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Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language instructions is critical for bridging communication gaps and facilitating more intuitive interactions. However, the absence of a comprehensive evaluation benchmark poses a significant challenge. We present Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive evaluation of instruction-based universal speech models. Building upon the first generation, this second version incorporates 125 new tasks contributed collaboratively by the global research community, expanding the benchmark to a total of 180 tasks, making it the largest benchmark for speech and audio evaluation. While the first generation of Dynamic-SUPERB was limited to classification tasks, Dynamic-SUPERB Phase-2 broadens its evaluation capabilities by introducing a wide array of novel and diverse tasks, including regression and sequence generation, across speech, music, and environmental audio. Evaluation results indicate that none of the models performed well universally. SALMONN-13B excelled in English ASR, while WavLLM demonstrated high accuracy in emotion recognition, but current models still require further innovations to handle a broader range of tasks. We will soon open-source all task data and the evaluation pipeline.

Objectives: This study examined whether currently existing clinical structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (18FDG-PET) capabilities and board-certified radiologists' reports and interpretations can assist with traumatic encephalopathy syndrome (TES) diagnosis. Design: retrospective case series. Setting: this study assessed six patients with TES criteria recruited from the Sydney area, Australia Main outcomes: patients' clinical history and clinical presentation along TES diagnostic criteria, board-certified radiologist reports of structural MRI and 18FDG-PET. Results: one patient was classified as possible CTE, and the others were classified as probable CTE with significant RHI exposure history and a spectrum of cognitive deficits and other neuropsychiatric disturbances consistent with TES diagnostic criteria. Most common radiological features included atrophy of posterior superior parietal region and Evans Index > 0.25. FDG-PET's pattern of common regions of hypometabolism and differing hypometabolism across participants suggested that FDG-PET and structural MRI have the potential for stratifying different TES stages. Conclusions: this study highlights the potential of a combined clinical and radiological approach using solely current capabilities to improve TES diagnosis and suggests a larger study. Such expanded investigation is crucial for advancing the ante-mortem diagnosis and management of CTE., Comment: 19 pages, 2 figures, 5 tables

Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high accuracy. These models offer versatile, one-for-many or one-for-all solutions, eliminating the need for developing task-specific AI models. Examples of such foundation models include the Chat Generative Pre-trained Transformer (ChatGPT) and the Segment Anything Model (SAM). These models have been trained on millions to billions of samples and have shown wide-ranging and accurate applications in numerous tasks such as text processing (using ChatGPT) and natural image segmentation (using SAM). In medical image segmentation - finding target regions in medical images - there is a growing need for these one-for-many or one-for-all foundation models. Such models could obviate the need to develop thousands of task-specific AI models, which is currently standard practice in the field. They can also be adapted to tasks with datasets too small for effective training. We discuss two paths to achieve foundation models for medical image segmentation and comment on progress, challenges, and opportunities. One path is to adapt or fine-tune existing models, originally developed for natural images, for use with medical images. The second path entails building models from scratch, exclusively training on medical images.

Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical limitations on the size of the biasing catalogue and consequently on accuracy improvements. This work proposes an approximation to cross-attention scoring based on vector quantization and enables compute- and memory-efficient use of large biasing catalogues. We propose to use this technique jointly with a retrieval based contextual biasing approach. First, we use an efficient quantized retrieval module to shortlist biasing entries by grounding them on audio. Then we use retrieved entries for biasing. Since the proposed approach is agnostic to the biasing method, we investigate using full cross-attention, LLM prompting, and a combination of the two. We show that retrieval based shortlisting allows the system to efficiently leverage biasing catalogues of several thousands of entries, resulting in up to 71% relative error rate reduction in personal entity recognition. At the same time, the proposed approximation algorithm reduces compute time by 20% and memory usage by 85-95%, for lists of up to one million entries, when compared to standard dot-product cross-attention., Comment: 13 pages, 7 figures, submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing

Consider a compact torsion free CR manifold $X$ and assume that $X$ admits a compact CR Lie group action $G$. Let $L$ be a $G$-equivariant rigid CR line bundle over $X$. It seems natural to consider the space of $G$-invariant CR sections in the high tensor powers as quantization space, on which a certain weighted $G$-invariant Fourier-Szeg\H{o} operator projects. Under certain natural assumptions, we show that the group invariant Fourier-Szeg\H{o} projector admits a full asymptotic expansion. As an application, if the tensor power of the line bundle is large enough, we prove that quantization commutes with reduction.

Most learned B-frame codecs with hierarchical temporal prediction suffer from the domain shift issue caused by the discrepancy in the Group-of-Pictures (GOP) size used for training and test. As such, the motion estimation network may fail to predict large motion properly. One effective strategy to mitigate this domain shift issue is to downsample video frames for motion estimation. However, finding the optimal downsampling factor involves a time-consuming rate-distortion optimization process. This work introduces lightweight classifiers to determine the downsampling factor. To strike a good rate-distortion-complexity trade-off, our classifiers observe simple state signals, including only the coding and reference frames, to predict the best downsampling factor. We present two variants that adopt binary and multi-class classifiers, respectively. The binary classifier adopts the Focal Loss for training, classifying between motion estimation at high and low resolutions. Our multi-class classifier is trained with novel soft labels incorporating the knowledge of the rate-distortion costs of different downsampling factors. Both variants operate as add-on modules without the need to re-train the B-frame codec. Experimental results confirm that they achieve comparable coding performance to the brute-force search methods while greatly reducing computational complexity.

