December 2024 arXiv papers — page 194

Guanghui Zhu, Zipeng Ji, Jingyan Chen, Limin Wang

GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such as node classification and link prediction. However, most existing GNAS methods cannot efficiently handle large-scale graphs containing more than million-scale nodes and edges due t

Xingzhong Xu

In this paper, we focus on Oliver's $p$-group conjecture. We use elementary method to prove that Oliver's $p$-group conjecture holds for Sylow $p$-subgroups of unitary groups.

Abdurrahman Javid Shaikh, Othman Sidek

Photonics caught world attention since channel capacity limit of metallic interconnects approached due to research and design in high speed digital processors. Use of dielectrics, instead, suitable for light propagation was more attractive due to its extremely wide bandwidth. Many of the devices, both active and passive, have been demonstrated using these in

Fan-Yun Sun, Weiyu Liu, Siyi Gu, Dylan Lim

Spatial reasoning is a fundamental aspect of human cognition, enabling intuitive understanding and manipulation of objects in three-dimensional space. While foundation models demonstrate remarkable performance on some benchmarks, they still struggle with 3D reasoning tasks like arranging objects in space according to open-ended language instructions, particu

Jayjeet Chakraborty, Matthieu Dorier, Philip Carns, Robert Ross

The volume of data generated and stored in contemporary global data centers is experiencing exponential growth. This rapid data growth necessitates efficient processing and analysis to extract valuable business insights. In distributed data processing systems, data undergoes exchanges between the compute servers that contribute significantly to the total dat

Lei Zhang, Wenhao Luo, Renxi Liu, Mohan Chen

Aluminum oxide (alumina, Al$_2$O$_3$) exists in various structures and has broad industrial applications. While the crystal structure of $\alpha$-Al$_2$O$_3$ is well-established, those of transitional aluminas remain highly debated. In this study, we propose a universal machine learning interatomic potential (MLIP) for aluminas, trained using the neuroevolut

Supriyo Jana, Soumen Sarkar

In this paper, we characterize arbitrary polynomial vector fields on $S^n$. We establish a necessary and sufficient condition for a degree one vector field on the odd-dimensional sphere $S^{2n-1}$ to be Hamiltonian. Additionally, we classify polynomial vector fields on $S^n$ up to degree two that possess an invariant great $(n-1)$-sphere. We present a class

Abu Bakar Siddik, Faisal R. Badal, Afroza Islam

A great deal of effort has been devoted to discovering a particular genetic disorder, but its classification across a broad spectrum of disorder classes and types remains elusive. Early diagnosis of genetic disorders enables timely interventions and improves outcomes. This study implements machine learning models using basic clinical indicators measurable at

Sanjay Mishra, Chander Mohan Bishnoi

Cardinal functions provide valuable insight into the topological properties of spaces, helping to analyze and compare spaces in terms of their covering, convergence and separation properties. This paper focuses on investigating cardinal functions like network weight, Lindel\"of degree, tightness, weak covering, pseudocharacter, and $i$-weight, for the spaces

Weiche Hsieh, Ziqian Bi, Keyu Chen, Benji Peng

Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores the theoretical foundations, methodological advancements, and practical implementations of these technologies, emphasizing their role in uncov

Kangsan Kim, Geon Park, Youngwan Lee, Woongyeong Yeo

Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training data. Traditional methods like fine-tuning on OOD datasets are impractical due to high computational costs. Whil

Juntao Du, Songxiao Li, Zuoling Liu

Using some estimates in [J. Funct. Anal. {\bf 278}(2020), Article No. 108401], we completely characterized the boundedness and compactness of the Stevi\'c-Sharma type operators with different weights and different composition symbols between Bergman spaces induced by two-sides doubling weights.

