February 2024 arXiv papers — page 71

Guoqi Yu, Jing Zou, Xiaowei Hu, Angelica I. Aviles-Rivero

Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pose challenges, and existing methods, relying on basic moving average kernels, may struggle with the non-linear structure and complex trends i

Joseph Poata, Fabio Taddei, Michele Governale

We consider multi-terminal transport through a flake of rectangular shape of a two-dimensional topological insulator in the presence of an in-plane magnetic field. This system has been shown to be a second-order topological insulator, thus exhibiting corner states at its boundaries. The position of the corner states and their decay length can be controlled b

Xiao Li, Bolin Zhu, Kaiwen Shi, Sichen Liu

The application of physics formulas is a fundamental human capability in numerical reasoning. While existing datasets often rely on implicit mathematical knowledge, they rarely explicitate the underlying formulas. To address this, we introduce FormulaReasoning, a new benchmark for formula-based numerical reasoning comprising 5,324 questions requiring calcula

Ryo Yoshida, Taiga Someya, Yohei Oseki

Syntactic Language Models (SLMs) can be trained efficiently to reach relatively high performance; however, they have trouble with inference efficiency due to the explicit generation of syntactic structures. In this paper, we propose a new method dubbed tree-planting: instead of explicitly generating syntactic structures, we "plant" trees into attention weigh

Zheng Wei Lim, Ekaterina Vylomova, Trevor Cohn, Charles Kemp

A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these objectives. On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be ac

Meng Kang, Meng Xiao, C. T. Chan

We propose a novel topological defect called Janus bound states in the continuum (BICs), featuring asymmetric topological charges in upward and downward radiation channels. Our approach involves a photonic crystal slab (PCS) that initially exhibits both out-of-plane and in-plane mirror symmetry, and this PCS possesses one BIC at the \Gamma point and two BICs

Runyi Hu, Jie Zhang, Ting Xu, Jiwei Li

Instruction-driven image editing allows users to quickly edit an image according to text instructions in a forward pass. Nevertheless, malicious users can easily exploit this technique to create fake images, which could cause a crisis of trust and harm the rights of the original image owners. Watermarking is a common solution to trace such malicious behavior

H. N. Mhaskar, Ryan O'Dowd

Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampled from an unknown submanifold of a high dimensional Euclidean space. A great deal of research deals with obtaining information about this manifold, such as the eigendecomposition

Negin Maddah, Babak Heydari

Within an increasingly digitalized organizational landscape, this research delves into the dynamics of decentralized collaboration, contrasting it with traditional collaboration models. An effective capturing of high-level collaborations (beyond direct massages) is introduced as the network construction methodology including both temporal and content dimensi

Yu Xiong, Zhipeng Hu, Ye Huang, Runze Wu

Reinforcement Learning (RL) has demonstrated substantial potential across diverse fields, yet understanding its decision-making process, especially in real-world scenarios where rationality and safety are paramount, is an ongoing challenge. This paper delves in to Explainable RL (XRL), a subfield of Explainable AI (XAI) aimed at unravelling the complexities

John Spitzer

Using a lemma of Davis on Gram matrices applied to the classical Orthogonal Polynomials to generate reproducing kernel interpolation over the classical domains for polynomials. These kernels have terms which are exact over the rational ring. The Condition Numbers are readily shown to get very large with the size of the Gram matrices as expected. The calculat

Jianguo Huang, Jianqing Song, Xuanning Zhou, Bingyi Jing

Conformal prediction (CP) is a powerful statistical framework that generates prediction intervals or sets with guaranteed coverage probability. While CP algorithms have evolved beyond traditional classifiers and regressors to sophisticated deep learning models like deep neural networks (DNNs), graph neural networks (GNNs), and large language models (LLMs), e

Hikari Otsuka, Daiki Chijiwa, Ángel López García-Arias, Yasuyuki Okoshi

Randomly initialized dense networks contain subnetworks that achieve high accuracy without weight learning--strong lottery tickets (SLTs). Recently, Gadhikar et al. (2023) demonstrated that SLTs could also be found within a randomly pruned source network. This phenomenon can be exploited to further compress the small memory size required by SLTs. However, th

