March 2024 arXiv papers — page 7

Luankang Zhang, Hao Wang, Suojuan Zhang, Mingjia Yin

Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start problems. Despite significant advances in representation disentanglement to capture diverse user preferences, existing methods usually neglect representation enhancement and lack

Zhivar Sourati, Meltem Ozcan, Colin McDaniel, Alireza Ziabari

Prior research has established associations between individuals' language usage and their personal traits; our linguistic patterns reveal information about our personalities, emotional states, and beliefs. However, with the increasing adoption of Large Language Models (LLMs) as writing assistants in everyday writing, a critical question emerges: are authors'

Abhishek Das, Santosha Pattanayak

We consider typical finite dimensional complex irreducible representations of a basic classical simple Lie superalgebra, and give a sufficient condition on when unique factorization of finite tensor products of such representations hold. We also prove unique factorization of tensor products of singly atypical finite dimensional irreducible modules for $\math

Zhiheng Yu, Jiancheng An, Lu Gan, Chau Yuen

Reconfigurable intelligent surface (RIS) provides a new electromagnetic response control solution, which can reshape the characteristics of wireless channels. In this paper, we propose a novel environment-aware codebook protocol for RIS-assisted multi-user multiple-input single-output (MU-MISO) systems. Specifically, we first introduce a channel training pro

Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi, Manabu Okumura

Dataset distillation aims to compress a training dataset by creating a small number of informative synthetic samples such that neural networks trained on them perform as well as those trained on the original training dataset. Current text dataset distillation methods create each synthetic sample as a sequence of word embeddings instead of a text to apply gra

Aki Mori

Let $\mathscr{O}(P)$ and $\mathscr{C}(P)$ denote the order polytope and chain polytope, respectively, associated with a finite poset $P$. We prove the following result: if $P$ is a maximal ranked poset, then the number of triangular $2$-faces of $\mathscr{O}(P)$ is less than or equal to that of $\mathscr{C}(P)$, with equality holding if and only if $P$ does

Yuan Wang, Rui Sun, Naisong Luo, Yuwen Pan

Open-vocabulary semantic segmentation (OVS) aims to segment images of arbitrary categories specified by class labels or captions. However, most previous best-performing methods, whether pixel grouping methods or region recognition methods, suffer from false matches between image features and category labels. We attribute this to the natural gap between the t

Qing Yin, Hui Fang, Zhu Sun, Yew-Soon Ong

Session-based recommender systems (SBRSs) have become extremely popular in view of the core capability of capturing short-term and dynamic user preferences. However, most SBRSs primarily maximize recommendation accuracy but ignore user minor preferences, thus leading to filter bubbles in the long run. Only a handful of works, being devoted to improving diver

Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu

Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-supervised constraint for single image super-resolution, termed SSC-SR. SSC-SR uniquely addresses the divergence in image complexity by employing a dual asymmetric paradigm and a t

Aleksey Ber, Fedor Sukochev, Dmitriy Zanin, Hongyin Zhao

In the study on the diagonality of an $n$-tuple $\alpha=(\alpha(j))_{j=1}^n$ of commuting self-adjoint operators modulo a given $n$-tuple $\Phi=(\mathcal{J}_1,\ldots,\mathcal{J}_n)$ of normed ideals in $B(H)$, Voiculescu introduced the notion of quasicentral modulus $k_{\Phi}(\alpha)$ and proved that $\alpha$ is diagonal modulo $(\mathcal{J}_1,\ldots,\mathca

Yohei Fujishima, Norisuke Ioku, Bernhard Ruf, Elide Terraneo

By introducing a new classification of the growth rate of exponential functions, singular solutions for semilinear elliptic equations in 2-dimensions with exponential nonlinearities are constructed. The strategy is to introduce a model nonlinearity which admits an explicit singular solution. Then, using a transformation as in [8], one obtains an approximate

Qian Wan, Xiang Xiang, Qinhao Zhou

Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently. The challenge is how can a model detect novel classes and then incrementally learn them without forgetting previously known classes. Previous approaches hinge on strongly-supervised or weakly-supervised novel-class data for novel-class detection, which

