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This study evaluated the implementation and effectiveness of My Math Academy and My Reading Academy, two digital learning programs, in supporting early math and literacy skill development during the 2021-2022 school year in Tyler Independent School District, Texas. In a district where 85% of students were eligible for free and reduced-price meals, the study followed 414 students using My Math Academy across 33 classrooms and 342 students using My Reading Academy across 29 classrooms in Head Start and pre-K programs. Data collection included program usage metrics, state-administered CIRCLE Progress Monitoring System assessments, teacher surveys, and end-of-study interviews. Students used My Math Academy for an average of 18.54 minutes per active week over 13.09 weeks, and My Reading Academy for 25.51 minutes per active week over 15.95 weeks. Multiple regression analyses controlled for student characteristics, including pretest scores, pre-K type, gender, age, race/ethnicity, free/reduced lunch status, English language learner status, disability status, and teacher effects. Results demonstrated significant positive outcomes. Among students using My Math Academy for at least 30 minutes weekly, 96.1% ended the year "On Track" in overall math skills. For My Reading Academy users with similar usage, 86% achieved "On Track" status in phonological awareness, a critical foundational reading skill. Statistical analyses revealed significant positive correlations between program usage and improvement across multiple math and reading sub-skills, with stronger effects associated with increased usage. These findings suggest that implementing personalized, game-based digital learning programs can effectively support early math and literacy development in high-need educational settings. Teacher feedback indicated that both programs increased student engagement and confidence while providing valuable resources for differentiated instruction. The research includes detailed statistical analyses, student performance data across multiple skill domains, and comprehensive qualitative findings from teacher surveys and interviews. This study represents the first examination of simultaneous implementation of both programs, corroborating and extending findings from previous research on their individual effectiveness.

Fault-tolerant quantum computation enables reliable quantum computation but incurs a significant overhead from both time and resource perspectives. To reduce computation time, Austin G. Fowler proposed time-optimal quantum computation by constructing a quantum circuit for a fault-tolerant $T$ gate without probabilistic $S$ gate correction. In this work, we introduce a resource-compact quantum circuit that significantly reduces resource requirements by more than 60% for a fault-tolerant $T$ gate without probabilistic $S$ gate correction. Consequently, we present a quantum circuit that minimizes resource utilization for time-optimal quantum computation, demonstrating efficient time-optimal quantum computation. Additionally, we describe an efficient form involving initialization, CNOTs, and measurements, laying the foundation for the development of an efficient compiler for fault-tolerant quantum computation.

The first science run of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time project chamber operating in the Sanford Underground Research Facility in South Dakota, USA, has reported leading limits on spin-independent WIMP-nucleon interactions and interactions described from a non-relativistic effective field theory (NREFT). Using the same 5.5~t fiducial mass and 60 live days of exposure we report on the results of a relativistic extension to the NREFT. We present constraints on couplings from covariant interactions arising from the coupling of vector, axial currents, and electric dipole moments of the nucleon to the magnetic and electric dipole moments of the WIMP which cannot be described by recasting previous results described by an NREFT. Using a profile-likelihood ratio analysis, in an energy region between 0~keV$_\text{nr}$ to 270~keV$_\text{nr}$, we report 90% confidence level exclusion limits on the coupling strength of five interactions in both the isoscalar and isovector bases., Comment: 7 pages, 4 figures

Crystal seeding enables a deeper understanding of phase behavior, leading to the development of methods for controlling and manipulating phase transitions in various applications such as materials synthesis, crystallization processes, and phase transformation engineering. How to seed a crystalline in time domain is an open question, which is of great significant and may provide an avenue to understand and control time-dependent quantum many-body physics. Here, we utilize a microwave pulse as a seed to induce the formation of a discrete time crystal in Floquet driven Rydberg atoms. In the experiment, the periodic driving on Rydberg states acts as a seeded crystalline order in subspace, which triggers the time-translation symmetry breaking across the entire ensemble. The behavior of the emergent time crystal is elaborately linked to alterations in the seed, such as the relative phase shift and the frequency difference, which result in phase dependent seeding and corresponding shift in periodicity of the time crystal, leading to embryonic synchronization. This result opens up new possibilities for studying and harnessing time-dependent quantum many-body phenomena, offering insights into the behavior of complex many-body systems under seeding.

V583 Lyr is an extremely low mass ratio Algol-type binary with an orbital period of 11.2580 days. We determined an effective temperature of T_{eff1} = 9000 \pm 350 K from newly observed spectra, which might be an underestimate due to binary mass transfer. The binary mass ratio q = 0.1 \pm 0.004 and the orbital inclination i = 85.5{\deg} are determined based on the assumption that the secondary fills its Roche lobe and rotates synchronously. The radial velocity curve is obtained from time series spectra, allowing for improved estimation of stellar masses and radii: M1 = 3.56 \pm 0.5 Msun, R1 = 2.4 \pm 0.2 Rsun; and M2 = 0.36 \pm 0.02 Msun, R2= 6.9 \pm 0.4 Rsun. The variations in the double-peaked H_{\alpha} emission indicate the formation of a stable disk during mass transfer. V583 Lyr appears to be a post-mass-reversal system, according to the estimated mass transfer using O-C period analysis. Its orbital period is slowly increasing, from which the rate of mass accretion by the primary star is estimated to be dM1/dt = 3.384 \times10^{-8} Msun/yr. The pulsation analysis was conducted on the residuals of the light curve. The primary component was found to be a g-mode pulsating star with 26 frequencies extracted lower than 9 d^{-1}. The frequency groups and rotational splitting properties of the g-mode were studied in detail. This study provides compelling evidence for an accretion disk surrounding the g-mode pulsating primary.

