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The antiferromagnetic layered compound EuCd$_2$As$_2$ is widely considered as a leading candidate of ideal Weyl semimetal, featuring a single pair of Weyl nodes in its field-induced ferromagnetic (FM) state. Nevertheless, this view has recently been challenged by an optical spectroscopy study, which suggests that it is a magnetic semiconductor. In this study, we have successfully synthesized highly insulating EuCd$_2$As$_2$ crystals with carrier density reaching as low as $2\times 10^{15}$ $\text{cm}^{-3}$. The magneto-transport measurements revealed a progressive decrease of the anomalous Hall conductivity (AHC) by several orders of magnitude as the carrier density decreases. This behavior contradicts with what is expected from the intrinsic AHC generated by the Weyl points, which is independent of carrier density as the Fermi level approaches the charge neutrality point. In contrast, the scaling relationship between AHC and longitudinal conductivity aligns with the characteristics of variable range hopping insulators. Our results suggest that EuCd$_2$As$_2$ is a magnetic semiconductor rather than a topological Weyl semimetal.

Within the extended vector meson dominance model, we investigate the $e^+ e^- \to \Lambda^+_c \bar{\Lambda}^-_c$ reaction and the electromagnetic form factors of the charmed baryon $\Lambda_c^+$. The model parameters are determined by fitting them to the cross sections of the process $e^+e^-\rightarrow \Lambda_c^+ \bar{\Lambda}_c^-$ and the magnetic form factor $|G_M|$ of $\Lambda^+_c$. By considering four charmoniumlike states, called $\psi(4500)$, $\psi(4660)$, $\psi(4790)$, and $\psi(4900)$, we can well describe the current data on the $e^+ e^- \to \Lambda^+_c \bar{\Lambda}^-_c$ reaction from the reaction threshold up to $4.96 \ \mathrm{GeV}$. In addition to the total cross sections and $|G_M|$, the ratio $|G_E/G_M|$ and effective form factor $|G_{\mathrm{eff}}|$ for $\Lambda^+_c$ are also calculated, and found that these calculations are consistent with the experimental data. Within the fitted model parameters, we have also estimated the charge radius of the charmed $\Lambda_c^+$ baryon., Comment: 7 pages, 6 figures. Published version

Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises. Conditional residual coding was thus proposed, creating a new school of thought to improve on conditional coding. Notably, conditional residual coding relies heavily on the assumption that the residual frame has a lower entropy rate than that of the intra frame. Recognizing that this assumption is not always true due to dis-occlusion phenomena or unreliable motion estimates, we propose a masked conditional residual coding scheme. It learns a soft mask to form a hybrid of conditional coding and conditional residual coding in a pixel adaptive manner. We introduce a Transformer-based conditional autoencoder. Several strategies are investigated with regard to how to condition a Transformer-based autoencoder for inter-frame coding, a topic that is largely under-explored. Additionally, we propose a channel transform module (CTM) to decorrelate the image latents along the channel dimension, with the aim of using the simple hyperprior to approach similar compression performance to the channel-wise autoregressive model. Experimental results confirm the superiority of our masked conditional residual transformer (termed MaskCRT) to both conditional coding and conditional residual coding. On commonly used datasets, MaskCRT shows comparable BD-rate results to VTM-17.0 under the low delay P configuration in terms of PSNR-RGB and outperforms VTM-17.0 in terms of MS-SSIM-RGB. It also opens up a new research direction for advancing learned video compression., Comment: Accepted for Publication in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for control systems. However, its practical deployment is impeded by state perturbations that can severely impact system performance. Addressing this critical challenge requires robustness verification about system performance, which involves tackling two quantitative questions: (i) how to establish guaranteed bounds for expected cumulative rewards, and (ii) how to determine tail bounds for cumulative rewards. In this work, we present the first approach for robustness verification of DRL-based control systems by introducing reward martingales, which offer a rigorous mathematical foundation to characterize the impact of state perturbations on system performance in terms of cumulative rewards. Our verified results provide provably quantitative certificates for the two questions. We then show that reward martingales can be implemented and trained via neural networks, against different types of control policies. Experimental results demonstrate that our certified bounds tightly enclose simulation outcomes on various DRL-based control systems, indicating the effectiveness and generality of the proposed approach.