Yoshiko Hayashi

Controlling the false discovery rate (FDR) is a critical challenge in large-scale data analysis, particularly in the presence of outliers. A common practice involves imposing a Student-$t$ distribution to eliminate the influence of outliers. Here, we developed a robust Bayesian analysis based on heavy-tailed modeling, applied it to large-scale studies in Bay

Jeong-Hoon Ju, Taehyeong Kim, Yeongrak Kim, Hayoung Choi

In this paper, we generalize the geometric mean of two positive definite matrices to that of third-order tensors using the notion of T-product. Specifically, we define the geometric mean of two T-positive definite tensors and verify several properties that "mean" should satisfy including the idempotence and the commutative property, and so on. Moreover, it i

Ruijie Quan, Wenguan Wang, Fan Ma, Hehe Fan

Protein representation learning is a challenging task that aims to capture the structure and function of proteins from their amino acid sequences. Previous methods largely ignored the fact that not all amino acids are equally important for protein folding and activity. In this article, we propose a neural clustering framework that can automatically discover

Zhanxuan Mei, Yun-Cheng Wang, C. -C. Jay Kuo

Image saliency detection is crucial in understanding human gaze patterns from visual stimuli. The escalating demand for research in image saliency detection is driven by the growing necessity to incorporate such techniques into various computer vision tasks and to understand human visual systems. Many existing image saliency detection methods rely on deep ne

Kanglong Fan, Wen Wen, Mu Li, Yifan Peng

Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit m

Ping Guo, Qingfu Zhang, Xi Lin

In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a solution set that approximates a small part of the PS and requires the solutions in this set share some similarities. This paper makes a first attempt to address this issue. We fi

Mingyu Jin, Haochen Xue, Zhenting Wang, Boming Kang

The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of La

Yusuke Okuma, Kiwamu Izumi, Kentaro Komori, Masaki Ando

The torsion pendulum is a prevailing instrument for measuring small forces acting on a solid body or those between solid bodies. While it offers powerful advantages, the measurement precision suffers from thermal noises of the suspending wires giving rise to stochastic torque noises. This paper proposes a new scheme to reduce the effect of such noise by empl

Vincent Abbott, Gioele Zardini

The study of abstraction and composition - the focus of category theory - naturally leads to sophisticated diagrams which can encode complex algebraic semantics. Consequently, these diagrams facilitate a clearer visual comprehension of diverse theoretical and applied systems. Complex algebraic structures - otherwise represented by a forest of symbols - can b

M. Nacianceno, T. Oraby, H. Rodrigo, Y. Sepulveda

In this work, a new relationship is established between the solutions of higher fractional differential equations and a Wright-type transformation. Solutions could be interpreted as expected values of functions in a random time process. As applications, we solve the fractional beam equation, fractional electric circuits with special functions as external sou

Runze Lin, Junghui Chen, Lei Xie, Hongye Su

In the context of Industry 4.0 and smart manufacturing, the field of process industry optimization and control is also undergoing a digital transformation. With the rise of Deep Reinforcement Learning (DRL), its application in process control has attracted widespread attention. However, the extremely low sample efficiency and the safety concerns caused by ex

Guande Wu, Chen Zhao, Claudio Silva, He He

Language agents that interact with the world on their own have great potential for automating digital tasks. While large language model (LLM) agents have made progress in understanding and executing tasks such as textual games and webpage control, many real-world tasks also require collaboration with humans or other LLMs in equal roles, which involves intent

Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Keshavan

Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like retrieval. We attribute this to a mismatch between LLMs' knowledge and the knowledge crucial for effective recommendations. While LLMs excel at natural language reasoning, they can

Pongwit Srisangyingcharoen

In this paper, we present a systematic derivation aimed at obtaining general expressions for on-shell recursion relations for tree-level open string amplitudes. Our approach involves applying the BCFW shift to an open string amplitude written in terms of multiple Gaussian hypergeometric functions. By employing binomial expansions, we demonstrate that the shi