Detecting microwave signals over a wide frequency range has numerous advantages as it enables simultaneous transmission of a large amount of information and access to more spectrum resources. This capability is crucial for applications such as microwave communication, remote sensing, and radar. However, conventional microwave receiving systems are limited by amplifiers and band-pass filters that can only operate efficiently in a specific frequency range. Typically, these systems can only process signals within a three-fold frequency range, which limits the data transfer bandwidth of the microwave communication systems. Developing novel atom-integrated microwave sensors, for example, radio frequency (RF)-chip coupled Rydberg atomic receiver, provides opportunities for a large working bandwidth of microwave sensing at the atomic level. Here, an ultra-wide dual-band RF sensing scheme is demonstrated by space-division multiplexing two RF-chip-integrated atomic receiver modules. The system can simultaneously receive dual-band microwave signals that span a frequency range exceeding 6 octaves (300 MHz and 24 GHz). This work paves the way for multi-band microwave reception applications within an ultra-wide range by RF-chip-integrated Rydberg atomic sensor., Comment: 11 pages, 5 figures

The identification of tipping points is essential for prediction of collapses or other sudden changes in complex systems. Applications include studies of ecology, thermodynamics, climatology, and epidemiology. However, detecting early signs of proximity to a tipping is made challenging by complexity and non-linearity. Strongly interacting Rydberg atom gases offer model systems that offer both complexity and non-linearity, including phase transition and critical slowing down. Here, via an external probe we observe prior warning of the proximity of a phase transition of Rydberg thermal gases. This warning signal is manifested as a deviation from linear growth of the variance with increasing probe intensity. We also observed the dynamics of the critical slowing down behavior versus different time scales, and atomic densities, thus providing insights into the study of a Rydberg atom system's critical behavior. Our experiment suggests that the full critical slowing down dynamics of strongly-interacting Rydberg atoms can be probed systematically, thus providing a benchmark with which to identify critical phenomena in quantum many-body systems.

For a polynomial function $f \colon \mathbb{C}^n \longrightarrow \mathbb{C}$, it is well-known in singularity (after Thom, Pham, Verdier,...) that outside a finite subset of $\mathbb{C}$, the function is a locally trivial $C^{\infty}$-fibration. The minimal such a finite set is called the bifurcation set associated with $f$ and determing the bifurcation sets is a difficult task in singularity theory. In his thesis, Raibaut attachs to such a function a virtual invariant called $\textit{motivic nearby cycles at infinity}$. This invariant lives in the some Grothendieck ring of varieties and measures the difference between the Euler characteristics of the general fiber and a fixed fiber. In this work, we show that the motivic nearby cycles at infinity constructed by Raibaut admits a functorial version in the context of motivic homotopy theory, called the $\textit{motivic nearby cycles functors at infinity}$. The nearby cycles functors at infinity live in the world of motives and hence capture cohomological information (not just Euler characteristics) of singularities at infinity and realizes to Raibaut's construction in the world of virtual motives.

The widespread application of Large Language Models (LLMs) across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of human value alignment, ranging from Reinforcement Learning with Human Feedback (RLHF), to constitutional learning, etc. there is an urgent need to understand the scope and nature of human values injected into these models before their release. There is also a need for model alignment without a costly large scale human annotation effort. We propose UniVaR, a high-dimensional representation of human value distributions in LLMs, orthogonal to model architecture and training data. Trained from the value-relevant output of eight multilingual LLMs and tested on the output from four multilingual LLMs, namely LlaMA2, ChatGPT, JAIS and Yi, we show that UniVaR is a powerful tool to compare the distribution of human values embedded in different LLMs with different langauge sources. Through UniVaR, we explore how different LLMs prioritize various values in different languages and cultures, shedding light on the complex interplay between human values and language modeling.

In this paper, we consider singular linear forms over global function fields of class number one and give an upper bound for the Hausdorff dimension of the set of singular linear forms by constructing an appropriate Margulis function over global function fields., Comment: 32 pages

Engineering a Terahertz (THz) frequency comb spectroscopy at atomic level advances the precisely measurement in spectroscopy and sensing. Current progresses on THz frequency comb rely on difference-frequency generation, optical parametric oscillation, and other methods. Generating a THz frequency comb poses challenges in source stability and achieving a narrow bandwidth, which traditional THz devices are difficult to achieve. Furthermore, accurately measuring the generated THz frequency comb necessitates a high-performance THz detector. Rydberg atoms are well-suited for electric field sensing due to their ultra-wide radio frequency transition energy levels, making them especially sensitive to external electric fields in the DC to THz bandwidth. However, there have been no reports about generating THz frequency comb spectroscopy at the atomic level until now. This work presents a THz frequency comb spectroscopy with Rydberg atoms, in which a Floquet comb-like transition is engineered through a time-periodic drive field. Our approach simplifies the setup required for THz frequency comb spectroscopy while extending the working bandwidth for Rydberg atomic sensors. The THz frequency comb spectroscopy at the atomic level reported in this article shows great potential for various applications in astronomy, remote sensing, spectral detection of biological samples, and other related fields.