In this paper, we first show that current learning-based video codecs, specifically the SSF codec, are not suitable for real-world applications due to the mismatch between the encoder and decoder caused by floating-point round-off errors. To address this issue, we propose the static quantization of the hyper prior decoding path. The quantization parameters are determined through an exhaustive search of all possible combinations of observers and quantization schemes from PyTorch. For the SSF codec, when encoding and decoding on different machines, the proposed solution effectively mitigates the mismatch issue and enhances compression efficiency results by preventing severe image quality degradation. When encoding and decoding are performed on the same machine, it constrains the average BD-rate increase to 9.93% and 9.02% for UVG and HEVC-B sequences, respectively., Comment: 4 pages, submitted to ISCAS2025

The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data. Such data is often scarce due to cost constraints and privacy concerns. Alleviating this burden, medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetically generating photo-realistic images based on existing sets of real medical images. However, the exact image set size required to efficiently train such a GAN is unclear. In this work, we experimentally establish benchmarks that measure the relationship between a sample dataset size and the fidelity of the generated images, given the dataset's distribution of image complexities. We analyze statistical metrics based on delentropy, an image complexity measure rooted in Shannon's entropy in information theory. For our pipeline, we conduct experiments with two state-of-the-art GANs, StyleGAN 3 and SPADE-GAN, trained on multiple medical imaging datasets with variable sample sizes. Across both GANs, general performance improved with increasing training set size but suffered with increasing complexity., Comment: Accepted to ACCV, Workshop on Generative AI for Synthetic Medical Data

In this Letter, we investigate the turbulence and energy injection in the extended nebulae surrounding two luminous obscured quasars, WISEA J100211.29$+$013706.7 ($z=1.5933$) and SDSS J165202.64$+$172852.3 ($z=2.9489$). Utilizing high-resolution data from the NIRSpec IFU onboard the James Webb Space Telescope, we analyze the velocity fields of line-emitting gas in and around these quasars and construct the second-order velocity structure functions (VSFs) to quantify turbulent motions across different spatial scales. Our findings reveal a notable flattening in the VSFs from $\approx\!3$ kpc up to a scale of 10--20 kpc, suggesting that energy injection predominantly occurs at a scale $\lesssim$10 kpc, likely powered by quasar outflows and jet-driven bubbles. The extended spatial range of flat VSFs may also indicate the presence of multiple energy injection sources at these scales. For J1652, the turbulent energy in the host interstellar medium (ISM) is significantly higher than in tidally stripped gas, consistent with the expectation of active galactic nucleus (AGN) activities stirring up the host ISM. Compared to the VSFs observed on spatial scales of 10--50 kpc around lower-redshift UV-bright quasars, these obscured quasars exhibit higher turbulent energies in their immediate surroundings, implying different turbulence drivers between the ISM and halo-scale gas. Future studies with an expanded sample are essential to elucidate further the extent and the pivotal role of AGNs in shaping the gas kinematics of host galaxies and beyond., Comment: 10 pages, 4 figures; submitted; comments welcome!

Quasar feedback may play a key role in the evolution of massive galaxies. The dust-reddened quasar, F2M110648.35$+$480712 at $z = 0.4352$ is one of the few cases at its redshift that exhibits powerful quasar feedback through bipolar outflows. Our new observation with the integral field unit mode of Near-infrared Spectrograph onboard JWST opens a new window to examine this spectacular outflow through Pa$\alpha$ emission line with $\sim$3$\times$ better spatial resolution than previous work. The morphology and kinematics of the Pa$\alpha$ nebula confirm the existence of a bipolar outflow extending on a scale of $\sim$17$\times$14 kpc and with a velocity reaching $\sim$1100 km s$^{-1}$. The higher spatial resolution of our new observation leads to more reliable measurements of outflow kinematics. Considering only the spatially resolved outflow and assuming an electron density of 100 cm$^{-2}$, the mass, momentum and kinetic energy outflow rates are $\sim$50-210 M$_{\odot}$ yr$^{-1}$, $\sim$0.3-1.7$\times$10$^{36}$ dynes ($\sim$14-78\% of the quasar photon momentum flux) and $\sim$0.16-1.27$\times$10$^{44}$ erg s$^{-1}$ ($\sim$0.02-0.20\% of the quasar bolometric luminosity), respectively. The local instantaneous outflow rates generally decrease radially. We infer that the quasar is powerful enough to drive the outflow, while stellar processes cannot be overlooked as a contributing energy source. The mass outflow rate is $\sim$0.4-1.5 times the star formation rate, and the ratio of kinetic energy outflow rate to the quasar bolometric luminosity is comparable to the minimum value required for negative quasar feedback in simulations. This outflow may help regulate the star formation activity within the system to some extent., Comment: 14 pages, 6 figures, 1 table, ApJ in review

