April 2024 arXiv papers — page 140

Jie Liao, M. Ghasemi-Nodehi, Lang Cui, Ashutosh Tripathi

Iron line spectroscopy has been one of the leading methods not only for measuring the spins of accreting black holes but also for testing fundamental physics. Basing on such a method, we present an analysis of a dataset observed simultaneously by NuSTAR and NICER for the black hole binary candidate MAXI J1803-298, which shows prominent relativistic reflectio

Tan Gan, Hongcheng Li

A platform charges a producer for disclosing quality evidence to consumers before trade. It aims to maximize its revenue guarantee across potentially multiple equilibria which arise from the interdependence of producer purchase decisions and consumer beliefs. The platform's optimal pricing strategy entrenches itself as a market gatekeeper: it induces a uniqu

Juan Zhang, Yiyi Luo

The article mainly introduces preprocessing algorithms for solving linear equation systems. This algorithm uses three algorithms as inner iterations, namely RPCG algorithm, ADI algorithm, and Kaczmarz algorithm. Then, it uses BA-GMRES as an outer iteration to solve the linear equation system. These three algorithms can indirectly generate preprocessing matri

Besnik Fetahu, Nachshon Cohen, Elad Haramaty, Liane Lewin-Eytan

Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem tha

Chanwoo Choi

This work demonstrates two Privacy Policy (PP) summarisation models based on two different clustering algorithms: K-means clustering and Pre-determined Centroid (PDC) clustering. K-means is decided to be used for the first model after an extensive evaluation of ten commonly used clustering algorithms. The summariser model based on the PDC-clustering algorith

Clifford Blakestad, YoungJu Choie

We introduce an infinite family of Kronecker series twisted by characters. As an application, we give a closed formula for the sum of all Hecke eigenforms on ${\Gamma}_0(N) $ multiplied by their twisted period polynomials in terms of the product of those twisted Kronecker series, when N is square free. This extends an identity of Zagier among period polynomi

Yunting Gui, Yuegang Fu, Xueming Xiao, Meibao Yao

Passive Non-Line-of-Sight (NLOS) imaging requires to reconstruct objects which cannot be seen in line without using external controllable light sources. It can be widely applied in areas like counter-terrorism, urban-Warfare, autonomous-driving and robot-vision. Existing methods for passive NLOS typically required extensive prior information and significant

Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann

Real-world optimization problems often involve stochastic and dynamic components. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to uncertain and changing environments but often uncertainty and dynamic changes are studied in isolation. In this paper, we explore the use of 3-objective evolutionary algorithms fo

Xuheng Li, Heyang Zhao, Quanquan Gu

Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorporating contextual information for decision-making and preference learning. Several algorithms based on the upper confidence bound (UCB) have been proposed for linear contextual dueli

Kai Luan, Chenghao Shi, Neng Wang, Yuwei Cheng

The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatly limits the development of mmWave radar technology. In this

Margaret-Anne Storey, Rashina Hoda, Alessandra Maciel Paz Milani, Maria Teresa Baldassarre

Mixed methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs. This paper provides guiding principles and advice on how to design mixed method research, and to encourage the intentional, rigorous, and innovative application of mixed methods in software engi

Zhuoliang Ni, Urban Seifert, Amanda V. Haglund, Nan Huang

In a collinear antiferromagnet, spins tend to cant towards the direction of an applied magnetic field, thereby decreasing the energy of the system. The canting angle becomes negligible when the magnetic field is small so that the induced anisotropic energy is substantially lower than the exchange energy. However, this tiny anisotropy can play a significant r

Samuel Grushevsky, Gabriele Mondello, Riccardo Salvati Manni, Jacob Tsimerman

We determine the maximal dimension of compact subvarieties of $\mathcal{A}_g$, the moduli space of complex principally polarized abelian varieties of dimension $g$, and the maximal dimension of a compact subvariety through a very general point of $\mathcal{A}_g$. This also allows us to draw some conclusions for compact subvarieties of the moduli space of com