Jiahao Yu, Yihai Duan, Longfei Xu, Chao Chen

Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to select pre-defined scenarios, which may hinder the downstream ranking performance. In addition, in the academic community, the multi-scenario ranking works only come from other fields,

Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You

Large language models (LLMs) are increasingly employed for complex tasks that process multiple generation calls in a tree structure with shared prefixes of tokens, including few-shot prompting, multi-step reasoning, speculative decoding, etc. However, existing inference systems for tree-based applications are inefficient due to improper partitioning of queri

Lorenzo Gallerani Resca

I consider electro-weak (EW) masses and interactions generated for photons by vacuum expectation values of Stueckelberg and Higgs fields. I provide a prescription to relate their parametric values to a cosmological range derived from a fundamental Heisenberg uncertainty principle and Einstein-de Sitter cosmological constant and horizon. This yields qualitati

Carla Farsi, Frederic Latremoliere

If two compact quantum metric spaces are close in the metric sense, then how similar are they, as noncommutative spaces? In the classical realm of Riemannian geometry, informally, if two manifolds are close in the Gromov-Hausdorff distance, and belong to a class of manifolds with bounded curvature and diameter, then the spectra of their Laplacian or Dirac op

Michael Grabchak, Xingnan Zhang

We derive a Dickman approximation for the small jumps of a large class of multivariate L\'evy processes. We then apply this approximation to develop a simulation method for the class of general multivariate gamma distributions (GMGD). A small-scale simulation study suggests that this method works very well.

Sol Kim, Kyuhwan Lee, Jongmin Kim, Y. Shin

We present a method for producing a flat, large-area Fermi gas of $^6$Li with a uniform area density. The method uses a programmable optical potential within a feedback loop to flatten the in-plane trapping potential for atoms. The optical potential is generated using a laser beam, whose intensity profile is adjusted by a spatial light modulator and optimize

Haotian Lin, Yixiao Wang, Mingxiao Huo, Chensheng Peng

Joint pedestrian trajectory prediction has long grappled with the inherent unpredictability of human behaviors. Recent investigations employing variants of conditional diffusion models in trajectory prediction have exhibited notable success. Nevertheless, the heavy dependence on accurate historical data results in their vulnerability to noise disturbances an

Wentao Xu, Qianqian Xie, Shuo Yang, Jiangxia Cao

In real-world applications, users express different behaviors when they interact with different items, including implicit click/like interactions, and explicit comments/reviews interactions. Nevertheless, almost all recommender works are focused on how to describe user preferences by the implicit click/like interactions, to find the synergy of people. For th

Jaydip Sen, Joceli Mayer, Subhasis Dasgupta, Subrata Nandi

In the era of generative artificial intelligence and the Internet of Things, while there is explosive growth in the volume of data and the associated need for processing, analysis, and storage, several new challenges are faced in identifying spurious and fake information and protecting the privacy of sensitive data. This has led to an increasing demand for m

Taegyeong Lee, Soyeong Kwon, Taehwan Kim

Recent advances in the diffusion models have significantly improved text-to-image generation. However, generating videos from text is a more challenging task than generating images from text, due to the much larger dataset and higher computational cost required. Most existing video generation methods use either a 3D U-Net architecture that considers the temp

Zhe Chen

Let $\mathbb{G}$ be a connected reductive group over $\mathcal{O}$, a complete discrete valuation ring with finite residue field $\mathbb{F}_q$. Let $R_{T_r,U_r}^{\theta}$ be a level $r$ Deligne--Lusztig representation of $\mathbb{G}(\mathcal{O})$, where $r$ is a positive integer. We show that, if $q$ is not small, and if $T$ is Coxeter and $\theta=1$, then

Baoyu Li, William Edwards, Kris Hauser

AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO). To address these issues, this paper proposes to employ a meta-learning approach called Portfolio that improves AutoMPC's efficiency a

Linchen Qian, Jiasong Chen, Linhai Ma, Timur Urakov

Lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential role on low back pain, a significant global health concern. Automated lumbar spine geometry reconstruction from MR images will enable fast measurement of medical parameters to evaluate the lumbar status, in order to determine a suitable