We measure the masses of the pseudoscalar flavour-singlet meson states in the $Sp(4)$ gauge theory coupled to two Dirac fermions transforming in the fundamental representation and three Dirac fermions in the antisymmetric representation. This theory provides a compelling ultraviolet completion for the minimal composite Higgs model implementing also partial compositeness for the top quark. The spectrum contains two, comparatively light, pseudoscalar flavour-singlet states, which mix with one another. One of them is a Nambu-Goldstone boson (in the massless limit), whereas the other receives a mass from the $U(1)_A$ axial anomaly. We demonstrate how to measure the mixing between these two states. For moderately heavy fermion masses, we find that the two wave functions are dominated by one of the fermion representations, mixing effects being small., Comment: 12 pages, 2 figures, 2 tables, Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), July 28th - August 3rd, 2024, University of Liverpool, UK

Spectral densities encode non-perturbative information crucial in computing physical observables in strongly coupled field theories. Using lattice gauge theory data, we perform a systematic study to demonstrate the potential of recent technological advances in the reconstruction of spectral densities. We develop, maintain and make publicly available dedicated analysis code that can be used for broad classes of lattice theories. As a test case, we analyse the Sp(4) gauge theory coupled to an admixture of fermions transforming in the fundamental and two-index antisymmetric representations. We measure the masses of mesons in energy-smeared spectral densities, after optimising the smearing parameters for available lattice ensembles. We present a summary of the mesons mass spectrum in all the twelve (flavored) channels available, including also several excited states., Comment: 9 pages, 5 figures, contribution for the 41th International Symposium on Lattice Field Theory (Lattice 2024), 28 July - 3 August 2024, Liverpool, UK, presenting the results of the paper: arXiv:2405.01388

This paper presents an innovative large language model (LLM) agent framework for enhancing diagnostic accuracy in simulated clinical environments using the AgentClinic benchmark. The proposed automatic correction enables doctor agents to iteratively refine their reasoning and actions following incorrect diagnoses, fostering improved decision-making over time. Experiments show that the implementation of the adaptive LLM-based doctor agents achieve correct diagnoses through dynamic interactions with simulated patients. The evaluations highlight the capacity of autonomous agents to adapt and improve in complex medical scenarios. Future enhancements will focus on refining the algorithm and expanding its applicability across a wider range of tasks and different large language models.

Current Artificial Intelligence (AI) computation systems face challenges, primarily from the memory-wall issue, limiting overall system-level performance, especially for Edge devices with constrained battery budgets, such as smartphones, wearables, and Internet-of-Things sensor systems. In this paper, we propose a new SRAM-based Compute-In-Memory (CIM) accelerator optimized for Spiking Neural Networks (SNNs) Inference. Our proposed architecture employs a multiport SRAM design with multiple decoupled Read ports to enhance the throughput and Transposable Read-Write ports to facilitate online learning. Furthermore, we develop an Arbiter circuit for efficient data-processing and port allocations during the computation. Results for a 128$\times$128 array in 3nm FinFET technology demonstrate a 3.1$\times$ improvement in speed and a 2.2$\times$ enhancement in energy efficiency with our proposed multiport SRAM design compared to the traditional single-port design. At system-level, a throughput of 44 MInf/s at 607 pJ/Inf and 29mW is achieved., Comment: DAC 2024 Research Manuscript

Type II supernovae (SNeII) mark the endpoint in the lives of hydrogen-rich massive stars. Their large explosion energies and luminosities allow us to measure distances, metallicities, and star formation rates into the distant Universe. To fully exploit their use in answering different astrophysical problems, high-quality low-redshift data sets are required. Such samples are vital to understand the physics of SNeII, but also to serve as calibrators for distinct - and often lower-quality - samples. We present uBgVri optical and YJH near-infrared (NIR) photometry for 94 low-redshift SNeII observed by the Carnegie Supernova Project (CSP). A total of 9817 optical and 1872 NIR photometric data points are released, leading to a sample of high-quality SNII light curves during the first ~150 days post explosion on a well-calibrated photometric system. The sample is presented and its properties are analysed and discussed through comparison to literature events. We also focus on individual SNeII as examples of classically defined subtypes and outlier objects. Making a cut in the plateau decline rate of our sample (s2), a new subsample of fast-declining SNeII is presented. The sample has a median redshift of 0.015, with the nearest event at 0.001 and the most distant at 0.07. At optical wavelengths (V), the sample has a median cadence of 4.7 days over the course of a median coverage of 80 days. In the NIR (J), the median cadence is 7.2 days over the course of 59 days. The fast-declining subsample is more luminous than the full sample and shows shorter plateau phases. Of the non-standard SNeII highlighted, SN2009A particularly stands out with a steeply declining then rising light curve, together with what appears to be two superimposed P-Cygni profiles of H-alpha in its spectra. We outline the significant utility of these data, and finally provide an outlook of future SNII science., Comment: Accepted for publication in A&A. Photometric data will be uploaded to the CDS and the CSP website, and can also be requested from the first author