Kui Wang, Zongdian Li, Kazuma Nonomura, Tao Yu

Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) p

Magd E. Kahil, Samah A. Ammar

A Clifford Space is counted to be a tempting approach to unify both micro-physics and macro-physics simultaneously. Such a tendency may be found in the realm of replacing vectors with poly-vectors. Accordingly, the problem of motion becomes essential to express the motion of extended particles rather than test particles. These equations are performed by usin

S. Jin, I. Y. Dodin

This paper explores structure formation in two-dimensional magnetohydrodynamic (MHD) turbulence as a modulational instability (MI) of turbulent fluctuations. We focus on the early stages of structure formation and consider simple backgrounds that allow for a tractable model of the MI while retaining the full chain of modulational harmonics. This approach all

Xiaojun Chen, Carl Kelley

This paper considers robust solutions to a class of nonlinear least squares problems using min-max optimization approach. We give an explicit formula for the value function of the inner maximization problem and show the existence of global minimax points. We establish error bounds from any solution of the nonlinear least squares problem to the solution set o

Junyi Xie

We get three basic results in algebraic dynamics: (1). We give the first algorithm to compute the dynamical degrees to arbitrary precision. (2). We prove that for a family of dominant rational self-maps, the dynamical degrees are lower semi-continuous with respect to the Zariski topology. This implies a conjecture of Call and Silverman. (3). We prove that th

Wenxiao Cai, Wankou Yang

The topic of stitching images with globally natural structures holds paramount significance, with two main goals: pixel-level alignment and distortion prevention. The existing approaches exhibit the ability to align well, yet fall short in maintaining object structures. In this paper, we endeavour to safeguard the overall OBJect-level structures within image

Nikolaos Smyrnakis, Tasos Karakostas, R. James Cotton

Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments. However, existing approaches for estimating gait parameters from videos can produce physically implausible results. To overcome this, we train a policy using reinforcement learning to control a physics simulation of h

Yupeng Zhang, Kaushik Bhattacharya

Neural network based models have emerged as a powerful tool in multiscale modeling of materials. One promising approach is to use a neural network based model, trained using data generated from repeated solution of an expensive small scale model, as a surrogate for the small scale model in application scale simulations. Such approaches have been shown to hav

Xiangyu Liu, Chenghao Deng, Yanchao Sun, Yongyuan Liang

In light of the burgeoning success of reinforcement learning (RL) in diverse real-world applications, considerable focus has been directed towards ensuring RL policies are robust to adversarial attacks during test time. Current approaches largely revolve around solving a minimax problem to prepare for potential worst-case scenarios. While effective against s

Vincent Vennin, David Wands

Quantum diffusion describes the inflow of vacuum quantum fluctuations as they get amplified by gravitational instability, and stretched to large distances during inflation. In this picture, the dynamics of the universe's expansion becomes stochastic, and the statistics of the curvature perturbation is encoded in the distribution of the duration of inflation.

Runxin Ouyang, Yu Yang, Chaohong Guan, Hong Zhu

Li ion diffusion is fundamentally a thermally activated ion hopping process. Recently, soft lattice, anharmonic phonon and paddlewheel mechanism have been proposed to potentially benefit the ion transport, while the understanding of vibrational couplings of mobile ion and anions is still limited but essential. Herein, we access the ionic conductivity, the st

Tanmay Vilas Samak, Chinmay Vilas Samak, Venkat Narayan Krovi

Autonomous vehicle platforms of varying spatial scales are employed within the research and development spectrum based on space, safety and monetary constraints. However, deploying and validating autonomy algorithms across varying operational scales presents challenges due to scale-specific dynamics, sensor integration complexities, computational constraints