Waqas Ahmed, Maria Mehmood, Mansoor Ur Rehman, Umer Zubair

We present a realistic supersymmetric $\mu$-hybrid inflation model within the framework of $SU(4)_C \times SU(2)_L \times U(1)_R$ gauge symmetry, wherein the symmetry breaking $SU(4)_C \times SU(2)_L \times U(1)_R\rightarrow SU(3)_C\times SU(2)_L \times U(1)_{B-L}\times U(1)_R$ occurs before observable inflation, effectively eliminating topologically stable

Pengfei Zhang, Dingzhu Wen, Guangxu Zhu, Qimei Chen

In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the noisy local feature vectors, which are then aggregated at each remote radio head (RRH) to suppress sensing noise. To rea

Chenyang Zhong

The Airy point process is a determinantal point process that arises from the spectral edge of the Gaussian Unitary Ensemble. In this paper, we establish a large deviation principle for the Airy point process. Our result also extends to point processes arising from the spectrum of the stochastic Airy operator.

Shaoyu Wang, Xiubing Li, Heng Zhang, Bo Chen

Nonlinear Hall effect (NLHE), as a new member of Hall effect family, has been realized in many materials, attracting a great deal of attention. Here, we report the observation of NLHE in magnetic topological insulator Sb-doped MnBi4Te7 flakes. The NLHE generation efficiency can reach up to 0.06 V^-1, which is comparable to that observed in MnBi2Te4. Differen

Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi, Vaneet Aggarwal

To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a global model through collaboration among $N$ agents using policy gradient (PG) updates. To address the challenge of lagged policies in asynchronous settings, we design a delay-adaptive

Kento Tatsuno, Daisuke Miyashita, Taiga Ikeda, Kiyoshi Ishiyama

Graph-based approximate nearest neighbor search (ANNS) algorithms work effectively against large-scale vector retrieval. Among such methods, DiskANN achieves good recall-speed tradeoffs using both DRAM and storage. DiskANN adopts product quantization (PQ) to reduce memory usage, which is still proportional to the scale of datasets. In this paper, we propose

Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang

The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency. Currently, there is a notable absence of a unified and adaptable framework that seamlessly integrates variou

Michael Gonzalez, Rosa Orellana, Mario Tomba

We study Stanley's chromatic symmetric function (CSF) for trees when expressed in the star-basis. We use the deletion-near-contraction algorithm recently introduced in \cite{ADOZ} to compute coefficients that occur in the CSF in the star-basis. In particular, one of our main results determines the smallest partition in lexicographic order that occurs as an i

Graham Wild

Safety is the priority for airlines. Airlines are sensitive to passengers' perceptions of safety, having randomly assigned the Boeing 737 Max to routes and times. Historically, Boeing has been considered more reliable and safer than Airbus. Hence, it is worth asking the question, are there any differences in the safety occurrences of the core narrow-body sin

Kennedy Edemacu, Xintao Wu

Pre-trained language models (PLMs) have demonstrated significant proficiency in solving a wide range of general natural language processing (NLP) tasks. Researchers have observed a direct correlation between the performance of these models and their sizes. As a result, the sizes of these models have notably expanded in recent years, persuading researchers to

Daisuke Hirota, Izuho Matsuzaki, Takeshi Miura

For a locally compact Hausdorff space $L$, we denote by $C_0(L,\mathbb{R})$ the Banach space of all continuous real-valued functions on $L$ vanishing at infinity equipped with the supremum norm. We prove that every surjective phase-isometry $T\colon C_0^+(X,\mathbb{R}) \to C_0^+(Y,\mathbb{R})$ between the positive cones of $C_0(X,\mathbb{R})$ and $C_0(Y,\mat

Alexander C. Ritter, Raymond R. Volkas

To properly solve the coincidence problem ($\Omega_\mathrm{DM} \simeq 5\Omega_\mathrm{VM}$) in a model of asymmetric dark matter, one cannot simply relate the number densities of visible and dark matter without also relating their particle masses. Following previous work, we consider a framework where the dark matter is a confined state of a dark QCD gauge g

Zhiyan Ding, Bowen Li, Lin Lin

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than treated as an approximation to weakly co