Developing microwave electric field sensing based on Rydberg atom has received significant attention due to its unique advantages. However, achieving effective coupling between Rydberg atom and the microwave electric field in the sensing process is a challenging problem that greatly impacts the sensitivity. To address this, we propose the use of a microwave resonant cavity to enhance the effective coupling between the Rydberg atoms and the microwave electric field. In our experiment, we use a three-photon excitation scheme to prepare Rydberg atoms, make measurements of electric fields without and with a microwave cavity in which the vapor cell is put inside. Through experimental testing, we achieve an 18 dB enhancement of power sensitivity. The experiment shows an effective enhancement in electric field pulse signal detection. This result provides a promising direction for enhancing the sensitivity of Rydberg atomic electric field sensors and paves the way for their application in precision electric field measurements.

As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view synthesis, it is a natural extension to deform a canonical 3DGS to multiple frames for representing a dynamic scene. However, previous works fail to accurately reconstruct complex dynamic scenes. We attribute the failure to the design of the deformation field, which is built as a coordinate-based function. This approach is problematic because 3DGS is a mixture of multiple fields centered at the Gaussians, not just a single coordinate-based framework. To resolve this problem, we define the deformation as a function of per-Gaussian embeddings and temporal embeddings. Moreover, we decompose deformations as coarse and fine deformations to model slow and fast movements, respectively. Also, we introduce a local smoothness regularization for per-Gaussian embedding to improve the details in dynamic regions. Project page: https://jeongminb.github.io/e-d3dgs/, Comment: ECCV 2024. Project page: https://jeongminb.github.io/e-d3dgs/

Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several years to complete, with a high probability of failure during the process. Recently, there has been a burgeoning interest in virtual clinical trials, which simulate real-world scenarios and hold the potential to significantly enhance patient safety, expedite development, reduce costs, and contribute to the broader scientific knowledge in healthcare. Existing research often focuses on leveraging electronic health records (EHRs) to support clinical trial outcome prediction. Yet, trained with limited clinical trial outcome data, existing approaches frequently struggle to perform accurate predictions. Some research has attempted to generate EHRs to augment model development but has fallen short in personalizing the generation for individual patient profiles. Recently, the emergence of large language models has illuminated new possibilities, as their embedded comprehensive clinical knowledge has proven beneficial in addressing medical issues. In this paper, we propose a large language model-based digital twin creation approach, called TWIN-GPT. TWIN-GPT can establish cross-dataset associations of medical information given limited data, generating unique personalized digital twins for different patients, thereby preserving individual patient characteristics. Comprehensive experiments show that using digital twins created by TWIN-GPT can boost the clinical trial outcome prediction, exceeding various previous prediction approaches.

In this paper, we study the submodularity of the covolume function in global function fields. The submodular property is often needed in the study of homogeneous dynamics, especially to define a Margulis function. We proved that the covolume function is submodular when the class group of the global function field is trivial., Comment: 19 pages

We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. In order to provide transparency to users, we advocate that there should be fine-grained and explainable measures of political biases generated by LLMs. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias. We measured the political bias in eleven open-sourced LLMs and showed that our proposed framework is easily scalable to other topics and is explainable., Comment: 16 pages

Recent years have witnessed extensive researches on developing two tower recommendation models for relieving information overload. Four building modules can be identified in such models, namely, user-item encoding, negative sampling, loss computing and back-propagation updating. To the best of our knowledge, existing algorithms have researched only on the first three modules, yet neglecting the backpropagation module. They all adopt a kind of two backpropagation strategy, which are based on an implicit assumption of equally treating users and items in the training phase. In this paper, we challenge such an equal training assumption and propose a novel one backpropagation updating strategy, which keeps the normal gradient backpropagation for the item encoding tower, but cuts off the backpropagation for the user encoding tower. Instead, we propose a moving-aggregation updating strategy to update a user encoding in each training epoch. Except the proposed backpropagation updating module, we implement the other three modules with the most straightforward choices. Experiments on four public datasets validate the effectiveness and efficiency of our model in terms of improved recommendation performance and reduced computation overload over the state-of-the-art competitors., Comment: 14 pages, 7 figures

The use of additive manufacturing (AM) processes for industrial fabrication has grown rapidly over the last ten years. The most well-known AM technologies are fused deposition modelling and stereolithography techniques. One particular industry where 3D printing is advantageous over traditional fabrication techniques is within ceramic components due to its flexibility. To establish a new and improved level of print quality and reduce resource consumption in the 3D printing ceramics industry, there is a need for fast integrated, sub-surface and non-destructive inspection (NDI) with high resolution. Several techniques have already been developed for high-resolution NDI, such as X-ray computed tomography (XCT), but none of them are both fast, integrable, and non-destructive while allowing deep penetration with high resolution. In this study, we demonstrate sub-surface monitoring of 3D printed alumina parts to a depth of $\sim$0.7 mm in images of 400$\times$2048 pixels with a lateral resolution of 30$~\mu$m and depth (or axial) resolution of 7$~\mu$m . The results were achieved using mid-infrared optical coherence tomography (MIR OCT) based on a MIR supercontinuum laser with a 4$~\mu$m center wavelength. We find that it is possible to detect individual printed ceramic layers and track predefined defects through all four processing steps: green, preconditioned, debinded, and sintered. Our results also demonstrate how a defect in the green phase could affect the final product. Based on the understanding of how defects develop in maturing printed parts, we pave the way for NDI integration in AM, which can be combined with artificial intelligence and machine learning algorithms for automatic defect classification in volume production of a new standard of high quality ceramic components., Comment: 15 pages, 8 figures, paper