New privacy concerns arise with chatbots on group messaging platforms. Chatbots may access information beyond their intended functionalities, such as messages unintended for chatbots or sender's identities. Chatbot operators may exploit such information to infer personal information and link users across groups, potentially leading to personal data breaches, pervasive tracking, and targeted advertising. Our analysis of conversation datasets shows that (1) chatbots often access far more messages than needed, and (2) when a user joins a new group with chatbots, there is a 3.4% chance that at least one of the chatbots can recognize and associate the user with their previous interactions in other groups. Although state-of-the-art group messaging protocols provide robust end-to-end security and some platforms have implemented policies to limit chatbot access, no platforms successfully combine these features. This paper introduces SnoopGuard, a secure group messaging protocol that ensures user privacy against chatbots while maintaining strong end-to-end security. Our method offers selective message access, preventing chatbots from accessing unrelated messages, and ensures sender anonymity within the group. SnoopGuard achieves $O(\log n + m)$ message-sending complexity for a group of $n$ users and $m$ chatbots, compared to $O(\log(n + m))$ in state-of-the-art protocols, with acceptable overhead for enhanced privacy. Our prototype implementation shows that sending a message in a group of 50 users and 10 chatbots takes about 30 milliseconds when integrated with Message Layer Security (MLS)., Comment: 18 pages, 5 figures

In this paper, we propose an efficient transformer architecture that uses reinforced positional embedding to obtain superior performance with half the number of encoder decoder layers. We demonstrate that concatenating positional encoding with trainable token embeddings, normalizing columns in the token embedding matrix, and using the normalized token embedding matrix as the value of the attention layer improve the training and validation loss and the training time in an encoder-decoder Transformer model for a Portuguese-English translation task with 10 epochs or 12 hours of training across 10 trials. Our method, with roughly a threefold parameter reduction compared to the baseline model, yields a mean training loss of 1.21, a mean validation loss of 1.51, and an average training time of 1352.27 seconds per epoch, surpassing the baseline model with the same embedding dimension that employs addition of positional encoding and token embeddings, which achieves a mean training loss of 1.96, a validation loss of 2.18, and an average training time of 4297.79 seconds per epoch. Additionally, we evaluated our proposed architecture and the baseline across 14 diverse translation datasets from TensorFlow. The results indicate that our method consistently achieves lower or comparable training and validation losses, suggesting enhanced learning efficiency.

This paper aims to delve into the rate-distortion-complexity trade-offs of modern neural video coding. Recent years have witnessed much research effort being focused on exploring the full potential of neural video coding. Conditional autoencoders have emerged as the mainstream approach to efficient neural video coding. The central theme of conditional autoencoders is to leverage both spatial and temporal information for better conditional coding. However, a recent study indicates that conditional coding may suffer from information bottlenecks, potentially performing worse than traditional residual coding. To address this issue, recent conditional coding methods incorporate a large number of high-resolution features as the condition signal, leading to a considerable increase in the number of multiply-accumulate operations, memory footprint, and model size. Taking DCVC as the common code base, we investigate how the newly proposed conditional residual coding, an emerging new school of thought, and its variants may strike a better balance among rate, distortion, and complexity., Comment: Accepted to MMSP 2024

Accurate diagnosis of ocular surface diseases is critical in optometry and ophthalmology, which hinge on integrating clinical data sources (e.g., meibography imaging and clinical metadata). Traditional human assessments lack precision in quantifying clinical observations, while current machine-based methods often treat diagnoses as multi-class classification problems, limiting the diagnoses to a predefined closed-set of curated answers without reasoning the clinical relevance of each variable to the diagnosis. To tackle these challenges, we introduce an innovative multi-modal diagnostic pipeline (MDPipe) by employing large language models (LLMs) for ocular surface disease diagnosis. We first employ a visual translator to interpret meibography images by converting them into quantifiable morphology data, facilitating their integration with clinical metadata and enabling the communication of nuanced medical insight to LLMs. To further advance this communication, we introduce a LLM-based summarizer to contextualize the insight from the combined morphology and clinical metadata, and generate clinical report summaries. Finally, we refine the LLMs' reasoning ability with domain-specific insight from real-life clinician diagnoses. Our evaluation across diverse ocular surface disease diagnosis benchmarks demonstrates that MDPipe outperforms existing standards, including GPT-4, and provides clinically sound rationales for diagnoses., Comment: Accepted to MICCAI 2024. Project Webpage: https://danielchyeh.github.io/MDPipe/