Arpit Babbar, Praveen Chandrashekar

This work introduces an extension of the high order, single stage Lax-Wendroff Flux Reconstruction (LWFR) of Babbar et al., JCP (2022) to solve second order time-dependent partial differential equations in conservative form on curvilinear meshes. The method uses BR1 scheme to reduce the system to first order so that the earlier LWFR scheme can be applied. Th

Brian Liu, Rahul Mazumder

We study the often overlooked phenomenon, first noted in \cite{breiman2001random}, that random forests appear to reduce bias compared to bagging. Motivated by an interesting paper by \cite{mentch2020randomization}, where the authors explain the success of random forests in low signal-to-noise ratio (SNR) settings through regularization, we explore how random

Derek Jacoby, Saiph Savage, Yvonne Coady

Increasing diversity in educational settings is challenging in part due to the lack of access to resources for non-traditional learners in remote communities. Post-pandemic platforms designed specifically for remote and hybrid learning -- supporting team-based collaboration online -- are positioned to bridge this gap. Our work combines the use of these new p

Dong Hu, Chao Huang, Jingda Wu, Hongbo Gao

Autonomous driving (AD) technology, leveraging artificial intelligence, strives for vehicle automation. End-toend strategies, emerging to simplify traditional driving systems by integrating perception, decision-making, and control, offer new avenues for advanced driving functionalities. Despite their potential, current challenges include data efficiency, tra

Linghang Sun, Michail A. Makridis, Alexander Genser, Cristian Axenie

The optimal operation of transportation systems is often susceptible to unexpected disruptions. Many established control strategies reliant on mathematical models can struggle with real-world disruptions, leading to significant divergence from their anticipated efficiency. This study integrates the cutting-edge concept of antifragility with learning-based tr

Jiaxin Zhang, Kamalika Das, Sricharan Kumar

Uncertainty estimation is a crucial aspect of deploying dependable deep learning models in safety-critical systems. In this study, we introduce a novel and efficient method for deterministic uncertainty estimation called Discriminant Distance-Awareness Representation (DDAR). Our approach involves constructing a DNN model that incorporates a set of prototypes

Varad Pimpalkhute, John Heyer, Xusen Yin, Sameer Gupta

We investigate the integration of Large Language Models (LLMs) into query encoders to improve dense retrieval without increasing latency and cost, by circumventing the dependency on LLMs at inference time. SoftQE incorporates knowledge from LLMs by mapping embeddings of input queries to those of the LLM-expanded queries. While improvements over various stron

Haifeng Yang, Manuel Fernández-López, Zhi-Yun Li, Ian W. Stephens

We investigate the crescent-shaped dust trap in the transition disk, Oph IRS 48, using well-resolved (sub)millimeter polarimetric observations at ALMA Band 7 (870 $\mu$m). The dust polarization map reveals patterns consistent with dust scattering-induced polarization. There is a relative displacement between the polarized flux and the total flux, which holds

Miguel Mercado, Kyle Chen, Parth Darekar, Aiichiro Nakano

Control of topological edge modes is desirable for encoding quantum information resiliently against external noise. Their implementation on quantum hardware, however, remains a long-standing problem due to current limitations of circuit depth and noise, which grows with the number of time steps. By utilizing recently developed constant-depth quantum circuits

Liumeng Xue, Chaoren Wang, Mingxuan Wang, Xueyao Zhang

In this study, we present SingVisio, an interactive visual analysis system that aims to explain the diffusion model used in singing voice conversion. SingVisio provides a visual display of the generation process in diffusion models, showcasing the step-by-step denoising of the noisy spectrum and its transformation into a clean spectrum that captures the desi

Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks. In this paper, we introduce FinBen, the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 f

Yuan Xie, Jiawei Ren, Ji Xu

Recognizing underwater targets from acoustic signals is a challenging task owing to the intricate ocean environments and variable underwater channels. While deep learning-based systems have become the mainstream approach for underwater acoustic target recognition, they have faced criticism for their lack of interpretability and weak generalization performanc