This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring. The transformation process, from one blurry state to another, leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion, as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks, where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://zero1778.github.io/blur2blur/, Comment: Accepted to CVPR 2024

This paper addresses the problem of detecting a moving target embedded in Gaussian noise with an unknown covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that obtaining a set of training data is available. Moreover, we propose three adaptive detectors in accordance with the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Rao criteria, namely OGLRT, TGLRT, and Rao. The LH adaptive matched filter (LHAMF) detector is also introduced when decomposing the Rao test. Next, all provided detectors have constant false alarm rate (CFAR) properties against the covariance matrix. Besides, the closed-form expressions for false alarm probability (PFA) and detection probability (PD) are derived. Finally, this paper substantiates the correctness of the aforementioned algorithms through numerical simulations.

Reference-based metrics such as BLEU and BERTScore are widely used to evaluate question generation (QG). In this study, on QG benchmarks such as SQuAD and HotpotQA, we find that using human-written references cannot guarantee the effectiveness of the reference-based metrics. Most QG benchmarks have only one reference; we replicate the annotation process and collect another reference. A good metric is expected to grade a human-validated question no worse than generated questions. However, the results of reference-based metrics on our newly collected reference disproved the metrics themselves. We propose a reference-free metric consisted of multi-dimensional criteria such as naturalness, answerability, and complexity, utilizing large language models. These criteria are not constrained to the syntactic or semantic of a single reference question, and the metric does not require a diverse set of references. Experiments reveal that our metric accurately distinguishes between high-quality questions and flawed ones, and achieves state-of-the-art alignment with human judgment., Comment: EMNLP 2024 Findings - Camera Ready

A novel tensor decomposition framework, termed Tensor Star (TS) decomposition, is proposed which represents a new type of tensor network decomposition based on tensor contractions. This is achieved by connecting the core tensors in a ring shape, whereby the core tensors act as skip connections between the factor tensors and allow for direct correlation characterisation between any two arbitrary dimensions. Uniquely, this makes it possible to decompose an order-$N$ tensor into $N$ order-$3$ factor tensors $\{\mathcal{G}_{k}\}_{k=1}^{N}$ and $N$ order-$4$ core tensors $\{\mathcal{C}_{k}\}_{k=1}^{N}$, which are arranged in a star shape. Unlike the class of Tensor Train (TT) decompositions, these factor tensors are not directly connected to one another. The so obtained core tensors also enable consecutive factor tensors to have different latent ranks. In this way, the TS decomposition alleviates the "curse of dimensionality" and controls the "curse of ranks", exhibiting a storage complexity which scales linearly with the number of dimensions and as the fourth power of the ranks.

The evolution of text to visual components facilitates people's daily lives, such as generating image, videos from text and identifying the desired elements within the images. Computer vision models involving the multimodal abilities in the previous days are focused on image detection, classification based on well-defined objects. Large language models (LLMs) introduces the transformation from nature language to visual objects, which present the visual layout for text contexts. OpenAI GPT-4 has emerged as the pinnacle in LLMs, while the computer vision (CV) domain boasts a plethora of state-of-the-art (SOTA) models and algorithms to convert 2D images to their 3D representations. However, the mismatching between the algorithms with the problem could lead to undesired results. In response to this challenge, we propose an unified VisionGPT-3D framework to consolidate the state-of-the-art vision models, thereby facilitating the development of vision-oriented AI. VisionGPT-3D provides a versatile multimodal framework building upon the strengths of multimodal foundation models. It seamlessly integrates various SOTA vision models and brings the automation in the selection of SOTA vision models, identifies the suitable 3D mesh creation algorithms corresponding to 2D depth maps analysis, generates optimal results based on diverse multimodal inputs such as text prompts. Keywords: VisionGPT-3D, 3D vision understanding, Multimodal agent, Comment: 12 pages, 7 figures, pending conference

With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains an open question. In this paper, we introduce VisionGPT to consolidate and automate the integration of state-of-the-art foundation models, thereby facilitating vision-language understanding and the development of vision-oriented AI. VisionGPT builds upon a generalized multimodal framework that distinguishes itself through three key features: (1) utilizing LLMs (e.g., LLaMA-2) as the pivot to break down users' requests into detailed action proposals to call suitable foundation models; (2) integrating multi-source outputs from foundation models automatically and generating comprehensive responses for users; (3) adaptable to a wide range of applications such as text-conditioned image understanding/generation/editing and visual question answering. This paper outlines the architecture and capabilities of VisionGPT, demonstrating its potential to revolutionize the field of computer vision through enhanced efficiency, versatility, and generalization, and performance. Our code and models will be made publicly available. Keywords: VisionGPT, Open-world visual perception, Vision-language understanding, Large language model, and Foundation model, Comment: 17 pages, 5 figures, and 1 table. arXiv admin note: substantial text overlap with arXiv:2311.10125