The Check tools automate formal memory consistency model and security verification of processors by analyzing abstract models of microarchitectures, called $\mu$SPEC models. Despite the efficacy of this approach, a verification gap between $\mu$SPEC models, which must be manually written, and RTL limits the Check tools' broad adoption. Our prior work, called RTL2$\mu$SPEC, narrows this gap by automatically synthesizing formally verified $\mu$SPEC models from SystemVerilog implementations of simple processors. But, RTL2$\mu$SPEC assumes input designs where an instruction (e.g., a load) cannot exhibit more than one microarchitectural execution path ($\mu$PATH, e.g., a cache hit or miss path) -- its single-execution-path assumption. In this paper, we first propose an automated approach and tool, called RTL2M$\mu$PATH, that resolves RTL2$\mu$SPEC's single-execution-path assumption. Given a SystemVerilog processor design, instruction encodings, and modest design metadata, RTL2M$\mu$PATH finds a complete set of formally verified $\mu$PATHs for each instruction. Next, we make an important observation: an instruction that can exhibit more than one $\mu$PATH strongly indicates the presence of a microarchitectural side channel in the input design. Based on this observation, we then propose an automated approach and tool, called SynthLC, that extends RTL2M$\mu$PATH with a symbolic information flow analysis to support synthesizing a variety of formally verified leakage contracts from SystemVerilog processor designs. Leakage contracts are foundational to state-of-the-art defenses against hardware side-channel attacks. SynthLC is the first automated methodology for formally verifying hardware adherence to them., Comment: Authors' version; to appear in the Proceedings of the 57th Annual IEEE/ACM International Symposium on Microarchitecture 57th (MICRO 2024)

Our team developed a riding ballbot (called PURE) that is dynamically stable, omnidirectional, and driven by lean-to-steer control. A hands-free admittance control scheme (HACS) was previously integrated to allow riders with different torso functions to control the robot's movements via torso leaning and twisting. Such an interface requires motor coordination skills and could result in collisions with obstacles due to low proficiency. Hence, a shared controller (SC) that limits the speed of PURE could be helpful to ensure the safety of riders. However, the self-balancing dynamics of PURE could result in a weak control authority of its motion, in which the torso motion of the rider could easily result in poor tracking of the command speed dictated by the shared controller. Thus, we proposed an interactive hands-free admittance control scheme (iHACS), which added two modules to HACS to improve the speed-tracking performance of PURE: control gain personalization module and interaction compensation module. Human riding tests of simple tasks, idle-keeping and speed-limiting, were conducted to compare the performance of HACS and iHACS. Two manual wheelchair users and two able-bodied individuals participated in this study. They were instructed to use "adversarial" torso motions that would tax the SC's ability to keep the ballbot idling or below a set speed. In the idle-keeping tasks, iHACS demonstrated minimal translational motion and low command speed tracking RMSE, even with significant torso lean angles. During the speed-limiting task with command speed saturated at 0.5 m/s, the system achieved an average maximum speed of 1.1 m/s with iHACS, compared with that of over 1.9 m/s with HACS. These results suggest that iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

This study introduces the development of hands-free control schemes for a riding ballbot, designed to allow riders including manual wheelchair users to control its movement through torso leaning and twisting. The hardware platform, Personal Unique Rolling Experience (PURE), utilizes a ballbot drivetrain, a dynamically stable mobile robot that uses a ball as its wheel to provide omnidirectional maneuverability. To accommodate users with varying torso motion functions, the hanads-free control scheme should be adjustable based on the rider's torso function and personal preferences. Therefore, concepts of (a) impedance control and (b) admittance control were integrated into the control scheme. A duo-agent optimization framework was utilized to assess the efficiency of this rider-ballbot system for a safety-critical task: braking from 1.4 m/s. The candidate control schemes were further implemented in the physical robot hardware and validated with two experienced users, demonstrating the efficiency and robustness of the hands-free admittance control scheme (HACS). This interface, which utilized physical human-robot interaction (pHRI) as the input, resulted in lower braking effort and shorter braking distance and time. Subsequently, 12 novice participants (six able-bodied users and six manual wheelchair users) with different levels of torso motion capability were then recruited to benchmark the braking performance with HACS. The indoor navigation capability of PURE was further demonstrated with these participants in courses simulating narrow hallways, tight turns, and navigation through static and dynamic obstacles. By exploiting pHRI, the proposed admittance-style control scheme provided effective control of the ballbot via torso motions. This interface enables PURE to provide a personal unique rolling experience to manual wheelchair users for safe and agile indoor navigation.

The Hall effect is one of the most fundamental but elusive phenomena in condensed matter physics due to the rich variety of underlying mechanisms. Here we report an exceptionally large Hall effect in a frustrated magnet GdCu$_2$ with high conductivity. The Hall conductivity at the base temperature is as high as 4 x 10$^4$ $\Omega^{-1}$cm$^{-1}$ and shows abrupt sign changes under magnetic fields. Remarkably, the giant Hall effect is rapidly suppressed as the longitudinal conductivity is lowered upon increasing temperature or introducing tiny amount of quenched disorder. Our systematic transport measurements together with neutron scattering measurements and ab initio band calculations indicate that the unusual Hall effect can be understood in terms of spin-splitting induced emergence/disappearance of Fermi pockets as well as skew scattering from spin-chiral cluster fluctuations in a field-polarized state. The present study demonstrates complex interplay among magnetization, spin-dependent electronic structure, and spin fluctuations in producing the giant Hall effect in highly conductive frustrated magnets.

