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1. Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction

Author: Chen, Peter Yichen, Liu, Chao, Ma, Pingchuan, Eastman, John, Rus, Daniela, Randle, Dylan, Ivanov, Yuri, and Matusik, Wojciech
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Computer Vision and Pattern Recognition, Physics - Computational Physics
Abstract: Differentiable simulation has become a powerful tool for system identification. While prior work has focused on identifying robot properties using robot-specific data or object properties using object-specific data, our approach calibrates object properties by using information from the robot, without relying on data from the object itself. Specifically, we utilize robot joint encoder information, which is commonly available in standard robotic systems. Our key observation is that by analyzing the robot's reactions to manipulated objects, we can infer properties of those objects, such as inertia and softness. Leveraging this insight, we develop differentiable simulations of robot-object interactions to inversely identify the properties of the manipulated objects. Our approach relies solely on proprioception -- the robot's internal sensing capabilities -- and does not require external measurement tools or vision-based tracking systems. This general method is applicable to any articulated robot and requires only joint position information. We demonstrate the effectiveness of our method on a low-cost robotic platform, achieving accurate mass and elastic modulus estimations of manipulated objects with just a few seconds of computation on a laptop., Comment: arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission
Published: 2024

2. BEAVER: An Enterprise Benchmark for Text-to-SQL

Author: Chen, Peter Baile, Wenz, Fabian, Zhang, Yi, Kayali, Moe, Tatbul, Nesime, Cafarella, Michael, Demiralp, Çağatay, and Stonebraker, Michael
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Databases
Abstract: Existing text-to-SQL benchmarks have largely been constructed using publicly available tables from the web with human-generated tests containing question and SQL statement pairs. They typically show very good results and lead people to think that LLMs are effective at text-to-SQL tasks. In this paper, we apply off-the-shelf LLMs to a benchmark containing enterprise data warehouse data. In this environment, LLMs perform poorly, even when standard prompt engineering and RAG techniques are utilized. As we will show, the reasons for poor performance are largely due to three characteristics: (1) public LLMs cannot train on enterprise data warehouses because they are largely in the "dark web", (2) schemas of enterprise tables are more complex than the schemas in public data, which leads the SQL-generation task innately harder, and (3) business-oriented questions are often more complex, requiring joins over multiple tables and aggregations. As a result, we propose a new dataset BEAVER, sourced from real enterprise data warehouses together with natural language queries and their correct SQL statements which we collected from actual user history. We evaluated this dataset using recent LLMs and demonstrated their poor performance on this task. We hope this dataset will facilitate future researchers building more sophisticated text-to-SQL systems which can do better on this important class of data.
Published: 2024

3. Neural Representation of Shape-Dependent Laplacian Eigenfunctions

Author: Chang, Yue, Benchekroun, Otman, Chiaramonte, Maurizio M., Chen, Peter Yichen, and Grinspun, Eitan
Subjects: Computer Science - Graphics
Abstract: The eigenfunctions of the Laplace operator are essential in mathematical physics, engineering, and geometry processing. Typically, these are computed by discretizing the domain and performing eigendecomposition, tying the results to a specific mesh. However, this method is unsuitable for continuously-parameterized shapes. We propose a novel representation for eigenfunctions in continuously-parameterized shape spaces, where eigenfunctions are spatial fields with continuous dependence on shape parameters, defined by minimal Dirichlet energy, unit norm, and mutual orthogonality. We implement this with multilayer perceptrons trained as neural fields, mapping shape parameters and domain positions to eigenfunction values. A unique challenge is enforcing mutual orthogonality with respect to causality, where the causal ordering varies across the shape space. Our training method therefore requires three interwoven concepts: (1) learning $n$ eigenfunctions concurrently by minimizing Dirichlet energy with unit norm constraints; (2) filtering gradients during backpropagation to enforce causal orthogonality, preventing earlier eigenfunctions from being influenced by later ones; (3) dynamically sorting the causal ordering based on eigenvalues to track eigenvalue curve crossovers. We demonstrate our method on problems such as shape family analysis, predicting eigenfunctions for incomplete shapes, interactive shape manipulation, and computing higher-dimensional eigenfunctions, on all of which traditional methods fall short.
Published: 2024

4. Making LLMs Work for Enterprise Data Tasks

Author: Demiralp, Çağatay, Wenz, Fabian, Chen, Peter Baile, Kayali, Moe, Tatbul, Nesime, and Stonebraker, Michael
Subjects: Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs' performance is tied to their training data, a crucial question is how useful they can be in improving enterprise database management and analysis tasks. To address this, we contribute experimental results on LLMs' performance for text-to-SQL and semantic column-type detection tasks on enterprise datasets. The performance of LLMs on enterprise data is significantly lower than on benchmark datasets commonly used. Informed by our findings and feedback from industry practitioners, we identify three fundamental challenges -- latency, cost, and quality -- and propose potential solutions to use LLMs in enterprise data workflows effectively., Comment: Poster at North East Database Day 2024
Published: 2024

5. Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs

Author: Viswanath, Hrishikesh, Chang, Yue, Berner, Julius, Chen, Peter Yichen, and Bera, Aniket
Subjects: Computer Science - Machine Learning
Abstract: We propose accelerating the simulation of Lagrangian dynamics, such as fluid flows, granular flows, and elastoplasticity, with neural-operator-based reduced-order modeling. While full-order approaches simulate the physics of every particle within the system, incurring high computation time for dense inputs, we propose to simulate the physics on sparse graphs constructed by sampling from the spatially discretized system. Our discretization-invariant reduced-order framework trains on any spatial discretizations and computes temporal dynamics on any sparse sampling of these discretizations through neural operators. Our proposed approach is termed Graph Informed Optimized Reduced-Order Modeling or \textit{GIOROM}. Through reduced order modeling, we ensure lower computation time by sparsifying the system by 6.6-32.0$\times$, while ensuring high-fidelity full-order inference via neural fields. We show that our model generalizes to a range of initial conditions, resolutions, and materials. The code and the demos are provided at \url{https://github.com/HrishikeshVish/GIOROM}
Published: 2024

6. MDCR: A Dataset for Multi-Document Conditional Reasoning

Author: Chen, Peter Baile, Zhang, Yi, Liu, Chunwei, Gupta, Sejal, Kim, Yoon, and Cafarella, Michael
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a scholarship depends on eligibility conditions, such as major or degree required. ConditionalQA was proposed to evaluate models' capability of reading a document and answering eligibility questions, considering unmentioned conditions. However, it is limited to questions on single documents, neglecting harder cases that may require cross-document reasoning and optimization, for example, "What is the maximum number of scholarships attainable?" Such questions over multiple documents are not only more challenging due to more context having to understand, but also because the model has to (1) explore all possible combinations of unmentioned conditions and (2) understand the relationship between conditions across documents, to reason about the optimal outcome. To evaluate models' capability of answering such questions, we propose a new dataset MDCR, which can reflect real-world challenges and serve as a new test bed for complex conditional reasoning that requires optimization. We evaluate this dataset using the most recent LLMs and demonstrate their limitations in solving this task. We believe this dataset will facilitate future research in answering optimization questions with unknown conditions.
Published: 2024

7. Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities

Author: Zayats, Vicky, Chen, Peter, Ferrari, Melissa, and Padfield, Dirk
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Integrating multiple generative foundation models, especially those trained on different modalities, into something greater than the sum of its parts poses significant challenges. Two key hurdles are the availability of aligned data (concepts that contain similar meaning but is expressed differently in different modalities), and effectively leveraging unimodal representations in cross-domain generative tasks, without compromising their original unimodal capabilities. We propose Zipper, a multi-tower decoder architecture that addresses these concerns by using cross-attention to flexibly compose multimodal generative models from independently pre-trained unimodal decoders. In our experiments fusing speech and text modalities, we show the proposed architecture performs very competitively in scenarios with limited aligned text-speech data. We also showcase the flexibility of our model to selectively maintain unimodal (e.g., text-to-text generation) generation performance by freezing the corresponding modal tower (e.g. text). In cross-modal tasks such as automatic speech recognition (ASR) where the output modality is text, we show that freezing the text backbone results in negligible performance degradation. In cross-modal tasks such as text-to-speech generation (TTS) where the output modality is speech, we show that using a pre-trained speech backbone results in superior performance to the baseline., Comment: Under review at NeurIPS
Published: 2024

8. A Declarative System for Optimizing AI Workloads

Author: Liu, Chunwei, Russo, Matthew, Cafarella, Michael, Cao, Lei, Chen, Peter Baille, Chen, Zui, Franklin, Michael, Kraska, Tim, Madden, Samuel, and Vitagliano, Gerardo
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Databases, H.2.3, I.2.5
Abstract: A long-standing goal of data management systems has been to build systems which can compute quantitative insights over large corpora of unstructured data in a cost-effective manner. Until recently, it was difficult and expensive to extract facts from company documents, data from scientific papers, or metrics from image and video corpora. Today's models can accomplish these tasks with high accuracy. However, a programmer who wants to answer a substantive AI-powered query must orchestrate large numbers of models, prompts, and data operations. For even a single query, the programmer has to make a vast number of decisions such as the choice of model, the right inference method, the most cost-effective inference hardware, the ideal prompt design, and so on. The optimal set of decisions can change as the query changes and as the rapidly-evolving technical landscape shifts. In this paper we present Palimpzest, a system that enables anyone to process AI-powered analytical queries simply by defining them in a declarative language. The system uses its cost optimization framework to implement the query plan with the best trade-offs between runtime, financial cost, and output data quality. We describe the workload of AI-powered analytics tasks, the optimization methods that Palimpzest uses, and the prototype system itself. We evaluate Palimpzest on tasks in Legal Discovery, Real Estate Search, and Medical Schema Matching. We show that even our simple prototype offers a range of appealing plans, including one that is 3.3x faster and 2.9x cheaper than the baseline method, while also offering better data quality. With parallelism enabled, Palimpzest can produce plans with up to a 90.3x speedup at 9.1x lower cost relative to a single-threaded GPT-4 baseline, while obtaining an F1-score within 83.5% of the baseline. These require no additional work by the user., Comment: 29 pages, 9 figures
Published: 2024

9. Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval

Author: Chen, Peter Baile, Zhang, Yi, and Roth, Dan
Subjects: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found either in a single table or multiple tables identified through question decomposition or rewriting. However, neither of these approaches is sufficient, as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself. If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. We use a novel re-ranking method formulated as a mixed-integer program that considers not only table-query relevance but also table-table relevance that requires inferring join relationships. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy., Comment: ACL 2024 camera ready
Published: 2024

10. IEBins: Iterative Elastic Bins for Monocular Depth Estimation and Completion

Author: Shao, Shuwei, Pei, Zhongcai, Chen, Weihai, Chen, Peter C. Y., and Li, Zhengguo
Published: 2024
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11. An aberrant immune–epithelial progenitor niche drives viral lung sequelae

Author: Narasimhan, Harish, Cheon, In Su, Qian, Wei, Hu, Sheng’en Shawn, Parimon, Tanyalak, Li, Chaofan, Goplen, Nick, Wu, Yue, Wei, Xiaoqin, Son, Young Min, Fink, Elizabeth, de Almeida Santos, Gislane, Tang, Jinyi, Yao, Changfu, Muehling, Lyndsey, Canderan, Glenda, Kadl, Alexandra, Cannon, Abigail, Young, Samuel, Hannan, Riley, Bingham, Grace, Arish, Mohammed, Sen Chaudhari, Arka, Im, Jun sub, Mattingly, Cameron L. R., Pramoonjago, Patcharin, Marchesvsky, Alberto, Sturek, Jeffrey, Kohlmeier, Jacob E., Shim, Yun Michael, Woodfolk, Judith, Zang, Chongzhi, Chen, Peter, and Sun, Jie
Published: 2024
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12. LLMs cannot find reasoning errors, but can correct them given the error location