It is well-known and easy to show that even the following version of the directed travelling salesman problem is NP-complete: Given a strongly connected complete digraph $D=(V,A)$, a cost function $w: A\rightarrow \{0,1\}$ and a natural number $K$; decide whether $D$ has a directed Hamiltonian cycle of cost at most $K$. We study the following variant of this problem for $\{0,1\}$-weighted semicomplete digraphs where the set of arcs which have cost 1 form a collection of vertex-disjoint complete digraphs. A digraph is \textbf{semicomplete multipartite} if it can be obtained from a semicomplete digraph $D$ by choosing a collection of vertex-disjoint subsets $X_1,\ldots{},X_c$ of $V(D)$ and then deleting all arcs both of whose end-vertices lie inside some $X_i$. Let $D$ be a semicomplete digraph with a cost function $w$ as above, where $w(a)=1$ precisely when $a$ is an arc inside one of the subsets $X_1,\ldots{},X_c$ and let $D^*$ be the corresponding \smd{} that we obtain by deleting all arcs inside the $X_i$'s. Then every cycle $C$ of $D$ corresponds to a {\bf generalized cycle} $C^g$ of $D^*$ which is either the cycle $C$ itself if $w(C)=0$ or a collection of two or more paths that we obtain by deleting all arcs of cost 1 on $C$. Similarly we can define a {\bf generalized path} $P^g$ in a semicomplete multipartite digraph. The purpose of this paper is to study structural and algorithmic properties of generalized paths and cycles in semicomplete multipartite digraphs. This allows us to identify classes of directed $\{0,1\}$-weighted TSP instances that can be solved in polynomial time as well as others for which we can get very close to the optimum in polynomial time. Along with these results we also show that two natural questions about properties of cycles meeting all partite sets in semicomplete multipartite digraphs are NP-complete.

Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content. However, it remains a formidable challenge pertaining to maintaining temporal consistency and ensuring action smoothness throughout the generated sequences. In this paper, we present an innovative video generation AI agent that harnesses the power of Sora-inspired multimodal learning to build skilled world models framework based on textual prompts and accompanying images. The framework includes two parts: prompt enhancer and full video translation. The first part employs the capabilities of ChatGPT to meticulously distill and proactively construct precise prompts for each subsequent step, thereby guaranteeing the utmost accuracy in prompt communication and accurate execution in following model operations. The second part employ compatible with existing advanced diffusion techniques to expansively generate and refine the key frame at the conclusion of a video. Then we can expertly harness the power of leading and trailing key frames to craft videos with enhanced temporal consistency and action smoothness. The experimental results confirm that our method has strong effectiveness and novelty in constructing world models from text and image inputs over the other methods., Comment: 11 pages, 2 figures, 2 tables

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks., Comment: 20 pages

In this letter, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). We employ conformal prediction to obtain theoretical safety guarantees and use real-world traffic data to validate our approach. Then, we design a controller that ensures effective merging of CAVs with HDVs with safety guarantees. We provide numerical simulations to illustrate the efficacy of the control approach., Comment: 6 pages, 5 figures

Many cyber-physical-human systems (CPHS) involve a human decision-maker who may receive recommendations from an artificial intelligence (AI) platform while holding the ultimate responsibility of making decisions. In such CPHS applications, the human decision-maker may depart from an optimal recommended decision and instead implement a different one for various reasons. In this letter, we develop a rigorous framework to overcome this challenge. In our framework, we consider that humans may deviate from AI recommendations as they perceive and interpret the system's state in a different way than the AI platform. We establish the structural properties of optimal recommendation strategies and develop an approximate human model (AHM) used by the AI. We provide theoretical bounds on the optimality gap that arises from an AHM and illustrate the efficacy of our results in a numerical example.

The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection. In this paper, we propose RadarDistill, a novel knowledge distillation (KD) method, which can improve the representation of radar data by leveraging LiDAR data. RadarDistill successfully transfers desirable characteristics of LiDAR features into radar features using three key components: Cross-Modality Alignment (CMA), Activation-based Feature Distillation (AFD), and Proposal-based Feature Distillation (PFD). CMA enhances the density of radar features by employing multiple layers of dilation operations, effectively addressing the challenge of inefficient knowledge transfer from LiDAR to radar. AFD selectively transfers knowledge based on regions of the LiDAR features, with a specific focus on areas where activation intensity exceeds a predefined threshold. PFD similarly guides the radar network to selectively mimic features from the LiDAR network within the object proposals. Our comparative analyses conducted on the nuScenes datasets demonstrate that RadarDistill achieves state-of-the-art (SOTA) performance for radar-only object detection task, recording 20.5% in mAP and 43.7% in NDS. Also, RadarDistill significantly improves the performance of the camera-radar fusion model., Comment: Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024, 10 pages, 3 figures

Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learning tasks, including EIF. Despite these efforts, there exists a lack of a unified understanding regarding the impact of various components-ranging from visual perception to action execution-on task performance. To address this gap, we introduce OPEx, a comprehensive framework that delineates the core components essential for solving embodied learning tasks: Observer, Planner, and Executor. Through extensive evaluations, we provide a deep analysis of how each component influences EIF task performance. Furthermore, we innovate within this space by deploying a multi-agent dialogue strategy on a TextWorld counterpart, further enhancing task performance. Our findings reveal that LLM-centric design markedly improves EIF outcomes, identify visual perception and low-level action execution as critical bottlenecks, and demonstrate that augmenting LLMs with a multi-agent framework further elevates performance.