We investigate the nonmonotonic behavior observed in the time-like nucleon electromagnetic form factors. Using a phenomenological extending vector meson dominance model, where the ground states $\rho$ and $\omega$ and their excited states $\rho(2D)$, $\omega(3D)$, and $\omega(5S)$ are taken into account, we have successfully reproduced the cross sections of $e^+ e^- \to p \bar{p}$ and $e^+ e^- \to n \bar{n}$ reactions. Furthermore, we have derived the nucleon electromagnetic form factors in the time-like region, and it is found that the so-called periodic behaviour of the nucleon effective form factors is not confirmed. However, there are indeed nonmonotonic structures in the line shape of nucleon effective form factors, which can be naturally reproduced by considering the contributions from the low-lying excited vector states., Comment: Published version

We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks. Despite the critical importance of these tasks, existing methodologies often struggle to generate high-caliber results. We begin by examining the inherent limitations in previous diffusion priors. We identify a divergence between the diffusion priors and the training procedures of diffusion models that substantially impairs the quality of 3D generation. To address this issue, we propose a novel, unified framework that iteratively optimizes both the 3D model and the diffusion prior. Leveraging the different learnable parameters of the diffusion prior, our approach offers multiple configurations, affording various trade-offs between performance and implementation complexity. Notably, our experimental results demonstrate that our method markedly surpasses existing techniques, establishing new state-of-the-art in the realm of text-to-3D generation. Furthermore, our approach exhibits impressive performance on both NeRF and the newly introduced 3D Gaussian Splatting backbones. Additionally, our framework yields insightful contributions to the understanding of recent score distillation methods, such as the VSD and DDS loss.

Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have become increasingly vital in representing complex stochastic processes in various domains such as gene expression analysis, healthcare, and traffic prediction. Structure learning of DBNs from data is challenging, particularly for datasets with thousands of variables. Most current algorithms for DBN structure learning are adaptations from those used in static Bayesian Networks (BNs), and are typically focused on small-scale problems. In order to solve large-scale problems while taking full advantage of existing algorithms, this paper introduces a novel divide-and-conquer strategy, originally developed for static BNs, and adapts it for large-scale DBN structure learning. In this work, we specifically concentrate on 2 Time-sliced Bayesian Networks (2-TBNs), a special class of DBNs. Furthermore, we leverage the prior knowledge of 2-TBNs to enhance the performance of the strategy we introduce. Our approach significantly improves the scalability and accuracy of 2-TBN structure learning. Experimental results demonstrate the effectiveness of our method, showing substantial improvements over existing algorithms in both computational efficiency and structure learning accuracy. On problem instances with more than 1,000 variables, our approach improves two accuracy metrics by 74.45% and 110.94% on average , respectively, while reducing runtime by 93.65% on average.

Catastrophic Forgetting (CF) is a prominent issue in continual learning. Parameter isolation addresses this challenge by masking a sub-network for each task to mitigate interference with old tasks. However, these sub-networks are constructed relying on weight magnitude, which does not necessarily correspond to the importance of weights, resulting in maintaining unimportant weights and constructing redundant sub-networks. To overcome this limitation, inspired by information bottleneck, which removes redundancy between adjacent network layers, we propose \textbf{\underline{I}nformation \underline{B}ottleneck \underline{M}asked sub-network (IBM)} to eliminate redundancy within sub-networks. Specifically, IBM accumulates valuable information into essential weights to construct redundancy-free sub-networks, not only effectively mitigating CF by freezing the sub-networks but also facilitating new tasks training through the transfer of valuable knowledge. Additionally, IBM decomposes hidden representations to automate the construction process and make it flexible. Extensive experiments demonstrate that IBM consistently outperforms state-of-the-art methods. Notably, IBM surpasses the state-of-the-art parameter isolation method with a 70\% reduction in the number of parameters within sub-networks and an 80\% decrease in training time.

