April 2024 arXiv papers — page 125

Ian Tenney, Ryan Mullins, Bin Du, Shree Pandya

We present Sequence Salience, a visual tool for interactive prompt debugging with input salience methods. Sequence Salience builds on widely used salience methods for text classification and single-token prediction, and extends this to a system tailored for debugging complex LLM prompts. Our system is well-suited for long texts, and expands on previous work

Ming Cheng, Bowen Zhang, Ziyu Wang, Ziyi Zhou

Trajectory similarity search plays an essential role in autonomous driving, as it enables vehicles to analyze the information and characteristics of different trajectories to make informed decisions and navigate safely in dynamic environments. Existing work on the trajectory similarity search task primarily utilizes sequence-processing algorithms or Recurren

Nayan Moni Baishya, B. R. Manoj

Data-driven deep learning (DL) techniques developed for automatic modulation classification (AMC) of wireless signals are vulnerable to adversarial attacks. This poses a severe security threat to the DL-based wireless systems, specifically for edge applications of AMC. In this work, we address the joint problem of developing optimized DL models that are also

K. Boyett, A. J. Bunker, J Chevallard, A. J. Battisti

We follow up emission line galaxies identified through the near-infrared slitless HST/WFC3 WISP survey with VLT/FORS2 optical spectroscopy. Over 4 WISP fields, we targetted 85 of 138 line emission objects at $0.4<z<2$ identified in WFC3 spectroscopy. Half the galaxies are fainter than $H_{AB}=24$mag, and would not have been included in many well-known survey

Weihu Ma, Yu-Gang Ma, Wanbing He, Bo Zhou

We present an integral density method for calculating the multifractal dimension spectrum for the nucleon distribution in atomic nuclei. This method is then applied to analyze the non-uniformity of the density distribution in several typical types of nuclear matter distributions, including the Woods-Saxon distribution, the halo structure and the tetrahedral

Nayan Moni Baishya, B. R. Manoj, Prabin K. Bora

The recent advancement in deep learning (DL) for automatic modulation classification (AMC) of wireless signals has encouraged numerous possible applications on resource-constrained edge devices. However, developing optimized DL models suitable for edge applications of wireless communications is yet to be studied in depth. In this work, we perform a thorough

Weisheng Xu, Sifan Zhou, Jiaqi Xiong, Ziyu Zhao

LiDAR-based 3D single object tracking (3D SOT) is a critical issue in robotics and autonomous driving. Existing 3D SOT methods typically adhere to a point-based processing pipeline, wherein the re-sampling operation invariably leads to either redundant or missing information, thereby impacting performance. To address these issues, we propose PillarTrack, a n

Xinyu Zhu, Lilin Zhang, Ning Yang

Personalized fairness in recommendations has been attracting increasing attention from researchers. The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suff

Kailong Wu, Yule Xie, Jiaxin Ding, Yuxiang Ren

Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the fundamental problem of understanding and analyzing how graph topology influences the performance of learning models on downstream tasks has not yet been well understood. In this pa

Viola Bolton-Lum, R. Cameron Dennis, Peter Morse, Eric Corwin

The ideal glass, a disordered system of particles with zero configurational entropy, cannot be realized through thermal processes. Nevertheless, we present a method for constructing ideal jammed packings of soft spheres, and thus the zero temperature ideal glass, in two dimensions. In line with the predicted properties, these critically jammed packings have

Domenico Cotroneo, Pietro Liguori

Traditional software fault injection methods, while foundational, face limitations in adequately representing real-world faults, offering customization, and requiring significant manual effort and expertise. This paper introduces a novel methodology that harnesses the capabilities of Large Language Models (LLMs) augmented with Reinforcement Learning from Hum

Xinyu Shi, Yi Cui, Yanyan Shangguan, Xiaoyu Xu

We performed 23Na NMR and magnetization measurements on an S = 1, quasi-2D honeycomb lattice antiferromagnet Na3Ni2BiO6. A large positive Curie-Weiss constant of 22.9 K is observed. The NMR spectra at low fields are consistent with a "zigzag" magnetic order, indicating a large easy-axis anisotropy. With field applied along the c* axis, the NMR spectra confir

