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Lin Qi,1,* Yanglei Li,2,* Chengqi Kong,2,* Siqi Li,2 Qinyue Wang,2 Hanqin Pan,2 Shuyi Zhang,3 Xinkai Qu,2 Ming Li,1 Mingxuan Li,2,4 Kailei Shi2 1Department of Computed Tomography, Huadong Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China; 2Department of Cardiology, Huadong Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China; 3Department of Cardiac Care Unit, Tongren Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 4Department of Cardiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kailei Shi; Mingxuan Li, Email shikailei@fudan.edu.cn; 19211280015@fudan.edu.cnBackground: Inflammation triggers atherosclerotic plaque rupture, leading to acute myocardial infarction (AMI). Following AMI, peri-coronary adipose tissue (PCAT) undergoes a transition from lipid-rich to hydrophilic characteristics due to vascular inflammation. This study investigates PCAT changes and neutrophil-to-lymphocyte ratio levels during AMI.Patients and Methods: 60 AMI patients undergoing coronary computed tomography angiography and angiography (Jan 2020-Jun 2022) were studied 60 age, gender, BMI-matched stable angina, and 60 non-coronary artery disease patients were included. Siemens VB20.0 measured PCAT-volume and fat attenuation index (FAI). Neutrophil-to-lymphocyte ratio levels were calculated by peripheral blood tests.Results: The PCAT volume and PCAT-FAI gradually increased across the control, stable angina, and AMI groups, with a corresponding gradual rise in NLR. NLR exhibited weak positive correlation with PCAT-FAI (r=0.35) and PCAT-volume (r=0.24). Multivariable logistic regression identified increased PCAT-volume, PCAT-FAI and neutrophil-to-lymphocyte ratio as possible independent AMI risk factors. No significant PCAT-volume differen

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling. © 2023 Elsevier Ltd

For elastomer-based tactile sensors, represented by visuotactile sensors, routine calibration of mechanical parameters (Young's modulus and Poisson's ratio) has been shown to be important for force reconstruction. However, the reliance on existing in-situ calibration methods for accurate force measurements limits their cost-effective and flexible applications. This article proposes a new in-situ calibration scheme that relies only on comparing contact deformation. Based on the detailed derivations of the normal contact and torsional contact theories, we designed a simple and low-cost calibration device, EasyCalib, and validated its effectiveness through extensive finite element analysis. We also explored the accuracy of EasyCalib in the practical application and demonstrated that accurate contact distributed force reconstruction can be realized based on the mechanical parameters obtained. EasyCalib balances low hardware cost, ease of operation, and low dependence on technical expertise and is expected to provide the necessary accuracy guarantees for wide applications of visuotactile sensors in the wild., Comment: 8 pages, 8 figures

Large language models (LLMs) with Transformer architectures have become phenomenal in natural language processing, multimodal generative artificial intelligence, and agent-oriented artificial intelligence. The self-attention module is the most dominating sub-structure inside Transformer-based LLMs. Computation using general-purpose graphics processing units (GPUs) inflicts reckless demand for I/O bandwidth for transferring intermediate calculation results between memories and processing units. To tackle this challenge, this work develops a fully customized vanilla self-attention accelerator, AttentionLego, as the basic building block for constructing spatially expandable LLM processors. AttentionLego provides basic implementation with fully-customized digital logic incorporating Processing-In-Memory (PIM) technology. It is based on PIM-based matrix-vector multiplication and look-up table-based Softmax design. The open-source code is available online: https://bonany.cc/attentionleg., Comment: for associated source codes, see https://bonany.cc/attentionleg

PURPOSE: Chordoma is a rare bone tumor with a high recurrence rate and limited treatment options. The aim of this study was to identify molecular subtypes of chordoma that may improve clinical management. EXPERIMENTAL DESIGN: We conducted RNA sequencing in 48 tumors from patients with Chinese skull-base chordoma and identified two major molecular subtypes. We then replicated the classification using a NanoString panel in 48 patients with chordoma from North America. RESULTS: Tumors in one subtype were more likely to have somatic mutations and reduced expression in chromatin remodeling genes, such as PBRM1 and SETD2, whereas the other subtype was characterized by the upregulation of genes in epithelial-mesenchymal transition and Sonic Hedgehog pathways. IHC staining of top differentially expressed genes between the two subtypes in 312 patients with Chinese chordoma with long-term follow-up data showed that the expression of some markers such as PTCH1 was significantly associated with survival outcomes. CONCLUSIONS: Our findings may improve the understanding of subtype-specific tumorigenesis of chordoma and inform clinical prognostication and targeted options.