We present a detailed study of the kinematics of OVI-bearing gas around 60 low-mass (median log(M*/Msun)~8.9) galaxies at low redshift (0.1 < z < 0.7) using background quasars (median impact parameter $\approx115$ kpc) as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the majority of the OVI absorbers detected within the virial radius have line-of-sight velocities smaller than the escape velocities and are thus consistent with being gravitationally bound, irrespective of the halo mass. However, the fraction of such absorbers declines at larger impact parameters. The Doppler $b$ parameter and the velocity width ($\Delta v_{90}$) of the OVI absorbers exhibit large scatter inside the virial radius of the host galaxies, but the scatter declines sharply at impact parameter $D \gtrsim 2R_{\rm vir}$. For high-mass galaxies (log(M*/Msun)>9), OVI absorption displays a larger kinematic spread, quantified by the pixel-velocity two-point correlation function (TPCF). However, this difference disappears for the isolated galaxies when the pixel velocities are scaled by the galaxy's circular velocity. We do not find any significant difference between the TPCF of isolated and group galaxies when the stellar mass is controlled for. A significant fraction of groups (4/6) with four or more member galaxies do not show any detectable OVI absorption, likely due to the passive nature of nearest galaxies., Comment: Submitted to ApJ. Comments are welcome

We present a study of OVI-bearing gas around 247 low-mass (median log(M*/Msun)~8.7) galaxies at low redshifts (0.1 < z < 0.7) using background quasars as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the average OVI column density, ${\rm log}_{10}$ = $14.14^{+0.09}_{-0.10}$, measured within the virial radius for our sample, is significantly lower than for L_* galaxies. Combining 253 star-forming galaxies (mostly more massive) from the literature with 176 star-forming galaxies from MUSEQuBES, we find that both and the average covering fraction peak at log(M*/Msun)~9.5. The virial temperature corresponding to this stellar mass is ideal for OVI production via collisional ionization. However, we argue that photoionization and/or non-equilibrium processes are necessary to produce the OVI associated with low-mass, dwarf galaxies (log(M*/Msun)<9). The average OVI mass within the virial radius of dwarf galaxies is measured to be $10^{5.2_{-0.1}^{+0.1}}$ Msun. The characteristic normalized impact parameter at which the OVI covering fraction drops to half of its peak value is the largest (~1.1) for galaxies with stellar mass log(M*/Msun)~9.5. We report the presence of a highly ionized metal floor with ${\rm log}_{10}(N({\rm OVI})/{\rm cm}^{-2}) = 13.2$ outside the virial radius of dwarf galaxies inferred from median spectral stacking., Comment: Submitted to ApJ. Comments are welcome

The proxy design pattern allows Ethereum smart contracts to be simultaneously immutable and upgradeable, in which an original contract is split into a proxy contract containing the data storage and a logic contract containing the implementation logic. This architecture is known to have security issues, namely function collisions and storage collisions between the proxy and logic contracts, and has been exploited in real-world incidents to steal users' millions of dollars worth of digital assets. In response to this concern, several previous works have sought to identify proxy contracts in Ethereum and detect their collisions. However, they all fell short due to their limited coverage, often restricting analysis to only contracts with available source code or past transactions. To bridge this gap, we present Proxion, an automated cross-contract analyzer that identifies all proxy smart contracts and their collisions in Ethereum. What sets Proxion apart is its ability to analyze hidden smart contracts that lack both source code and past transactions. Equipped with various techniques to enhance efficiency and accuracy, Proxion outperforms the state-of-the-art tools, notably identifying millions more proxy contracts and thousands of unreported collisions. We apply Proxion to analyze over 36 million alive contracts from 2015 to 2023, revealing that 54.2% of them are proxy contracts, and about 1.5 million contracts exhibit at least one collision issue.

The hydrogen Lyman-alpha (Lya) emission line, the brightest spectral feature of a photoionized gas, is considered an indirect tracer of the escape of Lyman continuum (LyC) photons, particularly when the intergalactic medium is too opaque for direct detection. However, resonant scattering complicates interpreting the empirical properties of Lya photons, necessitating radiative transfer simulations to capture their strong coupling with underlying gas kinematics. In this study, we leverage the exceptional spatial resolution from strong gravitational lensing to investigate the connection between Lya line profiles and LyC leakage on scales of a few 100 pc in the Sunburst Arc galaxy at $z\sim2.37$. New optical echelle spectra obtained using Magellan MIKE show that both the LyC leaking and non-leaking regions exhibit a classic double-peak Lya feature with an enhanced red peak, indicating outflows at multiple locations in the galaxy. Both regions also show a central Gaussian peak atop the double peaks, indicating directly escaped Lya photons independent of LyC leakage. We introduce a machine learning-based method for emulating Lya simulations to quantify intrinsic dynamics ($\sigma_{\mathrm{int}}$), neutral hydrogen column density ($N_{\mathrm{HI}}$), outflow velocity ($v_{\mathrm{exp}}$), and effective temperature ($T$) across continuous parameter spaces. By comparing the spatially and spectrally resolved Lya lines in Sunburst, we argue that the directly escaped Lya photons originate in a volume-filling, warm ionized medium spanning $\sim1$ kpc, while the LyC leakage is confined to regions of $\lesssim200$ pc. These sub-kpc variations in Lya profiles highlight the complexity of interpreting integrated properties in the presence of inhomogeneous mixtures of gas and young stars, emphasizing the need for spatially and spectrally resolved observations of distant galaxies., Comment: 19 pages, 7 figures, 8 tables; added full names of authors; references updated