Author: Tyen, Gladys, Mansoor, Hassan, Cărbune, Victor, Chen, Peter, and Mak, Tony
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: While self-correction has shown promise in improving LLM outputs in terms of style and quality (e.g. Chen et al., 2023b; Madaan et al., 2023), recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in worse performances overall (Huang et al., 2023). In this paper, we show that poor self-correction performance stems from LLMs' inability to find logical mistakes, rather than their ability to correct a known mistake. Firstly, we benchmark several state-of-the-art LLMs on their mistake-finding ability and demonstrate that they generally struggle with the task, even in highly objective, unambiguous cases. Secondly, we test the correction abilities of LLMs -- separately from mistake finding -- using a backtracking setup that feeds ground truth mistake location information to the model. We show that this boosts downstream task performance across our 5 reasoning tasks, indicating that LLMs' correction abilities are robust. Finally, we show that it is possible to obtain mistake location information without ground truth labels or in-domain training data. We train a small classifier with out-of-domain data, which exhibits stronger mistake-finding performance than prompting a large model. We release our dataset of LLM-generated logical mistakes, BIG-Bench Mistake, to enable further research into locating LLM reasoning mistakes., Comment: ACL 2024 Findings
Published: 2023

13. MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

Author: Shao, Shuwei, Pei, Zhongcai, Chen, Weihai, Sun, Dingchi, Chen, Peter C. Y., and Li, Zhengguo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, e.g., occlusion and dynamic objects. In this work, we introduce a novel self-supervised depth estimation framework, dubbed MonoDiffusion, by formulating it as an iterative denoising process. Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually adds noise to the depth map generated by a pre-trained teacher model. Moreover,the teacher model allows applying a distillation loss to guide the denoised depth. Further, we develop a masked visual condition mechanism to enhance the denoising ability of model. Extensive experiments are conducted on the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior state-of-the-art competitors. The source code will be available at https://github.com/ShuweiShao/MonoDiffusion., Comment: 10 pages, 8 figures
Published: 2023

14. NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion

Author: Shao, Shuwei, Pei, Zhongcai, Chen, Weihai, Chen, Peter C. Y., and Li, Zhengguo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Over the past few years, monocular depth estimation and completion have been paid more and more attention from the computer vision community because of their widespread applications. In this paper, we introduce novel physics (geometry)-driven deep learning frameworks for these two tasks by assuming that 3D scenes are constituted with piece-wise planes. Instead of directly estimating the depth map or completing the sparse depth map, we propose to estimate the surface normal and plane-to-origin distance maps or complete the sparse surface normal and distance maps as intermediate outputs. To this end, we develop a normal-distance head that outputs pixel-level surface normal and distance. Meanwhile, the surface normal and distance maps are regularized by a developed plane-aware consistency constraint, which are then transformed into depth maps. Furthermore, we integrate an additional depth head to strengthen the robustness of the proposed frameworks. Extensive experiments on the NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds in performance prior state-of-the-art monocular depth estimation and completion competitors. The source code will be available at https://github.com/ShuweiShao/NDDepth., Comment: Extension of previous work arXiv:2309.10592
Published: 2023

15. Neural Stress Fields for Reduced-order Elastoplasticity and Fracture

Author: Zong, Zeshun, Li, Xuan, Li, Minchen, Chiaramonte, Maurizio M., Matusik, Wojciech, Grinspun, Eitan, Carlberg, Kevin, Jiang, Chenfanfu, and Chen, Peter Yichen
Subjects: Computer Science - Graphics, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract: We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasticity and fracture mechanics. However, their long runtime and large memory consumption render them unsuitable for applications constrained by computation time and memory usage, e.g., virtual reality. To overcome these barriers, we propose a reduced-order framework. Our key innovation is training a low-dimensional manifold for the Kirchhoff stress field via an implicit neural representation. This low-dimensional neural stress field (NSF) enables efficient evaluations of stress values and, correspondingly, internal forces at arbitrary spatial locations. In addition, we also train neural deformation and affine fields to build low-dimensional manifolds for the deformation and affine momentum fields. These neural stress, deformation, and affine fields share the same low-dimensional latent space, which uniquely embeds the high-dimensional simulation state. After training, we run new simulations by evolving in this single latent space, which drastically reduces the computation time and memory consumption. Our general continuum-mechanics-based reduced-order framework is applicable to any phenomena governed by the elastodynamics equation. To showcase the versatility of our framework, we simulate a wide range of material behaviors, including elastica, sand, metal, non-Newtonian fluids, fracture, contact, and collision. We demonstrate dimension reduction by up to 100,000X and time savings by up to 10X.
Published: 2023
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16. LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields

Author: Chang, Yue, Chen, Peter Yichen, Wang, Zhecheng, Chiaramonte, Maurizio M., Carlberg, Kevin, and Grinspun, Eitan
Subjects: Computer Science - Graphics
Abstract: Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the behavior of a system using a simplified kinematic representation. Typically, ROM is trained on input simulations created with a specific spatial discretization, and then serves to accelerate simulations with the same discretization. This discretization-dependence is restrictive. Becoming independent of a specific discretization would provide flexibility to mix and match mesh resolutions, connectivity, and type (tetrahedral, hexahedral) in training data; to accelerate simulations with novel discretizations unseen during training; and to accelerate adaptive simulations that temporally or parametrically change the discretization. We present a flexible, discretization-independent approach to reduced-order modeling. Like traditional ROM, we represent the configuration as a linear combination of displacement fields. Unlike traditional ROM, our displacement fields are continuous maps from every point on the reference domain to a corresponding displacement vector; these maps are represented as implicit neural fields. With linear continuous ROM (LiCROM), our training set can include multiple geometries undergoing multiple loading conditions, independent of their discretization. This opens the door to novel applications of reduced order modeling. We can now accelerate simulations that modify the geometry at runtime, for instance via cutting, hole punching, and even swapping the entire mesh. We can also accelerate simulations of geometries unseen during training. We demonstrate one-shot generalization, training on a single geometry and subsequently simulating various unseen geometries.
Published: 2023

17. Exploring the protective capabilities developed by hospital nurses to deflect workplace violence: a qualitative study in Taiwan

Author: Wang, Tsu-Chi, Kuo, Ting-Ya, Tung, Tao-Hsin, Chen, Peter Y., and Chen, Fu-Li
Published: 2024
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18. Inorganic Cs3Bi2I9 lead-free halide perovskite film for large-area X-ray detector via low-cost ambient spray coating

Author: Chen, Yen-Ting, Wen, Zi-Xiang, Lin, Chen-Fu, Li, Ming-Hsien, and Chen, Peter
Published: 2024
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19. How Can Large Language Models Help Humans in Design and Manufacturing?

Author: Makatura, Liane, Foshey, Michael, Wang, Bohan, HähnLein, Felix, Ma, Pingchuan, Deng, Bolei, Tjandrasuwita, Megan, Spielberg, Andrew, Owens, Crystal Elaine, Chen, Peter Yichen, Zhao, Allan, Zhu, Amy, Norton, Wil J, Gu, Edward, Jacob, Joshua, Li, Yifei, Schulz, Adriana, and Matusik, Wojciech
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design. We investigate the application of this tool across the entire design and manufacturing workflow. Specifically, we scrutinize the utility of LLMs in tasks such as: converting a text-based prompt into a design specification, transforming a design into manufacturing instructions, producing a design space and design variations, computing the performance of a design, and searching for designs predicated on performance. Through a series of examples, we highlight both the benefits and the limitations of the current LLMs. By exposing these limitations, we aspire to catalyze the continued improvement and progression of these models.
Published: 2023

20. A wireless patch for the monitoring of C-reactive protein in sweat

Author: Tu, Jiaobing, Min, Jihong, Song, Yu, Xu, Changhao, Li, Jiahong, Moore, Jeff, Hanson, Justin, Hu, Erin, Parimon, Tanyalak, Wang, Ting-Yu, Davoodi, Elham, Chou, Tsui-Fen, Chen, Peter, Hsu, Jeffrey J, Rossiter, Harry B, and Gao, Wei
Subjects: Engineering, Biomedical Engineering, Heart Disease, Bioengineering, Cardiovascular, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Humans, Sweat, Wearable Electronic Devices, C-Reactive Protein, Gold, Monitoring, Physiologic, Metal Nanoparticles, Biomarkers, Biomedical engineering
Abstract: The quantification of protein biomarkers in blood at picomolar-level sensitivity requires labour-intensive incubation and washing steps. Sensing proteins in sweat, which would allow for point-of-care monitoring, is hindered by the typically large interpersonal and intrapersonal variations in its composition. Here we report the design and performance of a wearable and wireless patch for the real-time electrochemical detection of the inflammatory biomarker C-reactive (CRP) protein in sweat. The device integrates iontophoretic sweat extraction, microfluidic channels for sweat sampling and for reagent routing and replacement, and a graphene-based sensor array for quantifying CRP (via an electrode functionalized with anti-CRP capture antibodies-conjugated gold nanoparticles), ionic strength, pH and temperature for the real-time calibration of the CRP sensor. In patients with chronic obstructive pulmonary disease, with active or past infections or who had heart failure, the elevated concentrations of CRP measured via the patch correlated well with the protein's levels in serum. Wearable biosensors for the real-time sensitive analysis of inflammatory proteins in sweat may facilitate the management of chronic diseases.
Published: 2023

21. After the killing fields: Post-pandemic changes in journalism employment in Australia and Aotearoa/New Zealand

Author: Cokley, John, Chen, Peter John, Beresford, Joanna, and Bundy, Alexis
Published: 2024

22. Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers

Author: Yeung, Yumiki Y. K., Chen, Peter Q., Ng, Peter H. F., and Cheng, Andy S. K.
Published: 2024
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23. AudioPaLM: A Large Language Model That Can Speak and Listen

Author: Rubenstein, Paul K., Asawaroengchai, Chulayuth, Nguyen, Duc Dung, Bapna, Ankur, Borsos, Zalán, Quitry, Félix de Chaumont, Chen, Peter, Badawy, Dalia El, Han, Wei, Kharitonov, Eugene, Muckenhirn, Hannah, Padfield, Dirk, Qin, James, Rozenberg, Danny, Sainath, Tara, Schalkwyk, Johan, Sharifi, Matt, Ramanovich, Michelle Tadmor, Tagliasacchi, Marco, Tudor, Alexandru, Velimirović, Mihajlo, Vincent, Damien, Yu, Jiahui, Wang, Yongqiang, Zayats, Vicky, Zeghidour, Neil, Zhang, Yu, Zhang, Zhishuai, Zilka, Lukas, and Frank, Christian
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract: We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt. We release examples of our method at https://google-research.github.io/seanet/audiopalm/examples, Comment: Technical report
Published: 2023

24. Learning Preconditioner for Conjugate Gradient PDE Solvers

Author: Li, Yichen, Chen, Peter Yichen, Du, Tao, and Matusik, Wojciech
Subjects: Mathematics - Numerical Analysis
Abstract: Efficient numerical solvers for partial differential equations empower science and engineering. One of the commonly employed numerical solvers is the preconditioned conjugate gradient (PCG) algorithm which can solve large systems to a given precision level. One challenge in PCG solvers is the selection of preconditioners, as different problem-dependent systems can benefit from different preconditioners. We present a new method to introduce \emph{inductive bias} in preconditioning conjugate gradient algorithm. Given a system matrix and a set of solution vectors arise from an underlying distribution, we train a graph neural network to obtain an approximate decomposition to the system matrix to be used as a preconditioner in the context of PCG solvers. We conduct extensive experiments to demonstrate the efficacy and generalizability of our proposed approach in solving various 2D and 3D linear second-order PDEs.
Published: 2023

25. Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics

Author: Ma, Pingchuan, Chen, Peter Yichen, Deng, Bolei, Tenenbaum, Joshua B., Du, Tao, Gan, Chuang, and Matusik, Wojciech
Subjects: Computer Science - Machine Learning, Computer Science - Graphics
Abstract: We propose a hybrid neural network (NN) and PDE approach for learning generalizable PDE dynamics from motion observations. Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models). Without explicit PDE knowledge, these approaches cannot guarantee physical correctness and have limited generalizability. We argue that the governing PDEs are often well-known and should be explicitly enforced rather than learned. Instead, constitutive models are particularly suitable for learning due to their data-fitting nature. To this end, we introduce a new framework termed "Neural Constitutive Laws" (NCLaw), which utilizes a network architecture that strictly guarantees standard constitutive priors, including rotation equivariance and undeformed state equilibrium. We embed this network inside a differentiable simulation and train the model by minimizing a loss function based on the difference between the simulation and the motion observation. We validate NCLaw on various large-deformation dynamical systems, ranging from solids to fluids. After training on a single motion trajectory, our method generalizes to new geometries, initial/boundary conditions, temporal ranges, and even multi-physics systems. On these extremely out-of-distribution generalization tasks, NCLaw is orders-of-magnitude more accurate than previous NN approaches. Real-world experiments demonstrate our method's ability to learn constitutive laws from videos., Comment: Homepage: https://sites.google.com/view/nclaw
Published: 2023

26. ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation

Author: Zhao, Xiaoming, Wu, Xingming, Chen, Weihai, Chen, Peter C. Y., Xu, Qingsong, and Li, Zhengguo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Image keypoints and descriptors play a crucial role in many visual measurement tasks. In recent years, deep neural networks have been widely used to improve the performance of keypoint and descriptor extraction. However, the conventional convolution operations do not provide the geometric invariance required for the descriptor. To address this issue, we propose the Sparse Deformable Descriptor Head (SDDH), which learns the deformable positions of supporting features for each keypoint and constructs deformable descriptors. Furthermore, SDDH extracts descriptors at sparse keypoints instead of a dense descriptor map, which enables efficient extraction of descriptors with strong expressiveness. In addition, we relax the neural reprojection error (NRE) loss from dense to sparse to train the extracted sparse descriptors. Experimental results show that the proposed network is both efficient and powerful in various visual measurement tasks, including image matching, 3D reconstruction, and visual relocalization., Comment: Accepted by IEEE Transactions on Instrumentation & Measurement
Published: 2023

27. Constrained Exploration in Reinforcement Learning with Optimality Preservation

Author: Chen, Peter C. Y.
Subjects: Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract: We consider a class of reinforcement-learning systems in which the agent follows a behavior policy to explore a discrete state-action space to find an optimal policy while adhering to some restriction on its behavior. Such restriction may prevent the agent from visiting some state-action pairs, possibly leading to the agent finding only a sub-optimal policy. To address this problem we introduce the concept of constrained exploration with optimality preservation, whereby the exploration behavior of the agent is constrained to meet a specification while the optimality of the (original) unconstrained learning process is preserved. We first establish a feedback-control structure that models the dynamics of the unconstrained learning process. We then extend this structure by adding a supervisor to ensure that the behavior of the agent meets the specification, and establish (for a class of reinforcement-learning problems with a known deterministic environment) a necessary and sufficient condition under which optimality is preserved. This work demonstrates the utility and the prospect of studying reinforcement-learning problems in the context of the theories of discrete-event systems, automata and formal languages., Comment: 33 pages, and 6 figures
Published: 2023

28. Intravenous aviptadil and remdesivir for treatment of COVID-19-associated hypoxaemic respiratory failure in the USA (TESICO): a randomised, placebo-controlled trial.

Author: Brown, Samuel, Barkauskas, Christina, Grund, Birgit, Sharma, Shweta, Phillips, Andrew, Leither, Lindsay, Peltan, Ithan, Lanspa, Michael, Gilstrap, Daniel, Mourad, Ahmad, Lane, Kathleen, Beitler, Jeremy, Serra, Alexis, Garcia, Ivan, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley, Harris, Estelle, Middleton, Elizabeth, Barrios, Macy, Mathews, Kusum, Goel, Neha, Acquah, Samuel, Mosier, Jarrod, Hypes, Cameron, Salvagio Campbell, Elizabeth, Khan, Akram, Hough, Catherine, Wilson, Jennifer, Levitt, Joseph, Duggal, Abhijit, Dugar, Siddharth, Goodwin, Andrew, Terry, Charles, Chen, Peter, Torbati, Sam, Iyer, Nithya, Sandkovsky, Uriel, Johnson, Nicholas, Robinson, Bryce, Matthay, Michael, Aggarwal, Neil, Douglas, Ivor, Casey, Jonathan, Hache-Marliere, Manuel, Georges Youssef, J, Nkemdirim, William, Leshnower, Brad, Awan, Omar, Pannu, Sonal, OMahony, Darragh, Manian, Prasad, Awori Hayanga, J, Wortmann, Glenn, Tomazini, Bruno, Miller, Robert, Jensen, Jens-Ulrik, Murray, Daniel, Bickell, Nina, Zatakia, Jigna, Burris, Sarah, Higgs, Elizabeth, Natarajan, Ven, Dewar, Robin, Schechner, Adam, Kang, Nayon, Arenas-Pinto, Alejandro, Hudson, Fleur, Ginde, Adit, Self, Wesley, Rogers, Angela, Oldmixon, Cathryn, Morin, Haley, Sanchez, Adriana, Weintrob, Amy, Cavalcanti, Alexandre, Davis-Karim, Anne, Engen, Nicole, Denning, Eileen, Taylor Thompson, B, Gelijns, Annetine, Kan, Virginia, Davey, Victoria, Lundgren, Jens, Babiker, Abdel, Neaton, James, and Lane, H
Subjects: Adult, Humans, Female, Middle Aged, Male, COVID-19, SARS-CoV-2, Treatment Outcome, COVID-19 Drug Treatment, Respiratory Insufficiency, Oxygen
Abstract: BACKGROUND: There is a clinical need for therapeutics for COVID-19 patients with acute hypoxemic respiratory failure whose 60-day mortality remains at 30-50%. Aviptadil, a lung-protective neuropeptide, and remdesivir, a nucleotide prodrug of an adenosine analog, were compared with placebo among patients with COVID-19 acute hypoxaemic respiratory failure. METHODS: TESICO was a randomised trial of aviptadil and remdesivir versus placebo at 28 sites in the USA. Hospitalised adult patients were eligible for the study if they had acute hypoxaemic respiratory failure due to confirmed SARS-CoV-2 infection and were within 4 days of the onset of respiratory failure. Participants could be randomly assigned to both study treatments in a 2 × 2 factorial design or to just one of the agents. Participants were randomly assigned with a web-based application. For each site, randomisation was stratified by disease severity (high-flow nasal oxygen or non-invasive ventilation vs invasive mechanical ventilation or extracorporeal membrane oxygenation [ECMO]), and four strata were defined by remdesivir and aviptadil eligibility, as follows: (1) eligible for randomisation to aviptadil and remdesivir in the 2 × 2 factorial design; participants were equally randomly assigned (1:1:1:1) to intravenous aviptadil plus remdesivir, aviptadil plus remdesivir matched placebo, aviptadil matched placebo plus remdesvir, or aviptadil placebo plus remdesivir placebo; (2) eligible for randomisation to aviptadil only because remdesivir was started before randomisation; (3) eligible for randomisation to aviptadil only because remdesivir was contraindicated; and (4) eligible for randomisation to remdesivir only because aviptadil was contraindicated. For participants in strata 2-4, randomisation was 1:1 to the active agent or matched placebo. Aviptadil was administered as a daily 12-h infusion for 3 days, targeting 600 pmol/kg on infusion day 1, 1200 pmol/kg on day 2, and 1800 pmol/kg on day 3. Remdesivir was administered as a 200 mg loading dose, followed by 100 mg daily maintenance doses for up to a 10-day total course. For participants assigned to placebo for either agent, matched saline placebo was administered in identical volumes. For both treatment comparisons, the primary outcome, assessed at day 90, was a six-category ordinal outcome: (1) at home (defined as the type of residence before hospitalisation) and off oxygen (recovered) for at least 77 days, (2) at home and off oxygen for 49-76 days, (3) at home and off oxygen for 1-48 days, (4) not hospitalised but either on supplemental oxygen or not at home, (5) hospitalised or in hospice care, or (6) dead. Mortality up to day 90 was a key secondary outcome. The independent data and safety monitoring board recommended stopping the aviptadil trial on May 25, 2022, for futility. On June 9, 2022, the sponsor stopped the trial of remdesivir due to slow enrolment. The trial is registered with ClinicalTrials.gov, NCT04843761. FINDINGS: Between April 21, 2021, and May 24, 2022, we enrolled 473 participants in the study. For the aviptadil comparison, 471 participants were randomly assigned to aviptadil or matched placebo. The modified intention-to-treat population comprised 461 participants who received at least a partial infusion of aviptadil (231 participants) or aviptadil matched placebo (230 participants). For the remdesivir comparison, 87 participants were randomly assigned to remdesivir or matched placebo and all received some infusion of remdesivir (44 participants) or remdesivir matched placebo (43 participants). 85 participants were included in the modified intention-to-treat analyses for both agents (ie, those enrolled in the 2 x 2 factorial). For the aviptadil versus placebo comparison, the median age was 57 years (IQR 46-66), 178 (39%) of 461 participants were female, and 246 (53%) were Black, Hispanic, Asian or other (vs 215 [47%] White participants). 431 (94%) of 461 participants were in an intensive care unit at baseline, with 271 (59%) receiving high-flow nasal oxygen or non-invasive ventiliation, 185 (40%) receiving invasive mechanical ventilation, and five (1%) receiving ECMO. The odds ratio (OR) for being in a better category of the primary efficacy endpoint for aviptadil versus placebo at day 90, from a model stratified by baseline disease severity, was 1·11 (95% CI 0·80-1·55; p=0·54). Up to day 90, 86 participants in the aviptadil group and 83 in the placebo group died. The cumulative percentage who died up to day 90 was 38% in the aviptadil group and 36% in the placebo group (hazard ratio 1·04, 95% CI 0·77-1·41; p=0·78). The primary safety outcome of death, serious adverse events, organ failure, serious infection, or grade 3 or 4 adverse events up to day 5 occurred in 146 (63%) of 231 patients in the aviptadil group compared with 129 (56%) of 230 participants in the placebo group (OR 1·40, 95% CI 0·94-2·08; p=0·10). INTERPRETATION: Among patients with COVID-19-associated acute hypoxaemic respiratory failure, aviptadil did not significantly improve clinical outcomes up to day 90 when compared with placebo. The smaller than planned sample size for the remdesivir trial did not permit definitive conclusions regarding safety or efficacy. FUNDING: National Institutes of Health.
Published: 2023

29. PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification

Author: Li, Xuan, Qiao, Yi-Ling, Chen, Peter Yichen, Jatavallabhula, Krishna Murthy, Lin, Ming, Jiang, Chenfanfu, and Gan, Chuang
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Graphics, Computer Science - Machine Learning, Computer Science - Robotics
Abstract: Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos. We design PAC-NeRF to only ever produce physically plausible states by enforcing the neural radiance field to follow the conservation laws of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian representation of the neural radiance field, i.e., we use the Eulerian grid representation for NeRF density and color fields, while advecting the neural radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian representation seamlessly blends efficient neural rendering with the material point method (MPM) for robust differentiable physics simulation. We validate the effectiveness of our proposed framework on geometry and physical parameter estimation over a vast range of materials, including elastic bodies, plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate significant performance gain on most tasks., Comment: ICLR 2023 Spotlight. Project page: https://sites.google.com/view/PAC-NeRF
Published: 2023

30. How Should I Manage a Patient Who is Progressing at Low Levels of Intraocular Pressure?

Author: Chang, Ta Chen Peter, primary
Published: 2024
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31. Numerical Solutions of Nonlinear Schrödinger Equation: An Application Example of Nonlinear Analysis

Author: Y.P. Chen, Peter, primary
Published: 2024
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32. Researching COVID to enhance recovery (RECOVER) adult study protocol: Rationale, objectives, and design

Author: Horwitz, Leora I, Thaweethai, Tanayott, Brosnahan, Shari B, Cicek, Mine S, Fitzgerald, Megan L, Goldman, Jason D, Hess, Rachel, Hodder, SL, Jacoby, Vanessa L, Jordan, Michael R, Krishnan, Jerry A, Laiyemo, Adeyinka O, Metz, Torri D, Nichols, Lauren, Patzer, Rachel E, Sekar, Anisha, Singer, Nora G, Stiles, Lauren E, Taylor, Barbara S, Ahmed, Shifa, Algren, Heather A, Anglin, Khamal, Aponte-Soto, Lisa, Ashktorab, Hassan, Bassett, Ingrid V, Bedi, Brahmchetna, Bhadelia, Nahid, Bime, Christian, Bind, Marie-Abele C, Black, Lora J, Blomkalns, Andra L, Brim, Hassan, Castro, Mario, Chan, James, Charney, Alexander W, Chen, Benjamin K, Chen, Li Qing, Chen, Peter, Chestek, David, Chibnik, Lori B, Chow, Dominic C, Chu, Helen Y, Clifton, Rebecca G, Collins, Shelby, Costantine, Maged M, Cribbs, Sushma K, Deeks, Steven G, Dickinson, John D, Donohue, Sarah E, Durstenfeld, Matthew S, Emery, Ivette F, Erlandson, Kristine M, Facelli, Julio C, Farah-Abraham, Rachael, Finn, Aloke V, Fischer, Melinda S, Flaherman, Valerie J, Fleurimont, Judes, Fonseca, Vivian, Gallagher, Emily J, Gander, Jennifer C, Gennaro, Maria Laura, Gibson, Kelly S, Go, Minjoung, Goodman, Steven N, Granger, Joey P, Greenway, Frank L, Hafner, John W, Han, Jenny E, Harkins, Michelle S, Hauser, Kristine SP, Heath, James R, Hernandez, Carla R, Ho, On, Hoffman, Matthew K, Hoover, Susan E, Horowitz, Carol R, Hsu, Harvey, Hsue, Priscilla Y, Hughes, Brenna L, Jagannathan, Prasanna, James, Judith A, John, Janice, Jolley, Sarah, Judd, SE, Juskowich, Joy J, Kanjilal, Diane G, Karlson, Elizabeth W, Katz, Stuart D, Kelly, J Daniel, Kelly, Sara W, Kim, Arthur Y, Kirwan, John P, Knox, Kenneth S, Kumar, Andre, Lamendola-Essel, Michelle F, Lanca, Margaret, Lee-lannotti, Joyce K, Lefebvre, R Craig, and Levy, Bruce D
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Clinical Research, Emerging Infectious Diseases, Infectious Diseases, Infection, Good Health and Well Being
Abstract: Abstract: Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility– and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross– validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.
Published: 2023

33. Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease.

