March 2024 arXiv papers — page 166

Zeng Tao, Yan Wang, Junxiong Lin, Haoran Wang

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn't yield exceptional results as observed in other CLIP-based classification tasks. While CLIP's primary objective is to achieve alignment between images and text in the feature space, DFER poses challenges due to the abstract nature of text and the dynamic nature of video, maki

Pengzhou Cheng, Zongru Wu, Gongshen Liu

Control Area Network (CAN) is an essential communication protocol that interacts between Electronic Control Units (ECUs) in the vehicular network. However, CAN is facing stringent security challenges due to innate security risks. Intrusion detection systems (IDSs) are a crucial safety component in remediating Vehicular Electronics and Systems vulnerabilities

Knud Thomsen

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented in the form of a drawing for AI to understand. In a second vein, AI is challenged to produce something similar from verbal description. The aim of the paper is to highlight strengths and deficiencies of current Artificial Intelligenc

Fenghua Tong, Yongyong Cai

We propose and analyze a novel approach to construct structure preserving approximations for the Poisson-Nernst-Planck equations, focusing on the positivity preserving and mass conservation properties. The strategy consists of a standard time marching step with a projection (or correction) step to satisfy the desired physical constraints (positivity and mass

Chenlu Zhan, Yu Lin, Gaoang Wang, Hongwei Wang

Medical generative models, acknowledged for their high-quality sample generation ability, have accelerated the fast growth of medical applications. However, recent works concentrate on separate medical generation models for distinct medical tasks and are restricted to inadequate medical multi-modal knowledge, constraining medical comprehensive diagnosis. In

Jiuqiang Liu, Guihai Yu, Lihua Feng, Yongtao Li

In this paper, we derive a tight upper bound for the size of an intersecting $k$-Sperner family of subspaces of the $n$-dimensional vector space $\mathbb{F}_{q}^{n}$ over finite field $\mathbb{F}_{q}$ which gives a $q$-analogue of the Erd\H{o}s' $k$-Sperner Theorem, and we then establish a general relationship between upper bounds for the sizes of families o

Ondřej Finke, Jan Vábek, Matěj Dvořáček, Lucie Jurkovičová

High harmonic generation (HHG) has become a multipurpose source of coherent XUV radiation used in various applications. One of the notable aspects of HHG is its wide spectrum consisting of many harmonic orders. This might represent a bottleneck in HHG utility for applications requiring a single wavelength. We propose a method to generate radiation consisting

Leilei Ding, Dazhong Shen, Chao Wang, Tianfu Wang

Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node information and topology. However, these models often face the famous over-smoothing issue, leading to indistinct user and item embeddings and reduced personalization. Traditional desmoot

Naoya Enomoto, Takao Satoh

In this paper, we study the Johnson homomorphisms of basis-conjugating automorphism groups of free groups. We construct obstructions for the surjectivity of the Johnson homomorphisms. By using it, we determine its cokernels of degree up to four, and give further observations for degree greater than four. As applications, we give the affirmative answer for th

Huimin Sun, Shuhui Yang, Yan Lin

Via the new weight function $A_{\vec p}^{\theta }(\varphi )$, the authors introduce a new class of multilinear Littlewood--Paley $g_{\lambda}^{*}$ functions and establish the boundedness on weighted Lebesgue spaces. In addition, the authors obtain the boundedness of the multilinear commutator and multilinear iterated commutator generated by the multilinear L

Yiming Bian, Yang Li, Xuesong Xu, Tao Zhang

Quantum key distribution allows secret key generation with information theoretical security. It can be realized with photonic integrated circuits to benefit the tiny footprints and the large-scale manufacturing capacity. Continuous-variable quantum key distribution is suitable for chip-based integration due to its compatibility with mature optical communicat

Yu Zhu, Chuxiong Sun, Wenfei Yang, Wenqiang Wei

Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require a high computational cost, one main reason being that RLHF assigns both the generation and alignment tasks to the LLM simultaneously. In this paper, we introduce Proxy-RLHF, which