Jingyi Liao, Kalle Ruttik, Riku Jantti, Phan-Huy Dinh-Thuy

The 3GPP has recently conducted a study on the Ambient Internet of Things (AIoT), with a particular emphasis on examining backscatter communications as one of the primary techniques under consideration. Previous investigations into Ambient Backscatter Communications (AmBC) within the long term evolution (LTE) downlink have shown that it is feasible to utiliz

Hao Zhao, Zihan Qiu, Huijia Wu, Zili Wang

The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of experts for processing. Despite the success, most existing methods face a challenge for balance between sparsity and the availability of expert knowledge: enhancing performance through

Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. EvoRL integrates EAs and reinforcement learning, presenting a promising avenue for training intelligent agents. This systematic review firstly navigates throu

Lu Deng, JingJing Zhang, Yong Wang, Chuan Chen

Estimating the average treatment effect in social networks is challenging due to individuals influencing each other. One approach to address interference is ego cluster experiments, where each cluster consists of a central individual (ego) and its peers (alters). Clusters are randomized, and only the effects on egos are measured. In this work, we propose an

Yifan Peng, Yui Sudo, Muhammad Shakeel, Shinji Watanabe

There has been an increasing interest in large speech models that can perform multiple tasks in a single model. Such models usually adopt an encoder-decoder or decoder-only architecture due to their popularity and good performance in many domains. However, autoregressive models can be slower during inference compared to non-autoregressive models and also hav

Lu Deng, Yilin Li, JingJing Zhang, Yong Wang

In social media platforms, user behavior is often influenced by interactions with other users, complicating the accurate estimation of causal effects in traditional A/B experiments. This study investigates situations where an individual's outcome can be broken down into the sum of multiple pairwise outcomes, a reflection of user interactions. These outcomes,

Zhanhong Ye, Xiang Huang, Leheng Chen, Hongsheng Liu

This paper introduces PDEformer, a neural solver for partial differential equations (PDEs) capable of simultaneously addressing various types of PDEs. We propose to represent the PDE in the form of a computational graph, facilitating the seamless integration of both symbolic and numerical information inherent in a PDE. A graph Transformer and an implicit neu

Sinan Abdulhak, Wayne Hubbard, Karthik Gopalakrishnan, Max Z. Li

Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generatio

Qingmei Zhao

In this paper, we present a null controllability result for a class of stochastic semi-discrete parabolic equations. For this purpose, an observability estimate is established for backward stochastic semi-discrete parabolic equations, with an explicit observability constant that depends on the discretization parameter. This estimate is obtained by a new Carl

Kevin Moseni, Richard B Wilson, Sinisa Coh

The measured magnetization dynamics of ferromagnetic iron--cobalt Fe$_{1-x}$Co$_x$ alloys show a strong dependence on the alloy composition, especially near $x=0.25$. Here, we calculate from first principles the electron-phonon coupling strength in Fe$_{1-x}$Co$_x$ alloys for compositions ranging from $x=0$ to $x=0.75$. We find a strong, spin-dependent varia

Kristian Lum, Jacy Reese Anthis, Kevin Robinson, Chirag Nagpal

Standard benchmarks of bias and fairness in large language models (LLMs) measure the association between the user attributes stated or implied by a prompt and the LLM's short text response, but human-AI interaction increasingly requires long-form and context-specific system output to solve real-world tasks. In the commonly studied domain of gender-occupation

Mark L. Lewis, Quanfu Yan

Let $G$ be a finite group. The group pseudo-algebra of $G$ is defined as the multi-set $C(G)=\{(d,m_G(d))\mid d\in{\rm Cod}(G)\},$ where $m_G(d)$ is the number of irreducible characters of with codegree $d\in {\rm Cod}(G)$. We show that there exist two finite $p$-groups with distinct orders that have the same group pseudo-algebra, providing an answer to Ques

Takuya Ikeda, Sergey Zakharov, Tianyi Ko, Muhammad Zubair Irshad

This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects and when attempting to generalize to new environments solely through synthetic data training. In this work, we address these challenges by proposing a probabilistic model that relie

Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab, Azam Asilian Bidgoli

Training Artificial Neural Networks poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model ba

Shuichi Hirahara, Naoto Ohsaka

In the Minmax Set Cover Reconfiguration problem, given a set system $\mathcal{F}$ over a universe and its two covers $\mathcal{C}^\mathsf{start}$ and $\mathcal{C}^\mathsf{goal}$ of size $k$, we wish to transform $\mathcal{C}^\mathsf{start}$ into $\mathcal{C}^\mathsf{goal}$ by repeatedly adding or removing a single set of $\mathcal{F}$ while covering the univ

Shijie Lin, Xiang Zhang, Lei Yang, Lei Yu

Bimodal objects, such as the checkerboard pattern used in camera calibration, markers for object tracking, and text on road signs, to name a few, are prevalent in our daily lives and serve as a visual form to embed information that can be easily recognized by vision systems. While binarization from intensity images is crucial for extracting the embedded info

Isaac Kulp, Charlotte Ochanine, Logan Richard, Leonel Robert

We call a continuous path of polygons decreasing if the convex hulls of the polygons form a decreasing family of sets. For an arbitrary polygon of more than three vertices, we characterize the polygons contained in it that can be reached by a decreasing path (attainability problem), and we show that this can be done by a finite application of "pull-in" moves

Kohei Tsujio, Mohammad Abdullah Al Faruque, Yasser Shoukry

This paper presents a novel tool, named Rampo, that can perform binary code analysis to identify cyber kinetic vulnerabilities in CPS. The tool takes as input a Signal Temporal Logic (STL) formula that describes the kinetic effect, i.e., the behavior of the physical system, that one wants to avoid. The tool then searches the possible cyber trajectories in th

Xiaoyu Tang, Xingming Chen, Jintao Cheng, Jin Wu

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry

Gunther Uhlmann, Jian Zhai

Consider a compact Riemannian manifold in dimension $n$ with strictly convex boundary. We show the local invertibility near a boundary point of the transverse ray transform of $2$ tensors for $n\geq 3$ and the mixed ray transform of $2+2$ tensors for $n=3$. When the manifold admits a strictly convex function, this local invertibility result leads to global i

Yuzhen Qi, Jin Yan

In this paper, we show that for any positive integer $m$ and $k\in [2]$, let $G$ be a $(2m+2k+2)$-connected graph and let $a_1,\ldots , a_m, s, t$ be any distinct vertices of $G$, there are $k$ internally disjoint $s$-$t$ paths $P_1, \ldots, P_k$ in $G$ such that $\{a_1,\ldots , a_m\} \cap \bigcup^{k}_{i=1}V (P_i) = \emptyset$ and $G- \bigcup^{k}_{i=1}V (P_i

Ishan Bhanuka, Lionel Parreaux, David Binder, Jonathan Immanuel Brachthäuser

Creating good type error messages for constraint-based type inference systems is difficult. Typical type error messages reflect implementation details of the underlying constraint-solving algorithms rather than the specific factors leading to type mismatches. We propose using subtyping constraints that capture data flow to classify and explain type errors. O

Gaoxiang Cong, Yuankai Qi, Liang Li, Amin Beheshti

Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a reference audio track. Existing state-of-the-art V2C models break the phonemes in the script according to the divisions between video frames, which solves the temporal alignment prob

Sinan Abdulhak, Anthony Carvette, Kate Shen, Robert Goldman

The transition to a microservices-based Flow Management Data and Services (FMDS) architecture from the existing Traffic Flow Management System (TFMS) is a critical enabler of the vision for an Information-Centric National Airspace System (NAS). The need to design a user-centric interface for FMDS is a key technical gap, as this interface connects NAS data an