Author: Cerro-Chiang, Giuliana, Ayres, Matthew, Rivas, Alejandro, Romero, Tahmineh, Parker, Sarah, Mastali, Mitra, Elashoff, David, Chen, Peter, Van Eyk, Jennifer, Wolters, Paul, Boin, Francesco, and Zaman, Tanzira
Subjects: Humans, Lung Diseases, Interstitial, Scleroderma, Systemic, Immunosuppressive Agents, Biomarkers, Disease Progression, Lung
Abstract: Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of
Published: 2023

34. Efficient prime editing in mouse brain, liver and heart with dual AAVs

Author: Davis, Jessie R., Banskota, Samagya, Levy, Jonathan M., Newby, Gregory A., Wang, Xiao, Anzalone, Andrew V., Nelson, Andrew T., Chen, Peter J., Hennes, Andrew D., An, Meirui, Roh, Heejin, Randolph, Peyton B., Musunuru, Kiran, and Liu, David R.
Published: 2024
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35. Implicit Neural Spatial Representations for Time-dependent PDEs

Author: Chen, Honglin, Wu, Rundi, Grinspun, Eitan, Zheng, Changxi, and Chen, Peter Yichen
Subjects: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Graphics, Mathematics - Numerical Analysis
Abstract: Implicit Neural Spatial Representation (INSR) has emerged as an effective representation of spatially-dependent vector fields. This work explores solving time-dependent PDEs with INSR. Classical PDE solvers introduce both temporal and spatial discretizations. Common spatial discretizations include meshes and meshless point clouds, where each degree-of-freedom corresponds to a location in space. While these explicit spatial correspondences are intuitive to model and understand, these representations are not necessarily optimal for accuracy, memory usage, or adaptivity. Keeping the classical temporal discretization unchanged (e.g., explicit/implicit Euler), we explore INSR as an alternative spatial discretization, where spatial information is implicitly stored in the neural network weights. The network weights then evolve over time via time integration. Our approach does not require any training data generated by existing solvers because our approach is the solver itself. We validate our approach on various PDEs with examples involving large elastic deformations, turbulent fluids, and multi-scale phenomena. While slower to compute than traditional representations, our approach exhibits higher accuracy and lower memory consumption. Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive. By tapping into the rich literature of classic time integrators, e.g., operator-splitting schemes, our method enables challenging simulations in contact mechanics and turbulent flows where previous neural-physics approaches struggle. Videos and codes are available on the project page: http://www.cs.columbia.edu/cg/INSR-PDE/, Comment: ICML 2023. Project page: http://www.cs.columbia.edu/cg/INSR-PDE/
Published: 2022

36. Newark Kids Count Data Snapshot: The Impact of COVID-19--College Students and COVID-19

Author: Advocates for Children of New Jersey, Vega, Alana, and Chen, Peter
Abstract: This data snapshot is a second report in a two-part series, "Newark Kids Count Data Snapshot: The Impact of COVID-19." In 2021, data releases from the U.S. Bureau of Labor Statistics, as well as the U.S. Census Bureau, have demonstrated the ways in which college students have shifted their educational plans as a result of COVID-19. This snapshot seeks to investigate these trends at a more local level by examining the experiences of Newark's 2019 and 2020 high school graduates as they navigated their freshman and sophomore years in college during a global pandemic. [For the first data snapshot, see ED613560.]
Published: 2021

37. CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations

Author: Chen, Peter Yichen, Xiang, Jinxu, Cho, Dong Heon, Chang, Yue, Pershing, G A, Maia, Henrique Teles, Chiaramonte, Maurizio M., Carlberg, Kevin, and Grinspun, Eitan
Subjects: Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Graphics, Mathematics - Numerical Analysis, Physics - Computational Physics
Abstract: The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches reduce the dimensionality of discretized vector fields, our continuous reduced-order modeling (CROM) approach builds a low-dimensional embedding of the continuous vector fields themselves, not their discretization. We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations. We validate our approach on an extensive range of PDEs with training data from voxel grids, meshes, and point clouds. Compared to prior discretization-dependent ROM methods, such as linear subspace proper orthogonal decomposition (POD) and nonlinear manifold neural-network-based autoencoders, CROM features higher accuracy, lower memory consumption, dynamically adaptive resolutions, and applicability to any discretization. For equal latent space dimension, CROM exhibits 79$\times$ and 49$\times$ better accuracy, and 39$\times$ and 132$\times$ smaller memory footprint, than POD and autoencoder methods, respectively. Experiments demonstrate 109$\times$ and 89$\times$ wall-clock speedups over unreduced models on CPUs and GPUs, respectively. Videos and codes are available on the project page: https://crom-pde.github.io
Published: 2022