In this paper, we present a novel point generation model, referred to as Pillar-based Point Generation Network (PillarGen), which facilitates the transformation of point clouds from one domain into another. PillarGen can produce synthetic point clouds with enhanced density and quality based on the provided input point clouds. The PillarGen model performs the following three steps: 1) pillar encoding, 2) Occupied Pillar Prediction (OPP), and 3) Pillar to Point Generation (PPG). The input point clouds are encoded using a pillar grid structure to generate pillar features. Then, OPP determines the active pillars used for point generation and predicts the center of points and the number of points to be generated for each active pillar. PPG generates the synthetic points for each active pillar based on the information provided by OPP. We evaluate the performance of PillarGen using our proprietary radar dataset, focusing on enhancing the density and quality of short-range radar data using the long-range radar data as supervision. Our experiments demonstrate that PillarGen outperforms traditional point upsampling methods in quantitative and qualitative measures. We also confirm that when PillarGen is incorporated into bird's eye view object detection, a significant improvement in detection accuracy is achieved., Comment: Accepted by IEEE International Conference on Robotics and Automation (ICRA 2024), 8 pages, 3 figures

This paper presents an original energy management methodology to enhance the resilience of ship power systems. The integration of various energy storage systems (ESS), including battery energy storage systems (BESS) and super-capacitor energy storage systems (SCESS), in modern ship power systems poses challenges in designing an efficient energy management system (EMS). The EMS proposed in this paper aims to achieve multiple objectives. The primary objective is to minimize shed loads, while the secondary objective is to effectively manage different types of ESS. Considering the diverse ramp-rate characteristics of generators, SCESS, and BESS, the proposed EMS exploits these differences to determine an optimal long-term schedule for minimizing shed loads. Furthermore, the proposed EMS balances the state-of-charge (SoC) of ESS and prioritizes the SCESS's SoC levels to ensure the efficient operation of BESS and SCESS. For better computational efficiency, we introduce the receding horizon optimization method, enabling real-time EMS implementation. A comparison with the fixed horizon optimization (FHO) validates its effectiveness. Simulation studies and results demonstrate that the proposed EMS efficiently manages generators, BESS, and SCESS, ensuring system resilience under generation shortages. Additionally, the proposed methodology significantly reduces the computational burden compared to the FHO technique while maintaining acceptable resilience performance., Comment: 15 pages, 17 figures

Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. However, such a paradigm fails to comprehensively differentiate the fine-grained language and cognitive skills, rendering the lack of sufficient interpretation to LLMs' capabilities. In this paper, we present FAC$^2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation. Specifically, we formulate LLMs' evaluation in a multi-dimensional and explainable manner by dissociating the language-related capabilities and the cognition-related ones. Besides, through extracting the intermediate reasoning from LLMs, we further break down the process of applying a specific capability into three sub-steps: recalling relevant knowledge, utilizing knowledge, and solving problems. Finally, FAC$^2$E evaluates each sub-step of each fine-grained capability, providing a two-faceted diagnosis for LLMs. Utilizing FAC$^2$E, we identify a common shortfall in knowledge utilization among models and propose a straightforward, knowledge-enhanced method to mitigate this issue. Our results not only showcase promising performance enhancements but also highlight a direction for future LLM advancements., Comment: Accepted at EMNLP 2024 main conference

This paper addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. We introduce Resonance RoPE, a novel approach designed to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions, significantly improving the model performance without additional online computational costs. Furthermore, we present PosGen, a new synthetic benchmark specifically designed for fine-grained behavior analysis in TSTL scenarios, aiming to isolate the constantly increasing difficulty of token generation on long contexts from the challenges of recognizing new token positions. Our experiments on synthetic tasks show that after applying Resonance RoPE, Transformers recognize OOD position better and more robustly. Our extensive LLM experiments also show superior performance after applying Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on both upstream language modeling tasks and a variety of downstream long-text applications., Comment: 13 pages, 4 figures, accepted at ACL 2024 Findings

Large Language Model (LLM)-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected tasks, dynamic data adjustments and domain expertise remains challenging. Previous approaches primarily focus on individual tasks, making it difficult to assess the complete data science workflow. Moreover, they struggle to handle real-time changes in intermediate data and fail to adapt dynamically to evolving task dependencies inherent to data science problems. In this paper, we present Data Interpreter, an LLM-based agent designed to automatically solve various data science problems end-to-end. Our Data Interpreter incorporates two key modules: 1) Hierarchical Graph Modeling, which breaks down complex problems into manageable subproblems, enabling dynamic node generation and graph optimization; and 2) Programmable Node Generation, a technique that refines and verifies each subproblem to iteratively improve code generation results and robustness. Extensive experiments consistently demonstrate the superiority of Data Interpreter. On InfiAgent-DABench, it achieves a 25% performance boost, raising accuracy from 75.9% to 94.9%. For machine learning and open-ended tasks, it improves performance from 88% to 95%, and from 60% to 97%, respectively. Moreover, on the MATH dataset, Data Interpreter achieves remarkable performance with a 26% improvement compared to state-of-the-art baselines. The code is available at https://github.com/geekan/MetaGPT.