Zheling Meng, Bo Peng, Jing Dong

Watermarking is a tool for actively identifying and attributing the images generated by latent diffusion models. Existing methods face the dilemma of image quality and watermark robustness. Watermarks with superior image quality usually have inferior robustness against attacks such as blurring and JPEG compression, while watermarks with superior robustness u

Hui Chang Li, Xiang Zhou, Hai Lin Chi, Wen Wen Wang

The emerging field of topology has brought device effects to a new level. Higher-order topological insulators (HOTIs) go beyond traditional descriptions of bulk-edge correspondence, broadening the understanding of topologically insulating phases. In this paper, a second-order split-ring photonic crystal (SSPC) with zero-dimensional (0D) corner states and one

Yan-Shuo Liang, Wu-Jun Li

Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (stability) and the ability to adapt to new tasks continuously (plasticity). Recently, parameter-efficient fine-tuning (PEFT), which involves freezing a pre-trained model and injectin

Kalahasti Ganesh Srivatsa, Sabyasachi Mukhopadhyay, Ganesh Katrapati, Manish Shrivastava

Infrastructure as Code (IaC) is a revolutionary approach which has gained significant prominence in the Industry. IaC manages and provisions IT infrastructure using machine-readable code by enabling automation, consistency across the environments, reproducibility, version control, error reduction and enhancement in scalability. However, IaC orchestration is

Tongkun Su, Jun Li, Xi Zhang, Haibo Jin

Multimodal pre-training demonstrates its potential in the medical domain, which learns medical visual representations from paired medical reports. However, many pre-training tasks require extra annotations from clinicians, and most of them fail to explicitly guide the model to learn the desired features of different pathologies. In this paper, we utilize Vis

Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing foundation models benefit from the generalization capability of contrastive self-supervised learning by learning compact and high-quality representations without relying on any labe

Gustavo Bartz Guedes, Ana Estela Antunes da Silva

Scientific articles are long text documents organized into sections, each describing aspects of the research. Analyzing scientific production has become progressively challenging due to the increase in the number of available articles. Within this scenario, our approach consisted of fine-tuning transformer language models to generate sentence-level embedding

Luiz L. Lopes

In December 2023, the Fermi LAT Catalog announced the discovery of 33 new millisecond pulsars. Motivated by that, in this work, I study how different values of the symmetry energy slope $(L)$ affect the properties of static and slowly rotating neutron stars. For fixed values of angular velocity, I study how the slope influences the increase of the maximum ma

Dominique Guillot, Himanshu Gupta, Prateek Kumar Vishwakarma, Chi Hoi Yip

We resolve an algebraic version of Schoenberg's celebrated theorem [Duke Math.J., 1942] characterizing entrywise matrix transforms that preserve positive definiteness. Compared to the classical real and complex settings, we consider matrices with entries in a finite field and obtain a complete characterization of such preservers for matrices of a fixed dimen

Shosei Sakaguchi

Public policies and medical interventions often involve dynamic treatment assignments, in which individuals receive a sequence of interventions over multiple stages. We study the statistical learning of optimal dynamic treatment regimes (DTRs) that determine the optimal treatment assignment for each individual at each stage based on their evolving history. W

Haijie Xu, Xiaochen Xian, Chen Zhang, Kaibo Liu

Sequential change point detection for multivariate autocorrelated data is a very common problem in practice. However, when the sensing resources are limited, only a subset of variables from the multivariate system can be observed at each sensing time point. This raises the problem of partially observable multi-sensor sequential change point detection. For it

Fan Zhong

Phase transitions and critical phenomena are among the most intriguing phenomena in nature and society. They are classified as first-order phase transitions (FOPTs) and continuous ones. While the latter show marvelous phenomena of scaling and universality, whether the former behaves similarly is a long-standing controversial issue. Here we definitely demonst