Junlin Hou, Jilan Xu, Hao Chen

The black-box nature of deep learning models has raised concerns about their interpretability for successful deployment in real-world clinical applications. To address the concerns, eXplainable Artificial Intelligence (XAI) aims to provide clear and understandable explanations of the decision-making process. In the medical domain, concepts such as attributes

Telun Zhong, Heyang Ma, Peijun Zheng, Jie Zhang

Inspired by the stable bilayer water ice grown in the laboratory (Nature 577, 60, 2020), we propose a model representing water ice as a two-layer six-vertex model. Using the loop update Monte Carlo method, we unveil meaningful findings. While the square lattice six-vertex model exhibits an antiferromagnetic to disordered phase transition known as the Berezin

Runyu Zhang, Haitong Ma, Na Li

This paper studies the multi-agent coverage control (MAC) problem where agents must dynamically learn an unknown density function while performing coverage tasks. Unlike many current theoretical frameworks that concentrate solely on the regret occurring at specific targeted sensory locations, our approach additionally considers the regret caused by transient

Manash Jyoti Sarmah, Himangshu Prabal Goswami

We theoretically identify the noise-induced coherent contribution to the ergotropy of a four-level quantum heat engine coupled to a unimodal quantum cavity. We utilize a protocol where the passive state's quasiprobabilities can be analytically identified from the population-coherence coupled reduced density matrix. The reduced density matrix elements are eva

Shaona Ghosh, Prasoon Varshney, Erick Galinkin, Christopher Parisien

As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase. We find a notable deficiency in high-quality content safety datasets and benchmarks that comprehensively cover a wide range of critical safety areas. To address this, we define a broad content safety risk taxonomy, compr

Aristotelis Chaniotis, Babak Miraftab, Sophie Spirkl

A hole in a graph is an induced subgraph which is a cycle of length at least four. A graph is chordal if it contains no holes. Following McKee and Scheinerman (1993), we define the chordality of a graph $G$ to be the minimum number of chordal graphs on $V(G)$ such that the intersection of their edge sets is equal to $E(G)$. In this paper we study classes of

Fereshteh R. Dastjerdi, Liming Cai

Characterization of joint probability distribution for large networks of random variables remains a challenging task in data science. Probabilistic graph approximation with simple topologies has practically been resorted to; typically the tree topology makes joint probability computation much simpler and can be effective for statistical inference on insuffic

Seth Lazar

As rapid advances in Artificial Intelligence and the rise of some of history's most potent corporations meet the diminished neoliberal state, people are increasingly subject to power exercised by means of automated systems. Machine learning and related computational technologies now underpin vital government services. They connect consumers and producers in

Yanan Zhang, Xiaoling Bai, Tianhua Zhou

The embedding-based retrieval (EBR) approach is widely used in mainstream search engine retrieval systems and is crucial in recent retrieval-augmented methods for eliminating LLM illusions. However, existing EBR models often face the "semantic drift" problem and insufficient focus on key information, leading to a low adoption rate of retrieval results in sub

Valdemar Švábenský, Maciej Pankiewicz, Jiayi Zhang, Elizabeth B. Cloude

Programming courses can be challenging for first year university students, especially for those without prior coding experience. Students initially struggle with code syntax, but as more advanced topics are introduced across a semester, the difficulty in learning to program shifts to learning computational thinking (e.g., debugging strategies). This study ex

Tian Siyuan, Dai Renjie, Wang Junhao, He Zhengxiao

Shared mobility redefines urban transportation, offering economic and environmental benefits by reducing pollution and urban congestion. However, in the post-pandemic era, the shared mobility sector is grappling with a crisis of trust, particularly concerning passenger hesistancy towards shared transportation options. To address these problems, in this paper

Robert M. Guralnick, Pham Huu Tiep

We extend Gow's theorem on products of semisimple regular conjugacy classes to finite groups whose generalized Fitting subgroup is Z(G)S where S is a quasisimple group of Lie type in characteristic p and Z(G) has order prime to p.