Honbo Shao, Roberto Martinez-Maldonado, Vanessa Echeverria, Lixiang Yan

Data storytelling (DS) is rapidly gaining attention as an approach that integrates data, visuals, and narratives to create data stories that can help a particular audience to comprehend the key messages underscored by the data with enhanced efficiency and effectiveness. It has been posited that DS can be especially advantageous for audiences with limited vis

Simone Cecchini, Jinmin Wang, Zhizhang Xie, Bo Zhu

Let $(M,g)$ be a closed connected oriented (possibly non-spin) smooth four-dimensional manifold with scalar curvature bounded below by $n(n-1)$. In this paper, we prove that if $f$ is a smooth map of non-zero degree from $(M, g)$ to the unit four-sphere, then $f$ is an isometry. Following ideas of Gromov, we use $\mu$-bubbles and a version with coefficients

Mark L. Lewis, Quanfu Yan

Let $\chi$ be an irreducible character of a group $G,$ and $S_c(G)=\sum_{\chi\in {\rm Irr}(G)}{\rm cod}(\chi)$ be the sum of the codegrees of the irreducible characters of $G.$ Write ${\rm fcod} (G)=\frac{S_c(G)}{|G|}.$ We aim to explore the structure of finite groups in terms of ${\rm fcod} (G).$ On the other hand, we determine the lower bound of $S_c(G)$ f

Reuben Tate, Stephan Eidenbenz

We generalize Farhi et al.'s 0.6924-approximation result technique of the Max-Cut Quantum Approximate Optimization Algorithm (QAOA) on 3-regular graphs to obtain provable lower bounds on the approximation ratio for warm-started QAOA. Given an initialization angle $\theta$, we consider warm-starts where the initial state is a product state where each qubit po

Brian Liu, Rahul Mazumder

We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent additive models. The framework leverages a novel optimization procedure to fit these models $\sim$2 orders of magnitude faster than existing state-of-the-art methods, such as explaina

Anmol Agarwal, Pratyush Priyadarshi, Shiven Sinha, Shrey Gupta

In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and

Mark L. Lewis, Quanfu Yan

Let $\chi$ be an irreducible character of a group $G.$ We denote the sum of the codegrees of the irreducible characters of $G$ by $S_c(G)=\sum_{\chi\in {\rm Irr}(G)}{\rm cod}(\chi).$ We consider the question if $S_c(G)\leq S_c(C_n)$ is true for any finite group $G,$ where $n=|G|$ and $C_n$ is a cyclic group of order $n.$ We show this inequality holds for man

Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and

Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu

Machine learning models have achieved great success in supervised learning tasks for end-to-end training, which requires a large amount of labeled data that is not always feasible. Recently, many practitioners have shifted to self-supervised learning methods that utilize cheap unlabeled data to learn a general feature extractor via pre-training, which can be

Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we def

Angelo G. Menezes, Augusto J. Peterlevitz, Mateus A. Chinelatto, André C. P. L. F. de Carvalho

Continual Object Detection is essential for enabling intelligent agents to interact proactively with humans in real-world settings. While parameter-isolation strategies have been extensively explored in the context of continual learning for classification, they have yet to be fully harnessed for incremental object detection scenarios. Drawing inspiration fro

Renchi Yang

Given a network G, edge centrality is a metric used to evaluate the importance of edges in G, which is a key concept in analyzing networks and finds vast applications involving edge ranking. In spite of a wealth of research on devising edge centrality measures, they incur either prohibitively high computation costs or varied deficiencies that lead to sub-opt

Jonas Eschle

The Standard Model of particle physics is well established, yet recently showed tensions with experimental observations. A large part of this thesis is dedicated to the first measurement of the ratio of branching fractions of the decays $B^+ \rightarrow K^+ \mu^+ \mu^-$ and $B^+ \rightarrow K^+ e^+ e^-$ , referred to as $R_K$ , in the high dilepton invariant

Runlong Zhou, Simon S. Du, Beibin Li

As language models (LMs) demonstrate their capabilities in various fields, their application to tasks requiring multi-round interactions has become increasingly popular. These tasks usually have complex dynamics, so supervised fine-tuning (SFT) on a limited offline dataset does not yield good performance. However, only a few works attempted to directly train