Hikaru Watanabe, Akito Daido

Nonlinear optics has regained attention in recent years, especially in the context of optospintronics and topological materials. Nonlinear responses involved in various degrees of freedom manifest their intricacy more pronounced than linear responses. However, for a certain class of nonlinear responses, a connection can be established with linear-response co

Letizia Angeli, Dan Crisan, Martin Kolodziejczyk, Michela Ottobre

This paper is devoted to the problem of approximating non-linear Stochastic Partial Differential Equations (SPDEs) via interacting particle systems. In particular, we consider the Stochastic McKean-Vlasov equation, which is the McKean-Vlasov (MKV) PDE, perturbed by additive trace class noise. As is well-known, the MKV PDE can be obtained as mean field limit

Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank

Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) do

Yang Chen, Jingcai Guo, Tian He, Ling Wang

Skeleton-based zero-shot action recognition aims to recognize unknown human actions based on the learned priors of the known skeleton-based actions and a semantic descriptor space shared by both known and unknown categories. However, previous works focus on establishing the bridges between the known skeleton representation space and semantic descriptions spa

Sijie Ren, Jian Wang, Shipeng Wang, Weihua Yang

Let $G$ be an $n$-vertex triangle-free graph. The celebrated Mantel's theorem showed that $e(G)\leq \lfloor\frac{n^2}{4}\rfloor$. In 1962, Erd\H{o}s (together with Gallai), and independently Andr\'{a}sfai, proved that if $G$ is non-bipartite then $e(G)\leq \lfloor\frac{(n-1)^2}{4}\rfloor+1$. In this paper, we extend this result and show that if $G$ has chrom

Gurinder Singh, Rupam Barman

In this article, we study hook lengths of ordinary partitions and $t$-regular partitions. We establish hook length biases for the ordinary partitions and motivated by them we find a few interesting hook length biases in $2$-regular partitions. For a positive integer $k$, let $p_{(k)}(n)$ denote the number of hooks of length $k$ in all the partitions of $n$.

Yuan Zhang, Xiaomei Tao, Hanxu Ai, Tao Chen

In the Massive Open Online Courses (MOOC) learning scenario, the semantic information of instructional videos has a crucial impact on learners' emotional state. Learners mainly acquire knowledge by watching instructional videos, and the semantic information in the videos directly affects learners' emotional states. However, few studies have paid attention to

Hongfei Dai, Xuegeng Yang, Karin Schwarzenberger, Julian Heinrich

In this study, the dissolution of a single oxygen bubble on a solid surface, here Titianium alloy Ti64, in ultrapure water with different oxygen undersaturation levels is investigated. For that purpose, a combination of shadowgraph technique and planar laser-induced fluorescence is used to measure simultaneously the changes in bubble geometry and in the diss

Seok-Hyung Lee, Andrew Li, Stephen D. Bartlett

Two-dimensional color codes are a promising candidate for fault-tolerant quantum computing, as they have high encoding rates, transversal implementation of logical Clifford gates, and resource-efficient magic state preparation schemes. However, decoding color codes presents a significant challenge due to their structure, where elementary errors violate three

Sooshin Kim, Alexander Lukin, Matthew Rispoli, M. Eric Tai

We report on adiabatic state preparation in the one-dimensional quantum Ising model using ultracold bosons in a tilted optical lattice. We prepare many-body ground states of controllable system sizes and observe enhanced fluctuations around the transition between paramagnetic and antiferromagnetic states, marking the precursor of quantum critical behavior. F

Joshua Pickard, Cooper Stansbury, Amit Surana, Indika Rajapakse

In this paper we consider aspects of geometric observability for hypergraphs, extending our earlier work from the uniform to the nonuniform case. Hypergraphs, a generalization of graphs, allow hyperedges to connect multiple nodes and unambiguously represent multi-way relationships which are ubiquitous in many real-world networks including those that arise in