Anamitra Maiti, Subham Laha, Rishav Upadhaya, Soumyajit Biswas

In today's digital age, the internet is essential for communication and the sharing of information, creating a critical need for sophisticated data security measures to prevent unauthorized access and exploitation. Cryptography encrypts messages into a cipher text that is incomprehensible to unauthorized readers, thus safeguarding data during its transmissio

Aimin Tang, Xudong Wang, J. Andrew Zhang

Integrated sensing and communications (ISAC) has been visioned as a key technique for B5G/6G networks. To support monostatic sensing, a full-duplex radio is indispensable to extract echo signals from targets. Such a radio can also greatly improve network capacity via full-duplex communications. However, full-duplex radios in existing ISAC designs are mainly

Jiaqing Bai, Ji He, Yanping Chen, Yulong Shen

Covert communication's effectiveness critically depends on precise channel state information (CSI). This paper investigates the impact of imperfect CSI on achievable covert communication performance in a two-hop relay system. Firstly, we introduce a two-hop covert transmission scheme utilizing channel inversion power control (CIPC) to manage opportunistic in

Guangming Hu, Ziping Lei, Yu Sun, Puchun Zhou

In this paper, we study the existence and rigidity of (degenerated) circle pattern metric with prescribed total geodesic curvatures in spherical background geometry. To find the (degenerated) circle pattern metric with prescribed total geodesic curvatures, we define some prescribed combinatorial Ricci flows and study the convergence of flows for (degenerated

Jianhua Jiang, Ziying Zhao, Weihua Li, Keqin Li

The Grey Wolf Optimizer (GWO) is recognized as a novel meta-heuristic algorithm inspired by the social leadership hierarchy and hunting mechanism of grey wolves. It is well-known for its simple parameter setting, fast convergence speed, and strong optimization capability. In the original GWO, there are two significant design flaws in its fundamental optimiza

Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman, Michael Coss

Although accuracy and computation benchmarks are widely available to help choose among neural network models, these are usually trained on datasets with many classes, and do not give a good idea of performance for few (< 10) classes. The conventional procedure to predict performance involves repeated training and testing on the different models and dataset v

Yao Yu, Hong-Song Xie, Han Zhang, Bai-Cian Ke

Substantial contribution of the tensor current in semileptonic decays is regarded as a clear signal for new physics. In this work, we propose a model-independent approach to unambiguously test contribution of the tensor current in semileptonic five-body decays $\bar{D}_{(s)}/\bar{B}_{(s)}\to V\ell\bar{\nu}_{\ell}\,(\ell=e,\mu,\tau)$ with $V\to \pi^0\pi^+\pi^

Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini

Recent developments in diffusion models have advanced conditioned image generation, yet they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in medical images, causing "image hallucination" and risking misdiagnosis. We hypothesize such hallucinations result from local OOD regions in the conditional images. We verify that

Ming Tao, Bing-Kun Bao, Hao Tang, Yaowei Wang

Story visualization aims to generate a series of realistic and coherent images based on a storyline. Current models adopt a frame-by-frame architecture by transforming the pre-trained text-to-image model into an auto-regressive manner. Although these models have shown notable progress, there are still three flaws. 1) The unidirectional generation of auto-reg

L. E. S. Machado, F. C. E. Lima, C. A. S. Almeida

Considering the relativistic scenario, we dedicate our study to the relativistic quantum description of one-dimensional Majorana fermions. Thus, we focus on aspects related to exciton-like particles. Seeking to reach our purpose, one analyzes the relativistic quantum mechanical system characterized by an effective mass distribution. In this context, we adopt

Conghe Wang, Xia Wang, Yujie Fang, Changda Yan

Non-line-of-sight (NLOS) imaging with intelligent sensors emerges as a novel technique in imaging and sensing occluded objects around corners. With the innovation of bio-inspired neuromorphic sensors, the applications of novel sensors in unconventional imaging tasks like NLOS imaging have shown promising prospects in intelligent perception, encompassing auto

Yutian Ren, Yuqi He, Xuyin Zhang, Aaron Yen

Machine Learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality enabled self