Yu Oshima, Tianyi Wang, Masaya Kato, Haruto Kobayashi

We demonstrate the feasibility of the radar-based measurement of body movements in scenarios involving multiple students using a pair of 79-GHz millimeter-wave radar systems with array antennas. We quantify the body motion using the Doppler frequency calculated from radar echoes. The measurement accuracy is evaluated for two experimental scenarios, namely un

Mengsi Gao

This paper investigates the identification and inference of treatment effects in randomized controlled trials with social interactions. Two key network features characterize the setting and introduce endogeneity: (1) latent variables may affect both network formation and outcomes, and (2) the intervention may alter network structure, mediating treatment effe

Paula Gablenz, Matteo Sesia, Tianshu Sun, Chiara Sabatti

We introduce local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. By expanding upon the model-X knockoff filter, we show how to adaptively discover these local associations, all while controlling the false discovery rate. Our enhanced inferences can h

Dongkwan Kim, Alice Oh

Subgraph representation learning has been effective in solving various real-world problems. However, current graph neural networks (GNNs) produce suboptimal results for subgraph-level tasks due to their inability to capture complex interactions within and between subgraphs. To provide a more expressive and efficient alternative, we propose WLKS, a Weisfeiler

Y. Zhao, M. Vardaro, E. Capocasa, J. Ding

Reducing optical losses is crucial for reducing quantum noise in gravitational-wave detectors. Losses are the main source of degradation of the squeezed vacuum. Frequency dependent squeezing obtained via a filter cavity is currently used to reduce quantum noise in the whole detector bandwidth. Such filter cavities are required to have high finesse in order t

Takumi Gomyou, Shin Nayatani

Given a length function on the set of edges of a finite graph, the corresponding Fujiwara Laplacian is defined. We consider a problem of maximizing the first nonzero eigenvalue of this graph Laplacian over all choices of edge-length function subject to a certain normalization. In this paper we prove that the supremum of the first nonzero eigenvalue is finite

Seungwon Baek, P. Ko, Yuji Omura, Chaehyun Yu

In this paper, we study two Higgs doublet models with gauged U(1)_H symmetry, motivated by the excesses around 96 GeV reported by the CMS collaboration in the searches for light resonances decaying to two photons and two \tau's. In this model, one Higgs doublet field is charged under the U(1)_H symmetry to avoid tree-level flavor changing neutral currents. T

R. Mahmood, K. C. L. Wong, D. M. Reyes, N. D'Souza

With the emergence of large-scale vision-language models, realistic radiology reports may be generated using only medical images as input guided by simple prompts. However, their practical utility has been limited due to the factual errors in their description of findings. In this paper, we propose a novel model for explainable fact-checking that identifies

Shahab Shokouhi, Oguzhan Oruc, May-Win Thein

This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic architecture to process limited LIDAR inpu

Ruichen Jiang, Aryan Mokhtari, Francisco Patitucci

We study the problem of finding an $\epsilon$-first-order stationary point (FOSP) of a smooth function, given access only to gradient information. The best-known gradient query complexity for this task, assuming both the gradient and Hessian of the objective function are Lipschitz continuous, is ${O}(\epsilon^{-7/4})$. In this work, we propose a method with

Lily Chung, Erik D. Demaine, Jenny Diomidova, Tonan Kamata

We prove that, for any two polyhedral manifolds $\mathcal P,\mathcal Q$, there is a polyhedral manifold $\mathcal I$ such that $\mathcal P,\mathcal I$ share a common unfolding and $\mathcal I,\mathcal Q$ share a common unfolding. In other words, we can unfold $\mathcal P$, refold (glue) that unfolding into $\mathcal I$, unfold $\mathcal I$, and then refold i

Nader Karayanni, Aya Awwad, Chein-Lien Hsiao, Surish P Shanmugam

Since the emergence of Large Language Models (LLMs), the challenge of effectively leveraging their potential in healthcare has taken center stage. A critical barrier to using LLMs for extracting insights from unstructured clinical notes lies in the prompt engineering process. Despite its pivotal role in determining task performance, a clear framework for pro

Xueqing Wu, Yuheng Ding, Bingxuan Li, Pan Lu

The ability of large vision-language models (LVLMs) to critique and correct their reasoning is an essential building block towards their self-improvement. However, a systematic analysis of such capabilities in LVLMs is still lacking. We propose VISCO, the first benchmark to extensively analyze the fine-grained critique and correction capabilities of LVLMs. C

