October 2023 arXiv papers — page 55

Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee

Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (.i.e, generating large-scale harmful and misleading content). To combat this emerging risk of LLMs, we propose a novel "Fighting Fire with Fire" (F3) strategy that harnesses modern LLMs' generative and emergent reasoning capabiliti

A. V. Parafilo, V. M. Kovalev, I. G. Savenko

We study the influence of many-body interactions on the transport properties in a two-site charge Kondo circuit recently implemented in a hybrid metal-semiconductor double-quantum dot device [W. Pouse {\it et al.}, Nat. Phys. {\bf 19}, 492 (2023)]. There emerge two principal types of interactions: (i) an intrinsic one, described by the Luttinger liquid model

Zheng Zhao, Yftah Ziser, Bonnie Webber, Shay B. Cohen

We present an analysis tool based on joint matrix factorization for comparing latent representations of multilingual and monolingual models. An alternative to probing, this tool allows us to analyze multiple sets of representations in a joint manner. Using this tool, we study to what extent and how morphosyntactic features are reflected in the representation

Denis Chetverikov, Daniel Wilhelm

The slope coefficient in a rank-rank regression is a popular measure of intergenerational mobility. In this article, we first show that commonly used inference methods for this slope parameter are invalid. Second, when the underlying distribution is not continuous, the OLS estimator and its distribution may be sensitive to how ties in the ranks are handled.

Marah I Abdin, Suriya Gunasekar, Varun Chandrasekaran, Jerry Li

We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., 'a list of ice cream shops in San Diego'). In the past, such queries were considered to be tasks that could only be solved via web-search or knowledge bases. More recently, large language models (LLMs) have demonstrated initial emergent

Chunguang Tang, Shunxin Fei, G. David Lin, Yun Liu

Liquid organic hydrogen carriers (LOHCs) represent a promising approach for hydrogen storage due to their favorable properties including stability and compatibility with the existing infrastructure. However, fossil-based LOHC molecules are not green or sustainable. Here we examined the possibility of using norbelladine and trisphaeridine, two typical structu

Shreeharshini Dharanesh Murthy, Angel Jurado, Michael Betz, Qiang Du

The ongoing work to upgrade ALS to ALS-U demands strict RF requirements such as low jitter and low spurs frequency reference to meet its accelerator and science goals. A low phase noise dual frequency Master Oscillator (MO), where the two frequencies are related by a fractional ratio of 608/609 and flexible divide by four frequency outputs has been consolida

Wei-Chen Lin, Dong-han Yeom, Dejan Stojkovic

We construct an explicit model for the black hole to white hole transition (known as the black hole fireworks scenario) using the cut-and-paste technique. We model a black hole collapse using the evolution of a time-like shell in the background of the loop quantum gravity inspired metric. We then use the space-like shell analysis to construct the firework ge

Shunyao Zhang, Yonggan Fu, Shang Wu, Jyotikrishna Dass

Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployments of TNNs on edge devices which are constrained by strict limitations in terms of memory, computation, bandwidth, and power supply. To this end, we propose a framework called NetDistiller to boost the achievable accuracy of TNNs by treating

Kun Hu, Makishi Yamakoshi, Taishi Katsuragawa, Shin'ichi Nojiri

$f(Q)$ gravity is an extension of the symmetric teleparallel equivalent to general relativity (STEGR). This work shows that based on the scalar-nonmetricity formulation, a scalar mode in $f(Q)$ gravity has a negative kinetic energy. This conclusion holds regardless of the coincident gauge frequently used in STEGR and $f(Q)$ gravity. To study the scalar mode,

Shengze Zhong, Parinya Punpongsanon, Daisuke Iwai, Kosuke Sato

We propose an approach for the generation of topology-optimized structures with text-guided appearance stylization. This methodology aims to enrich the concurrent design of a structure's physical functionality and aesthetic appearance. Users can effortlessly input descriptive text to govern the style of the structure. Our system employs a hash-encoded neural