Weiwei Gu, Jinqiang Hou, Weiyi Gu

Complex networks are widely used to represent an abundance of real-world relations ranging from social networks to brain networks. Inferring missing links or predicting future ones based on the currently observed network is known as the link prediction task.Recent network embedding based link prediction algorithms have demonstrated ground-breaking performanc

Yasha Neiman, David O'Connell

In this paper we study topology-changing spacetimes occurring from pointlike sources. Following an old idea of Penrose, we will opt for a non-Hausdorff model of topology change in which an initial pointlike source is ``doubled" and allowed to propagate along null rays into an eventual cobordism. By appealing to recent developments in non-Hausdorff differenti

Qusai Abo Obaidah, Muhy Eddin Za'ter, Adnan Jaljuli, Ali Mahboub

This work is an attempt to introduce a comprehensive benchmark for Arabic speech recognition, specifically tailored to address the challenges of telephone conversations in Arabic language. Arabic, characterized by its rich dialectal diversity and phonetic complexity, presents a number of unique challenges for automatic speech recognition (ASR) systems. These

Pu Cao, Feng Zhou, Qing Song, Lu Yang

In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guided generative functions. However, relying solely on text for conditioning these models does not fully cater to the varied and complex requirements of different applications and sce

Chi Zhang, Qilong Han, Rui Chen, Xiangyu Zhao

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored mo

Tomasz Kołodziej, Mariusz Mrózek, Saravanan Sengottuvel, Maciej J. Głowacki

Cells and tissues are constantly exposed to various chemical and physical signals that intricately regulate various physiological and pathological processes. This study explores the integration of two biophysical methods, Traction Force Microscopy (TFM) and Optically-Detected Magnetic Resonance (ODMR), to concurrently assess cellular traction forces and loca

D. Chandra, P. Das, S. Das

We study selection principles related to bornological covers using the notion of ideals. We consider ideals $\mathcal I$ and $\mathcal J$ on $\omega$ and standard ideal orderings $KB, K$. Relations between cardinality of a base of a bornology with certain selection principles related to bornological covers are established using cardinal invariants such as mo

Chungang Shi, Mengmeng Wang, Yan Lv, Wei Wang

The small mass limit is derived for a McKean-Vlasov equation with state-dependent friction in $d$-dimensional space. By applying the averaging approach to a non-autonomous slow-fast system with the microscopic and macroscopic scales, the convergence in distribution is obtained.

Wenyu Jiao, Ke Wang, Fengwei Xu, Chao Wang

Context: Magnetic fields can play crucial roles in high-mass star formation. Nonetheless, the significance of magnetic fields at various scales and their relationship with gas structures is largely overlooked. Aims: Our goal is to examine the relationship between the magnetic field and molecular gas structures within the Orion A giant molecular cloud at diff

Xiang Qu, Hui Zhao, Wenjie Cai, Gongyi Wang

Mittag-Leffler correlated noise (M-L noise) plays a crucial role in the dynamics of complex systems, yet the scientific community has lacked tools for its direct generation. Addressing this gap, our work introduces GenML, a Python library specifically designed for generating M-L noise. We detail the architecture and functionalities of GenML and its underlyin

Shijie Ma, Fei Zhu, Zhun Zhong, Xu-Yao Zhang

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently fa

Nicola Nesa

We reformulate the ranks that appear in the dimension formula for the linear space of periods of a 1-motive established by Huber and W\"ustholz in a more conceptual and categorical way, as dimensions of Ext$^1$ vector spaces. This constitutes the first step towards rewriting the dimension formula purely in general categorical terms, rather than through defin

M. Iskin

We investigate the impacts of the quantum geometry of Bloch states, specifically through the band-resolved quantum-metric tensor, on Cooper pairing and flat-band superconductivity in a three-dimensional pyrochlore-Hubbard model. First we analyze the low-lying two-body spectrum exactly, and show that the pairing order parameter is uniform in this four-band la

Jun Tang, Cunhua Pan, Yang Zhang, Hong Ren

This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system's physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR)

Jialin Chen, Zhiqiang Cai, Ke Xu, Di Wu

Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates. As the classical neural architecture search (NAS), quantum architecture search methods (QAS) employ methods like reinforcement learning, evolutionary algorithms and supe