Tianyi Wang, Zichen Wang, Cong Wang, Yuanchao Shu

Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally known as misclassifying attacks. By targeting the real-time pr

Hong-Jian Wang, Pei-Rong Li, Xiao-Rui Lyu, Jusak Tandean

A sizable strong-interaction phase shift in weak two-body nonleptonic baryon decay would enhance the possibility of discovering charge-conjugation parity ($CP$) violation in the baryon sector, which might help in the quest for understanding the matter-antimatter asymmetry in the universe. Over the past 60 years, empirical analyses involving different types o

Konstantin A. Kouzakov, Fedor M. Lazarev, Alexander I. Studenikin

A thorough account of electromagnetic interactions of massive Dirac neutrinos as well as their spin-flavor state in the theoretical formulation of elastic neutrino-nucleon scattering is given. The formalism of neutrino charge, magnetic, electric, and anapole form factors defined as matrices in the mass basis is employed under the assumption of three-neutrino

Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri

Image generation today can produce somewhat realistic images from text prompts. However, if one asks the generator to synthesize a specific camera setting such as creating different fields of view using a 24mm lens versus a 70mm lens, the generator will not be able to interpret and generate scene-consistent images. This limitation not only hinders the adopti

Amitash Nanda, Sree Bhargavi Balija, Debashis Sahoo

Large language models have transformed the comprehension and generation of natural language tasks, but they come with substantial memory and computational requirements. Quantization techniques have emerged as a promising avenue for addressing these challenges while preserving accuracy and making energy efficient. We propose CPTQuant, a comprehensive strategy

Ben Ward, Deepshikha Bhati, Fnu Neha, Angela Guercio

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these featu

Woojay Jeon

This paper provides a theoretical framework for interpreting acoustic neighbor embeddings, which are representations of the phonetic content of variable-width audio or text in a fixed-dimensional embedding space. A probabilistic interpretation of the distances between embeddings is proposed, based on a general quantitative definition of phonetic similarity b

K. R. Zhu, J. M. Chen, L. Zhang

We present the statistic results of GeV spectral breaks of bright gamma-ray flat-spectrum radio quasars (FSRQs) in the energy range of 0.1-10 GeV based on New Pass 8 date of the Large Area Telescope abroad on Fermi Gamma-ray Space Telescope. We have fitted the 15-year average gamma-ray spectra of 755 FSRQs by using both a broken power-law (BPL) and the Logar

Jaime Garza, Yizao Wang

We investigate the random permutation matrices induced by the Chinese restaurant processes with $(\alpha,\theta)$-seating. When $\alpha=0,\theta>0$, the permutations are those following Ewens measures on symmetric groups, and have been extensively studied in the literature. Here, we consider $\alpha\in(0,1)$ and $\theta>-\alpha$. In an accompanying paper, a

Chengpeng Fu, Tong Li, Hao Chen, Wen Du

Epidemic prediction is of practical significance in public health, enabling early intervention, resource allocation, and strategic planning. However, privacy concerns often hinder the sharing of health data among institutions, limiting the development of accurate prediction models. In this paper, we develop a general privacy-preserving framework for node-lev

Jaime Garza, Yizao Wang

A functional central limit theorem is established for weighted occupancy processes of the Karlin model. The weighted occupancy processes take the form of, with $D_{n,j}$ denoting the number of urns with $j$-balls after the first $n$ samplings, $\sum_{j=1}^na_jD_{n,j}$ for a prescribed sequence of real numbers $(a_j)_{j\in\mathbb N}$. The main applications ar

Tony T. Wang, John Hughes, Henry Sleight, Rylan Schaeffer

Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the difficulty of jailbreak-defense when we only want to forbid a narrowly-defined set of behaviors. As a case study, we focus on preventing an LLM from helping a user make a bomb. We f

Liqiong Wang, Teng Jin, Jinyu Yang, Ales Leonardis

In the general domain, large multimodal models (LMMs) have achieved significant advancements, yet challenges persist in applying them to specific fields, especially agriculture. As the backbone of the global economy, agriculture confronts numerous challenges, with pests and diseases being particularly concerning due to their complexity, variability, rapid sp