Haijie Xu, Chen Zhang

Contrasts with existing works which all consider nodes as functions and use edges to represent the relationships between different functions. We target at network modeling whose edges are functional data and transform the adjacency matrix into a functional adjacency tensor, introducing an additional dimension dedicated to function representation. Tucker func

Haoyuan Li, Snigdha Chaturvedi

Opinion summarization aims to generate concise summaries that present popular opinions of a large group of reviews. However, these summaries can be too generic and lack supporting details. To address these issues, we propose a new paradigm for summarizing reviews, rationale-based opinion summarization. Rationale-based opinion summaries output the representat

Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei

As the modern tools of choice for text understanding and generation, large language models (LLMs) are expected to accurately output answers by leveraging the input context. This requires LLMs to possess both context-faithfulness and factual accuracy. Extensive efforts have been made to enable better outputs from LLMs by mitigating hallucinations through fact

John C. Sunil, Richard A. Blythe, Martin R. Evans, Satya N. Majumdar

We introduce the profligacy of a search process as a competition between its expected cost and the probability of finding the target. The arbiter of the competition is a parameter $\lambda$ that represents how much a searcher invests into increasing the chance of success. Minimizing the profligacy with respect to the search strategy specifies the optimal sea

Ren Oshima, Tatsuto Hatanaka, Shinichi Nishihaya, Takuya Nomoto

Technical advancement of oxide molecular beam epitaxy (MBE) has opened new avenues for studying various quantum transport phenomena in correlated transition-metal oxides, as exemplified by the exotic superconductivity of Sr$_2$RuO$_4$ and quantum oscillations of SrRuO$_3$. On the other hand, film research of another Ruddlesden-Popper strontium ruthenate Sr$_

Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein

In recent years, Large Language Models (LLMs) have shown remarkable performance in generating human-like text, proving to be a valuable asset across various applications. However, adapting these models to incorporate new, out-of-domain knowledge remains a challenge, particularly for facts and events that occur after the model's knowledge cutoff date. This pa

Yue Niu, Jonathan Sterling, Robert Harper

We study a cost-aware programming language for higher-order recursion dubbed $\textbf{PCF}_\mathsf{cost}$ in the setting of synthetic domain theory (SDT). Our main contribution relates the denotational cost semantics of $\textbf{PCF}_\mathsf{cost}$ to its computational cost semantics, a new kind of dynamic semantics for program execution that serves as a mat

Pouya Pezeshkpour, Estevam Hruschka

Utilizing large language models (LLMs) to rank a set of items has become a common approach in recommendation and retrieval systems. Typically, these systems focus on ordering a substantial number of documents in a monotonic order based on a given query. However, real-world scenarios often present a different challenge: ranking a comparatively smaller set of

Daeun Song, Jing Liang, Amirreza Payandeh, Amir Hossain Raj

We propose VLM-Social-Nav, a novel Vision-Language Model (VLM) based navigation approach to compute a robot's motion in human-centered environments. Our goal is to make real-time decisions on robot actions that are socially compliant with human expectations. We utilize a perception model to detect important social entities and prompt a VLM to generate guidan

Cheng Jiayang, Lin Qiu, Chunkit Chan, Xin Liu

Narrative reasoning relies on the understanding of eventualities in story contexts, which requires a wealth of background world knowledge. To help machines leverage such knowledge, existing solutions can be categorized into two groups. Some focus on implicitly modeling eventuality knowledge by pretraining language models (LMs) with eventuality-aware objectiv

Ahmad Ayaz Amin

Natural evolution strategies are a class of approximate-gradient black-box optimizers that have been successfully used for continuous parameter spaces. In this paper, we derive NES algorithms for discrete parameter spaces and demonstrate their effectiveness in tasks involving discrete parameters.