Huacheng Li, Zongyu Yue, Nan Zhang, Jinhai Zhang

Impact craters are the primary geomorphic features on the surfaces of celestial bodies such as the Moon, and their formation has significant implications for the evolutionary history of the celestial body. The study of the impact crater formation process relies mainly on numerical simulation methods, with two-dimensional simulations capable of reproducing ge

Barkha Baloda, Jitender Kumar

Let $R$ be a ring with unity. The upper ideal relation graph $\Gamma_U(R)$ of the ring $R$ is a simple undirected graph whose vertex set is the set of all non-unit elements of $R$ and two distinct vertices $x, y$ are adjacent if and only if there exists a non-unit element $z \in R$ such that the ideals $(x)$ and $(y)$ contained in the ideal $(z)$. In this ar

Susobhan Bandopadhyay, Aritra Banik, Sushmita Gupta, Pallavi Jain

In the standard model of fair allocation of resources to agents, every agent has some utility for every resource, and the goal is to assign resources to agents so that the agents' welfare is maximized. Motivated by job scheduling, interest in this problem dates back to the work of Deuermeyer et al. [SIAM J. on Algebraic Discrete Methods'82]. Recent works con

Hoang Giang Pham, Tien Thanh Dam, Ngan Ha Duong, Tien Mai

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection

Dhanyamol Antony, Yixin Cao, Sagartanu Pal, R. B. Sandeep

In a graph, the switching operation reverses adjacencies between a subset of vertices and the others. For a hereditary graph class $\mathcal{G}$, we are concerned with the maximum subclass and the minimum superclass of $\mathcal{G}$ that are closed under switching. We characterize the maximum subclass for many important classes $\mathcal{G}$, and prove that

Pham Duy Khanh, Boris S. Mordukhovich, Vo Thanh Phat

This paper proposes and develops new Newton-type methods to solve structured nonconvex and nonsmooth optimization problems with justifying their fast local and global convergence by means of advanced tools of variational analysis and generalized differentiation. The objective functions belong to a broad class of prox-regular functions with specification to c

Hui Zong, Rongrong Wu, Jiaxue Cha, Weizhe Feng

Objective: This study aims to review the recent advances in community challenges for biomedical text mining in China. Methods: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative

Yuling Wang, Changxin Tian, Binbin Hu, Yanhua Yu

Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations empowered by LLMs. Firstly, user behavior patterns are often complex, and rel

Ivan Lau, Shiqian Ma, César A. Uribe

This paper considers the decentralized (discrete) optimal transport (D-OT) problem. In this setting, a network of agents seeks to design a transportation plan jointly, where the cost function is the sum of privately held costs for each agent. We reformulate the D-OT problem as a constraint-coupled optimization problem and propose a single-loop decentralized

Weihuang Liu, Xi Shen, Haolun Li, Xiuli Bi

Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model on large-scale video datasets, which struggle to generalize to unseen videos. In this work, we introduce a test-time training (TTT) strategy to address the problem. Our key insigh

Ningfei Wang, Yupin Huang, Han Cheng, Jiri Gesi

Information retrieval (IR) is a pivotal component in various applications. Recent advances in machine learning (ML) have enabled the integration of ML algorithms into IR, particularly in ranking systems. While there is a plethora of research on the robustness of ML-based ranking systems, these studies largely neglect commercial e-commerce systems and fail to

Huimin Zeng, Zhenrui Yue, Qian Jiang, Dong Wang

Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations. However, traditional FR systems usually represent users/items with discrete identities (IDs), suffering from performance degradation due to the data sparsity and heterogeneity in FR. On the other hand, Large Language Models (LLMs) as recommenders have p

K. Uzawa, K. Hagino

We present a microscopic modeling for a decay of a heavy compound nucleus, starting from a nucleonic degree of freedom. To this end, we develop an approach based on a non-equilibrium Green's function, which is combined with a configuration interaction (CI) approach based on a constrained density-functional theory (DFT). We apply this approach to a barrier-to

Steffen Lempp, Yiqun Liu, Yong Liu, Keng Meng Ng

We prove that every finite distributive lattice is isomorphic to a final segment of the d.c.e. Turing degrees (i.e., the degrees of differences of computably enumerable sets). As a corollary, we are able to infer the undecidability of the EAE-theory of the d.c.e. degrees in the language of partial ordering.

Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar

Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solve tasks involving extended time gaps between actions and outcomes, or tasks demanding the recalling of distant observations to inform current actions. To improve temporal coherence, we integrate a new family of state spa

I. B. Abdurakhmanov, N. W. Antonio, M. Cytowski, A. S. Kadyrov

We present our experience of porting the code used in the wave-packet convergent-close-coupling (WP-CCC) approach to run on NVIDIA V100 and AMD MI250X GPUs. The WP-CCC approach is a method used in the field of ion-atom collision physics to describe various processes such as elastic scattering, target excitation and electron-capture by the projectile. It has

Dong-Meng Zhang, Xiao-Yuan Hu, Lin-Jing Qi, Hong-Ming Liu

In the present study, proton emission half-lives have been investigated for the deformed proton emitters with $53\leq Z \leq 83$ in the deformed Gamow-like model, where the deformation effect has been included in the Coulomb potential. The experimental half-lives of proton emitters can be reproduced within a factor of 3.45. For comparison, other results from

Zaid Bin Tariq, Teviet Creighton, Louis P. Dartez, Naofal Al-Dhahir

With the ongoing growth in radio communications, there is an increased contamination of radio astronomical source data, which hinders the study of celestial radio sources. In many cases, fast mitigation of strong radio frequency interference (RFI) is valuable for studying short lived radio transients so that the astronomers can perform detailed observations

Dong-Meng Zhang, Lin-Jing Qi, Hai-Feng Gui, Song Luo

In the present work, we systematically study the spectroscopic factor of proton radioactivity ($S_p$) with $A>100$ using the deformed two-potential approach (D-TPA). It is found that there is a link between the quadrupole deformation parameter of proton emitter and $S_p$. Based on this result, we propose a simple analytic formula for estimating the spectrosc

Rui Tuo, Lu Zou

An asymptotic theory is established for linear functionals of the predictive function given by kernel ridge regression, when the reproducing kernel Hilbert space is equivalent to a Sobolev space. The theory covers a wide variety of linear functionals, including point evaluations, evaluation of derivatives, $L_2$ inner products, etc. We establish the upper an

Yangning Li, Qingsong Lv, Tianyu Yu, Yinghui Li

Entity Set Expansion (ESE) aims to identify new entities belonging to the same semantic class as the given set of seed entities. Traditional methods solely relied on positive seed entities to represent the target fine-grained semantic class, rendering them tough to represent ultra-fine-grained semantic classes. Specifically, merely relying on positive seed e

Shuaiyu Li, Yang Ruan, Changzhou Long, Yuzhong Cheng

This study addresses the challenges in parameter estimation of stochastic differential equations driven by non-Gaussian noises, which are critical in understanding dynamic phenomena such as price fluctuations and the spread of infectious diseases. Previous research highlighted the potential of LSTM networks in estimating parameters of alpha stable Levy drive

Yusheng Dai, Hang Chen, Jun Du, Ruoyu Wang

Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames, performing even worse than single-modality models. While applying the dropout technique to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. In this

Na Su, Shuang Liu, Yingjie He, Yan Liu

We report the observation of both resonant quantum tunneling of magnetization (RQTM) and resonant quantum magnetodielectric (RQMD) effect in the perovskite multiferroic metal-organic framework [CH3NH3]Co(HCOO)3. An intrinsic magnetic phase separation emerges at low temperatures due to hydrogen-bond-modified long range super-exchange interaction, leading to t

Tarun Pati, Seunghoon Hwang, Sze Zheng Yong

Cyber-physical and autonomous systems are often equipped with mechanisms that provide predictions/projections of future disturbances, e.g., road curvatures, commonly referred to as preview or lookahead, but this preview information is typically not leveraged in the context of deriving control barrier functions (CBFs) for safety. This paper proposes a novel l