Xia Su, Han Zhang, Kaiming Cheng, Jaewook Lee

The safety and accessibility of our homes is critical to quality of life and evolves as we age, become ill, host guests, or experience life events such as having children. Researchers and health professionals have created assessment instruments such as checklists that enable homeowners and trained experts to identify and mitigate safety and access issues. Wi

Jiayin Pan

For a Gromov-Hausdorff convergent sequence of closed manifolds $M_i^n\overset{GH}\longrightarrow X$ with $\mathrm{Ric}\ge-(n-1)$, $\mathrm{diam}(M_i)\le D$, and $\mathrm{vol}(M_i)\ge v>0$, we study the relation between $\pi_1(M_i)$ and $X$. It was known before that there is a surjective homomorphism $\phi_i:\pi_1(M_i)\to \pi_1(X)$ by the work of Pan-Wei. In

Huan Huang, Hongliang Zhang, Weidong Mei, Jun Li

Integrated sensing and communication (ISAC) systems traditionally presuppose that sensing and communication (S&C) channels remain approximately constant during their coherence time. However, a "DISCO" reconfigurable intelligent surface (DRIS), i.e., an illegitimate RIS with random, time-varying reflection properties that acts like a "disco ball," introduces

Xiao Wang, Yutong Wang, Jing Yang, Xiaofeng Jia

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, b

Evan Shieh, Faye-Marie Vassel, Cassidy Sugimoto, Thema Monroe-White

The rapid deployment of generative language models (LMs) has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative LMs has primarily examined bias via explicit identity prompting. However, prior research on bias in earlier language-based technology platforms, including search engines, has shown

Drici Mourad, Kazeem Oluwakemi Oseni

Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This w

Kamaljeet Kaur Sidhu, Habeeb Balogun, Kazeem Oluwakemi Oseni

Air pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the

Zixiong Huang, Qi Chen, Libo Sun, Yifan Yang

Novel view synthesis aims to generate new view images of a given view image collection. Recent attempts address this problem relying on 3D geometry priors (e.g., shapes, sizes, and positions) learned from multi-view images. However, such methods encounter the following limitations: 1) they require a set of multi-view images as training data for a specific sc

Songkai Sun, Qingshan She, Yuliang Ma, Rihui Li

In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to its high complexity. To address this challenge, we proposed a new light

Chunwei Meng, Dingyou Ma, Xu Chen, Zhiyong Feng

A generic modular array architecture is proposed, featuring uniform/non-uniform subarray layouts that allows for flexible deployment. The bistatic near-field sensing system is considered, where the target is located in the near-field of the whole modular array and the far-field of each subarray. Then, the closed-form expressions of Cramer-Rao bounds (CRBs) f

Jiayi Wu, Renyu Zhu, Nuo Chen, Qiushi Sun

Over the past few years, we have witnessed remarkable advancements in Code Pre-trained Models (CodePTMs). These models achieved excellent representation capabilities by designing structure-based pre-training tasks for code. However, how to enhance the absorption of structural knowledge when fine-tuning CodePTMs still remains a significant challenge. To fill

Bohao Peng, Zhuotao Tian, Shu Liu, Mingchang Yang

Continual learning has gained increasing importance as it facilitates the acquisition and refinement of scalable knowledge and skills in language models. However, existing methods typically encounter strict limitations and challenges in real-world scenarios, such as reliance on experience replay, optimization constraints, and inference task-ID. In this study

Yucong Huang, Itsuko Hashimoto, Shinya Nishibata

We investigate an inflow problem for the multi-dimensional isentropic compressible Navier-Stokes equations. The fluid under consideration occupies the exterior domain of unit ball, $\Omega=\{x\in\mathbb{R}^n\,\vert\, |x|\ge 1\}$, and a constant stream of mass is flowing into the domain from the boundary $\partial\Omega=\{|x|=1\}$. It is shown in Hashimoto-Ma