Bohan Zhang, Yixin Wang, Paramveer S. Dhillon

In this paper, we examine the collaborative dynamics between humans and language models (LMs), where the interactions typically involve LMs proposing text segments and humans editing or responding to these proposals. Productive engagement with LMs in such scenarios necessitates that humans discern effective text-based interaction strategies, such as editing

Dean Doron, S. Venkitesh

We prove that Reed-Solomon (RS) codes with random evaluation points are list recoverable up to capacity with optimal output list size, for any input list size. Namely, given an input list size $\ell$, a designated rate $R$, and any $\varepsilon > 0$, we show that a random RS code is list recoverable from $1-R-\varepsilon$ fraction of errors with output list

Ben Zhou, Hongming Zhang, Sihao Chen, Dian Yu

Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large language models' capability to perform conceptual reasoning. In this work, we bridge this gap and propose a novel conceptualization framework that forces models to perform conceptual r

Junyang Zhang, Cristian Emanuel Ocampo Rivera, Kyle Tyni, Steven Nguyen

Navigation precision, speed and stability are crucial for safe Unmanned Aerial Vehicle (UAV) flight maneuvers and effective flight mission executions in dynamic environments. Different flight missions may have varying objectives, such as minimizing energy consumption, achieving precise positioning, or maximizing speed. A controller that can adapt to differen

Larkin Liu, Yuming Rong

We introduce the application of online learning in a Stackelberg game pertaining to a system with two learning agents in a dyadic exchange network, consisting of a supplier and retailer, specifically where the parameters of the demand function are unknown. In this game, the supplier is the first-moving leader, and must determine the optimal wholesale price o

Ronen Weiss, Alessandro Baroni, Joseph Carlson, Ionel Stetcu

The description of quantum many-body dynamics is extremely challenging on classical computers, as it can involve many degrees of freedom. On the other hand, the time evolution of quantum states is a natural application for quantum computers that are designed to efficiently perform unitary transformations. In this paper, we study quantum algorithms for respon

Belle Collaboration, D. Ferlewicz, P. Urquijo, I. Adachi

We perform an angular analysis of the $B\to K^* e^+ e^-$ decay for the dielectron mass squared, $q^2$, range of $0.0008$ to $1.1200 ~\text{GeV}^2 /c^4$ using the full Belle data set in the $K^{*0} \to K^+ \pi^-$ and $K^{*+} \to K_S^0 \pi^+$ channels, incorporating new methods of electron identification to improve the statistical power of the data set. This a

Robert Parker, Carleton Coffrin

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment prob

Ziming Wang, Xinghua Zhu

This paper studies the sparse identification problem of unknown sparse parameter vectors in stochastic dynamic systems. Firstly, a novel sparse identification algorithm is proposed, which can generate sparse estimates based on least squares estimation by adaptively adjusting the threshold. Secondly, under a possibly weakest non-persistent excited condition,

Daniel J. Tibben, Enrico Della Gaspera, Joel van Embden, Philipp Reineck

Quantum batteries, quantum systems for energy storage, have gained interest due to their potential scalable charging power density. A quantum battery proposal based on the Dicke model has been explored using organic microcavities, which enable a cavity-enhanced energy transfer process called superabsorption. However, energy storage lifetime in these devices

Yu-Hsiu Huang, Elisabeth Krause, Jiachuan Xu, Tim Eifler

Kinematic lensing (KL) is a new weak lensing technique that reduces shape noise for disk galaxies by including spectroscopically measured galaxy kinematics in addition to photometrically measured galaxy shapes. Since KL utilizes the Tully-Fisher relation, any correlation of this relation with the local environment may bias the cosmological interpretation. Fo

Chris Porter, Sharjeel Khan, Kangqi Ni, Santosh Pande

Software debloating can effectively thwart certain code reuse attacks by reducing attack surfaces to break gadget chains. Approaches based on static analysis enable a reduced set of functions reachable at a callsite for execution by leveraging static properties of the callgraph. This achieves low runtime overhead, but the function set is conservatively compu

Qi Deng

We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adj

Md Hassanuzzaman, Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I Ahmed

Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duratio

Chou-Ying Hsieh, Po-Chieh Lin, Sy-Yen Kuo

The push-relabel algorithm is an efficient algorithm that solves the maximum flow/ minimum cut problems of its affinity to parallelization. As the size of graphs grows exponentially, researchers have used Graphics Processing Units (GPUs) to accelerate the computation of the push-relabel algorithm further. However, prior works need to handle the significant m