Ziqian Li, Ke Chen, Xudong Wang, Kan Luo

Two-dimensional transition metal carbides and nitrides (MXenes) have gained popularity in fields such as energy storage, catalysis, and electromagnetic interference due to their diverse elemental compositions and variable surface terminations (T). Generally, the synthesis of MXene materials involves etching the weak M-A metallic bonds in the ternary layered

Ranyang Zhou, Jacqueline T. Liu, Sabbir Ahmed, Shaahin Angizi

Recent advancements in side-channel attacks have revealed the vulnerability of modern Deep Neural Networks (DNNs) to malicious adversarial weight attacks. The well-studied RowHammer attack has effectively compromised DNN performance by inducing precise and deterministic bit-flips in the main memory (e.g., DRAM). Similarly, RowPress has emerged as another eff

Xiaojie Yang, Hangli Ge, Jiawei Wang, Zipei Fan

Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they often interfered by non-periodic public events, such as disasters and occasional celebrations. Since regular human mobility patterns are heavily affected by these eve

Harrison Delecki, Sydney M. Katz, Mykel J. Kochenderfer

Estimating the probability of failure is a critical step in developing safety-critical autonomous systems. Direct estimation methods such as Monte Carlo sampling are often impractical due to the rarity of failures in these systems. Existing importance sampling approaches do not scale to sequential decision-making systems with large state spaces and long hori

Abulikemu Abuduweili, Changliu Liu

Adaptive gradient optimization methods, such as Adam, are prevalent in training deep neural networks across diverse machine learning tasks due to their ability to achieve faster convergence. However, these methods often suffer from suboptimal generalization compared to stochastic gradient descent (SGD) and exhibit instability, particularly when training Tran

Lijie Ji, Zhichao Peng, Yanlai Chen

Nonlinear and nonaffine terms in parametric partial differential equations can potentially lead to a computational cost of a reduced order model (ROM) that is comparable to the cost of the original full order model (FOM). To address this, the Reduced Residual Reduced Over-Collocation method (R2-ROC) is developed as a hyper-reduction method within the framewo

Yuang Tian, Jiajin Sun, Yinqiu He

This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates

Yanjun Chen

We generalize the classification of isomorphism classes of Schubert varieties in complete flag varieties G/B to a class of partial flag varieties G/P. In particular, we classify all Schubert varieties in G/P where P is a minimal parabolic subgroup and all Schubert surfaces. We also obtain several pairs of isomorphisms of Schubert varieties from folding the r

Fernando Gabriela Garcia, Spencer Burns, Harrison Fuller

In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective at generating concise summaries, fail t

Ashutosh Hathidara, Gaurav Atavale, Suyash Chaudhary

Bitcoin has increased investment interests in people during the last decade. We have seen an increase in the number of posts on social media platforms about cryptocurrency, especially Bitcoin. This project focuses on analyzing user tweet data in combination with Bitcoin price data to see the relevance between price fluctuations and the conversation between m

Hongfei Shu

In this review (written in Chinese), we introduce the computation of the minimal surface area in the scattering amplitude/Wilson loop duality, where the minimal surface ends on a light-like polygonal Wilson loop at the boundary of anti-de Sitter space (AdS). Due to its nonlinearity and the complexity of the boundary conditions, directly solving the equations

Jing Liu, Fangfei Li, Xin Jin, Yang Tang

This paper addresses dynamic task allocation in resource-constrained multi-agent systems (MASs) with sequentially updated assignments. We develop a submodular maximization framework integrated with $q$-independence systems, demonstrating greater flexibility than conventional matroid-based constraints for modeling heterogeneous resource limitations. The propo

Yuxuan Shi, Hongbo Cheng

We study the effects of neutrino lensing induced by a Damour-Solodukhin wormhole on the neutrino oscillation. We derive and calculate the flavour transition probabilities in the presence of Damour-Solodukhin factor $\Lambda$ as a shift in the massive source to show that the neutrino flavour oscillation is also sensitive not only to the sign of difference bet