Junqiang Ge, Youjun Lu, Changshuo Yan, Jifeng Liu

Context. Periodic QSOs are considered as candidates of supermassive binary black hole (BBH) systems in galactic centers. Further confirmation of these candidates may require different lines of observational evidences. Aims. Assuming the Doopler boosting scenario, in this paper we investigate the (coherent) variations of both broad emission lines (BELs) and c

Jinlin Wang, Licheng Lou, Kang Yin, Fanqi Meng

Atomic disorder, a widespread problem in compound crystalline materials, is a imperative affecting the performance of multi-chalcogenide Cu2ZnSn(S, Se)4 (CZTSSe) photovoltaic device known for its low cost and environmental friendliness. Cu-Zn disorder is particularly abundantly present in CZTSSe due to its extraordinarily low formation energy, having induced

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

Using 7.33~fb$^{-1}$ of $e^{+}e^{-}$ collision data collected by the BESIII detector at center-of-mass energies in the range of $\sqrt{s}=4.128 - 4.226$~GeV, we search for the rare decays $D_{s}^+\to h^+(h^{0})e^{+}e^{-}$, where $h$ represents a kaon or pion. By requiring the $e^{+}e^{-}$ invariant mass to be consistent with a $\phi(1020)$, $0.98<M(e^{+}e^{-

Rongli Huang

This is a sequel to [2] and [3], which study the second boundary value problems for mean curvature flow. Consequently, we construct the translating solitons with prescribed Gauss image in Minkowski space.

Gonçalo Paulo, Thomas Marshall, Nora Belrose

Recent advances in recurrent neural network architectures, such as Mamba and RWKV, have enabled RNNs to match or exceed the performance of equal-size transformers in terms of language modeling perplexity and downstream evaluations, suggesting that future systems may be built on completely new architectures. In this paper, we examine if selected interpretabil

Alireza Salemi, Surya Kallumadi, Hamed Zamani

This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to large language models for the purpose of personalized generation

Qiao Huang, Nicolas Privault

The main goal of this paper is to provide an algorithm for the random sampling of Butcher trees and the probabilistic numerical solution of ordinary differential equations (ODEs). This approach complements and simplifies a recent approach to the probabilistic representation of ODE solutions, by removing the need to generate random branching times. The random

Ying Qin, Kai-Fan Ji, Hui Liu, Xiao-Guang Yu

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency. The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic fie

Masato Fujitake

In this paper, we present a method for enhancing the accuracy of scene text recognition tasks by judging whether the image and text match each other. While previous studies focused on generating the recognition results from input images, our approach also considers the model's misrecognition results to understand its error tendencies, thus improving the text

Yizhou Chi, Kevin Yang, Dan Klein

We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential solutions using Monte Carlo Tree Search (MCTS), building solutions one action at a time and evaluating according to any domain-specific heuristic, which in practice is often simply an

María Fernanda Espinal, María del Mar González

In this paper we study the $\sigma_2$--Yamabe equation, $n>4$, for solutions with a prescribed singular set $\Lambda$ given by a disjoint union of closed submanifolds whose dimension is positive and strictly less than $(n-\sqrt{n}-2)/2$. The $\sigma_2$--curvature in conformal geometry is defined as the second elementary symmetric polynomial of the eigenvalue

Van Nguyen, Xingliang Yuan, Tingmin Wu, Surya Nepal

Software vulnerabilities (SVs) have become a common, serious, and crucial concern to safety-critical security systems. That leads to significant progress in the use of AI-based methods for software vulnerability detection (SVD). In practice, although AI-based methods have been achieving promising performances in SVD and other domain applications (e.g., compu

Paul Becker, Thomas R. Cameron, Derek Hanely, Boon Ong

In 2018, a fort of a graph was introduced as a non-empty subset of vertices in which no vertex outside of the set has exactly one neighbor in the set. Since then, forts have been used to characterize zero forcing sets, model the zero forcing number as an integer program, and generate lower bounds on the zero forcing number of a Cartesian product. In this art