Shaobo Di, Sergei O. Ivanov, Lev Mukoseev, Mengmeng Zhang

We develop a theory of covering digraphs, similar to the theory of covering spaces. By applying this theory to Cayley digraphs, we build a "bridge" between GLMY-theory and group homology theory, which helps to reduce path homology calculations to group homology computations. We show some cases where this approach allows us to fully express path homol

Yilei Chen, Aldo Pacchiano, Ioannis Ch. Paschalidis

We study the multiple-policy evaluation problem where we are given a set of $K$ policies and the goal is to evaluate their performance (expected total reward over a fixed horizon) to an accuracy $\epsilon$ with probability at least $1-\delta$. We propose an algorithm named $\mathrm{CAESAR}$ for this problem. Our approach is based on computing an approximate

Joanna A. Ellis-Monaghan, Iain Moffatt, Steven Noble

We give an analogue of the Tutte polynomial for hypermaps. This polynomial can be defined as either a sum over subhypermaps, or recursively through deletion-contraction reductions where the terminal forms consist of isolated vertices. Our Tutte polynomial extends the classical Tutte polynomial of a graph as well as the Tutte polynomial of an embedded graph (

Ezra Huscher, Kristian Finlator, Samir Kušmić, Maya Steen

We implement a treatment of Helium {\sc ii} absorption and re-emission into the \TD cosmological simulations to study its impact on the metagalactic ultraviolet background (UVB) in three dimensions. By comparing simulations with and without He {\sc ii} reprocessing, we show that it weakens the mean UVB by $\sim$3 dex from $z = 10$ to $z = 5$ between 3.5 and

Sayeri Lala, Hanlin Goh, Christopher Sandino

Sleep staging is a clinically important task for diagnosing various sleep disorders, but remains challenging to deploy at scale because it because it is both labor-intensive and time-consuming. Supervised deep learning-based approaches can automate sleep staging but at the expense of large labeled datasets, which can be unfeasible to procure for various sett

Yichun Zhao, Miguel A. Nacenta

Our previous interview study explores the needs and uses of diagrammatic information by the Blind and Low Vision (BLV) community, resulting in a framework called the Ladder of Diagram Access. The framework outlines five levels of information access when interacting with a diagram. In this paper, we connect this framework to include the global activity of sen

Krishnanshu Gupta, Devon Bolt, Ben Hinchliff

This research project investigates the application of several computer vision techniques for playing card detection and recognition in the context of the popular casino game, blackjack. The primary objective is to develop a robust system that is capable of detecting and accurately classifying playing cards in real-time, and displaying the optimal move recomm

Sandra Siby, Sina Abdollahi, Mohammad Maheri, Marios Kogias

Machine-learning (ML) models are increasingly being deployed on edge devices to provide a variety of services. However, their deployment is accompanied by challenges in model privacy and auditability. Model providers want to ensure that (i) their proprietary models are not exposed to third parties; and (ii) be able to get attestations that their genuine mode

Xiao Liu, Jiawei Zhang

This study introduces GPTA, a Large Language Model assistance training framework, that enhances the training of downstream task models via prefix prompt. By minimizing data exposure to LLM, the framework addresses the security and legal challenges of applying LLM in downstream task model training. GPTA utilizes a new synergistic training approach, optimizing

Manit Mishra, Abderrahman Braham, Charles Marsom, Bryan Chung

Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from undertaking more nuanced labor and high-level projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "L

Bahar Arslan, Vanni Noferini, Spyridon Vrontos

We investigate an application of network centrality measures to portfolio optimization, by generalizing the method in [Pozzi, Di Matteo and Aste, \emph{Spread of risks across financial markets: better to invest in the peripheries}, Scientific Reports 3:1665, 2013], that however had significant limitations with respect to the state of the art in network theor