Junda Wu, Hanjia Lyu, Yu Xia, Zhehao Zhang

Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their ar

Yuci Liang, Xinheng Lyu, Wenting Chen, Meidan Ding

Recent advancements in computational pathology have produced patch-level Multi-modal Large Language Models (MLLMs), but these models are limited by their inability to analyze whole slide images (WSIs) comprehensively and their tendency to bypass crucial morphological features that pathologists rely on for diagnosis. To address these challenges, we first intr

Junqiu Yu, Xinlin Ren, Yongchong Gu, Haitao Lin

Language-guided robotic grasping is a rapidly advancing field where robots are instructed using human language to grasp specific objects. However, existing methods often depend on dense camera views and struggle to quickly update scenes, limiting their effectiveness in changeable environments. In contrast, we propose SparseGrasp, a novel open-vocabulary robo

Liu Hong

The Boltzmann equation is one of the most famous equations and has vast applications in modern science. In the current study, we take the randomness of binary collisions into consideration and generalize the classical Boltzmann equation into a stochastic framework. The corresponding Kolmogorov forward equations and Liouville equation in either discrete or co

Xiaocun Zong, Binchao Zhang, Fan Yang, Shenheng Xu

This paper analyzes the working principle of X-Shaped reconfigurable intelligent surface (RIS) in detail and reveals the different types of RIS that can be designed based on this structure. Combined with the design examples using this structure in the currently published articles, this paper summarizes and organizes them, and finally, based on this X-Shaped

Zhihan Cao, Hiroaki Yamada, Simone Teufel, Takenobu Tokunaga

WordNet provides a carefully constructed repository of semantic relations, created by specialists. But there is another source of information on semantic relations, the intuition of language users. We present the first systematic study of the degree to which these two sources are aligned. Investigating the cases of misalignment could make proper use of WordN

Dillon Z. Chen, Mingyu Hao, Sylvie Thiébaux, Felipe Trevizan

Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary number of objects. In this paper, we study the usage of graph learning for planning thus far by studying the theoretical and empirical effects on learning and plan

Changqing Teng, Guanglian Li

Existing deep learning-based calibration scheme for rough volatility models predominantly rely on supervised learning frameworks, which incur significant computational costs due to the necessity of generating massive synthetic training datasets. In this work, we propose a novel unsupervised learning-based calibration scheme for rough volatility models that e

Byeongseon Go, Hyukjoon Kwon, Siheon Park, Dhrumil Patel

Density matrix exponentiation (DME) is a quantum algorithm that processes multiple copies of a program state $\sigma$ to realize the Hamiltonian evolution $e^{-i \sigma t}$. Wave matrix Lindbladization (WML) similarly processes multiple copies of a program state $\psi_L$ in order to realize a Lindbladian evolution. Both algorithms are prototypical sample-bas

Lane G. Gunderman, Troy Borneman, David G. Cory

Hybrid quantum systems consisting of a collection of N spin-1/2 particles uniformly interacting with an electromagnetic field, such as one confined in a cavity, are important for the development of quantum information processors and will be useful for metrology, as well as tests of collective behavior. Such systems are often modeled by the Tavis-Cummings mod

Ke-Tai Wu, Zi-Jun Zhong, Yi Li, Chong-Ye Chen

We investigate spontaneous scalarization in the Einstein-Born-Infeld-Scalar (EBIS) model with asymptotically AdS boundary conditions, revealing novel dynamical critical phenomena in black hole evolution. Through numerical analysis, we discover a distinctive ``flip" phenomenon where the scalar field exhibits critical transitions between different stable confi

Gong Chen, Yang Lan, Xu Yuan

For the 2D cubic (mass-critical) Zakharov-Kuznetsov equation, \begin{equation*} \partial_t\phi+\partial_{x_1}(\Delta \phi+\phi^3)=0,\quad (t,x)\in [0,\infty)\times \mathbb{R}^{2}, \end{equation*} we prove that there exist no finite/infinite time blow-up solution with minimal mass in the energy space. This nonexistence result is in contrast to the one obtaine