Haoxuan Li, Yuanxin Ouyang, Zhuang Liu, Wenge Rong

Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertai

Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau

Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM i

Baojie Fan, Wuyang Zhou, Kai Wang, Shijun Zhou

Most of 3D single object trackers (SOT) in point clouds follow the two-stream multi-stage 3D Siamese or motion tracking paradigms, which process the template and search area point clouds with two parallel branches, built on supervised point cloud backbones. In this work, beyond typical 3D Siamese or motion tracking, we propose a neat and compact one-stream t

Shijie Rao, Kaiyu Cui, Yidong Huang, Jiawei Yang

Subwavelength photonic structures and metamaterials provide revolutionary approaches for controlling light. The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices. However, most of the existing inverse design methods cannot realize direct mapping from optical properties to photonic structur

Tao-Ran Hu, Su Chen, Feng-Kun Guo

Recently entanglement suppression was proposed to be one possible origin of emergent symmetries. Here we test this conjecture in the context of heavy meson scatterings. The low-energy interactions of $D^{(*)}\bar D^{(*)}$ and $D^{(*)} D^{(*)}$ are closely related to the hadronic molecular candidates $X(3872)$ and $T_{cc}(3875)^+$, respectively, and can be de

Zelin Zhang, Songbai Chen, Jiliang Jing

With the observed data of the S2 orbit around the black hole Sgr A$^*$ and the Markov Chain Monte Carlo method, we make a constraint on parameters of a disformal Schwarzschild black hole in quadratic degenerate higher-order scalar-tensor (DHOST) theories. This black hole belongs to a class of non-stealth solutions and owns an extra disformal parameter descri

Daniela Calvetti, Erkki Somersalo

We exploit the similarities between Tikhonov regularization and Bayesian hierarchical models to propose a regularization scheme that acts like a distributed Tikhonov regularization where the amount of regularization varies from component to component. In the standard formulation, Tikhonov regularization compensates for the inherent ill-conditioning of linear

Junpeng Liu, Yifan Song, Bill Yuchen Lin, Wai Lam

Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. Existing benchmarks are either designed for general multimodal tasks, failing to capture the unique characteristics of web pages, or focus on end-to-end web agent

Yuji Matsumoto, Kosuke Kurosawa, Sota Arakawa

A leading candidate for the heating source of chondrules and igneous rims is shock waves. This mechanism generates high relative velocities between chondrules and dust particles. We have investigated the possibility of the chondrule destruction in collisions with dust particles behind a shock wave using a semianalytical treatment. We find that the chondrules

Tian Qiu, Alan Zoubi, Nikolai Spine, Lailiang Cheng

Robotic branch pruning is a significantly growing research area to cope with the shortage of labor force in the context of agriculture. One fundamental requirement in robotic pruning is the perception of detailed geometry and topology of branches. However, the point clouds obtained in agricultural settings often exhibit incompleteness due to several constrai

Zetao Lu, Kaijun Feng, Jun Xu, Haoyao Chen

Implementing obstacle avoidance in dynamic environments is a challenging problem for robots. Model predictive control (MPC) is a popular strategy for dealing with this type of problem, and recent work mainly uses control barrier function (CBF) as hard constraints to ensure that the system state remains in the safe set. However, in crowded scenarios, effectiv

Yasmin Sarita, Avaljot Singh, Shaurya Gomber, Gagandeep Singh

Synthesizing ranking functions is a common technique for proving the termination of loops. A ranking function must be bounded and decrease by a specified amount with each iteration for all reachable program states. However, the set of reachable program states is often unknown, and loop invariants are typically used to overapproximate it. So, proving the term

Jinyuan Feng, Min Chen, Zhiqiang Pu, Tenghai Qiu

Multi-task reinforcement learning (MTRL) demonstrate potential for enhancing the generalization of a robot, enabling it to perform multiple tasks concurrently. However, the performance of MTRL may still be susceptible to conflicts between tasks and negative interference. To facilitate efficient MTRL, we propose Task-Specific Action Correction (TSAC), a gener

Nosratollah Jafari

We review the history of the curved momentum space from Max Born to the recent relative locality, \k{appa}-Poincare and noncommutative geometric proposals. We found that the concept of the curvature in the momentum space and motivations has been evolved during 80 years from the Max Born time. Motivations has been evolved from introducing general relativistic