38. Measurement of circulating viral antigens post-SARS-CoV-2 infection in a multicohort study

Author: Abraham, Rachael, Ager, Arijan, Aguilar, Franchesca A., Ahmadi-Izad, Ghazal, Ahmed, Dilshad R., Alvarez, Alicarmen, Anderson, Blake, Asencios, Walter D., Atha, Mary, Beaty, Casey L., Bedi, Brahmchetna, Berry, Jasmine A., Boone, Donchel, Bower, Mary, Bremner, James D., Brent, Corbin, Brown-Smith, Ke'Ara, Bull, Rachel, Bush, Patricia A., Capo, Gustavo, Carl-Igwe, Kelechi, Chitadze, Calista, Chukwumerije, Nachi, Clyburn, Erna, Collins, Shelby, Costello, Julie, Couture, Grace, Craft, Angel, Cribbs, Sushma K., Cui, Xiangqin, Dandy, Annette, Rio, Carlos del, Jasarevic, Rijalda, Detelich, Joshua F., Dixon, Cartia, Dow, Jeanne, Doyle, D'Andrea, Elchommali, Jannah, Ibeawuchi, Carmel, Elsey, Imani, Fineman, Rebecca, Francis, Anyssa G., Franks, Nicole, Gallini, Julia, Gander, Jennifer C., Gray, Natalie, Grimes, Ash, Gutter, Evan, Han, Jenny E., Hang, Tina P., Harding, Jess, Hernandez, Liliana, Hewitt, Lauren N., Holloway, Carla, Hudgins, Alex F., Huerta, Christopher, Ifejika, Cynthia, Ingram, Ketteria D., Javia, Vidhi N., Jeter, Mykayla, Johnson, Brandi, Joseph, Yasha, Juarez, Monica, Kajan, Dana, Khalil, Lana, Kirkpatrick, Caitlin M., Kleinhenz, Dean, Kolailat, Imanii, Koumanelis, Athena, Koumanelis, Alexandra, Kozoman, Rebecca, Krishnan, Shilpa, Lainez, Jordi, Lawrence, Brianna, Lee, Matthew A., Leon, Jose D., Lew, Valerie, Lewis, Kennedy C., Litvack, Matthew, Maroney, Mara, Maier, Cheryl L., Makkaoui, Nour, Marconi, Vincent C., Martin, Christopher F., Martinez, Monica, Mbogo, Loice, McCaslin, Atuarra, McIntyre, Jerrod, Moanna, Abeer, Montoya, Miranda, Morales, Elena, Moran, Caitlin A., Morgan-Billingslea, Jan, Murray, Calista, Nelson, Roslin, Neuman, Robert B., Nguyen, Tran, Ofotokun, Ighovwerha, Ojemakinde, Elizabeth I., Ojoawo, Bukkie, Osinski, Eileen, Oviedo, Sofia, Panganiban, Bernadine, Paredes-Gaitan, Yolanda, Patzer, Rachel E., Pemu, Priscilla, Prude, Michael, Rahman, Kazi, Ramakrishnan, Grace, Rebolledo, Paulina A., Roberts, Marjorie, Robinson, Keysha, Rogers, Chantrice, Rouphael, Nadine G., Searles, Charles, Shah, Anand, Segall, Marni, Shaw, Renata M., Silva, Ruvina, Simpson, Cheryl, Simpson-Derrell, Krystal, Sirajud-Deen, Talib, Smith, Veronica E., Stringer, Andre, Stroud, Jacob, Suthar, Mehul S., Sylber, Cory, Sylvera, Ashley, Tanner, Tehquin, Teunis, Larissa J., Tolbert, Maliya, Thomas, Kodasha M., Thompson, Sierra G., Titanji, Kehmia, Toy, Christopher, Traenkner, Jessica, Truong, Alex, Unterberger, Kristen, Vaccarino, Viola, Varney, Kris, Vyas, Kartavya, Vyas, Kurt, Walker, Tiffany A., Walkow, Max, Wang, Dongli, Wesley, Tamara, Wiley, Zanthia, Wimberly, Erika, Winston, Juton R., Winter, Terra J., Wongtrakool, Cherry, Aikawa, Masanori, Alba, George A., Aung, Taing N., Baden, Lindsey, Baslet, Gaston, Bassett, Ingrid V., Bennett, Lindsey, Bhattacharyya, Shamik, Blazey-Martin, Deborah, Buring, Julie, Cagnina, Rebecca E., Chen, Li Qing, Clark, Cheryl R., Cohen, Pieter, Collier, Ai-Ris, Czeisler, Charles, Duffy, Elizabeth, Estill, Peter, Fong, Tamara, Gay, Elizabeth, Ghamloush, Maher, Ginns, Leo C., Haack, Monika, Haas, Jennifer, Hamburg, Naomi, Hauser, Kristine S., John, Janice, Jordan, Michael, Juelg, Boris D., Kanjilal, Diane G., Kim, Arthur Y., Klerman, Elizabeth B., Kobayashi, Misaki ., Kogelman, Laura, Lamas, Daniela, Levy, Bruce D., Levy-Carrick, Nomi, Lewis, Gregory, Maley, Jason H., Manson, JoAnn, Marathe, Jai G., Mullington, Janet M., O'Connor, George T., Ojikutu, Bisola, Perlis, Roy, Quintana, Yuri, Redline, Susan, Remis, Elijah J., Rosand, Jonathan, Sesso, Howard D., Shaughnessy, Lynn, Shepherd, Fitzgerald M., Solomon, Scott, Sparks, Jeffrey A., Spencer, Lia L., Stephenson, Kathryn, Systrom, David, Thomas, Robert J., Min Thu, Phyo Phyo, Ticotsky, Amberly, Torres, Robert, Wallace, Zachary S., Walt, David, Ward, Honorine D., Washko, George, Whittelsey, Maureen, Wiener, Rebecca, Williams, Charles T., Xerras, Dean, Zhang, Haihua, Zionts, Danielle, Armstrong, Donna, Binkley, Susan E., Blackwell, Kenneth, Brown, Todd, Carton, Thomas W., Causey, Annalia, Cook, Felice, Daniel, Casey L., Datri, Paula, Domingo, Julio, Donahue, Conner, Eady, Maitlyn, Edberg, Jeffrey, Erdmann, Nathaniel, Fuloria, Jyotsna, Garcia-McClaney, Noah, Garner, Melissa, Gillespie, Mark, Gray, Brandon, Hagensee, Michael, Hall, Wanda, Hansel, Jamie, Hart, Cady, Hebson, Camden L., Hidalgo, Bertha, Holtzapfel, Kaylen, Jinright, Alexis, Judd, Suzanne E., Kennedy, Teri, Kirkwood, Leigh, Leggio, Cathryn, Levitan, Emily B., Maier, Megan, McCormack, Patricia, Miele, Lucio, Mitchell, Kevin, Montgomery, Aoyjai, Peralta-Carcelen, Myriam, Perkins, Allen, Pilco, Juan P., Powell, Leigh, Shevin, Rachael, Skipworth, Sidney, Spurgeon, Leah, Sutherland, Erica, Tita, Alan T., Trauth, Amber, Trotter, Siobhan, Van Deerlin, Alexander, Ware, Gregory, Weiser, Sharon, Wilson, Rosanne, Woodruff, Dana, Wu, Jing, Young, Madeline, Alemu, Mhret, Anderson, Jordan, Ashktorab, Hassan, Brim, Hassan, Chang, Linda, Chauhan, Mahak, Cho, Sung, Durrani, Saima, Gentil, Monique P., Goodman, Karli, Laiyemo, Adeyinka O., Lanke, Gandi, Lebron, Ralph, Maheshwari, Anurag, Mehari, Alem, Nezamloo, Ali, Ngwa, Julius, Njoku, Noelle, Ok, Jina, Sherif, Zaki A., Solemani, Akbar, Thuluvath, Paul, To, Chau, Spikes, Leslie A., James, Judith A., Luciano Roman, Carlos A., Chow, Dominic C., Marshall, Gailen D., Dickinson, John D., Hoover, Susan E., Warren, David E., Emery, Ivette F., Sukhera, Fatima I., Rosen, Clifford J., Greenway, Frank L., Hodder, Sally L., Shikuma, Cecilia M., VanWagoner, Timothy M., Bardes, James M., Kirwan, John P., Wood, Jeremy P., Whiteheart, Sidney W., Shellito, Judd, Roelke, Theresa, Black, Lora, Tjarks, Brian, Fonseca, Vivian, Gupta, Shaveeta, Longo, Michele, Yang, Mei, MarGangcuangco, Louis, Bengtson, Charles, Castro, Mario, Howard, Theresa, Garvy, Beth, Simmons, Christopher, Garla, Vishnu, Kuebler, Joy, Nandi, Utsav, Vasey, Andrew, Bogie, Amanda, Scott, James, Frontera, Sigrid Perez, Bagur, Jorge Santana, Dominique-Villanueva, Daphne, Juskowich, Joy, Reece, Rebecca, Sarwari, Arif, Aponte-Soto, Lisa, Adams, Dara, Baker, Aileen, Barbera, Sunni, Basu, Sanjib, Bleasdale, Susan, Bolliger, Dawn, Boyd, Andrew D., Boyineni, Jerusha, Breiter, Taylor, Brown, Daniel, Buhimschi, Irina A., Carrithers, Michael D., Certa, Marta, Chalamalla, Rashmika, Chebrolu, Praneeth, Chestek, David, Chessier, Erica, Cook, Judith A., Cranford, Savannah, Curry, Hannah L., Darbar, Dawood, Dasgupta, Raktima, Blakley, Felicia Davis, DeLisa, Julie A., Del Rios, Marina, Diaz, Maya Z., Diviak, Kathleen R., Dixon, Jennifer, Donlon, Meghan F., Donohue, Sarah E., Dworkin, Mark S., Edmonds, Sherrie, Ellison, Angela, Everett, Emily, Flanigan, Clarie, Freedman, Michael B., Gale, Lisa, Gerald, Lynn B., Giles, Wayne H., Gordon, Howard S., Hafner, John, Hammad, Bayan, Hanson, Keith A., Harris, Pastor C., Hartwig, Kimberly, Hasek, Sharon, Hasse, Wendy, Hendrickson, Monica, Hobbs, Brianna, Hryniewicka, Martyna, Hammerl, Savannah, Hutton, Robert, Ibanez, Alejandra L., Illendula, Sai D., Ismail, Nahed, Jain, Akash, Jennette, Kyle J., Kadubek, Grace, Kent, Denise, Kotini-Shah, Pavitra, Kelly, Sara W., Kent, Denise A., Kim, Keri S., Kindred, Elijah, Klein, Jonathan D., Krishnan, Jerry A., Large, Lucia, Lash, James ., Lin, Janet Y., Lu, Jun, Mahamed, Abeer M., Maholovich, Phoebe, Malchenko, Sergey, Martinez, Miriam, Mauntel-Medici, Cammeo, Madineni, Abhigna, McCauley, Mark, Menchaca, Martha, Mermelstein, Robin, Moreno, David J., Morrissy, Liam, Muramatsu, Naoko, Musick, Hugh, Noland, Seth, Norwick, Lourdes, Novak, Richard M., Olds, Lela, Ortiz, Marilyn, Patel, Khushboo, Perez, Nicolas L., Pliskin, Neil H., Pope, Sam, Prabhakar, Bellur S., Prasad, Bharati, Predki, Barbara, Prendergast, Heather M., Quigley, John G., Ramchandran, Ramaswamy, Ramirez, Ana, Rappe, Sarah, Rehman, Jalees, Rolon, Cesar, Rowley, Matthew, Rudraraju, Gowrisree, Rutherfoord, Melissa, Sader, Samer B., Sculley, Jennifer A., Smith-Mack, Jerisha, Swearingen, Peyton, Stewart de Ramirez, Sarah A., Sudhindra, Praveen, Sun, Jun, Tartt, Nancy, Terlinde, Tracy, Thompson, Tiffany, Vanden Hoek, Terry L., Kelly, Sara Warfield, Villanueva, Laura, Welter, Hannah, Woolley, Brittany, Yazici, Cemal, Charney, Alexander W., Kovatch, Patricia, Merad, Miriam, Nadkarni, Girish N., Wisnivesky, Juan P., Aberg, Judith A., Ascolillo, Steven, Assenso, Esther, Bagiella, Emilia, Bartram, Logan, Becker, Jacqueline, Beckmann, Noam D., Bendl, Ashley, Chen, Benjamin K., Civil, Alyssa, Dhar, Kaberi, Evo-Ortega, Lorraine, Fierer, Daniel, Gallagher, Emily J., Garcia-Sastre, Adolfo, Gnjatic, Sacha, Guliyeva, Sabina, Harvey-Ingram, Lori, Herrera-Moreno, Julia, Hill, Matthew, Horowitz, Carol R., Jackson, Rachel, Kastrat, Din, Lala-Trindade, Anu, Lin, Jenny, Macaluso, Nick, Marcon, Kathryn, Meyer, Dara, Morinigo, Janice, Natelson, Benjamin H., Nussenzweig, Maya, Padua, Tiffani, Putrino, David, Quazi, Nawreen, Ramos, Michelle, Richardson, Lynne, Russo, Scott, Seifert, Alan C., Serri, Abdullah, Walker, Jordan, Yee, Michell, Adolphi, Natalie L., Alekhina, Natalya, Archuleta, David A., Barlocker, Jackson, Bateman, Lucinda, Bradfute, Steven B., Brito, Rebecca, Bryan, Tanner W., Buck, Kaitlin E., Davis, Debra, Deakyne Davies, Sara J., Decker, Lauren A., Elifritz, Jamie, Erlandson, Kristine M., Facelli, Julio C., Fudge, Harrison Z., Tran, Huong, Pitch, Chloe, Feuerriegel, Elen M., Ford, Isaac, Friedman, Naomi P., Garcia-Soberanez, Noella D., Gardner, Edward M., Stringham, Caitlyn, Ling, Leah, Gebremariam, Tewodros H., Gentry, Frederick D., Gouripeddi, Ramkiran, Graham, Paige, Gronert, Eve G., Harkins, Michelle S., Hawkins, Kellie L., Hess, Rachel, Johnny, Jace D., Johnson, Brandon M., Jolley, Sarah E., Lloyd, Jennifer, Ludwig, Katelyn R., Martinez, Noah I., McCandless, Sean A., Montoya, Lorenzo A., Oakes, Judy L., Parada, Alisha N., Quinn, Davin K., Raissy, Hengameh, Ramos, Alfredo, Reid, Kayleigh M., Reusch, Jane E., Sheehan, Elyce B., Sokol, Ron J., Treacher, Irena S., Trinity, Joel D., Truong, Dongngan T., West, Shelby C., Molden, Joie, Sharareh, Nasser, Weaver, Lisa J., Spivak, Adam M., Brown, Jeanette P., Shah, Kevin S., Pace, Laura A., Scholand, Mary Beth, Velinder, Matt, Cortez, Melissa, Morimoto, Sarah Shizuko, Vernon, Suzanne D., Lu, Yue, Owen, Megan, Hermansen, Jessica A., Lindsay, Ashton M., Donohue, Dagny K., Garg, Lohit, Wodushek, Thomas, Higgins, Janine, Lockie, Tim, Brightman, Marisa, Thurman, Brook, Powell, Jenny M., Freston, Dylan C., Medina, Juliemar C., Aguirre, Bailee, Anderson, Jeff, Bair, Tami, Bosh, Lindsay, Evans, Lorlie, Garrett, Chase, Harris, Dixie, Herrera, Katherine, Horne, Benjamin D., Juan, James, Knight, Stacey, Knowlton, Kirk, Leither, Lindsay, Maestas, Heather, May, Heidi T., Najarian, Gabriel, Woller, Scott C., Zubal, Shyanne, Jensen, McKenna M., Webb, Tiaura, Iverson, Leslie, Ayache, Mirna, Baloi, Alexis, Barnboym, Emma, Boldt, Nicholas, Bukulmez, Hulya, Chesnick, Hailey, Conrad, Ann, Consolo, Mary, Curtis, Lynette, D’anza, Brian, DiFrancesco, Kathryn, Edminston, Marissa, Eteshola, Ebenezer, Gallagher, Michelle, Gibson, Kelly S., Gordesky, Larraine, Greenwood, Carla, Haghiac, Maricela, Harris, Paul, Hernandez, Carla, Iqbal, Shirin, Kaelber, David C., Kaufman, Elizabeth S., Kennedy, Olivia, Labbato, Danielle, Lengu, Ketrin, Levert, Antonio, Levin, Jennifer, Lowenthal, Rebecca, Mackin, Bridget, Malakooti, Shahdi K., McComsey, Grace A., Minium, Judy, Mouchati, Christian, Oleson, Christine, Pearman, Ann, Hershey, Morgan, Rivera, Amanda, Rodgers, Michael, Rodgers, Theresa, Roy, Arnab, Russ, Kris, Scott, Sarah, Sheth, Niyati, Singer, Nora G., Smith, Beth, Smith, Cheryl, Stancin, Terry, Temple, Daniel, Tribout, Megan, Weinberger, Elisheva, Zhang, David, Zisis, Sokratis N., Atieh, Ornina, Yendewa, George, Baissary, Jhony, Pettinato, KImberly, Lim, Joaquin, Jacob, Joshua, Adams, Cara, Tejani, Viral, Algren, Heather A., Alicic, Radica, Baxter, Joni, Brennan, Conor, Caudill, Antonina, Chen, Peter, Chopra, Tananshi, Chu, Helen Y., Del Alcazar, James, Duven, Alexandria M., Edmark, Rick, Emerson, Sarah, Goldman, Jason D., Gutierrez, Vanessa, Hadlock, Jennifer, Harteloo, Alex, Heath, James R., Hood, Susan, Jackman, Susan, Kaneko, John, Kemp, Megan, Kim, Christina, Kuykendall, Kelli, Li, Sarah, Logue, Jennifer K., Magis, Andrew T., Manner, Paula, Mason, Carly, McCaffrey, Kathryn, McDonald, Connor, McDonald, Dylan, Murray, Kim M., Nackviseth, Callista, Nguyen, Helen, Parimon, Tanyalak, Poussier, Rachel, Rowen, Lee, Satira, Richard, Torbati, Sam, Tuttle, Katherine R., Wallick, Julie A., Yuan, Dan, Watanabe, Kino, Wilcox, Lauren E., Contreras, Fatima, Dahlke, Lea, Gudipudi, Lasya, Modes, Matthew, Muttera, Nicole, Salinas, Nancy, Tadeo, Josie, White, Shane, Alvarado, Stephanie, Anderson, Reed, Arellanes, Azaneth, Barajas, Rose A., Chauhan, Suneet P., Clarke, Geoffrey D., Farner, Cheryl E., Fischer, Melinda S., Goldberg, Mark P., Hasbani, Keren, Hastings, Gabrielyd, Heard, Patricia, Herrera, Italia, Infante, Edgar, Johnson, Hillary, Jones, Johnnie, Kellogg, Dean L., Kraig, Ellen, Longoria, Lisa, Nambiar, Anoop M., Okafor, Emeka, Paredes, Claudia C., Patterson, Thomas F., Patterson, Jan E., Pinones, Alexis, Potter, Jennifer S., Reeves, W.B., Saade, George R., Salehi, Marzieh, Scholler, Irma, Seshadri, Sudha, Shah, Dimpy P., Shah, Pankil, Sharma, Kumar, Sharma, Kavita, Soileau, Bridgette, Solis, Pamela, Stoebner, Carmen, Sullivan, Michael, Taylor, Barbara S., Tragus, Robin, Tsevat, Joel, Verduzco-Gutierrez, Monica, Ahuja, Neera, Blish, Catherine A., Blomkalns, Andra L., Bonilla, Hector, Brotherton, Richard, Clinton, Kimberly, Dingankar, Vaidehi, Geng, Linda N., Go, Minjoung, Haddad, Francois, Jagannathan, Prasanna, Jamero, Christopher, Jee, Kathryn, Jia, Xiaolin K., Khurana, Naresh, Kumar, Andre, Maldonado, Yvonne, Miglis, Mitchell G., O'Conor, Ellen, Olszewski, Kelly, Pathak, Divya, Quintero, Orlando, Scott, Jake, Singh, Upinder, Urdaneta, Alfredo E., Utz, Paul J., Varkey, Mary R., Saperia, Corey, Autry, Lynn, Bime, Christian, Borwege, Sabine, Copeland, Jacquelynn, DiLise-Russo, Marjorie, Ernst, Kacey C., Esquivel, Denise R., Fadden, Susan, Gomez, Isaias, Grischo, Garrett, Hansen, Lillian, Harris, David T., Harris, Stefanie, Hartley, William, Hernandez, Michael, Hillier, Leah, Hsu, Harvey, Hughes, Trina, Ismail, Hira, Iusim, Stephanie, James, Michelle, Kala, Mrinalini, Karnafel, Maria, Kim, Daniel, Knox, Kenneth S., Koleski, Alison, LaFleur, Bonnie, Lambert, Brenda, LaRue, Sicily, Lee-Iannotti, Joyce K., Lieberman, David, Lutrick, Karen, Merchant, Nirav, Morton, Christopher, Mosier, Jarrod M., Murthy, Ganesh, Nikolich, Janko Z., Olorunnisola, Toluwanimi, Parthasarathy, Sairam, Peralta, Jeanette, Pilling, William, Pogreba-Brown, Kristen, Reiman, Eric M., Rischard, Franz P., Ryan, Lee T., Smith, Terry, Snyder, Manuel, Soto, Francisco, Subbian, Vignesh, Suhr, Kyle, Unzek, Samuel, Vadovicky, Sheila, Velarde, Deanna, Veres, Sharry, Wilson, Cathleen, Anderson, Grace, Anglin, Khamal, Argueta, Urania, Asare, Kofi, Buitrago, Melissa, Chang Song, Celina, Clark, Alexus, Conway, Emily, Deeks, Steven G., Del Castillo, Nicole, Deswal, Monika, Durstenfeld, Matthew S., Eilkhani, Elnaz, Eun, Avery, Fehrman, Emily, Figueroa, Tony, Flores, Diana, Grebe, Halle, Henrich, Timothy J., Hoh, Rebecca, Hsue, Priscilla, Huang, Beatrice, Ibrahim, Rania, Kelly, John D., Kerbleski, Marian, Kirtikar, Raushun, Lew, Megan T., Lombardo, James, Lopez, Monica, Luna, Michael, Marquez, Carina, Martin, Jeffrey N., Munter, Sadie, Ngo, Lynn, Peluso, Michael J., Pineda-Ramirez, Jesus, Rhoads, Kim, Rodriguez, Antonio, Romero, Justin, Ryder, Dylan, So, Matthew, Somsouk, Ma, Tai, Viva, Tran, Brandon, Uy, Julian, Valdivieso, Daisy, Verma, Deepshika, Williams, Meghann, Zamora, Andhy, Newman, Lisa T., Abella, Julie, Barnette, Quinn, Bevc, Christine, Beverly, Jennifer, Ceger, Patricia, Croxford, Julie, Enger, Mike, Fain, Katie, Farris, Tonya, Hanlon, Sean, Hines, David, Johnson-Lawrence, Vicki, Jordan, Kevin, Lefebvre, Craig, Linas, Beth, Luukinen, Bryan, Mandal, Meisha, McKoy, Nikki J., Nance, Susan, Pasquarelli, Demian, Quiner, Claire, Sembajwe, Rita, Shaw, Gwendolyn, Thornburg, Vanessa, Tosco, Kendall, Wright, Hannah, Gross, Rachel S., Hochman, Judith S., Horwitz, Leora I., Katz, Stuart D., Troxel, Andrea B., Adler, Lenard, Akinbo, Precious, Almenana, Ramona, Aschalew, Malate, Balick, Lara, Bello, Ola, Bhuiyan, Sultana, Blachman, Nina, Branski, Ryan, Briscoe, Jasmine, Brosnahan, Shari, Bueler, Elliott, Burgos, Yvette, Caplin, Nina, Chaplin, Domonique, Chen, Yu, Cheng, Shen, Choe, Peter, Choi, Jess, Chung, Alicia, Church, Richard, Cobos, Stanley, Croft, Nakia, Irving, Angelique Cruz, Del Boccio, Phoebe, Díaz, Iván, Divers, Jasmin, Doshi, Vishal, Dreyer, Benard, Ebel, Samantha, Esquenazi-Karonika, Shari, Faustin, Arline, Febres, Elias, Fine, Jeffrey, Fink, Sandra, Freeland, Catherine, Frontera, Jennifer, Gallagher, Richard, Gonzalez-Duarte, Alejandra, Gross, Rachel, Hasson, Denise, Hill, Sophia, Hochman, Judith, Horwitz, Leora, Hossain, Jennifer, Islam, Shahidul, Jean, Christina Saint, Johnson, Stephen, Kansal, Neha, Katz, Stuart, Kenney, Rachel, Kershner, Tammy, Kewlani, Deepshikha, Kwak, Judy, Lamendola-Essel, Michelle F., Laury, Sarah, Laynor, Gregory, Lei, Lei, Leon, Terry, Linton, Janelle, Logan, Max, Malik, Nadia, Mamistvalova, Lia, Mandel, Hannah, Maranga, Gabrielle, Mattoo, Aprajita, Mei, Tony, Mendelsohn, Alan, Mercier, Emmanuelle, Vernetti, Patricio Millar, Miller, Marc, Mitchell, Maika, Moreira, Andre, Mudumbi, Praveen C., Nahin, Erica, Nair, Nandini, Nekulak, Joseph, Owens, Kellie, Parent, Brendan, Patibandla, Nandan, Petrov, Peter, Postelnicu, Radu, Pratt, Francesca, Randall, Isabelle, Rao, Priyatha, Rapkiewicz, Amy, Rizzo, JohnRoss, Rosas, Johana, Rose, Chelsea, Saint-Jean, Christina, Santacatterina, Michelle, Shah, Binita, Shaukat, Aasma, Simon, Naomi, Simsir, Aylin, Stinson, Miranda, Tang, Wenfei, Tatapudi, Vasishta, Thawani, Sujata, Thomas, Mary, Thorpe, Lorna, Tom, MeeLee, Treiha, Ethan, Troxel, Andrea, Truong, Jennifer, Udosen, Mmekom, Valencia, Carlos, Velazquez-Perez, Jessica, Vernetti, Patricio M., Vidal, Crystal, Viswanathan, Anand, Willerford, Amy, Williams, Natasha, Wong, Crystal, Wood, Marion J., Wuller, Shannon, Yin, Shonna H., Young, Chloe, Zaretsky, Jonah, Zavlunova, Susanna, Ahirwar, Shreya, Ahmed, Shifa, Ainsworth, Layne L., Atchley-Challenner, Rachel, Avilach, Paul, Balan, Trisha T., Benik, Nicholas, Benoit, Barbara, Bind, Marie-Abèle C., Bonaventura, William J., Boutin, Natalie, Brion, Beverly, Cagan, Andrew, Cai, Tianrun, Cao, Tingyi, Castro, Victor M., Cerretani, Xander R., Chan, James G., Cheng, David, Chibnik, Lori B., Ciriello, Mark, Costenbader, Karen, Dimitrov, Dimitar S., Estiri, Hossein, Fayad, Maria, Feldman, Candace H., Foulkes, Andrea, Gainer, Vivian, Ghosh, Bhaswati, Gollub, Randy, Guan, Zoe, Harris, Alan, Helmer, Karl, Hendrix, Andrew, Holzbach, Ana, Huang, Weixing, Karlson, Elizabeth W., Kaufman, Daniel, Keogh, Diane, Kerr, James D., Klann, Jeffrey G., Krishnamoorthy, Aparna, Lasky-Su, Jessica A., Liao, Katherine P., MacFadden, Doug, Maram, Anupama, Martel, Megan W., Mendis, Michael, Metta, Reeta, Monteiro, Jonathan, Morales, Eduardo, Morse, Richard E., Murphy, Shawn, Nazaire, Marc-Danie, Neils, Gregory, Nguyen, Amber N., Norman, James, Paik, Henry H., Pant, Deepti, Park, HeeKyong, Rabideau, Dustin J., Reeder, Harrison T., Rossi-Roh, Kathleen, Santacroce, Leah M., Schlepphorst, Katherine, Schulte, Carolin, Selvaggi, Caitlin A., Shinnick, Daniel J., Simons, William, Simpson, Lynn A., St. Jean Flanders, Mary L., Strasser, Zachary, Thakrar, Mansi R., Thaweethai, Tanayott, Thorn, Madeleine, Trewett, Philip, Van Fleet, Dustin, Wagholikar, Kavishwar B., Wang, Taowei D., Wattanasin, Nich, Weber, Griffin, Williams, Michael A., Zhang, Ren Zhe, Cicek, Mine, Chang, Nancy, Wirkus, Samantha, Zahnle, Nicole, Flotte, Thomas J., Frisch, Erika, Boysen, Erik M., Welch, Gary, Akintonwa, Teresa, Blancero, Frank, Brown, Heather-Elizabeth, Carmilani, Megan, Cerda, Marta, Clash, Victor H., Copeland, Debra, Hall, Yvonka, kevin kondo, Lerma, Lydia, Lindsay, Jacqui, Marti, Heather, Maughan, Christine, Minor, Tony, Taylor, Brittany, Vincent, Hyatt, Zissis, Mike, Anderson, Brett, Bardhan, Sujata, Castro-Baucom, Leah, Chisolm, Deena, Corchado, Claudia, Damian, April Joy, Daniel, Casey, DasGupta, Soham, Dehority, Walter, Feldman, Candace, Fessel, Josh, Rosas, Lisa Goldman, Horowitz, Carol, Khullar, Dhruv, Lopez, Keila, McDonald Pinkett, Shelly, Myaskovsky, Larissa, Regino, Lidia, St John Thomas, Gelise, Stewart de Ramirez, Sarah, Vangeepuram, Nita, Walden, Anita, Williams, Neely, Yin, Shonna, Burton, Phoebe, Catallozzi, Marina, Clark, Cheryl, Dworetzky, Beth, Edwards, Belinda, Ferrer, Robert L., Judd, Suzanne, Rothman, Russell, Wagner, Laura, Wallace, Ann, Adams, Sonseeahray (Ray), Aragon, Leyna, Bander, Bryan, Bishof, Karyn, Brooks, Gail, Carignan, Etienne, Coombs, Krista, Davis, Hannah, Blakley, Felicia D., Diggs, Marissa, Brown, Heather E., Favors, Umar, Fields, Whitney, Fisher, Liza, Fitzgerald, Megan, Gaffney, Alicia, Witvliet, Margot Gage, Garcia, Roberto, Gustafson, Tyler, Guthe, Nick, Holmes, Verna, Hornig, Mady, Hornig, Maxwell, Jefferson, Wendy, Kochis, Nancy, Kondo, Kevin, Lam, Julie, Lawrence, Fadwa, Letts, Rebecca, Lewis, Juan, Lopez, Silcia, Martinez, Thomas, McCorkell, Lisa, McGrath, Rebecca, Minor, Thomas T., Moore, Charita, Nguyen, Kian, Nichols, Lauren, O'Brien, Lisa, Olson, Holly, Peddie, Aimee, Perlowski, Alice, Lorenzo, Elizabeth P., Prentiss, Lisa, Raytselis, Nadia, Rochez, Nitza, Rockwell, Megan, Rutter, Jacqueline, Seibert, Elle, Sekar, Anisha, Smith, Chimere, Stiles, Lauren, Taylor, Emily, Thompson, Julie, Trapp, Stephen, Valdiva, Stephen, Wilensky, Rochelle, Williams, Melissa, Dawson, Kay W., Wylam, Andrew, Swank, Zoe, Borberg, Ella, Chen, Yulu, Senussi, Yasmeen, Chalise, Sujata, Manickas-Hill, Zachary, Yu, Xu G., Li, Jonathan Z., Alter, Galit, Kelly, J. Daniel, Goldberg, Sarah A., Talla, Aarthi, Li, Xiaojun, Skene, Peter, Bumol, Thomas F., Torgerson, Troy R., Czartoski, Julie L., McElrath, M. Juliana, and Walt, David R.
Published: 2024
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39. Endocannabinoid dysfunction in neurological disease: neuro-ocular DAGLA-related syndrome (NODRS)