Chaoxiong Ma, Yan Liang, Xinyu Yang, Han Wu

The theory of evidence reasoning has been applied to collective decision-making in recent years. However, existing distributed evidence fusion methods lead to participants' preference leakage and fusion failures as they directly exchange raw evidence and do not assess evidence credibility like centralized credible evidence fusion (CCEF) does. To do so, a pri

Yifan Jiao, Yunhao Li, Junhua Ding, Qing Yang

In this paper, we present a novel benchmark, GSOT3D, that aims at facilitating development of generic 3D single object tracking (SOT) in the wild. Specifically, GSOT3D offers 620 sequences with 123K frames, and covers a wide selection of 54 object categories. Each sequence is offered with multiple modalities, including the point cloud (PC), RGB image, and de

Sen Niu, Yang Peng, D. N. Sheng

Experimental realizations of Abelian fractional Chern insulators (FCIs) have demonstrated the potentials of moir\'e systems in synthesizing exotic quantum phases. Remarkably, twisted multilayer graphene system may also host non-Abelian states competing with charge density wave under Coulomb interaction. Here, through larger scale exact diagonalization simula

L. G. A dos Reis, V. L. P. S. Caminha, T. J. P. Penna

Symbolic regression is a machine learning technique, and it has seen many advancements in recent years, especially in genetic programming approaches (GPSR). Furthermore, it has been known for many years that constant optimization of parameters, during the evolutionary search, greatly increases GPSR performance However, different authors approach such tasks d

Guangyu Zhao, Kewei Lian, Haoxuan Ru, Borong Zhang

Goal-conditioned policies enable decision-making models to execute diverse behaviors based on specified goals, yet their downstream performance is often highly sensitive to the choice of instructions or prompts. To bypass the limitations of discrete text prompts, we formulate post-training adaptation as a latent control problem, where the goal embedding serv

Long Wang

Accurate $N$-body simulations of multiple systems such as binaries and triples are essential for understanding the formation and evolution of interacting binaries and binary mergers, including gravitational wave sources, blue stragglers and X-ray binaries. The logarithmic time-transformed explicit symplectic integrator (LogH), also known as algorithmic regul

Jian-Ci Xiao

We prove that any non-degenerate Bedford-McMullen carpet does not admit oblique self-embedding similitudes; that is, if $f$ is a similitude sending the carpet into itself, then the image of the $x$-axis under $f$ must be parallel to one of the principal axes. This result leads to a logarithmic commensurability result on the contraction ratios of such embeddi

Luyi Ma, Aashika Padmanabhan, Anjana Ganesh, Shengwei Tang

Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shopping. However, the growing transition between online and in-store becomes a challenge to sequential recommender systems for future online interaction prediction due to the lack of hol

Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh

Self Supervised learning (SSL) has demonstrated its effectiveness in feature learning from unlabeled data. Regarding this success, there have been some arguments on the role that mutual information plays within the SSL framework. Some works argued for increasing mutual information between representation of augmented views. Others suggest decreasing mutual in

Warley M. S. Alves, Leonardo Neves

Quantum ptychography is a method for estimating an unknown pure quantum state by subjecting it to overlapping projections, each one followed by a projective measurement on a single prescribed basis. Here, we present a comprehensive study of this method applied for estimating $n$-qubit states in a circuit-based quantum computer, including numerical simulation

Zeqing Zhang, Guangze Zheng, Xuebo Ji, Guanqi Chen

Granular materials (GMs) are ubiquitous in daily life. Understanding their properties is also important, especially in agriculture and industry. However, existing works require dedicated measurement equipment and also need large human efforts to handle a large number of particles. In this paper, we introduce a method for estimating the relative values of par

Ozgur Guldogan, Jackson Kunde, Kangwook Lee, Ramtin Pedarsani

As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become increasingly critical. Batching LLM requests is a critical step in scheduling the inference jobs on servers (e.g. GPUs), enabling the system to maximize throughput by allowing multiple requests to be processed in p