Baoling Ning, Jianzhong Li

Graph partitioning is a key fundamental problem in the area of big graph computation. Previous works do not consider the practical requirements when optimizing the big data analysis in real applications. In this paper, motivated by optimizing the big data computing applications, two typical problems of graph partitioning are studied. The first problem is to

Yuanyuan Yang, XinYu Lyu, Sida He, Xiliang Lu

We demonstrate that, even when there are moderate overlaps in the inputs of sloppy or accurate double-word addition algorithms in the QD library, these algorithms still guarantee error bounds of $O(u^2(|a|+|b|))$ in faithful rounding. Furthermore, the accurate algorithm can achieve a relative error bound of $O(u^2)$ in the presence of moderate overlaps in th

Yoshimichi Teratani, Kazuhiko Tsutsumi, Kaiji Motoyama, Rui Sakano

We present a comprehensive Fermi-liquid description for thermoelectric transport and current noise, applicable to multilevel quantum dots (QD) and magnetic alloys (MA) without electron-hole or time-reversal symmetry. Our formulation for the low-energy transport is based on an Anderson model with $N$ discrete impurity levels, and is asymptotically exact at lo

Jayson R Vavrek, Luozhong Zhou, Joshua Boverhof, Elisa R Heymann

In future nuclear arms control treaties, it will be necessary to authenticate the hardware and software components of verification measurement systems, i.e., to ensure these systems are functioning as intended and have not been tampered with by malicious actors. While methods such as source code hashing and static analysis can help verify the integrity of so

Hong-Ming Cui, Zhong-Ying Fan

Employing extended phase space formalism, we study critical phenomenon of A-charge and C-charge for holographic theories dual to Gauss-Bonnet black holes. We find a universal critical Gauss-Bonnet coupling, giving rise to a universal ratio between the two central charges at the critical point. This leads to a new intepretation for critical behavior of Gauss-

K. Suzuki, K. Nomura

Evolution and coexistence of shape and the related spectroscopic properties of even-even Te isotopes are investigated within the quadrupole collective model that is based on the nuclear density functional theory. By means of the constrained self-consistent mean-field calculations performed within the relativistic Hartree-Bogoliubov method with a choice of th

Wei-Rui Chen, Ife Adebara, Muhammad Abdul-Mageed

We investigate two research questions: (1) how do machine translation (MT) and diacritization influence the performance of each other in a multi-task learning setting (2) the effect of keeping (vs. removing) diacritics on MT performance. We examine these two questions in both high-resource (HR) and low-resource (LR) settings across 55 different languages (36

Yongchun Lu, Yongchun Lu, Liying Kang

Given a family of graphs $\mathcal{F}$, the Tur\'{a}n number $ex(n, \mathcal{F})$ denotes the maximum number of edges in any $\mathcal{F}$-free graph on $n$ vertices. Recently, Alon and Frankl studied of maximum number of edges in an $n$-vertex $\{K_{k+1}, M_{s+1}\}$-free graph, where $K_{k+1}$ is a complete graph on $k+1$ vertices and $M_{s+1}$ is a matchin

Jun-Muk Hwang

We propose a conjecture that a general member of a bracket-generating family of rational curves in a complex manifold satisfies the formal principle with convergence, namely, any formal equivalence between such curves is convergent. If the normal bundles of the rational curves are positive, the conjecture follows from the results of Commichau-Grauert and Hir

Meng-Yun Mao, Zheng Cheng, Liangsheng Li, Ning Wu

Achieving precise preparation of quantum many-body states is crucial for the practical implementation of quantum computation and quantum simulation. However, the inherent challenges posed by unavoidable excitations at critical points during quench processes necessitate careful design of control fields. In this work, we introduce a promising and versatile dyn

Zavareh Bozorgasl, Mohammad Javad Dehghani

Despite many advantages of direction-of-arrivals (DOAs) in sparse representation domain, they have high computational complexity. This paper presents a new method for real-valued 2-D DOAs estimation of sources in a uniform circular array configuration. This method uses a transformation based on phase mode excitation in uniform circular arrays which called re