Kaiqi Yang, Hang Li, Hongzhi Wen, Tai-Quan Peng

The prediction has served as a crucial scientific method in modern social studies. With the recent advancement of Large Language Models (LLMs), efforts have been made to leverage LLMs to predict the human features in social life, such as presidential voting. These works suggest that LLMs are capable of generating human-like responses. However, we find that t

Hassan Cheraghpour, Nader M. Ghosseiri, Madineh Jafari, Farnaz Seyfpour

Let $R$ be a noncommutative ring with identity. The commuting graph of $R$, denoted by $\Gamma(R)$, is a graph with vertex set $R \setminus Z(R)$, and two vertices $a$, $b$ are adjacent if $a\neq b$ and $ab=ba$. Let $T=Tr(R)$ be the ring of all $2\times 2$ upper triangular matrices over $R$ and $\Gamma(T)$ be the commuting graph of $T$. In this article, we f

Ziqi Li, Kai Tian

For a 2-component Camassa-Holm equation, as well as a 2-component generalization of the modified Camassa-Holm equation, nonlocal infinitesimal symmetries quadratically depending on eigenfunctions of linear spectral problems are constructed from functional gradients of spectral parameters. With appropriate pseudo-potentials, these nonlocal infinitesimal symme

Banghua Zhu, Norman Mu, Jiantao Jiao, David Wagner

Generative AI's expanding footprint across numerous industries has led to both excitement and increased scrutiny. This paper delves into the unique security challenges posed by Generative AI, and outlines potential research directions for managing these risks.

Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is comput

Karsten Kruse

We study the problem of existence of preduals of locally convex Hausdorff spaces. We derive necessary and sufficient conditions for the existence of a predual with certain properties of a bornological locally convex Hausdorff space $X$. Then we turn to the case that $X=\mathcal{F}(\Omega)$ is a space of scalar-valued functions on a non-empty set $\Omega$ and

Tinggui Zhang, Hong Yang, Shao-Ming Fei

Bell nonlocality is the key quantum resource in some device-independent quantum information processing. It is of great importance to study the efficient sharing of this resource. Unsharp measurements are widely used in sharing the nonlocality of an entangled state shared among several sequential observers. Recently, the authors in [Phys. Rev. Lett.129, 23040

Chungpa Lee, Joonhwan Chang, Jy-yong Sohn

Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achie

Christian Pilato, Subhadeep Banik, Jakub Beranek, Fabien Brocheton

Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly executed in data centers with energy-efficient hardware accelerators since FPGAs are well-suited for this task

Hassan Cheraghpour, Madineh Jafari

Triangular matrix rings are example of trivial extensions. In this article we describe the Jordan superderivations of the trivial extensions and upper triangular matrix rings. We deduce then that any Jordan superderivation of an upper triangular matrix ring, under some conditions, is a derivation, and any Jordan super-biderivation of a trivial extension, and

Huanzhi Hu, Frank Krüger

We investigate the nature of the topological quantum phase transition between the gapless and gapped Kitaev quantum spin liquid phases away from the exactly solvable point. The transition is driven by anisotropy of the Kitaev couplings. At the critical point the two Dirac points of the gapless Majorana modes merge, resulting in the formation of a semi-Dirac

Huaxin Lin

Let $H$ be an infinite dimensional separable Hilbert space and $B(H)$ the C*-algebra of bounded operators on $H.$ Suppose that $T_1,T_2,..., T_n$ are self-adjoint operators in $B(H).$ We show that, if commutators $[T_i, T_j]$ are sufficiently small in norm, then ``Approximately Macroscopically Unique" states always exist for any values in a synthetic spectru

Hassan S. Al Khatib, Subash Neupane, Harish Kumar Manchukonda, Noorbakhsh Amiri Golilarz