Hussein Behzadipour, Henk Koppelaar, Peyman Nasehpour

In this paper, we introduce Indigenous semirings and show that they are examples of information algebras. We also attribute a graph to them and discuss their diameters, girths, and clique numbers. On the other hand, we prove that the Zariski topology of any Indigenous semiring is the Sierpi\'{n}ski space. Next, we investigate their algebraic properties (incl

Anne C. Bronzi, Cecilia F. Mondaini, Ricardo M. S. Rosa

In this work, a recently introduced general framework for trajectory statistical solutions is considered, and the question of convergence of families of such solutions is addressed. Conditions for the convergence are given which rely on natural assumptions related to a priori estimates for the individual solutions of typical approximating problems. The first

Md Hafizur Rahman, Md Mashfiq Rizvee, Sumaiya Shomaji, Prabuddha Chakraborty

Artificial intelligence (AI) is widely used in various fields including healthcare, autonomous vehicles, robotics, traffic monitoring, and agriculture. Many modern AI applications in these fields are multi-tasking in nature (i.e. perform multiple analysis on same data) and are deployed on resource-constrained edge devices requiring the AI models to be effici

A. Bogomyagkov, V. Druzhinin, E. Levichev, A. Milstein

The paper presents solution of quantum problem of neutron propagation in the magnetic field with multipole field expansion. Rigorous solution of the Pauli equation for neutron reveals existence of two solutions, finite and infinite, for any multipole configuration. As an example, we present detailed study of neutron motion in quadrupole and sextupole magnets

Harold Haodong Chen, Harry Yang, Ser-Nam Lim

Recent advances in video generation have outpaced progress in video editing, which remains constrained by several limiting factors, namely: (a) the task's dependency on supervision severely limits generality, (b) an unnecessary artificial separation between the generation and editing task, and (c) the high computational costs of training a video model. In th

Li Haochen, Yuanwei Liu, Xidong Mu, Yue Chen

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the

Doohee You, Dan Chon

In recent years, Large Language Models (LLMs) have garnered considerable attention for their remarkable abilities in natural language processing tasks. However, their widespread adoption has raised concerns pertaining to trust and safety. This systematic review investigates the current research landscape on trust and safety in LLMs, with a particular focus o

Richard Cloete, Peter Vereš, Abraham Loeb

The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of ma

Chenke Luo, Jiang Ming, Mengfei Xie, Guojun Peng

In this paper, we present PXoM, a practical technique to seamlessly retrofit XoM into stripped binaries on the x86-64 platform. As handling the mixture of code and data is a well-known challenge for XoM, most existing methods require the strict separation of code and data areas via either compile-time transformation or binary patching, so that the unreadable

Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh

The success of self-supervised learning (SSL) has been the focus of multiple recent theoretical and empirical studies, including the role of data augmentation (in feature decoupling) as well as complete and dimensional representation collapse. While complete collapse is well-studied and addressed, dimensional collapse has only gain attention and addressed in

Valdemar Švábenský, Conrad Borchers, Elizabeth B. Cloude, Atsushi Shimada

In supervised machine learning (SML) research, large training datasets are essential for valid results. However, obtaining primary data in learning analytics (LA) is challenging. Data augmentation can address this by expanding and diversifying data, though its use in LA remains underexplored. This paper systematically compares data augmentation techniques an

Mako Bates, Shun Kashiwa, Syed Jafri, Gan Shen

Choreographic programming (CP) is a paradigm for implementing distributed systems that uses a single global program to define the actions and interactions of all participants. Library-level CP implementations, like HasChor, integrate well with mainstream programming languages but have several limitations: Their conditionals require extra communication; they

Mikolaj K. Schmidt, Alexander A. High, Michael J. Steel

A typical surface-enhanced Raman scattering (SERS) system relies on deeply subwavelength field localization in nanoscale plasmonic cavities to enhance both the excitation and emission of Raman-active molecules. Here, we demonstrate that a germanium-vacancy (GeV) defect in diamond can efficiently mediate the excitation process, by acting as a bright atomic an

Yunkai Dang, Kaichen Huang, Jiahao Huo, Yibo Yan

The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing, respectively. The convergence of these technologies has catalyzed the rise of multimodal AI, enabling richer, cross-modal und