Albert Wu, Ruocheng Wang, Sirui Chen, Clemens Eppner

Extrinsic manipulation, the use of environment contacts to achieve manipulation objectives, enables strategies that are otherwise impossible with a parallel jaw gripper. However, orchestrating a long-horizon sequence of contact interactions between the robot, object, and environment is notoriously challenging due to the scene diversity, large action space, a

Kashish Jain, Manthan Juthani, Jash Jain, Anant V. Nimkar

The illegal disposal of trash is a major public health and environmental concern. Disposing of trash in unplanned places poses serious health and environmental risks. We should try to restrict public trash cans as much as possible. This research focuses on automating the penalization of litterbugs, addressing the persistent problem of littering in public pla

Osama Muhammad Raisuddin, Suvranu De

Quantum linear system algorithms (QLSAs) can provide exponential speedups for the solution of linear systems, but the growth of the condition number for finite element problems can eliminate the exponential speedup. QLSAs are also incapable of using an initial guess of a solution to improve upon it. To circumvent these issues, we present a Quantum Multigrid

Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama

Offline reinforcement learning (RL) is vital in areas where active data collection is expensive or infeasible, such as robotics or healthcare. In the real world, offline datasets often involve multiple domains that share the same state and action spaces but have distinct dynamics, and only a small fraction of samples are clearly labeled as belonging to the t

Xinxing Zhao, Kar Wai Fok, Vrizlynn L. L. Thing

Network intrusion detection systems (NIDS) play a pivotal role in safeguarding critical digital infrastructures against cyber threats. Machine learning-based detection models applied in NIDS are prevalent today. However, the effectiveness of these machine learning-based models is often limited by the evolving and sophisticated nature of intrusion techniques

Clifton Cunningham, Sarah Dijols, Andrew Fiori, Qing Zhang

If $\pi$ is a representation of a $p$-adic group $G(F)$, and $\phi$ is its Langlands parameter, can we use the moduli space of Langlands parameters to find a geometric property of $\phi$ that will detect when $\pi$ is generic? In this paper we show that if $G$ is classical or if we assume the Kazhdan-Lusztig hypothesis for $G$, then the answer is yes, and th

Benjamin Leinwand, Vince Lyzinski

Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, we propose a new method for generating and estimating unweighted networks. This approach can describe a broader class of potential networks than existing models, including those where nodes in different subnetwork

Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term `hallucination' in the field of NLP. Additionally, to compliment our audit, we conduct a survey

Yan Pei, Wei Luo

Although deep learning algorithms have proven their efficiency in automatic sleep staging, the widespread skepticism about their "black-box" nature has limited its clinical acceptance. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that reasons in a similar way to sleep experts. In this network, we utilize the laten

Yutong Dai, Xiaoyi Qu, Daniel P. Robinson

We present a new algorithm for solving optimization problems with objective functions that are the sum of a smooth function and a (potentially) nonsmooth regularization function, and nonlinear equality constraints. The algorithm may be viewed as an extension of the well-known proximal-gradient method that is applicable when constraints are not present. To ac

Pan Shang, Lingchen Kong

Matrix form data sets arise in many areas, so there are lots of works about the matrix regression models. One special model of these models is the adaptive nuclear norm regularized trace regression, which has been proven have good statistical performances. In order to accelerate the computation of this model, we consider the technique called screening rule.