In this paper, we propose a novel coarse-to-fine continuous pose diffusion method to enhance the precision of pick-and-place operations within robotic manipulation tasks. Leveraging the capabilities of diffusion networks, we facilitate the accurate perception of object poses. This accurate perception enhances both pick-and-place success rates and overall manipulation precision. Our methodology utilizes a top-down RGB image projected from an RGB-D camera and adopts a coarse-to-fine architecture. This architecture enables efficient learning of coarse and fine models. A distinguishing feature of our approach is its focus on continuous pose estimation, which enables more precise object manipulation, particularly concerning rotational angles. In addition, we employ pose and color augmentation techniques to enable effective training with limited data. Through extensive experiments in simulated and real-world scenarios, as well as an ablation study, we comprehensively evaluate our proposed methodology. Taken together, the findings validate its effectiveness in achieving high-precision pick-and-place tasks., Comment: 8 pages, 7 figures. Project webpage: https://tony2guo.github.io/precise-pick-and-place/

Heavy precipitation from tropical cyclones (TCs) may result in disasters, such as floods and landslides, leading to substantial economic damage and loss of life. Prediction of TC precipitation based on ensemble post-processing procedures using machine learning (ML) approaches has received considerable attention for its flexibility in modeling and its computational power in managing complex models. However, when applying ML techniques to TC precipitation for a specific area, the available observation data are typically insufficient for comprehensive training, validation, and testing of the ML model, primarily due to the rapid movement of TCs. We propose to use the convolutional neural network (CNN) as a deep ML model to leverage the spatial information of precipitation. The proposed model has three distinct features that differentiate it from traditional CNNs applied in meteorology. First, it utilizes data augmentation to alleviate challenges posed by the small sample size. Second, it contains geographical and dynamic variables to account for area-specific features and the relative distance between the study area and the moving TC. Third, it applies unequal weights to accommodate the temporal structure in the training data when calculating the objective function. The proposed CNN-all model is then illustrated with the TC Soudelor's impact on Taiwan. Soudelor was the strongest TC of the 2015 Pacific typhoon season. The results show that the inclusion of augmented data and dynamic variables improves the prediction of heavy precipitation. The proposed CNN-all outperforms traditional CNN models, based on the continuous probability skill score (CRPSS), probability plots, and reliability diagram. The proposed model has the potential to be utilized in a wide range of meteorological studies., Comment: 21 pages, 6 figures, 2 tables, 2 supplementary figures

Mobile Charge Scheduling for wirelessly charging nodes in Wireless Rechargeable Sensor Networks (WRSNs) is a promising but still evolving research area. Existing research mostly assumes a symmetric environment, where the routing costs in opposite directions between two locations are considered identical. However, various factors such as terrain restrictions and wind or water flows may invalidate the routing-symmetric assumption in practical environments, thereby significantly limiting the performance of these solutions in routing-asymmetric WRSNs (RA-WRSNs). To address the routing-asymmetric challenges in mobile charge scheduling for WRSNs, this paper systematically investigates the underlying Asymmetric Directional Mobile Charger (DMC) Charge Scheduling (ADMCCS) problem, aiming to minimize energy loss while satisfying the charging demands of the network nodes. The DMC model is assumed because its results can be easily applied to the specialized case of an Omnidirectional Mobile Charger (OMC). To solve the ADMCCS problem, we propose a four-step framework. First, a minimum-size efficient charging position set is selected using our designed K-means-based Charging Position Generation (KCPG) algorithm, addressing the challenge of the unlimited charging position selection space. Next, minimum-size functional-equivalent direction sets at these positions are determined using an optimal algorithm, tackling the challenge of infinite charging directions. Subsequently, the optimal energy transmission time lengths for all directions at the positions are obtained by formulating and solving a Nonlinear Program (NLP) problem. Finally, the Lin-Kernighan Heuristic (LKH) algorithm for the Asymmetric Traveling Salesman Problem is adapted to obtain a highly probable optimal loop tour, addressing the routing-asymmetric challenge., Comment: 15 pages, 5 figures

This study introduces a shared-control approach for collision avoidance in a self-balancing riding ballbot, called PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrated with a sensor array and a novel Passive Artificial Potential Field (PAPF) method, PURE provides intuitive navigation with deceleration assistance and haptic/audio feedback, effectively mitigating collision risks. This approach addresses the limitations of traditional APF methods, such as control oscillations and unnecessary speed reduction in challenging scenarios. A human-robot interaction experiment, with 20 manual wheelchair users and able-bodied individuals, was conducted to evaluate the performance of indoor navigation and obstacle avoidance with the proposed shared-control algorithm. Results indicated that shared-control significantly reduced collisions and cognitive load without affecting travel speed, offering intuitive and safe operation. These findings highlight the shared-control system's suitability for enhancing collision avoidance in self-balancing mobility devices, a relatively unexplored area in assistive mobility research., Comment: 7 pages and 7 figures, IEEE ICRA format