Shuang Ji, Jing Lu, Fanfei Meng

We study the dynamics of the focusing nonlinear Hartree equation with a Kato potential $$ i\partial_t u +\Delta u - Vu = -(|\cdot|^{-\gamma} \ast |u|^2)u, \quad x \in \mathbb{R}^d $$ under some assumptions on the potential $V$. We prove the blow up versus global existence dichotomy for solutions beyond the threshold, based on the method from Duyckaerts-Roude

Ka Wai Ho, Alex Lazarian

Super-Alfvenic turbulence is important for many astrophysical objects, particularly galaxy clusters. In this paper, we explore the accuracy of Synchrotron Intensity Gradients (SIGs) and X-ray intensity gradients to map magnetic fields in super-Alfvenic turbulence for a set of astrophysically relevant parameters of turbulent driving. Analyzing our synthetic o

Sachin Vaidya, Martin Kruczenski

In a previous article, we studied stationary solutions to the dynamics of a Bose-Einstein condensate (BEC) corresponding to acoustic (or Unruh) black/white holes, namely configurations where the flow becomes supersonic creating a horizon for phonons. In this paper, we consider again the Gross-Pitaevskii Equation (GPE) but looking for stationary numerical sol

Zhi Qu, Yiran Wang, Chenchen Ding, Hideki Tanaka

Existing multilingual neural machine translation (MNMT) approaches mainly focus on improving models with the encoder-decoder architecture to translate multiple languages. However, decoder-only architecture has been explored less in MNMT due to its underperformance when trained on parallel data solely. In this work, we attribute the issue of the decoder-only

Hongming Liang, Shobhit Jain, Mingwu Li

A 1:2 internally resonant mechanical system can undergo secondary Hopf (Neimark-Sacker) bifurcations, resulting in a quasi-periodic response when the system is subject to harmonic excitation. While these quasi-periodic orbits have been observed in practice, their bifurcations are not well studied, especially in high-dimensional mechanical systems. This is ma

Sandra Johnson, David Hyland-Wood

This paper provides a primer on Large Language Models (LLMs) and identifies their strengths, limitations, applications and research directions. It is intended to be useful to those in academia and industry who are interested in gaining an understanding of the key LLM concepts and technologies, and in utilising this knowledge in both day to day tasks and in m

Zhihang Lin, Mingbao Lin, Wengyi Zhan, Rongrong Ji

Diffusion models suffer severe object repetition and local distortion when the inference resolution differs from its pre-trained resolution. We propose AccDiffusion v2, an accurate method for patch-wise higher-resolution diffusion extrapolation without training. Our in-depth analysis in this paper shows that using an identical text prompt for different patch

Sarika Sasidharan Nair, Giedrius Žlabys, Wen-Bin He, Thomás Fogarty

We investigate the nonequilibrium dynamics of a groundstate fermionic many body gas subjected to a quench between parameter regimes of a topologically nontrivial Hamiltonian. By focusing on the role of the chiral edge states inherent to the system, we calculate the many body overlap and show that the characteristic monotonic decay of the orthogonality catast

Mingming Zhang, Jiahao Hu, Pengfei Shi, Ningtao Wang

Tabular data plays a critical role in real-world financial scenarios. Traditionally, tree models have dominated in handling tabular data. However, financial datasets in the industry often encounter some challenges, such as data heterogeneity, the predominance of numerical features and the large scale of the data, which can range from tens of millions to hund

Meng-Yuan Li, Ning Zheng, Yan-Wei Li

The migration of active particles in slowly moving, crowded, and heterogeneous media is fundamental to various biological processes and technological applications, such as cargo transport. In this study, we numerically investigate the dynamics of a single active particle in a medium composed of mixtures of rigid and flexible rings. We observe a non-monotonic

Zewen Jiang, Wei Wang, Guo Chen, Yaqing Shi

Atmospheric studies are essential for elucidating the formation history, evolutionary processes, and atmospheric dynamics of exoplanets. High-resolution transmission spectroscopy offers the advantage of detecting subtle variations in stellar spectral profiles, thereby enabling the identification of the sources of observed signals. In this study, we present t