Oleg Evnin, Weerawit Horinouchi

In a recent article J. Phys. Compl. 4 (2023) 035005, Kawamoto evoked statistical physics methods for the problem of counting graphs with a prescribed degree sequence. This treatment involved truncating a particular Taylor expansion at the first two terms, which resulted in the Bender-Canfield estimate for the graph counts. This is surprisingly successful sin

Na Su, Yinina Ma, Shuang Liu, Wei Wu

The influence of pressure on the hydrogen bond ordering in the perovskite metal-organic framework [(CH3)2NH2]Mn(HCOO)3 has been investigated by dielectric, pyroelectric adn magnetic measurements in a piston-cylinder cell. Under ambient pressure the ordering of hydrogen bonds takes place at TC = 188 K and induces a first-order ferroelectric phase transition.

Pengcheng Zheng, Songqian Zhang, Zhu Ma, Haipo Niu

The noise in absorption imaging of cold atoms significantly impacts measurement accuracy across a range of applications with ultracold atoms. It is crucial to adopt an approach that offers effective denoising capabilities without compromising the unique structure of the atoms. Here we introduce a novel image enhancement algorithm for cold atomic absorption i

Yunyang Luo, Zihao Bo, Shibo Zhang, Abdusalam Abdukerim

PandaX-4T experiment is a deep-underground dark matter direct search experiment that employs a dual-phase time projection chamber with a sensitive volume containing 3.7 tonne of liquid xenon. The detector of PandaX-4T is capable of simultaneously collecting the primary scintillation and ionization signals, utilizing their ratio to discriminate dark matter si

Ryan Hogan, Giulia Marcucci, Akbar Safari, A. Nicholas Black

Fully describing light propagation in a rotating, anisotropic medium with thermal nonlinearity requires modeling the interplay between nonlinear refraction, birefringence, and the nonlinear group index. Incorporating these factors into a generalized nonlinear Schr\"odinger equation and fitting them to recent experimental results reveals two key relationships

Chungang Shi, Wei Wang

An approximation is derived for a Langevin equation with distribution-dependent potential and state-dependent, randomly fast oscillation. By some estimates and a diffusion approximation the limiting equation is shown to be distribution-dependent stochastic differential equation (SDEs) driven by white noise.

Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang

In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. While recent advancements in machine learning have introduced flexible methods for IV estimation, they often encounter one or more of the following limitations: (1) restricting the IV regression to be uniquely identified; (2) requiring minimax computation oracle, whic

Ling Wang, Bin Zhou

In this paper, we establish the interior $C^{1,\alpha}$ regularity of minimizers of a class of functionals with a convexity constraint, which includes the principal-agent problems studied by Figalli-Kim-McCann (\textit{J. Econom. Theory} \textbf{146} (2011), no. 2, 454-478). The $C^{1,1}$ regularity was previously proved by Caffarelli-Lions in an unpublished

Pierre Mergny, Justin Ko, Florent Krzakala, Lenka Zdeborová

We consider the task of estimating a low-rank matrix from non-linear and noisy observations. We prove a strong universality result showing that Bayes-optimal performances are characterized by an equivalent Gaussian model with an effective prior, whose parameters are entirely determined by an expansion of the non-linear function. In particular, we show that t

Xingwei Qu, Yiming Liang, Yucheng Wang, Tianyu Zheng

Large Language Models (LLMs) exhibit the ability to perform in-context learning (ICL), where they acquire new tasks directly from examples provided in demonstrations. This process is thought to operate through an implicit task selection mechanism that involves extracting and processing task definitions from these demonstrations. However, critical questions r

Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid

Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends with a multitude of diverse traffic scenarios that are challenging for these planning algorithms. Model-free Deep Reinforcement Learning (DRL) presents a promising avenue in this

Shan Shan

Global concern over food prices and security has been exacerbated by the impacts of armed conflicts such as the Russia Ukraine War, pandemic diseases, and climate change. Traditionally, analyzing global food prices and their associations with socioeconomic factors has relied on static linear regression models. However, the complexity of socioeconomic factors

Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel

Equivalence testing, a fundamental problem in the field of distribution testing, seeks to infer if two unknown distributions on $[n]$ are the same or far apart in the total variation distance. Conditional sampling has emerged as a powerful query model and has been investigated by theoreticians and practitioners alike, leading to the design of optimal algorit

Benjamin R. Ecclestone, James A. Tummon Simmons, James E. D. Tweel, Channprit Kaur

Label-free optical absorption microscopy techniques have evolved as effective tools for non-invasive chemical specific structural, and functional imaging. Yet most modern label-free microscopy modalities target only a fraction of the contrast afforded by an optical absorption interaction. We introduce a comprehensive optical absorption microscopy technique,

Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung

We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task. This benchmark focuses on syntax-aware completions of program structures such as code blocks and conditional expressions, and includes 17,720 examples from multiple programming languages, sourced from re

Huafeng Li, Zhenmei Yang, Yafei Zhang, Dapeng Tao

The reconstruction of high dynamic range (HDR) images from multi-exposure low dynamic range (LDR) images in dynamic scenes presents significant challenges, especially in preserving and restoring information in oversaturated regions and avoiding ghosting artifacts. While current methods often struggle to address these challenges, our work aims to bridge this

Brandon Curd, Richard Anantua, Nathaniel Lujan, T. Kenneth Fowler

We show scenarios in which primordial black hole accretion under the magnetorotational instability (MRI) uniquely relates the density of the early Universe to the abundance of present day dark matter. We demonstrate via long duration general relativistic magnetohydrodynamic (GRMHD) simulations that MRI-dominated accretion at least hundreds of gravitational r

Sina Fazelpour

This paper examines two prominent formal trade-offs in artificial intelligence (AI) -- between predictive accuracy and fairness, and between predictive accuracy and interpretability. These trade-offs have become a central focus in normative and regulatory discussions as policymakers seek to understand the value tensions that can arise in the social adoption

Dharma KC, Clayton T. Morrison

Learning to generate textures for a novel 3D mesh given a collection of 3D meshes and real-world 2D images is an important problem with applications in various domains such as 3D simulation, augmented and virtual reality, gaming, architecture, and design. Existing solutions either do not produce high-quality textures or deform the original high-resolution in

Ali Abu-Nada, Subhashish Banerjee, Vivek Balasaheb Sabale

The non-Markovian depolarizing channel is explored from the perspective of understanding its non-Markovian behavior as well as the occurrence of singularities. The study brings together the various ways to identify and quantify non-Markovianity. This includes dynamical techniques such as quantum information backflow witness, Breuer-Laine-Piilo, Rivas-Huelga-

Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens

Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. Alignment is challenging, costly, and needs to be repeated for every LLM and alignment criterion. We propose to decouple LLMs and alignment by training aligner models that can be used to align any LLM for a given criteria on an as-

Oscar Perdomo

Let $M\subset S^{n+1}$ be the hypersurface generated by rotating a hypersurface $M_0$ contained in the interior of the unit ball of $\mathbb{R}^{n-k+1}$. More precisely, $M=\{(\sqrt{1-|m|^2}\, y, m):y\in S^k, m\in M_0\}$. We derive the equation for the mean curvature of $M$ in terms of the principal curvatures of $M_0$. For the particular case when $M_0$ is

Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang

The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect, model-aware glass-box features, is overlooked. In this study, we explore the utility of glass-box features under the

Oliver Schulte, Pascal Poupart

Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while the goal of reinforcement learning is to select interventions that maximize the rewards the agent receives from the environment. Reinforcement learning includes the two most powerfu

Weiqing Cao, Jiaqun Wei, Kaili Wu

In the present paper, we study the relationships of $n$-cotorsion pairs among three abelian categories in a recollement. Under certain conditions, we present an explicit construction of gluing of $n$-cotorsion pairs in an abelian category $\mathcal{D}$ with respect to $n$-cotorsion pairs in abelian categories $\mathcal{D}^{'}$, $\mathcal{D}^{''}$ respectivel