Sukwoong Choi, William S. Moses, Neil Thompson

Quantum computing promises transformational gains for solving some problems, but little to none for others. For anyone hoping to use quantum computers now or in the future, it is important to know which problems will benefit. In this paper, we introduce a framework for answering this question both intuitively and quantitatively. The underlying structure of t

Ryogo Yamamoto, Kanji Tanaka

Cross-view self-localization is a challenging scenario of visual place recognition in which database images are provided from sparse viewpoints. Recently, an approach for synthesizing database images from unseen viewpoints using NeRF (Neural Radiance Fields) technology has emerged with impressive performance. However, synthesized images provided by these tec

Zhejin Liu, Zebo Tang, Xin Wu, Wangmei Zha

The contribution of J/\psi production from coherent and incoherent photon-induced production is calculated in Au+Au and Cu+Cu collisions at $\sqrt{s_{NN}} = 200$ GeV. The yield and nuclear modification factors ($R_{AA}$) contributed by photoproduction are presented as functions of transverse momentum ($p_T$) and centrality at both mid- and forward rapidity.

Hiroshi Hirai, Yuni Iwamasa, Taihei Oki, Tasuku Soma

We address the computation of the degrees of minors of a noncommutative symbolic matrix of form \[ A[c] := \sum_{k=1}^m A_k t^{c_k} x_k, \] where $A_k$ are matrices over a field $\mathbb{K}$, $x_i$ are noncommutative variables, $c_k$ are integer weights, and $t$ is a commuting variable specifying the degree. This problem extends noncommutative Edmonds' probl

Ramiz Aktar, Kuo-Chuan Pan, Toru Okuda

We perform axisymmetric, two-dimensional magnetohydrodynamic (MHD) simulations to investigate accretion flows around spinning AGN. To mimic the space-time geometry of spinning black holes, we consider effective Kerr potential, and the mass of the black holes is $10^8 M_{\odot}$. We initialize the accretion disc with a magnetized torus by adopting the toroida

Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alexander Tessier

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of sin

E. Can Artun, Deniz Sarman, A. Nihat Berker

A nematic phase, previously seen in the d=3 classical Heisenberg spin-glass system, occurs in the n-component cubic-spin spin-glass system, between the low-temperature spin-glass phase and the high temperature disordered phase, for number of components n >= 3, in spatial dimension d=3, thus constituting a liquid-crystal phase in a dirty (quenched-disordered)

Chunguang Tang, Gang Sun, Yun Liu

Hydrogen diffusion is critical to the performance of metals for hydrogen storage as well as other important applications. As compared to its crystalline counterpart which follows the Arrhenius relation, hydrogen diffusion in amorphous metals sometimes are experimentally found to be non-Arrhenian. In this work we studied the diffusion of hydrogen in amorphous

Olivia Xiao, Stacy Wang, Min Chen

Meta-analysis is the aggregation of data from multiple studies to find patterns across a broad range relating to a particular subject. It is becoming increasingly useful to apply meta-analysis to summarize these studies being done across various fields. In meta-analysis, it is common to use the mean and standard deviation from each study to compare for analy

Arkadii Slinko

Fishburn's alternating scheme domains occupy a special place in the theory of Condorcet domains. Karpov (2023) generalised these domains and made an interesting observation proving that all of them are single-picked on a circle. However, an important point that all generalised Fishburn domains are maximal Condorcet domain remained unproved. We fill this gap

Zhen Li, Hao Zhou, Lingli Wang

Approximate computing is a promising approach to reduce the power, delay, and area in hardware design for many error-resilient applications such as machine learning (ML) and digital signal processing (DSP) systems, in which multipliers usually are key arithmetic units. Due to the underlying architectural differences between ASICs and FPGAs, existing ASIC-bas

Haofei Yu, Cunxiang Wang, Yue Zhang, Wei Bi

The Transformer architecture is crucial for numerous AI models, but it still faces challenges in long-range language modeling. Though several specific transformer architectures have been designed to tackle issues of long-range dependencies, existing methods like Transformer-XL are plagued by a high percentage of ineffective memories. In this study, we presen