Author: Bainbridge, Matthew N, Mazumder, Aloran, Ogasawara, Daisuke, Jamra, Rami Abou, Bernard, Geneviève, Bertini, Enrico, Burglen, Lydie, Cope, Heidi, Crawford, Ali, Derksen, Alexa, Dure, Leon, Gantz, Emily, Koch-Hogrebe, Margarete, Hurst, Anna CE, Mahida, Sonal, Marshall, Paige, Micalizzi, Alessia, Novelli, Antonio, Peng, Hongfan, Medicine, Rady Children's Institute for Genomic, Rodriguez, Diana, Robbins, Shira L, Rutledge, S Lane, Scalise, Roberta, Schließke, Sophia, Shashi, Vandana, Srivastava, Siddharth, Thiffault, Isabella, Topol, Sarah, Acosta, Maria T, Adam, Margaret, Adams, David R, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G, Deardorff, Matthew, Dell’Angelica, Esteban C, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L, Douine, Emilie D, Duncan, Laura, and Earl, Dawn
Subjects: Biotechnology, Brain Disorders, Genetics, Neurosciences, Clinical Research, Rare Diseases, Eye Disease and Disorders of Vision, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Neurological, Humans, Child, Endocannabinoids, Phenotype, Nervous System Diseases, Heterozygote, Syndrome, Mutant Proteins, endocannabinoid, episodic ataxia, paroxysmal tonic upgaze, nystagmus, developmental delay, Rady Children's Institute for Genomic Medicine, Undiagnosed Disease Network, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract: The endocannabinoid system is a highly conserved and ubiquitous signalling pathway with broad-ranging effects. Despite critical pathway functions, gene variants have not previously been conclusively linked to human disease. We identified nine children from eight families with heterozygous, de novo truncating variants in the last exon of DAGLA with a neuro-ocular phenotype characterized by developmental delay, ataxia and complex oculomotor abnormality. All children displayed paroxysms of nystagmus or eye deviation accompanied by compensatory head posture and worsened incoordination most frequently after waking. RNA sequencing showed clear expression of the truncated transcript and no differences were found between mutant and wild-type DAGLA activity. Immunofluorescence staining of patient-derived fibroblasts and HEK cells expressing the mutant protein showed distinct perinuclear aggregation not detected in control samples. This report establishes truncating variants in the last DAGLA exon as the cause of a unique paediatric syndrome. Because enzymatic activity was preserved, the observed mislocalization of the truncated protein may account for the observed phenotype. Potential mechanisms include DAGLA haploinsufficiency at the plasma membrane or dominant negative effect. To our knowledge, this is the first report directly linking an endocannabinoid system component with human genetic disease and sets the stage for potential future therapeutic avenues.
Published: 2022