Xiaoyutao Luo

We consider the patch problem of the $\alpha$-SQG equation with $\alpha=0$ being the 2D Euler and $\alpha= \frac{1}{2}$ the SQG equations respectively. In the Eulerian setting, we prove the uniqueness of patch solutions of regularity $W^{2, \frac{1}{1-2\alpha} +} $ when $0<\alpha< \frac{1}{2}$ and $C^{1, 4\alpha+ }$ when $0<\alpha< \frac{1}{4} $. The proof i

Bing Wu, Xiang-Kun Dong, Feng-Kun Guo, Bing-Song Zou

We investigate the possibility of deuteron-like $\Sigma_c^*\bar{\Sigma}$ bound states within the one-boson-exchange model and systematically analyze the effects of the contact-range $\delta^{3}(\vec{r}\,)$ potential, the tensor term from the vector-meson exchange, and nonlocal potentials due to the dependence on the sum of the initial and final state center-

Bruno Focassio, Luis Paulo Mezzina Freitas, Gabriel R. Schleder

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and co

Zhaoang Deng, Zhenhua Li, Jie Liu, Chuyao Bian

The advancement of artificial intelligence demands flexible multimodal data processing with high throughput and energy efficiency. Photonic integrated circuits (PIC) has demonstrated promising potentials in terms of low latency and low power consumption per operation for linear operations such as matrix-vector multiplication. However, the existing schemes fa

Xi Yan, Ru-Sen Lu, Wu Jiang, Thomas P. Krichbaum

We report multi-frequency and multi-epoch VLBI studies of the sub-parsec jet in Sombrero galaxy (M 104, NGC 4594). Using Very Long Baseline Array data at 12, 22, 44, and 88 GHz, we study the kinematics of the jet and the properties of the compact core. The sub-parsec jet is clearly detected at 12 and 22 GHz, and the inner jet base is resolved down to $\sim70

Kazuyuki Wada

Under an abstract setting, we show that eigenvectors belong to discrete spectra of unitary operators have exponential decay properties. We apply the main theorem to multi-dimensional quantum walks and show that eigenfunctions belong to a discrete spectrum decay exponentially at infinity.

Chunguang Xia, Tianyu Ma, Xiao Dong, Mingjing Zhang

In this paper, we study representations of non-finitely graded Lie algebras $\mathcal{W}(\epsilon)$ related to Virasoro algebra, where $\epsilon = \pm 1$. Precisely speaking, we completely classify the free $\mathcal{U}(\mathfrak h)$-modules of rank one over $\mathcal{W}(\epsilon)$,and find that these module structures are rather different from those of othe

Jian Chen, Petra Isenberg, Robert S. Laramee, Tobias Isenberg

We present and discuss the results of a qualitative analysis of visualization images to derive an image-based typology of visualizations. For each image, we seek to identify its main focus or the essential stimuli. As a result, we derived 10 image-based visualization types. We describe coding decisions we made in the derivation process. The resulting image t

Seunghee Han, Se Jin Park, Chae Won Kim, Yong Man Ro

Providing emotional support through dialogue systems is becoming increasingly important in today's world, as it can support both mental health and social interactions in many conversation scenarios. Previous works have shown that using persona is effective for generating empathetic and supportive responses. They have often relied on pre-provided persona rath

Megha Sharma, Daniel J. Price, Alexander Heger

In our Galactic Center, about 10,000 to 100,000 stars are estimated to have survived tidal disruption events, resulting in partially disrupted remnants. These events occur when a supermassive black hole (SMBH) tidally interacts with a star, but not enough to completely disrupt the star. We use the 1D stellar evolution code Kepler and the 3D smoothed particle

Ohad Lib, Kfir Sulimany, Mateus Araújo, Michael Ben-Or

High-dimensional quantum key distribution (QKD) offers higher information capacity and stronger resilience to noise compared to its binary counterpart. However, these advantages are often hindered by the difficulty of realizing the required high-dimensional measurements and transformations. Here, we implement a large-scale multi-plane light converter (MPLC)