Low-light image enhancement remains a critical challenge in computer vision, as does the lightweight design for edge devices with the computational burden for deep learning models. In this article, we introduce an extended version of Channel-Prior and Gamma-Estimation Network (CPGA-Net), termed CPGA-Net+, which incorporates an attention mechanism driven by a reformulated Atmospheric Scattering Model and effectively addresses both global and local image processing through Plug-in Attention with gamma correction. These innovations enable CPGA-Net+ to achieve superior performance on image enhancement tasks, surpassing lightweight state-of-the-art methods with high efficiency. Our results demonstrate the model's effectiveness and show the potential applications in resource-constrained environments.

We investigate the resonant baryonic $B$ decay $B^-\to {\cal P}_{cs}^0\bar p,{\cal P}_{cs}^0\to J/\psi \Lambda$, where ${\cal P}_{cs}^0\equiv {\cal P}_{cs}(4338)^0$ is identified as a hidden charm pentaquark candidate with strangeness. By interpreting ${\cal P}_{cs}^0$ as the $\Xi_c\bar D$ molecule that strongly decays into $J/\psi\Lambda$ and $\eta_c\Lambda$, we discover a dominant triangle rescattering effect for $B^-\to {\cal P}_{cs}^0\bar p$, initiated by $B^-\to J/\psi K^-$. Through the exchange of a $\bar \Lambda$ anti-baryon, $J/\psi$ and $K^-$ undergo rescattering, transforming into ${\cal P}_{cs}^0$ and $\bar p$, respectively. Based on this rescattering mechanism, we calculate ${\cal B}(B^-\to {\cal P}_{cs}^0\bar p,{\cal P}_{cs}^0\to J/\psi \Lambda) =(1.7^{+1.2}_{-0.8})\times10^{-6}$, which is consistent with the measured data., Comment: 11 pages, 1 figure, 2 tables

Investigating the metal enrichment in the early universe helps us constrain theories about the first stars and study the ages of galaxies. The lensed galaxy MACS0647$-$JD at $z=10.17$ is the brightest galaxy known at $z > 10$. Previous work analyzing JWST NIRSpec and MIRI data yielded a direct metallicity $\rm{12+log(O/H)}=7.79\pm0.09$ ($\sim$ 0.13 $Z_\odot$) and electron density $\rm{log}(n_e / \rm{cm^{-3}}) = 2.9 \pm 0.5$, the most distant such measurements to date. Here we estimate the direct C/O abundance for the first time at $z > 10$, finding a sub-solar ${\rm log(C/O)}=-0.44^{+0.06}_{-0.07}$. This is higher than other $z>6$ galaxies with direct C/O measurements, likely due to higher metallicity. It is also slightly higher than galaxies in the local universe with similar metallicity. This may suggest a very efficient and rapid burst of star formation, a low effective oxygen abundance yield, or the presence of unusual stellar populations including supermassive stars. Alternatively, the strong CIII]${\rm {\lambda}{\lambda}}$1907,1909 emission ($14\pm 3\,{\r{A}}$ rest-frame EW) may originate from just one of the two component star clusters JDB ($r \sim 20$ pc). Future NIRSpec IFU spectroscopic observations of MACS0647$-$JD will be promising for disentangling C/O in the two components to constrain the chemistry of individual star clusters just 460 Myr after the Big Bang., Comment: 9 pages, 2 figures, submitted

This paper presents a detailed study of the physical properties of seven C IV absorbers identified at z_abs = 0.68-1.28 along the line of sight toward QSO PG 1522+101 (z_QSO = 1.330). The study leverages high-quality QSO spectra from HST COS and STIS, and Keck HIRES to resolve component structures and to constrain the gas density and elemental abundances of individual components. Under the assumption of photoionization equilibrium (PIE), five of the 12 C IV components require a mixture of high- and low-density phases to fully explain the observed relative abundances between low-, intermediate-, and high-ionization species. In addition, galaxy surveys carried out using VLT MUSE and Magellan LDSS3C are utilized to characterize the galaxy environments. The results of this analysis are summarized as follows: (1) no luminous galaxies (> 0.1 L*) are found within 100 kpc in projected distance from the C IV absorbers; (2) the C IV selection preferentially targets high-metallicity (near solar) and chemically-evolved gas (~ solar [C/O] elemental abundances) in galaxy halos; (3) the observed narrow line widths of individual C IV components, places a stringent limit on the gas temperature (< 5e4 K) and supports a photoionization origin; (4) additional local ionizing sources beyond the UV ionizing background may be necessary for at least one absorber based on the observed deficit of He I relative to H I; and (5) a PIE assumption may not apply when the gas metallicity exceeds the solar value and the component line width implies a warmer temperature than expected from PIE models., Comment: 31 pages, 16 figures, accepted to The Open Journal of Astrophysics with revisions