Maksym Lazirko

The intersection of quantum theory and accounting presents a novel and intriguing frontier in exploring financial valuation and accounting practices. This paper applies quantum theory to cost accounting, specifically Work in Progress (WIP) valuation. WIP is conceptualized as materials in a quantum superposition state whose financial value remains uncertain u

X. M. Zhong, X. L. Zou, A. D. Liu, Y. T. Song

I-mode is a promising regime for future fusion reactors due to the high energy confinement and the moderate particle confinement. However, the effect of lithium, which has been widely applied for particle recycling and impurity control, on I-mode plasma is still unclear. Recently, experiments of real-time lithium powder injection on I-mode plasma have been c

Yingying Yang, Niloufar Dousti Mousavi, Zhou Yu, Jie Yang

The negative binomial distribution has been widely used as a more flexible model than the Poisson distribution for count data. However, when the true data-generating process is Poisson, it is often challenging to distinguish it from a negative binomial distribution with extreme parameter values, and existing maximum likelihood estimation procedures for the n

Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang

Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning or decision-making abilities through well-designed prompt engineering or task-specific fine-tuning, ignoring the procedure of exploration and exploitation. When addressing complex

Noam Soker

I further study the manner by which a pair of opposite jets shape the keyhole morphological structure of the core-collapse supernova (CCSN) SN 1997A, now the CCSN remnant (CCSNR) 1987A. By doing so, I strengthen the claim that the jittering-jet explosion mechanism (JJEM) accounts for most, likely all, CCSNe. The keyhole structure comprises a northern low-int

Tao Duan, Junzhou Zhao, Shuo Zhang, Jing Tao

Key-value sequence data has become ubiquitous and naturally appears in a variety of real-world applications, ranging from the user-product purchasing sequences in e-commerce, to network packet sequences forwarded by routers in networking. Classifying these key-value sequences is important in many scenarios such as user profiling and malicious applications id

Saichao Liu, Geng Sun, Jiahui Li, Shuang Liang

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi

Jing-Xiao Liao, Chao He, Jipu Li, Jinwei Sun

Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despite BD's desirable feature in adaptability and mathematical interpretability, a significant challenge persists: How to effectively integrate BD with fault-diagnosing classifiers? Th

Yupeng Cao, Zhi Chen, Prashant Kumar, Qingyun Pei

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering, and stock movement prediction (binary classification), the application of LLMs to f

Shubo Sun, Zifeng Zhao, Feiyu Jiang, Xiaofeng Shao

Time series segmentation aims to identify potential change-points in a sequence of temporally dependent data, so that the original sequence can be partitioned into several homogeneous subsequences. It is useful for modeling and predicting non-stationary time series and is widely applied in natural and social sciences. Existing segmentation methods primarily

Jiahui Li, Geng Sun, Qingqing Wu, Dusit Niyato

In this paper, we propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas, forming a virtual antenna array to enha

Kanchana Ranasinghe, Satya Narayan Shukla, Omid Poursaeed, Michael S. Ryoo

Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA). However, existing V-LLMs (e.g. BLIP-2, LLaVA) demonstrate weak spatial reasoning and localization awareness. Despite generating highly descript

Jingxuan Xu, Wuyang Chen, Yao Zhao, Yunchao Wei

Recent success of pre-trained foundation vision-language models makes Open-Vocabulary Segmentation (OVS) possible. Despite the promising performance, this approach introduces heavy computational overheads for two challenges: 1) large model sizes of the backbone; 2) expensive costs during the fine-tuning. These challenges hinder this OVS strategy from being w

Botao He, Guofei Chen, Wenshan Wang, Ji Zhang

This paper introduces a real-time algorithm for navigating complex unknown environments cluttered with movable obstacles. Our algorithm achieves fast, adaptable routing by actively attempting to manipulate obstacles during path planning and adjusting the global plan from sensor feedback. The main contributions include an improved dynamic Directed Visibility

Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati, Anand Rangarajan

Traffic congestion has significant economic, environmental, and social ramifications. Intersection traffic flow dynamics are influenced by numerous factors. While microscopic traffic simulators are valuable tools, they are computationally intensive and challenging to calibrate. Moreover, existing machine-learning approaches struggle to provide lane-specific

Qian Yu, Xiaoqi Zhao, Youwei Pang, Lihe Zhang

Dichotomous Image Segmentation (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. When designing an effective DIS model, the main challenge is how to balance the semantic dispersion of high-resolution targets in the small receptive field and the loss of high-precision details in the large receptive fiel