Jemin Park, HaRu K. Park, SungBin Lee

Antiferromagnetic(AFM) skyrmions have been in the spotlight as ideal topological magnetic bits. Although they are topologically protected, they do not exhibit the skyrmion Hall effect unlike the ferromagnetic ones. Thus, AFM skyrmions are considered to provide a better control of the skyrmion's motion due to the absence of the skyrmion Magnus effect. In this

Gyudong Kim, Mehdi Ghasemi, Soroush Heidari, Seungryong Kim

Federated Learning (FL) is a practical approach to train deep learning models collaboratively across user-end devices, protecting user privacy by retaining raw data on-device. In FL, participating user-end devices are highly fragmented in terms of hardware and software configurations. Such fragmentation introduces a new type of data heterogeneity in FL, name

Tolga Dimlioglu, Anna Choromanska

We study distributed training of deep learning models in time-constrained environments. We propose a new algorithm that periodically pulls workers towards the center variable computed as a weighted average of workers, where the weights are inversely proportional to the gradient norms of the workers such that recovering the flat regions in the optimization la

Lokesh Krishna, Nikhil Sobanbabu, Quan Nguyen

The efficacy of reinforcement learning for robot control relies on the tailored integration of task-specific priors and heuristics for effective exploration, which challenges their straightforward application to complex tasks and necessitates a unified approach. In this work, we define a general class for priors called oracles that generate state references

Xinpeng Wang, Shitong Duan, Xiaoyuan Yi, Jing Yao

Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns. Addressing such concerns, alignment technologies were introduced to make these models conform to human preferences and values. Despite considerable advancements in the past year, various challenges lie in establishing the optimal alignment st

Jake P. Vu, Ming Chen

Density Functional Theory (DFT) has become a cornerstone in the modeling of metals. However, accurately simulating metals, particularly under extreme conditions, presents two significant challenges. First, simulating complex metallic systems at low electron temperatures is difficult due to their highly delocalized density matrix. Second, modeling metallic wa

Elizaveta Tennant, Stephen Hailes, Mirco Musolesi

Growing concerns about safety and alignment of AI systems highlight the importance of embedding moral capabilities in artificial agents: a promising solution is the use of learning from experience, i.e., Reinforcement Learning. In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning ag

Vijaya Yajnanarayana, Philipp Geuer

The sixth generation (6G) systems will likely employ orthogonal frequency division multiplexing (OFDM) waveform for performing the joint task of sensing and communication. In this paper, we design an OFDM system for integrated sensing and communication (ISAC) and propose a novel approach for passive target detection in an indoor deployment using a data drive

Nabil Ibtehaz, Ning Yan, Masood Mortazavi, Daisuke Kihara

Transformers have elevated to the state-of-the-art vision architectures through innovations in attention mechanism inspired from visual perception. At present two classes of attentions prevail in vision transformers, regional and sparse attention. The former bounds the pixel interactions within a region; the latter spreads them across sparse grids. The oppos

Aina Mayumi, Gen Kimura, Hiromichi Ohno, Dariusz Chruściński

By employing a weighted Frobenius norm with a positive matrix $\omega$, we introduce natural generalizations of the famous B\"ottcher-Wenzel (BW) inequality. Based on the combination of the weighted Frobenius norm $\|A\|_\omega := \sqrt{{\rm tr}(A^\ast A \omega)}$ and the standard Frobenius norm $\|A\| := \sqrt{{\rm tr}(A^\ast A)}$, there are exactly five po

Guanlin Shen, Jingwei Huang, Zhihua Hu, Bin Wang

This paper introduces CN-RMA, a novel approach for 3D indoor object detection from multi-view images. We observe the key challenge as the ambiguity of image and 3D correspondence without explicit geometry to provide occlusion information. To address this issue, CN-RMA leverages the synergy of 3D reconstruction networks and 3D object detection networks, where

Tixuan Tan, Trithep Devakul

We study a model of electrons moving in a parent band of uniform Berry curvature. At sufficiently high parent Berry curvature, we show that strong repulsive interactions generically lead to the formation of an anomalous Hall crystal: a topological state with spontaneously broken continuous translation symmetry. Our results are established via a mapping to a