Anshuman Sinha, Spencer H. Bryngelson

We demonstrate that neural networks can be FLOP-efficient integrators of one-dimensional oscillatory integrands. We train a feed-forward neural network to compute integrals of highly oscillatory 1D functions. The training set is a parametric combination of functions with varying characters and oscillatory behavior degrees. Numerical examples show that these

Yousuf Aborahama, Karteekeya Sastry, Manxiu Cui, Yang Zhang

Noninvasive transcranial photoacoustic computed tomography (PACT) of the human brain, despite its clinical potential as a complementary technology to functional MRI, remains impeded by the acoustic distortion induced by the human skull. The distortion, which is attributed to the markedly different material properties of the skull relative to soft tissue, res

Jing Zhou, D. L. Zhou

Hamiltonian Learning is a process of recovering system Hamiltonian from measurements, which is a fundamental problem in quantum information processing. In this study, we investigate the problem of learning the symmetric Hamiltonian from its eigenstate. Inspired by the application of group theory in block diagonal secular determination, we have derived a meth

Guo-Jian Qiao, Xin Yue, C. P. Sun

The low-energy theory of hybrid nanowire systems fails to define Majorana fermion (MF) in the strong tunneling and magnetic field strength. To address this limitation, we propose a holistic approach to define MF in which the quasi-excitation in nanowire and superconductor constitutes together its own ``antiparticles''. This definition is general, beyond the

Mingsheng Ying, Zhicheng Zhang

Recursive techniques have recently been introduced into quantum programming so that a variety of large quantum circuits and algorithms can be elegantly and economically programmed. In this paper, we present a proof system for formal verification of the correctness of recursively defined quantum circuits. The soundness and (relative) completeness of the proof

Xingchi Li, Xianyang Zhang

Change point analysis is concerned with detecting and locating structure breaks in the underlying model of a sequence of observations ordered by time, space or other variables. A widely adopted approach for change point analysis is to minimize an objective function with a penalty term on the number of change points. This framework includes several well-estab

Shintaro Inoue, Kento Kawaharazuka, Kei Okada, Masayuki Inaba

In this study, a chair-type asymmetric tripedal low-rigidity robot was designed based on the three-legged chair character in the movie "Suzume" and its gait was generated. Its body structure consists of three legs that are asymmetric to the body, so it cannot be easily balanced. In addition, the actuator is a servo motor that can only feed-forward rotational

Jun-Muk Hwang

We show that any smooth surface germ in the moduli of abelian surfaces arises from a Lagrangian fibration of abelian surfaces. By Donagi-Markman's cubic condition, the key issue of the proof is to find a suitable affine structure with a compatible cubic form on the base space of the family. We achieve this by analyzing the properties of cubic forms in two va

Karim Adiprasito, Igor Pak

We address two longstanding open problems, one originating in PL topology, another in birational geometry. First, we prove the weighted version of Oda's \emph{strong factorization conjecture} (1978), and prove that every two birational toric varieties are related by a common iterated blowup (at rationally smooth points). Second, we prove that every two PL ho

Aria Nguyen, Oscar McMullin, Joseph T. Lizier, Ben D. Fulcher

Quantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditional methods for detecting pairwise dependence of time series, such as Pearson correlation, Granger causality, and mutual information, are computed directly in the space of measure

Nicol Leong, Michael J. Mossinghoff

Non-negative trigonometric polynomials satisfying certain properties are employed when studying a number of aspects of the Riemann zeta function. When establishing zero-free regions in the critical strip, the classical polynomial $3+4\cos(\theta)+\cos(2\theta)$ used by de la Vall\'ee Poussin has since been replaced by more beneficial polynomials with larger

Luca Boccioli, Giacomo Fragione

The explosion of core-collapse supernovae (CCSNe) is an extremely challenging problem, and there are still large uncertainties regarding which stars lead to successful explosions that leave behind a neutron star, and which ones will form a black hole instead. In this paper, we simulate 341 progenitors at three different metallicities using spherically symmet