Jiahui Li, Geng Sun, Qingqing Wu, Shuang Liang

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we a

Yu Shao, Haiqi Gao, Yipeng Chen, Yujie liu

Optical Diffraction Neural Networks (DNNs), a subset of Optical Neural Networks (ONNs), show promise in mirroring the prowess of electronic networks. This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs, synergizing the benefits of conventional ONNs with those of DNNs to su

Jie Fan

In the literature, the question about how to axiomatize the transitive logic of false belief is thought of as hard and left as an open problem. In this paper, among other contributions, we deal with this problem. In more details, although the standard doxastic operator is undefinable with the operator of false belief, the former is {\em almost definable} wit

Richard Kelley

Language models trained on internet-scale data sets have shown an impressive ability to solve problems in Natural Language Processing and Computer Vision. However, experience is showing that these models are frequently brittle in unexpected ways, and require significant scaffolding to ensure that they operate correctly in the larger systems that comprise "la

Sandra Rodríguez-Villalobos

In this paper, we use big Cohen-Macaulay algebras to define a characteristic free analog of the $F$-thresholds, which we call BCM-thresholds, in the case of principal ideals. We prove that, similarly to the case of the $F$-thresholds, the set of BCM-thresholds and the set of BCM-jumping numbers agree. We also relate some BCM-thresholds to splittings of maps

Kishore Rajasekar, Randolph Loh, Kar Wai Fok, Vrizlynn L. L. Thing

MLaaS (Machine Learning as a Service) has become popular in the cloud computing domain, allowing users to leverage cloud resources for running private inference of ML models on their data. However, ensuring user input privacy and secure inference execution is essential. One of the approaches to protect data privacy and integrity is to use Trusted Execution E

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The Born cross sections for the process $e^{+}e^{-}\to \omega\eta^{\prime}$ are measured at 22 center-of-mass energies from 2.000 to 3.080 GeV using data collected with the BESIII detector at the BEPCII collider. A resonant structure is observed with a statistical significance of 9.6$\sigma$. A Breit-Wigner fit determines its mass to be $M_R=(2153\pm30\pm31)

Xinwei Zhuang, Zixun Huang, Wentao Zeng, Luisa Caldas

As the global population and urbanization expand, the building sector has emerged as the predominant energy consumer and carbon emission contributor. The need for innovative Urban Building Energy Modeling grows, yet existing building archetypes often fail to capture the unique attributes of local buildings and the nuanced distinctions between different citie

Mohammad Alipour-Vaezi, Kwok-Leung Tsui

Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the box office of each project. Moreover, for an accurate box office prediction, it is critical to consider the effect of the celebrities involved in each MPI project, which was impos

Li-Hua Zhang, Zong-Kai Liu, Bang Liu, Qi-Feng Wang

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 narr

Jiachang Zhang, Tong Bao, Zhiyuan Li

We present a systematic search for periodic X-ray sources in the bulge of M31, using ~ 2 Ms of archival Chandra observations spanning a temporal baseline of 16 years. Utilizing the Gregory-Loredo algorithm that is designed for photon-counting, phase-folded light curves, we detect seven periodic X-ray sources, among which four are newly discovered. Three of t

Hyun Joe Jeong, Zheng Gong, Somil Bansal, Sylvia Herbert

Fast and Safe Tracking (FaSTrack) is a modular framework that provides safety guarantees while planning and executing trajectories in real time via value functions of Hamilton-Jacobi (HJ) reachability. These value functions are computed through dynamic programming, which is notorious for being computationally inefficient. Moreover, the resulting trajectory d

Ke Wang, Chao-Jun Feng

The curved spacetime induced by gravitational waves can give rise to visual effects such as geometric distortions and redshift structures in the observed image. By establishing a mapping from the object's surface coordinates to the observer's screen coordinates, we study these effects in the context of plane gravitational waves. The simulation reveals that t