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling

Tom Boot, Didier Nibbering

In theory, two-stage least squares (TSLS) identifies a weighted average of covariate-specific local average treatment effects (LATEs) from a saturated specification, without making parametric assumptions on how available covariates enter the model. In practice, TSLS is severely biased as saturation leads to a large number of control dummies and an equally la

Anna E. McCoy, Mark A. Caprio, Pieter Maris, Patrick J. Fasano

The spectrum of 12Be exhibits exotic features, e.g., an intruder ground state and shape coexistence, normally associated with the breakdown of a shell closure. While previous phenomenological treatments indicated the ground state has substantial contributions from intruder configurations, it is only with advances in computational abilities and improved inter

Guillermo Puebla, Jeffrey S. Bowers

Achieving visual reasoning is a long-term goal of artificial intelligence. In the last decade, several studies have applied deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of generalization of the relations learned. However, in recent years, object-centric representation learning has been put for

Daniel de Roux, Robert Carr, R. Ravi

We introduce a generic technique to obtain linear relaxations of semidefinite programs with provable guarantees based on the commutativity of the constraint and the objective matrices. We study conditions under which the optimal value of the SDP and the proposed linear relaxation match, which we then relax to provide a flexible methodology to derive effectiv

Elliot Murphy

In order to design strong paradigms for isolating lexical access and semantics, we need to know what a word is. Surprisingly few linguists and philosophers have a clear model of what a word is, even though words impact basically every aspect of human life. Researchers that regularly publish academic papers about language often rely on outdated, or inaccurate

Gunnar Blohm, Benjamin Peters, Ralf Haefner, Leyla Isik

Generative adversarial collaborations (GACs) are a form of formal teamwork between groups of scientists with diverging views. The goal of GACs is to identify and ultimately resolve the most important challenges, controversies, and exciting theoretical and empirical debates in a given research field. A GAC team would develop specific, agreed-upon avenues to r

Ziyu Niu, Xiang-Long Yu, Dingfu Shao, Xixiang Jing

Van der Waals (vdW) assembly could efficiently modulate the symmetry of two-dimensional (2D) materials that ultimately governs their physical properties. Of particular interest is the ferroelectric polarization being introduced by proper vdW assembly that enables the realization of novel electronic, magnetic and transport properties of 2D materials. Four-lay

Matteo Nerini, Bruno Clerckx

Stacked intelligent metasurface (SIM) has emerged as a technology enabling wave domain beamforming through multiple stacked reconfigurable intelligent surfaces (RISs). SIM has been implemented so far with diagonal RIS (D-RIS), while SIM implemented with beyond diagonal RIS (BD-RIS) remains unexplored. Furthermore, a model of SIM accounting for mutual couplin

Lauren I. Biddle, Brendan P. Bowler, Yifan Zhou, Kyle Franson

Giant planets grow by accreting gas through circumplanetary disks, but little is known about the timescale and mechanisms involved in the planet assembly process because few accreting protoplanets have been discovered. Recent visible and infrared (IR) imaging revealed a potential accreting protoplanet within the transition disk around the young intermediate-

C. V. Flores, C. H. Lenzi, M. Dutra, O. Lourenço

The influence of the dark matter mass~($M_{\chi}$) and the Fermi momentum~($k_{F}^{\dm}$) on the $f_0$-mode oscillation frequency, damping time parameter, and tidal deformability of hadronic stars are studied by employing a numerical integration of hydrostatic equilibrium, nonradial oscillation, and tidal deformability equations. The matter inside the hadron

Hejie Cui, Xinyu Fang, Ran Xu, Xuan Kan

Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous information, such as structural data in tabular form and unstructured data in textual notes. Different types of information in EHRs can complement each other and provide a more complete pic

D Panchal, P Verma, I Baran, D Musgrove

Although Machine Learning model building has become increasingly accessible due to a plethora of tools, libraries and algorithms being available freely, easy operationalization of these models is still a problem. It requires considerable expertise in data engineering, software development, cloud and DevOps. It also requires planning, agreement, and vision of