In this work, we tackle the challenge of enhancing the realism and expressiveness in talking head video generation by focusing on the dynamic and nuanced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual facial styles. To address these issues, we propose EMO, a novel framework that utilizes a direct audio-to-video synthesis approach, bypassing the need for intermediate 3D models or facial landmarks. Our method ensures seamless frame transitions and consistent identity preservation throughout the video, resulting in highly expressive and lifelike animations. Experimental results demonsrate that EMO is able to produce not only convincing speaking videos but also singing videos in various styles, significantly outperforming existing state-of-the-art methodologies in terms of expressiveness and realism.

Layered transition metal chalcogenides have stimulated a wide research interest due to their many exotic physical properties. In this paper, we studied the magnetotransport properties of the exfoliated TaNiTe5, a recently discovered Dirac nodal-line semimetal. A giant positive magnetoresistance (MR) is observed when the current is parallel to the crystallographic c axis, while it is strongly diminished when the current flows along the a axis. The observed giant MR is gradually suppressed either on reducing the thickness of nanoflake or on increasing temperature. By performing MR measurement in tilted magnetic fields, the interlayer coupling is found to be weakened both by reducing the thickness and by increasing temperature. We propose a mechanism of electron-electron interaction-assisted interlayer transport as a origin of the giant MR. The mechanism is likely to provide a explanation for the giant MR in other layered materials., Comment: 21 pages, 7 figures, 1 table

[Abridged] The extension of conceptual DFT to include external fields in chemical systems is utilised to investigate the effects of strong magnetic fields on the electronic charge distribution and its consequences on the reactivity of $\pi$-systems. Formaldehyde, H$_2$CO, is considered as a prototypical example and current-DFT calculations are used to evaluate the electric dipole moment together with the electron density and the Fukui functions, which provide insight into how H$_2$CO behaves chemically in a magnetic field. In particular, the symmetries of these quantities are analysed based on group, representation, and corepresentation theories using QSym$^2$. This allows us to leverage the simple symmetry constraints on the macroscopic electric dipole moments to make profound predictions on the more nuanced symmetry transformation properties of the microscopic frontier MOs, electron densities, and Fukui functions. This is especially useful for complex-valued MOs in magnetic fields whose detailed symmetry analyses lead us to define the new concepts of modular and phasal symmetry breaking. Through these, the connection between the vanishing constraints on the electric dipole moments and the symmetry of electron densities and Fukui functions can be formalised, and the inability of the magnetic field in all three orientations considered to induce asymmetry with respect to the molecular plane can be understood from a molecular perspective. Furthermore, the detailed forms of the Fukui functions reveal remarkable reversals in the direction of the C=O dipole moment in the presence of a parallel or perpendicular magnetic field, which can be attributed to the mixing between frontier MOs due to their subduced symmetries in magnetic fields. The findings in this work are also discussed in the wider context of a long-standing debate on the possibility to create enantioselectivity by external fields., Comment: Main text: 26 pages (two-column), 7 figures, 9 tables; Supplementary information: 7 pages (one-column), 4 figures, 2 tables

The Center for Underground Physics of the Institute for Basic Science (IBS) in Korea has been planning the construction of a deep underground laboratory since 2013 to search for extremely rare interactions such as dark matter and neutrinos. In September 2022, a new underground laboratory, Yemilab, was finally completed in Jeongseon, Gangwon Province, with a depth of 1,000 m and an exclusive experimental area spanning 3,000 m$^3$. The tunnel is encased in limestone and accommodates 17 independent experimental spaces. Over two years, from 2023 to 2024, the Yangyang Underground Laboratory facilities will be relocated to Yemilab. Preparations are underway for the AMoRE-II, a neutrinoless double beta decay experiment, scheduled to begin in Q2 2024 at Yemilab. Additionally, Yemilab includes a cylindrical pit with a volume of approximately 6,300 m$^3$, designed as a multipurpose laboratory for next-generation experiments involving neutrinos, dark matter, and related research. This article provides a focused overview of the construction and structure of Yemilab., Comment: 12 pages, 3 figures, 1 table

Higher-order and fractional discrete time crystals (DTCs) are exotic phases of matter where the discrete time translation symmetry is broken into higher-order and non-integer category. Generation of these unique DTCs has been widely studied theoretically in different systems. However, no current experimental methods can probe these higher-order and fractional DTCs in any quantum many-body systems. We demonstrate an experimental approach to observe higher-order and fractional DTCs in Floquet-driven Rydberg atomic gases. We have discovered multiple $n$-DTCs with integer values of $n$ = 2, 3, and 4, and others ranging up to 14, along with fractional $n$-DTCs with $n$ values beyond the integers. The system response can transition between adjacent integer DTCs, during which the fractional DTCs are investigated. Study of higher-order and fractional DTCs expands fundamental knowledge of non-equilibrium dynamics and is promising for discovery of more complex temporal symmetries beyond the single discrete time translation symmetry., Comment: 17 pages, 10 figures, to be published in Nature Communications