Edward L. Zhu, Francesco Borrelli

Dynamic games can be an effective approach for modeling interactive behavior between multiple competitive agents in autonomous racing and they provide a theoretical framework for simultaneous prediction and control in such scenarios. In this work, we propose DG-SQP, a numerical method for the solution of local generalized Nash equilibria (GNE) for open-loop

Amitangshu Mukherjee, Timur Ibrayev, Kaushik Roy

Current Deep Neural Networks are vulnerable to adversarial examples, which alter their predictions by adding carefully crafted noise. Since human eyes are robust to such inputs, it is possible that the vulnerability stems from the standard way of processing inputs in one shot by processing every pixel with the same importance. In contrast, neuroscience sugge

Behnam Mohammadi

The emergence of large language models (LLMs) has opened up exciting possibilities for simulating human behavior and cognitive processes, with potential applications in various domains, including marketing research and consumer behavior analysis. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain due to glaring divergen

Aashish Ghimire, John Edwards

The burgeoning development of generative artificial intelligence (GenAI) and the widespread adoption of large language models (LLMs) in educational settings have sparked considerable debate regarding their efficacy and acceptability.Despite the potential benefits, the assimilation of these cutting-edge technologies among educators exhibits a broad spectrum o

Marianna Marcella Bolognesi, Claudia Collacciani, Andrea Ferrari, Francesca Genovese

Word Ladders is a free mobile application for Android and iOS, developed for collecting linguistic data, specifically lists of words related to each other through semantic relations of categorical inclusion, within the Abstraction project (ERC-2021-STG-101039777). We hereby provide an overview of Word Ladders, explaining its game logic, motivation and expect

Andrew Balthrop, Hyunseok Jung

Jarring events inspiring reflection, known as ``shocks" in the literature, are the motive force in explaining changes in employee embeddedness and retention within the unfolding model of labor turnover. Substantial research effort has examined strategies for insulating valued employees from adverse shocks. However, this paper provides empirical evidence that

Duván Cardona, Alexandre Kirilov, Wagner A. A. de Moraes, André Pedroso Kowacs

We analyze the sharpness of the Sobolev order for left-invariant vector fields on compact Riemannian manifolds. Utilizing techniques from pseudo-differential operator theory and microlocal analysis, we investigate the asymptotic behavior of eigenvalues associated with these vector fields. As an application, we demonstrate the ill-posedness of a class of Cauc

Luis A. Escamilla, Supriya Pan, Eleonora Di Valentino, Andronikos Paliathanasis

The main aim of this work is to use a model-independent approach, along with late-time observational probes, to reconstruct the dark energy (DE) equation of state $w_{\rm DE}(z)$. Our analysis showed that, for a late time universe, $w_{\rm DE}$ deviates from being a constant but in contrast exhibits an oscillatory behavior, hence both quintessence ($w_{\rm D

Pablo Barham Alzás, Radi Radev

Neutrino-nucleus interactions play an important role in present and future neutrino experiments. The accurate simulation of these interactions at low energies ($<$100 MeV) is crucial for the detection and study of supernova, solar and atmospheric neutrinos. In particular, the reconstruction of the incoming neutrino properties depends on the ability to measur

Hannah Kerner, Saketh Sundar, Mathan Satish

The goal of field boundary delineation is to predict the polygonal boundaries and interiors of individual crop fields in overhead remotely sensed images (e.g., from satellites or drones). Automatic delineation of field boundaries is a necessary task for many real-world use cases in agriculture, such as estimating cultivated area in a region or predicting end

Ali Hassanzadeh, Mojtaba Hosseini, John G. Turner

Problem definition: Professional sports leagues may be suspended due to various reasons such as the recent COVID-19 pandemic. A critical question the league must address when re-opening is how to appropriately select a subset of the remaining games to conclude the season in a shortened time frame. Academic/practical relevance: Despite the rich literature on

Stan Gudder

Transition probabilities are an important and useful tool in quantum mechanics. However, in their present form, they are limited in scope and only apply to pure quantum states. In this article we extend their applicability to mixed states and to transitions between quantum effects. We also present their dependence on a measured operation or instrument. We be