We use a Rydberg quantum simulator to demonstrate a new form of spectroscopy, called quench spectroscopy, which probes the low-energy excitations of a many-body system. We illustrate the method on a two-dimensional simulation of the spin-1/2 dipolar XY model. Through microscopic measurements of the spatial spin correlation dynamics following a quench, we extract the dispersion relation of the elementary excitations for both ferro- and anti-ferromagnetic couplings. We observe qualitatively different behaviors between the two cases that result from the long-range nature of the interactions, and the frustration inherent in the antiferromagnet. In particular, the ferromagnet exhibits elementary excitations behaving as linear spin waves. In the anti-ferromagnet, spin waves appear to decay, suggesting the presence of strong nonlinearities. Our demonstration highlights the importance of power-law interactions on the excitation spectrum of a many-body system., Comment: Main text 8 pages with 4 figures ; Supplemental Material 16 pages and 13 figures

This paper generalizes the Fan-Knaster-Kuratowski-Mazurkiewicz (FKKM) lemma to the case of weak topology, and obtains the Ky Fan minimax inequality defined on non-empty non-compact convex subsets in reflexive Banach spaces, then we apply it to game theory and obtain Nash's existence theorem for non-compact strategy sets, together with John von Neumann's existence theorem in two-player zero-sum games., Comment: 8 pages

Subsurface fluid flow, essential in various natural and engineered processes, is largely governed by a rock's permeability, which describes its ability to allow fluid passage. While convolutional neural networks (CNNs) have been employed to estimate permeability from high-resolution 3D rock images, our novel visualization technology reveals that they occasionally miss higher-level characteristics, such as nuanced connectivity and flow paths, within porous media. To address this, we propose a novel fusion model to integrate CNN with the graph neural network (GNN), which capitalizes on graph representations derived from pore network model to capture intricate relational data between pores. The permeability prediction accuracy of the fusion model is superior to the standalone CNN, whereas its total parameter number is nearly two orders of magnitude lower than the latter. This innovative approach not only heralds a new frontier in the research of digital rock property predictions, but also demonstrates remarkable improvements in prediction accuracy and efficiency, emphasizing the transformative potential of hybrid neural network architectures in subsurface fluid flow research.

Uncertainty estimation plays an important role for future reliable deployment of deep segmentation models in safety-critical scenarios such as medical applications. However, existing methods for uncertainty estimation have been limited by the lack of explicit guidance for calibrating the prediction risk and model confidence. In this work, we propose a novel fine-grained reward maximization (FGRM) framework, to address uncertainty estimation by directly utilizing an uncertainty metric related reward function with a reinforcement learning based model tuning algorithm. This would benefit the model uncertainty estimation through direct optimization guidance for model calibration. Specifically, our method designs a new uncertainty estimation reward function using the calibration metric, which is maximized to fine-tune an evidential learning pre-trained segmentation model for calibrating prediction risk. Importantly, we innovate an effective fine-grained parameter update scheme, which imposes fine-grained reward-weighting of each network parameter according to the parameter importance quantified by the fisher information matrix. To the best of our knowledge, this is the first work exploring reward optimization for model uncertainty estimation in safety-critical vision tasks. The effectiveness of our method is demonstrated on two large safety-critical surgical scene segmentation datasets under two different uncertainty estimation settings. With real-time one forward pass at inference, our method outperforms state-of-the-art methods by a clear margin on all the calibration metrics of uncertainty estimation, while maintaining a high task accuracy for the segmentation results. Code is available at \url{https://github.com/med-air/FGRM}.

Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities exposed by this collaboration and consider adversarial attacks on a few agents with the goal of influencing the decisions of the rest. More specifically, we study adversarial attacks on CMA2B in both homogeneous settings, where agents operate with the same arm set, and heterogeneous settings, where agents have distinct arm sets. In the homogeneous setting, we propose attack strategies that, by targeting just one agent, convince all agents to select a particular target arm $T-o(T)$ times while incurring $o(T)$ attack costs in $T$ rounds. In the heterogeneous setting, we prove that a target arm attack requires linear attack costs and propose attack strategies that can force a maximum number of agents to suffer linear regrets while incurring sublinear costs and only manipulating the observations of a few target agents. Numerical experiments validate the effectiveness of our proposed attack strategies.