A time crystal is an exotic phase of matter where time-translational symmetry is broken; this phase differs from the spatial symmetry breaking induced in crystals in space. Lots of experiments report the transition from a thermal equilibrium phase to time crystal phase. However, there is no experimental method to probe the bifurcation effect of distinct time crystals in quantum many-body systems. Here, in a driven and dissipative many-body Rydberg atom system, we observe multiple continuous dissipative time crystals and emergence of more complex temporal symmetries beyond the single time crystal phase. Bifurcation of time crystals in strongly interacting Rydberg atoms is observed; the process manifests as a transition from a time crystal state of long temporal order to one of short temporal order, or vice versa. By manipulating the driving field parameters, we observe the time crystal's bistability and a hysteresis loop. These investigations indicate new possibilities for control and manipulation of the temporal symmetries of non-equilibrium systems., Comment: Added references

When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm. This unfairness can easily introduce biases in subsequent decision-making given broad adoptions of learning-based solutions in practice. However, locational biases in AI are largely understudied. To mitigate biases over locations, we propose a locational meta-referee (Meta-Ref) to oversee the few-shot meta-training and meta-testing of a deep neural network. Meta-Ref dynamically adjusts the learning rates for training samples of given locations to advocate a fair performance across locations, through an explicit consideration of locational biases and the characteristics of input data. We present a three-phase training framework to learn both a meta-learning-based predictor and an integrated Meta-Ref that governs the fairness of the model. Once trained with a distribution of spatial tasks, Meta-Ref is applied to samples from new spatial tasks (i.e., regions outside the training area) to promote fairness during the fine-tune step. We carried out experiments with two case studies on crop monitoring and transportation safety, which show Meta-Ref can improve locational fairness while keeping the overall prediction quality at a similar level.

We find a surprising link between Maz'ya-Shaposhnikova's well-known asymptotic formula concerning fractional Sobolev seminorms and the generalized Bishop-Gromov inequality. In the setting of abstract metric measure spaces we prove the validity of a large family of asymptotic formulas concerning non-local energies. Important examples which are covered by our approach are for instance Carnot groups, Riemannian manifolds with Ricci curvature bounded from below and non-collapsed RCD spaces. We also extend the classical Maz'ya-Shaposhnikova's formula on Euclidean spaces to a wider class of mollifiers.

We prove two Liouville type theorems for the stationary Navier-Stokes equations in $\mathbb{R}^3$ under some assumptions on 1) the growth of the $L^s$ mean oscillation of a potential function of the velocity field, or 2) the relative decay of the head pressure and the square of the velocity field at infinity. The main idea is to use Saint-Venant type estimates to characterize the growth of Dirichlet energy of nontrivial solutions. These assumptions are weaker than those previously known of a similar nature., Comment: 16 pages

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals., Comment: 9 pages, 4 figures

Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challenge was introduced, providing a benchmark dataset of over 5,000 rib fractures from 660 CT scans, with voxel-level instance mask annotations and diagnosis labels for four clinical categories (buckle, nondisplaced, displaced, or segmental). The challenge includes two tracks: a detection (instance segmentation) track evaluated by an FROC-style metric and a classification track evaluated by an F1-style metric. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts. Nevertheless, the current rib fracture classification solutions are hardly clinically applicable, which can be an interesting area in the future. As an active benchmark and research resource, the data and online evaluation of the RibFrac Challenge are available at the challenge website. As an independent contribution, we have also extended our previous internal baseline by incorporating recent advancements in large-scale pretrained networks and point-based rib segmentation techniques. The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis., Comment: Challenge paper for MICCAI RibFrac Challenge (https://ribfrac.grand-challenge.org/)

Searches for dark matter with liquid xenon time projection chamber experiments have traditionally focused on the region of the parameter space that is characteristic of weakly interacting massive particles, ranging from a few GeV/$c^2$ to a few TeV/$c^2$. Models of dark matter with a mass much heavier than this are well motivated by early production mechanisms different from the standard thermal freeze-out, but they have generally been less explored experimentally. In this work, we present a re-analysis of the first science run (SR1) of the LZ experiment, with an exposure of $0.9$ tonne$\times$year, to search for ultraheavy particle dark matter. The signal topology consists of multiple energy deposits in the active region of the detector forming a straight line, from which the velocity of the incoming particle can be reconstructed on an event-by-event basis. Zero events with this topology were observed after applying the data selection calibrated on a simulated sample of signal-like events. New experimental constraints are derived, which rule out previously unexplored regions of the dark matter parameter space of spin-independent interactions beyond a mass of 10$^{17}$ GeV/$c^2$., Comment: 9 pages, 7 figures

The well-known Cluster Vertex Deletion problem (CVD) asks for a given graph $G$ and an integer $k$ whether it is possible to delete a set $S$ of at most $k$ vertices of $G$ such that the resulting graph $G-S$ is a cluster graph (a disjoint union of cliques). We give a complete characterization of graphs $H$ for which CVD on $H$-free graphs is polynomially solvable and for which it is NP-complete. Moreover, in the NP-completeness cases, CVD cannot be solved in sub-exponential time in the vertex number of the $H$-free input graphs unless the Exponential-Time Hypothesis fails. We also consider the connected variant of CVD, the Connected Cluster Vertex Deletion problem (CCVD), in which the set $S$ has to induce a connected subgraph of $G$. It turns out that CCVD admits the same complexity dichotomy for $H$-free graphs. Our results enlarge a list of rare dichotomy theorems for well-studied problems on $H$-free graphs., Comment: Extended version of a MFCS 2022 paper. To appear in Theory of Computing Systems