Jiasheng Cai,1,* Mingxuan Li,2,* Wei Wang,1,* Rong Luo,1 Zheng Zhang,1 Haibo Liu1 1Department of Cardiology, QingPu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, 201700, People’s Republic of China; 2Department of Cardiology, Shanghai Medical College Fudan University, Huadong Hospital, Shanghai, 200040, People’s Republic of China*These authors contributed equally to this workCorrespondence: Haibo Liu; Zheng Zhang, Department of Cardiology, QingPu Branch of Zhongshan Hospital Affiliated to Fudan University, 1158 Park, Shanghai, 201700, People’s Republic of China, Email haiboliu13@fudan.edu.cn; zhangzheng@qphospital.comBackgrounds and Aims: Atrial fibrillation (AF) is the most common cardiac arrhythmia among the older patients (≥ 80 years) in clinical practice. The index of neutrophil percentage-to-albumin ratio (NPAR) is a reliable predictor of adverse outcomes in cardiovascular diseases. There is scarce evidence regarding the association between NPAR and mortality among the older patients with AF.Methods: The research was conducted among 1141 patients with AF between January 2015 and June 2020, hospitalized at Huadong Hospital affiliated with Fudan University. The primary outcome were 28-day all-cause and cardiovascular mortality. Cox regression analysis and Kaplan-Meier survival curves were used to explore the correlation between NPAR and 28-day all-cause or cardiovascular mortality. Receiver operating characteristic (ROC) curve and the area under the curve (AUC) were performed for the predictive values of NPAR on prognosis.Results: The 28-day death rate from cardiovascular disease and all-causes were 3.3% and 8.7%, respectively. Continuous NPAR levels were positively associated with all-cause (HR 1.13, 95% CI 1.09, 1.16) and cardiovascular (HR 1.16, 95% CI 1.10, 1.23) mortality after adjustment for confounding variables. Relative to patients in the T1 group, those in higher NPAR tertiles also exhibit

Compiler optimization level recognition can be applied to vulnerability discovery and binary analysis. Due to the exists of many different compilation optimization options, the difference in the contents of the binary file is very complicated. There are thousands of compiler optimization algorithms and multiple different processor architectures, so it is very difficult to manually analyze binary files and recognize its compiler optimization level with rules. This paper first proposes a CNN-based compiler optimization level recognition model: BinEye. The system extracts semantic and structural differences and automatically recognize the compiler optimization levels. The model is designed to be very suitable for binary file processing and is easy to understand. We built a dataset containing 80,028 binary files for the model training and testing. Our proposed model achieves an accuracy of over 97%. At the same time, BinEye is a fully CNN-based system and it has a faster forward calculation speed, at least 8 times faster than the normal RNN-based model. Through our analysis of the model output, we successfully found the difference in assembly codes caused by the different compiler optimization level. This means that the model we proposed is interpretable. Based on our model, we propose a method to analyze the code differences caused by different compiler optimization levels, which has great guiding significance for analyzing closed source compilers and binary security analysis.

Visuotactile sensing technology has received much attention in recent years. This article proposes a feature detection method applicable to visuotactile sensors based on continuous marker patterns (CMP) to measure 3-d deformation. First, we construct the feature model of checkerboard-like corners under contact deformation, and design a novel double-layer circular sampler. Then, we propose the judging criteria and response function of corner features by analyzing sampling signals' amplitude-frequency characteristics and circular cross-correlation behavior. The proposed feature detection algorithm fully considers the boundary characteristics retained by the corners with geometric distortion, thus enabling reliable detection at a low calculation cost. The experimental results show that the proposed method has significant advantages in terms of real-time and robustness. Finally, we have achieved the high-density 3-d contact deformation visualization based on this detection method. This technique is able to clearly record the process of contact deformation, thus enabling inverse sensing of dynamic contact processes., Comment: 17 pages, 14 figures. Submitted to Measurement

While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks. Tremendous research efforts have been dedicated to mitigating the threats of adversarial attacks, but the essential trait of adversarial examples is not yet clear, and most existing methods are yet vulnerable to hybrid attacks and suffer from counterattacks. In light of this, in this paper, we first reveal a gradient-based correlation between sensitivity analysis-based DNN interpreters and the generation process of adversarial examples, which indicates the Achilles's heel of adversarial attacks and sheds light on linking together the two long-standing challenges of DNN: fragility and unexplainability. We then propose an interpreter-based ensemble framework called X-Ensemble for robust adversary defense. X-Ensemble adopts a novel detection-rectification process and features in building multiple sub-detectors and a rectifier upon various types of interpretation information toward target classifiers. Moreover, X-Ensemble employs the Random Forests (RF) model to combine sub-detectors into an ensemble detector for adversarial hybrid attacks defense. The non-differentiable property of RF further makes it a precious choice against the counterattack of adversaries. Extensive experiments under various types of state-of-the-art attacks and diverse attack scenarios demonstrate the advantages of X-Ensemble to competitive baseline methods., Comment: 10 pages, accepted to KDD'20

Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nanophotonic structures, electrodynamic simulations are essential. These simulations enable us to model electromagnetic fields over time and calculate optical properties. In this work, we introduce frameworks and benchmarks to evaluate nanophotonic structures in the context of parametric structure design problems. The benchmarks are instrumental in assessing the performance of optimization algorithms and identifying an optimal structure based on target optical properties. Moreover, we explore the impact of varying grid sizes in electrodynamic simulations, shedding light on how evaluation fidelity can be strategically leveraged in enhancing structure designs., Comment: 31 pages, 31 figures, 4 tables. Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), Datasets and Benchmarks Track

In typical in-hand manipulation tasks represented by object pivoting, the real-time perception of rotational slippage has been proven beneficial for improving the dexterity and stability of robotic hands. An effective strategy is to obtain the contact properties for measuring rotation angle through visuotactile sensing. However, existing methods for rotation estimation did not consider the impact of the incipient slip during the pivoting process, which introduces measurement errors and makes it hard to determine the boundary between stable contact and macro slip. This paper describes a generalized 2-d contact model under pivoting, and proposes a rotation measurement method based on the line-features in the stick region. The proposed method was applied to the Tac3D vision-based tactile sensors using continuous marker patterns. Experiments show that the rotation measurement system could achieve an average static measurement error of 0.17 degree and an average dynamic measurement error of 1.34 degree. Besides, the proposed method requires no training data and can achieve real-time sensing during the in-hand object pivoting., Comment: 7 pages, 9 figures, Accepted by ICRA 2024

With the development of intelligent operation and maintenance of substations, the daily inspection of substations needs to process massive video and image data. This puts forward higher requirements on the processing speed and accuracy of defect detection. Based on the end-to-end learning paradigm, this paper proposes an intelligent detection method for substation insulator defects based on CenterMask. First, the backbone network VoVNet is improved according to the residual connection and eSE module, which effectively solves the problems of deep network saturation and gradient information loss. On this basis, an insulator mask generation method based on a spatial attentiondirected mechanism is proposed. Insulators with complex image backgrounds are accurately segmented. Then, three strategies of pixel-wise regression prediction, multi-scale features and centerness are introduced. The anchor-free single-stage target detector accurately locates the defect points of insulators. Finally, an example analysis is carried out with the substation inspection image of a power supply company in a certain area to verify the effectiveness and robustness of the proposed method., Comment: 3 figures,1 table

Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modifications of their environments or unfamiliar situations. The black-box nature of the neural network learning dynamics makes it impossible to audit trained deep agents and recover from such failures. In this paper, we propose a novel representation and learning approach to capture environment dynamics without using neural networks. It originates from the observation that, in games designed for people, the effect of an action can often be perceived in the form of local changes in consecutive visual observations. Our algorithm is designed to extract such vision-based changes and condense them into a set of action-dependent descriptive rules, which we call ''visual rewrite rules'' (VRRs). We also present preliminary results from a VRR agent that can explore, expand its rule set, and solve a game via planning with its learned VRR world model. In several classical games, our non-deep agent demonstrates superior performance, extreme sample efficiency, and robust generalization ability compared with several mainstream deep agents., Comment: AAAI 2022 Workshop on Reinforcement Learning in Games

Mingxuan Li,1,&ast; Yutao Shen,1,&ast; Yujia Xiong,1,&ast; Jiwei Bai,2 Shuai Wang,1 Chuzhong Li,1 Yazhuo Zhang1– 5 1Beijing Neurosurgical Institute, Capital Medical University, Beijing, People’s Republic of China; 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, People’s Republic of China; 4China National Clinical Research Center for Neurological Diseases, Beijing, People’s Republic of China; 5Key Laboratory of Central Nervous System Injury Research, Capital Medical University, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yazhuo Zhang Email zyz2004520@yeah.netObjective: Accumulating studies report that levels of mean corpuscular volume (MCV) and red cell distribution width (RDW) are associated with outcomes in cancer patients, while studies including MCV and RDW in chordoma are lacking so far. Therefore, our study aims to investigate the prognostic impact of MCV and RDW on survival in skull base chordoma patients.Methods: Levels of preoperative MCV and RDW in 187 primary skull base chordoma patients were collected. X-tile software was used to find the cutoff values of MCV and RDW. Progression-free survival (PFS) and overall survival (OS) analyses were performed using the Kaplan–Meier methods, Cox analysis, and nomogram model.Results: Low MCV level (MCV < 84.2) was more commonly observed in classical chordoma patients (p=0.022). High RDW level (RDW≥ 12.7) was correlated with older patient age (p=0.022) and a tough tumor texture (p=0.035). Low MCV level and high RDW level were associated with poor PFS (p=0.045 and 0.007, respectively) and OS (p=0.023 and < 0.001, respectively). Multivariate Cox analysis demonstrated that RDW was an independent prognostic indicator for both PFS (p=0.001) and OS (p< 0.001). Importantly, a nomogram based

Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to obtain the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is established to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on the exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable., Comment: 11 pages, 10 figures

Artificial neural network has achieved the state-of-art performance in fault detection on the Tennessee Eastman process, but it often requires enormous memory to fund its massive parameters. In order to implement online real-time fault detection, three deep compression techniques (pruning, clustering, and quantization) are applied to reduce the computational burden. We have extensively studied 7 different combinations of compression techniques, all methods achieve high model compression rates over 64% while maintain high fault detection accuracy. The best result is applying all three techniques, which reduces the model sizes by 91.5% and remains a high accuracy over 94%. This result leads to a smaller storage requirement in production environments, and makes the deployment smoother in real world.

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics.

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics.

In this work, we propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents using a message-passing mechanism. The main idea is that the agent should be guided by structured non-neural-network algorithms like dynamic programming. According to recent advances in algorithmic alignment, neural networks with structured computation procedures can be trained efficiently. We demonstrate the potential of graph neural network in supporting sample efficient learning by showing that Deep Graph Value Network can outperform unstructured baselines by a large margin in solving the Markov Decision Process (MDP). We believe this would open up a new avenue for structured agent design. See https://github.com/drmeerkat/Deep-Graph-Value-Network for the code.

Deep Neural Network (DDN) has achieved notable success in various tasks, including many security concerning scenarios. However, a considerable amount of work has proved its vulnerability to adversaries. We unravel the properties that can intrinsically differentiate adversarial examples and normal inputs through theoretical analysis. That is, adversarial examples generated by $L_2$ attacks usually have larger input sensitivity which can be used to identify them efficiently. We also found that those generated by $L_\infty$ attacks will be different enough in the pixel domain to be detected empirically. To verify our analysis, we proposed a \textbf{G}uided \textbf{C}omplementary \textbf{D}efense module (\textbf{GCD}) integrating detection and recovery processes. When compared with adversarial detection methods, our detector achieves a detection AUC of over 0.98 against most of the attacks. When comparing our guided rectifier with commonly used adversarial training methods and other rectification methods, our rectifier outperforms them by a large margin. We achieve a recovered classification accuracy of up to 99\% on MNIST, 89\% on CIFAR-10, and 87\% on ImageNet subsets against $L_2$ attacks. Furthermore, under the white-box setting, our holistic defensive module shows a promising degree of robustness. Thus, we confirm that at least $L_2$ adversarial examples are intrinsically different enough from normal inputs both theoretically and empirically. And we shed light upon designing simple yet effective defensive methods with these properties.

Mingxuan Li,1 Jiwei Bai,2 Shuai Wang,1 Yixuan Zhai,3 Shuheng Zhang,1 Chuzhong Li,1 Jiang Du,4 Yazhuo Zhang1,2,5– 7 1Beijing Neurosurgical Institute, Capital Medical University, Beijing, People’s Republic of China; 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 4Department of Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People’s Republic of China; 5Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, People’s Republic of China; 6China National Clinical Research Center for Neurological Diseases, Beijing, People’s Republic of China; 7Key Laboratory of Central Nervous System Injury Research, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Yazhuo Zhang Email zyz2004520@yeah.netObjective: Inflammation and malnutrition have been shown to be correlated with tumor progression and a poor prognosis in various cancers. However, the clinical implications of biomarkers of inflammation and malnutrition in chordoma have not been elucidated. We attempted to characterize the fibrinogen and albumin levels in skull base chordoma and investigate their correlations with clinicopathological data and survival.Methods: The preoperative levels of fibrinogen and albumin were assessed in 183 primary skull base chordoma patients. The cutoff values were determined by X-tile software, and their correlations with patient prognosis were further explored using the Kaplan–Meier curve and Cox proportional hazards regression analysis. In addition, the predictive performances of these markers in survival were evaluated by receiver operating characteristic curves.Results: The values of fibrinogen and albumin in skull base chordoma patients

Joan Flasch Artists' Book Collection

Joan Flasch Artists' Book Collection