In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particularly intriguing application of LLMs is their role as evaluators for texts produced by various generative models. In this study, we delve into the potential of LLMs as reliable assessors of factual consistency in summaries generated by text-generation models. Initially, we introduce an innovative approach for factuality assessment using LLMs. This entails employing a singular LLM for the entirety of the question-answering-based factuality scoring process. Following this, we examine the efficacy of various LLMs in direct factuality scoring, benchmarking them against traditional measures and human annotations. Contrary to initial expectations, our results indicate a lack of significant correlations between factuality metrics and human evaluations, specifically for GPT-4 and PaLM-2. Notable correlations were only observed with GPT-3.5 across two factuality subcategories. These consistent findings across various factual error categories suggest a fundamental limitation in the current LLMs' capability to accurately gauge factuality. This version presents the information more concisely while maintaining the main points and findings of the original text., Comment: accepted by Generation, Evaluation & Metrics (GEM) Workshop at EMNLP 2023

This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, namely, GPT-4, GPT- 3.5, PaLM-2, and LLaMA-2. Our findings reveal that most closed-source LLMs are generally better in terms of performance. However, much smaller open-source models like LLaMA- 2 (7B and 13B) could still achieve performance comparable to the large closed-source models even in zero-shot scenarios. Considering the privacy concerns of closed-source models for only being accessible via API, alongside the high cost associated with using fine-tuned versions of the closed-source models, the opensource models that can achieve competitive performance are more advantageous for industrial use. Balancing performance with associated costs and privacy concerns, the LLaMA-2-7B model looks more promising for industrial usage. In sum, this paper offers practical insights on using LLMs for real-world business meeting summarization, shedding light on the trade-offs between performance and cost., Comment: EMNLP 2023 Industry Track

The dominant mechanism for generating free-floating planets has so far remained elusive. One suggested mechanism is that planets are ejected from planetary systems due to planet-planet interactions. However, instability around a single star requires a very compactly spaced planetary system. We find that around binary star systems instability can occur even with widely separated planets that are on tilted orbits relative to the binary orbit due to combined effects of planet-binary and planet-planet interactions, especially if the binary is on an eccentric orbit. We investigate the orbital stability of planetary systems with various planet masses and architectures. We find that the stability of the system depends upon the mass of the highest mass planet. The order of the planets in the system does not significantly affect stability but, generally, the most massive planet remains stable and the lower mass planets are ejected. The minimum planet mass required to trigger the instability is about that of Neptune for a circular orbit binary and a super-Earth of about $10$ Earth masses for highly eccentric binaries. Hence, we suggest that planet formation around misaligned binaries can be an efficient formation mechanism for free-floating planets. While most observed free-floating planets are giant planets, we predict that there should be more low-mass free floating planets that are as yet unobserved than higher mass planets., Comment: 10 pages, 2 figures

Robotic technologies are becoming increasingly popular in dentistry due to the high level of precision required in delicate dental procedures. Most dental robots available today are designed for implant surgery, helping dentists to accurately place implants in the desired position and depth. In this paper, we introduce the DentiBot, the first robot specifically designed for dental endodontic treatment. The DentiBot is equipped with a force and torque sensor, as well as a string-based Patient Tracking Module, allowing for real-time monitoring of endodontic file contact and patient movement. We propose a 6-DoF hybrid position/force controller that enables autonomous adjustment of the surgical path and compensation for patient movement, while also providing protection against endodontic file fracture. In addition, a file flexibility model is incorporated to compensate for file bending. Pre-clinical evaluations performed on acrylic root canal models and resin teeth confirm the feasibility of the DentiBot in assisting endodontic treatment.

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction., Comment: Accepted to IEEE ICASSP 2024