Jie Cheng, Min Li, Yu-Feng Li, Gao-Song Li

Atmospheric neutrinos play a vital role in generating irreducible backgrounds in liquid-scintillator (LS) detectors via their neutral-current (NC) interactions with $^{12}$C nuclei. These interactions may affect a wide range of research areas from the MeV to GeV energy range, such as the reactor and geo neutrinos, diffuse supernova neutrino background (DSNB)

Tongzhou Mu, Yijie Guo, Jie Xu, Ankit Goyal

Encouraged by the remarkable achievements of language and vision foundation models, developing generalist robotic agents through imitation learning, using large demonstration datasets, has become a prominent area of interest in robot learning. The efficacy of imitation learning is heavily reliant on the quantity and quality of the demonstration datasets. In

Shandler A. Mason, Sandeep Kaur Kuttal

Remote pair programming is widely used in software development, but no research has examined how race affects these interactions. We embarked on this study due to the historical under representation of Black developers in the tech industry, with White developers comprising the majority. Our study involved 24 experienced developers, forming 12 gender-balanced

Haimanti Chakraborty, Ranga Vemuri

Logic Obfuscation is a well renowned design-for-trust solution to protect an Integrated Circuit (IC) from unauthorized use and illegal overproduction by including key-gates to lock the design. This is particularly necessary for ICs manufactured at untrusted third-party foundries getting exposed to security threats. In the past, several logic obfuscation meth

Rui Sun, Li You, An-An Lu, Chen Sun

In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) instead of all the BSs, facilitating the implementation of the system and lowering the dimension

Sheng Wang, Tianming Du, Katherine Fischer, Gregory E Tasian

Computer-aided diagnosis systems hold great promise to aid radiologists and clinicians in radiological clinical practice and enhance diagnostic accuracy and efficiency. However, the conventional systems primarily focus on delivering diagnostic results through text report generation or medical image classification, positioning them as standalone decision-make

Lena Podina, Ali Ghodsi, Mohammad Kohandel

Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Phy

Xian-Peng Zhang, Yugui Yao

We develop a comprehensive logical framework for effectively handling the overcomplete basis set in the Bogoliubov-de Gennes equation that contains two orthonormal basis sets conjugate with each other, such as particle and hole orthonormal basis sets. We highlight the significant implications of our logical framework from theoretical concepts and experimenta

Aleksandr Bekshaev

Optical fields with rich and well-developed spatio-temporal structure, including ultra-short structured light pulses, are essentially non-monochromatic and contain a continuous spectrum of monochromatic constituents. However, some substantial features of such fields and physical mechanisms determining their behavior can be understood based on simplified mode

Zi-Qi Zeng, Shi-Xin You, Zi-Xiang Yang, Chenzhi Yuan

Entangled photon pairs are crucial resources for quantum information processing protocols. Via the process of spontaneous parametric down-conversion (SPDC), we can generate these photon pairs using bulk nonlinear crystals. Traditionally, the crystal is designed to satisfy specific type of phase-matching condition. Here, we report controllable transitions amo

Tonghua Liu, Shuo Cao, Marek Biesiada, Yilong Zhang

In this Letter, we propose a model-independent method to determine the Hubble constant and curvature simultaneously taking advantage of the possibilities of future space-borne gravitational wave (GW) detector DECIGO in combination with the radio quasars as standard rulers. Similarly to the redshift drift in the electromagnetic domain, accelerating expansion

Laura N. Driessen, Joshua Pritchard, Tara Murphy, George Heald

We present the Sydney Radio Star Catalogue, a new catalogue of stars detected at megahertz to gigahertz radio frequencies. It consists of 839 unique stars with 3,405 radio detections, more than doubling the previously known number of radio stars. We have included stars from large area searches for radio stars found using circular polarisation searches, cross

Lihui Liu, Jinha Kim, Vidit Bansal

Recent advancements in contrastive learning have revolutionized self-supervised representation learning and achieved state-of-the-art performance on benchmark tasks. While most existing methods focus on applying contrastive learning to input data modalities such as images, natural language sentences, or networks, they overlook the potential of utilizing outp