40. The underwriter's conflict of interest and earnings forecast bias in prospectus: Evidence from Hong Kong

Author: Chen, Peter F., Hou, Qingchuan, Wang, Yihong, and Xu, Lifang
Published: 2024
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41. Shock prediction with dipeptidyl peptidase-3 and renin (SPiDeR) in hypoxemic patients with COVID-19

Author: Busse, Laurence W., Teixeira, J. Pedro, Schaich, Christopher L., ten Lohuis, Caitlin C., Nielsen, Nathan D., Sturek, Jeffrey M., Merck, Lisa H., Self, Wesley H., Puskarich, Michael A., Khan, Akram, Semler, Matthew W., Moskowitz, Ari, Hager, David N., Duggal, Abhijit, Rice, Todd W., Ginde, Adit A., Tiffany, Brian R., Iovine, Nicole M., Chen, Peter, Safdar, Basmah, Gibbs, Kevin W., Javaheri, Ali, de Wit, Marjolein, Harkins, Michelle S., Joly, Meghan M., and Collins, Sean P.
Published: 2025
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42. Bioaccessibility of avocado polyhydroxylated fatty alcohols

Author: Ahmed, Nawaz, Smith, Richard W., Chen, Peter X., Rogers, Michael A., and Spagnuolo, Paul A.
Published: 2025
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43. Risk-Adapted Intraoperative Radiation Therapy (IORT) for Breast Cancer: A Novel Analysis

Author: Silverstein, Melvin J., Kim, Brian, Lin, Kevin, Lloyd, Shane, Snyder, Lincoln, Khan, Sadia, Kramme, Katherine, and Chen, Peter
Published: 2023
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44. Efficacy and Safety of Ensovibep for Adults Hospitalized With COVID-19

Author: Barkauskas, Christina, Mylonakis, Eleftherios, Poulakou, Garyfallia, Young, Barnaby E, Vock, David M, Siegel, Lianne, Engen, Nicole, Grandits, Greg, Mosaly, Nilima R, Vekstein, Andrew M, Rogers, Ralph, Shehadeh, Fadi, Kaczynski, Matthew, Mylona, Evangelia K, Syrigos, Konstantinos N, Rapti, Vasiliki, Lye, David C, Hui, Diong Shiau, Leither, Lindsay, Knowlton, Kirk U, Jain, Mamta K, Marines-Price, Rubria, Osuji, Alice, Overcash, J Scott, Kalomenidis, Ioannis, Barmparessou, Zafeiria, Waters, Michael, Zepeda, Karla, Chen, Peter, Torbati, Sam, Kiweewa, Francis, Sebudde, Nicholus, Almasri, Eyad, Hughes, Alyssa, Bhagani, Sanjay R, Rodger, Alison, Sandkovsky, Uriel, Gottlieb, Robert L, Nnakelu, Eriobu, Trautner, Barbara, Menon, Vidya, Lutaakome, Joseph, Matthay, Michael, Robinson, Philip, Protopapas, Konstantinos, Koulouris, Nikolaos, Kimuli, Ivan, Baduashvili, Amiran, Braun, Dominique L, Günthard, Huldrych F, Ramachandruni, Srikanth, Kidega, Robert, Kim, Kami, Hatlen, Timothy J, Phillips, Andrew N, Murray, Daniel D, Jensen, Tomas O, Padilla, Maria L, Accardi, Evan X, Shaw-Saliba, Katy, Dewar, Robin L, Teitelbaum, Marc, Natarajan, Ven, Laverdure, Sylvain, Highbarger, Helene C, Rehman, M Tauseef, Vogel, Susan, Vallée, David, Crew, Page, Atri, Negin, Schechner, Adam J, Pett, Sarah, Hudson, Fleur, Badrock, Jonathan, Touloumi, Giota, Brown, Samuel M, Self, Wesley H, North, Crystal M, Ginde, Adit A, Chang, Christina C, Kelleher, Anthony, Nagy-Agren, Stephanie, Vasudeva, Shikha, Looney, David, Nguyen, Hien H, Sánchez, Adriana, Weintrob, Amy C, Grund, Birgit, Sharma, Shweta, Reilly, Cavan S, Paredes, Roger, Bednarska, Agnieszka, Gerry, Norman P, Babiker, Abdel G, Davey, Victoria J, Gelijns, Annetine C, Higgs, Elizabeth S, Kan, Virginia, Matthews, Gail, and Thompson, B Taylor
Subjects: Clinical Research, Clinical Trials and Supportive Activities, Lung, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Good Health and Well Being, Adult, Designed Ankyrin Repeat Proteins, Double-Blind Method, Humans, Recombinant Fusion Proteins, SARS-CoV-2, Treatment Outcome, COVID-19 Drug Treatment, ACTIV-3/TICO Study Group, Clinical Sciences, Public Health and Health Services
Abstract: BackgroundEnsovibep (MP0420) is a designed ankyrin repeat protein, a novel class of engineered proteins, under investigation as a treatment of SARS-CoV-2 infection.ObjectiveTo investigate if ensovibep, in addition to remdesivir and other standard care, improves clinical outcomes among patients hospitalized with COVID-19 compared with standard care alone.DesignDouble-blind, randomized, placebo-controlled, clinical trial. (ClinicalTrials.gov: NCT04501978).SettingMultinational, multicenter trial.ParticipantsAdults hospitalized with COVID-19.InterventionIntravenous ensovibep, 600 mg, or placebo.MeasurementsEnsovibep was assessed for early futility on the basis of pulmonary ordinal scores at day 5. The primary outcome was time to sustained recovery through day 90, defined as 14 consecutive days at home or place of usual residence after hospital discharge. A composite safety outcome that included death, serious adverse events, end-organ disease, and serious infections was assessed through day 90.ResultsAn independent data and safety monitoring board recommended that enrollment be halted for early futility after 485 patients were randomly assigned and received an infusion of ensovibep (n = 247) or placebo (n = 238). The odds ratio (OR) for a more favorable pulmonary outcome in the ensovibep (vs. placebo) group at day 5 was 0.93 (95% CI, 0.67 to 1.30; P = 0.68; OR > 1 would favor ensovibep). The 90-day cumulative incidence of sustained recovery was 82% for ensovibep and 80% for placebo (subhazard ratio [sHR], 1.06 [CI, 0.88 to 1.28]; sHR > 1 would favor ensovibep). The primary composite safety outcome at day 90 occurred in 78 ensovibep participants (32%) and 70 placebo participants (29%) (HR, 1.07 [CI, 0.77 to 1.47]; HR
Published: 2022

45. A novel DPH5-related diphthamide-deficiency syndrome causing embryonic lethality or profound neurodevelopmental disorder

Author: Shankar, Suma P, Grimsrud, Kristin, Lanoue, Louise, Egense, Alena, Willis, Brandon, Hörberg, Johanna, AlAbdi, Mayer, Klaus, Ütkür, Koray, Monaghan, Kristin G, Krier, Joel, Stoler, Joan, Alnemer, Maha, Shankar, Prabhu R, Schaffrath, Raffael, Alkuraya, Fowzan S, Brinkmann, Ulrich, Eriksson, Leif A, Lloyd, Kent, Rauen, Katherine A, Network, Undiagnosed Diseases, Acosta, Maria T, Adam, Margaret, Adams, David R, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G, Deardorff, Matthew, Dell'Angelica, Esteban C, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L, Douine, Emilie D, Duncan, Laura, Earl, Dawn, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Esteves, Cecilia, Falk, Marni, Fernandez, Liliana, Fieg, Elizabeth L, and Fisher, Paul G
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Genetics, Pediatric, Human Genome, 2.1 Biological and endogenous factors, Adenosine Diphosphate, Animals, Histidine, Humans, Methyltransferases, Mice, Mice, Inbred C57BL, Neurodevelopmental Disorders, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Syndrome, Nonverbal neurodevelopment delays, Novel gene discovery, Precision animal modeling, Precision genomics, Translational genetics, Undiagnosed Diseases Network, Clinical Sciences, Genetics & Heredity
Abstract: PurposeDiphthamide is a post-translationally modified histidine essential for messenger RNA translation and ribosomal protein synthesis. We present evidence for DPH5 as a novel cause of embryonic lethality and profound neurodevelopmental delays (NDDs).MethodsMolecular testing was performed using exome or genome sequencing. A targeted Dph5 knockin mouse (C57BL/6Ncrl-Dph5em1Mbp/Mmucd) was created for a DPH5 p.His260Arg homozygous variant identified in 1 family. Adenosine diphosphate-ribosylation assays in DPH5-knockout human and yeast cells and in silico modeling were performed for the identified DPH5 potential pathogenic variants.ResultsDPH5 variants p.His260Arg (homozygous), p.Asn110Ser and p.Arg207Ter (heterozygous), and p.Asn174LysfsTer10 (homozygous) were identified in 3 unrelated families with distinct overlapping craniofacial features, profound NDDs, multisystem abnormalities, and miscarriages. Dph5 p.His260Arg homozygous knockin was embryonically lethal with only 1 subviable mouse exhibiting impaired growth, craniofacial dysmorphology, and multisystem dysfunction recapitulating the human phenotype. Adenosine diphosphate-ribosylation assays showed absent to decreased function in DPH5-knockout human and yeast cells. In silico modeling of the variants showed altered DPH5 structure and disruption of its interaction with eEF2.ConclusionWe provide strong clinical, biochemical, and functional evidence for DPH5 as a novel cause of embryonic lethality or profound NDDs with multisystem involvement and expand diphthamide-deficiency syndromes and ribosomopathies.
Published: 2022

46. Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation

Author: Zhao, Xiaoming, Chen, Weihai, Wu, Xingming, Chen, Peter C. Y., and Li, Zhengguo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Deep stereo matching has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D correlation maps and iterative disparity updates have been proposed. Regarding that in real-world platforms, such as self-driving cars and robots, the Lidar is usually installed. Thus we further introduce the sparse Lidar point into the iterative updates, which alleviates the burden of network updating the disparity from zero states. Furthermore, we propose training the network in a self-supervised way so that it can be trained on any captured data for better generalization ability. Experiments and comparisons show that the presented method is effective and achieves comparable results with related methods.
Published: 2021

47. ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction

Author: Zhao, Xiaoming, Wu, Xingming, Miao, Jinyu, Chen, Weihai, Chen, Peter C. Y., and Li, Zhengguo
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Existing methods detect the keypoints in a non-differentiable way, therefore they can not directly optimize the position of keypoints through back-propagation. To address this issue, we present a partially differentiable keypoint detection module, which outputs accurate sub-pixel keypoints. The reprojection loss is then proposed to directly optimize these sub-pixel keypoints, and the dispersity peak loss is presented for accurate keypoints regularization. We also extract the descriptors in a sub-pixel way, and they are trained with the stable neural reprojection error loss. Moreover, a lightweight network is designed for keypoint detection and descriptor extraction, which can run at 95 frames per second for 640x480 images on a commercial GPU. On homography estimation, camera pose estimation, and visual (re-)localization tasks, the proposed method achieves equivalent performance with the state-of-the-art approaches, while greatly reduces the inference time., Comment: 11 pages, 11 figures
Published: 2021

48. Validation of Patient-Reported Outcomes in Patients With Nonmetastatic Breast Cancer Receiving Comprehensive Nodal Irradiation in the RadComp Trial

Author: Godette, Karen D., Patel, Sagar, Langen, Katja M., Zielan, Ryan R., DeBlois, David, Pang, Dalong, Rudra, Sonali, Benhabib, Sidi, Chawla, Ashish, Chen, Kuanling, Dadkhah, Hossein, Majithia, Lonika, Rao, Avani D., Stephenson, Lisa, Wang, Peng, Croog, Victoria J., Li, Heng, Smith, Karen, Stinson, Susan, Walker, Amanda J., Devisetty, Kiran, Hyde, Christian, Depauw, Nicholas, Godishala, Anuradha, Ho, Alice, Paganetti, Harald, Soto, Daniel, Suero-Abreu, Giselle, Taghian, Alphonse G., Corbin, Kimberly S., Halyard, Michele Y., Jackson, Amanda, Liang, Xiaoying, Manke, Heather, McGee, Lisa A., Pafundi, Deanna H., Remmes, Nicholas B., Shumway, Dean A., Yan, Elizabeth S., Bakhoum, Samuel F., Barron, David A., Bernstein, Michael, Cuaron, John J., Dover, Laura, Gelblum, Daphna Y., Gilbo, Philip, Gewanter, Richard M., Guttman, David M., Hong, Linda, Khan, Atif, LaPlant, Quincey, Mann, Justin, McCormick, Beryl, Mueller, Boris A., Mychalczak, Borys, Parikh, Dhwani, Powell, Simon N., Romesser, Paul B., Roth O'Brien, Diana, Schupak, Karen D., Shepherd, Annemarie F., Xu, Amy, Yu, Anthony, Zinovoy, Melissa, Bennouna, Jaafar, Fagundes, Marcio, Panoff, Joseph, Amelia Rodrigues, Maria, Yu, Jen, Lee, Choonsik, Chen, Chin-Cheng, Choi, J. Isabelle, Lin, Haibo, Akthar, Adil S., Akhter, Nausheen, Mihalcik, Stephen A., Pankuch, Mark, Parhar, Preeti K., Stutz, Michael D., Thukral, Arpi D., Mah, Dennis, Tsai, Henry K., Paulus, Rebecca, Haffty, Bruce G., Goyal, Sharad, Yue, Ning J., Sturgeon, Jared D., Wilkinson, J. Ben, Boggs, D. Hunter, Cardan, Rex A., Dalton, Allison, McDonald, Andrew, Prior, Fred, Mayadev, Jyoti S., Moiseenko, Vitali, Narezkina, Anya, Phreaner, Nicholas, Rash, Dominique, Seibert, Tyler, Yashar, Catheryn, Daugherty, Emily C., Mascia, Anthony, Medek, Sara A., Meier, Teresa, Bradley, Julie A., Lockney, Natalie, Mailhot, Raymond B., Mendenhall, Nancy, Rutenberg, Michael, Harris, Eleanor E., Lyons, Janice, Hong, Jack J., Kunaprayoon, Dan K., McAvoy, Sarah A., Manu Mysore, Nowak Choi, Kamila A., Patel, Akshar N., Vyfhuis, Melissa A.L., Wilson, Joelle, Algan, Ozer, Chen, Yong, Henson, Christina E., Dvorak, Tomas, Willoughby, Twyla, Zeidan, Omar, Berman, Abigail T., Chen, Et-tsu, Dong, Lei, Driscoll, Amanda, Feigenberg, Steven J., Feriozzi, Ashley, Hencek, Carolyn, Kolker, James D., Konski, Andre A., Ky, Bonnie, Lin, Lilie L., Novick, Kristina L., Reza, Nosheen, Siegal, Ann Marie, Tabakha, Sara, Taunk, Neil K., Wilcox, Nicholas, Zou, Wei, Bhooshan, Neha, Croley, Richard, Weksberg, David C., Ahmad, Neelour, Hoffman, Karen E., Joyner, Melissa M., Mitchell, Melissa P., Poenisch, Falk, Smith, Benjamin D., Strom, Eric A., Vallabhaneni, Srilakshmi, Cheng, Richard, Kang, Kylie H., Kim, H. Katherine, Kim, Janice N., Wang, Waylene A., Wong, Tony, Bosch, Walter, Mitchell, Joshua, Straube, William, Zoberi, Imran, Balanescu, Dinu, Chen, Peter Y., Dilworth, Joshua T., Ding, Xuanfeng, Donisan, Teodora, Hamstra, Daniel A., Daniella Dang, Phuong, Katz, Stanford, Wang, Chiachien J., Terry Wu, Hahn, Elizabeth A., Pugh, Stephanie L., Lu, Hien L., Vela, Alyssa M., Gillespie, Erin F., Nichols, Elizabeth M., Wright, Jean L., MacDonald, Shannon M., Cahlon, Oren, Baas, Carole, Braunstein, Lior Z., Fang, L. Christine, Freedman, Gary M., Jimenez, Rachel B., Kesslering, Christy M., Mishra, Mark V., Mutter, Robert W., Ohri, Nisha, Rosen, Lane R., Urbanic, James J., Jagsi, Reshma, Mitchell, Sandra A., Bekelman, Justin E., and Cella, David
Published: 2024
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49. Loss of function of FAM177A1, a Golgi complex localized protein, causes a novel neurodevelopmental disorder

Author: Acosta, Maria T., Adam, Margaret, Adams, David R., Alvarez, Raquel L., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Bacino, Carlos A., Bademci, Guney, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bellen, Hugo J., Bennett, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Cassini, Thomas, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Chinn, Ivan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Sessions Cole, F., Colley, Heather A., Cope, Heidi, Corner, Brian, Corona, Rosario, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Dell’Angelica, Esteban C., Dickson, Patricia, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Ezell, Kimberly, Falk, Marni, Fieg, Elizabeth L., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Goddard, Page C., Godfrey, Rena A., Golden-Grant, Katie, Grajewski, Alana, Hadley, Don, Hahn, Sihoun, Halley, Meghan C., Hamid, Rizwan, Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Hutchison, Sarah, Introne, Wendy, Isasi, Rosario, Izumi, Kosuke, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Jean-Marie, Orpa, Jobanputra, Vaidehi, Karaviti, Lefkothea, Ketkar, Shamika, Kiley, Dana, Kilich, Gonench, Kobren, Shilpa N., Kohane, Isaac S., Kohler, Jennefer N., Korrick, Susan, Kozuira, Mary, Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Lalani, Seema R., Lam, Byron, Lam, Christina, Lanpher, Brendan C., Lanza, Ian R., LeBlanc, Kimberly, Lee, Brendan H., Levitt, Roy, Lewis, Richard A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Maghiro, AudreyStephannie, Mahoney, Rachel, Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Lawrence Merritt, J., Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Mulvihill, John, Nakano-Okuno, Mariko, Nelson, Stanley F., Neumann, Serena, Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Carl Pallais, J., Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Pusey Swerdzewski, Barbara N., Quinlan, Aaron, Rao, Deepak A., Raper, Anna, Raskind, Wendy, Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenfeld, Jill A., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Saporta, Mario, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Ron Scott, C., Seto, Elaine, Shashi, Vandana, Shin, Jimann, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Kathleen, Sullivan, Jennifer A., Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Queenie K.-G., Tan, Amelia L.M., Tarakad, Arjun, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Ungar, Rachel A., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Walker, Melissa, Wallace, Stephanie, Walley, Nicole M., Wambach, Jennifer, Wan, Jijun, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Hubshman, Monika Weisz, Wener, Mark, Wenger, Tara, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Worley, Kim, Xiao, Changrui, Yamamoto, Shinya, Yang, John, Zhang, Zhe, Zuchner, Stephan, Legro, Nicole R., Bowman, Angela, Ugur, Berrak, Jackstadt, Madelyn M., Shriver, Leah P., Patti, Gary J., Zhang, Bo, Feng, Wenjia, McAdow, Anthony R., Goddard, Pagé, Jensen, Tanner, Fresard, Laure, Alvarez, Raquel, McCormack, Colleen, Holt, James M., Worthey, Elizabeth A., Montgomery, Stephen B., Postlethwait, John, De Camilli, Pietro, and Solnica-Krezel, Lila
Published: 2024
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50. Non-nasal, atypical retinochoroidal coloboma in pediatric patients: Case series and review

Author: Serena Shah, Natasha Ferreira Santos da Cruz, Patrick Staropoli, Francisco Lopez-Font, Ta Chen Peter Chang, and Audina Berrocal
Subjects: Atypical coloboma, Retinochoroidal coloboma, Retinal detachment, Pediatric retina, Ophthalmology, RE1-994
Abstract: Purpose: To report 2 cases of atypically located, non-nasal colobomas in the pediatric population. Observations: A 3-week-old female neonate with no known past ocular or medical history was diagnosed with temporal iris and chorioretinal coloboma with tractional membranes upon examination under anesthesia and imaging. A 9-year-old female with a history of bilateral sensorineural hearing loss and left mild hydronephrosis presented with a temporal chorioretinal coloboma associated with retinal detachment. Conclusions and importance: Very few cases of atypically located, non-nasal pediatric colobomas have been reported, and they lack a clear cause or mechanism of formation. Continued documentation of their occurrence and research into their formation at a molecular and embryological level are warranted to better understand their pathogenesis.
Published: 2024
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