Suichu Huang, Kan Yao, Hao Wang, Xumin Ding

Surface lattice resonances (SLRs) in metasurfaces have become a transformative platform for subwavelength optical devices, leveraging their high quality (Q)-factors, pronounced local field enhancement, and extensive long-range interactions. However, current high-Q SLR implementations are fundamentally limited by their dependence on homogeneous dielectric env

Rebekah Hermsmeier, Timur V. Tscherbul

We propose a general technique to produce cold spin-polarized molecules, in which rotationally excited states are first populated by coherent microwave excitation, and then allowed to spin-flip and relax via collisional quenching, which populates a single final spin state. We illustrate the high selectivity of the technique for $^{13}$C$^{16}$O molecules imm

Neelay Junnarkar, Emily Jensen, Xiaofan Wu, Suat Gumussoy

The problem of maintaining power system stability and performance after the failure of any single line in a power system (an "N-1 contingency") is investigated. Due to the large number of possible N-1 contingencies for a power network, it is impractical to optimize controller parameters for each possible contingency a priori. A method to partition a set of c

Li Yin, Hong Tang, Tom Berlijn, Adrienn Ruzsinszky

Charge density waves (CDWs) in transition metal dichalcogenides are the subject of growing scientific interest due to their rich interplay with exotic phases of matter and their potential technological applications. Here, using density functional theory with advanced meta-generalized gradient approximations (meta-GGAs) and linear response time-dependent dens

Yikang Shen, Zhen Guo, Tianle Cai, Zengyi Qin

Large Language Models (LLMs) have achieved remarkable results, but their increasing resource demand has become a major obstacle to the development of powerful and accessible super-human intelligence. This report introduces JetMoE-8B, a new LLM trained with less than $0.1 million, using 1.25T tokens from carefully mixed open-source corpora and 30,000 H100 GPU

Jing Mao, Shijie Zhang

In this paper, by imposing suitable assumptions on the weighted function, (under the constraint of fixed weighted volume) a Brock-type isoperimetric inequality for Steklov-type eigenvalues of the Witten-Laplacian on bounded domains in a Euclidean space or a hyperbolic space has been proven. This conclusion is actually an interesting extension of F. Brock's c

Vanessa McNealis, Erica E. M. Moodie, Nema Dean

Causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption frequently does not hold. Existing methods developed in the context of network interference rely upon the assumption of no unmeasured confounding. However, when faced with multilevel network data, there may be a latent factor i

Sourajit Saha, Tejas Gokhale

Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small pixel-level shift. Through a large-scale correlation analysis framework, we study shift invariance of CNNs by inspecting existing downsampling operators in terms of their maximum-sa

Benjamin Lebrun, Andrew Vonasch, Christoph Bartneck

A recent psychology study found that people sometimes reject overly generous offers from people because they imagine hidden ''phantom costs'' must be part of the transaction. Phantom costs occur when a person seems overly generous for no apparent reason. This study aims to explore whether people can imagine phantom costs when interacting with a robot. To thi

Savindu Wannigama, Arunan Sivanathan, Ayyoob Hamza, Hassan Habibi Gharakheili

Behavioral transparency for Internet-of-Things (IoT) networked assets involves two distinct yet interconnected tasks: (a) characterizing device types by discerning the patterns exhibited in their network traffic, and (b) assessing vulnerabilities they introduce to the network. While identifying communication protocols, particularly at the application layer,

Shuying Zhou, Mouyuan Sun, Tong Liu, Jian-Min Wang

Stellar black holes (sBHs) are widely believed to exist in the accretion disks of active galactic nuclei (AGNs). Previous studies often focus on the transient emission produced by embedded sBHs. Here, we explore the possible observational consequences of an AGN accretion disk that contains a population of accreting sBHs. Embedded accreting sBHs change the ef