Kai Xu, Kanokphon Phumphan, Wiriya Ruangyoo, Chia-Chu Chen

We systematically study hidden charm pentaquark states in the constituent quark model with a general Hamiltonian for multiquark systems, considering the coupling between the $\Sigma_c^{(*)}\bar{D}^{(*)}$ molecular states and the $q^3c\bar c$ compact pentaquark states by the one-gluon exchange hyperfine interaction. The ground state hidden-charm pentaquark ma

Minfang Lu, Yuchen Jiang, Huihui Dong, Qi Li

In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies with

Suchitra Narayanan, Jonathan P. Williams, John J. Tobin, Jes K. Jorgensen

We present the first results from the Early Planet Formation in Embedded Disks (eDisk) ALMA Large Program toward Oph IRS43, a binary system of solar mass protostars. The 1.3 mm dust continuum observations resolve a compact disk, ~6au radius, around the northern component and show that the disk around the southern component is even smaller, <~3 au. CO, 13CO,

Charles Cadieux, Mykhaylo Plotnykov, René Doyon, Diana Valencia

The two-planet transiting system LHS 1140 has been extensively observed since its discovery in 2017, notably with $Spitzer$, HST, TESS, and ESPRESSO, placing strong constraints on the parameters of the M4.5 host star and its small temperate exoplanets, LHS 1140 b and c. Here, we reanalyse the ESPRESSO observations of LHS 1140 with the novel line-by-line fram

M. Chakraborty, S. Ahmad, A. Chandra, S. R. Dugad

GRAPES-3 is a mid-altitude (2200 m) and near equatorial ($11.4^{\circ}$ North) air shower array, overlapping in its field of view for cosmic ray observations with experiments that are located in Northern and Southern hemispheres. We analyze a sample of $3.7\times10^9$ cosmic ray events collected by the GRAPES-3 experiment between 1 January 2013 and 31 Decemb

Wenqiang Zhu, Xin Wang, Chaoqian Wang, Longzhao Liu

A good group reputation often facilitates more efficient synergistic teamwork in production activities. Here we translate this simple motivation into a reputation-based synergy and discounting mechanism in the public goods game. Specifically, the reputation type of a group, either good or bad determined by a reputation threshold, modifies the nonlinear payof

Xia Gu, Xianjin Xie

We propose a systematic procedure to work out systems of topological defect lines (TDLs) in minimal models. The only input of this method is the modular invariant partition function. For diagonal and permutation diagonal models, we prove there is a bijection between simple TDLs and primary fields preserving fusion rules. For block-diagonal models, we work ou

Shuhao Zeng, Boya Di, Hongliang Zhang, Jiahao Gao

As one enabling technique of the future sixth generation (6G) network, ultra-massive multiple-input-multiple-output (MIMO) can support high-speed data transmissions and cell coverage extension. However, it is hard to realize the ultra-massive MIMO via traditional phased arrays due to unacceptable power consumption. To address this issue, reconfigurable intel

Octavio Arizmendi, Daniel Perales

We prove that the rate of convergence for the central limit theorem in finite free convolution is of order $n^{1/2}$

Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu, Hyunwoo J. Kim

Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based approaches formulate this task as a KG path searching problem, where messages are sequentially propagated from the seed node towards the answer nodes. However, these messages are past-

Linfang Wang, Caleb Terrill, Richard Wesel, Dariush Divsalar

Recently, neural networks have improved MinSum message-passing decoders for low-density parity-check (LDPC) codes by multiplying or adding weights to the messages, where the weights are determined by a neural network. The neural network complexity to determine distinct weights for each edge is high, often limiting the application to relatively short LDPC cod

Ao Mou, Yukang Lu, Jiahao He, Dingyao Min

Given the widespread adoption of depth-sensing acquisition devices, RGB-D videos and related data/media have gained considerable traction in various aspects of daily life. Consequently, conducting salient object detection (SOD) in RGB-D videos presents a highly promising and evolving avenue. Despite the potential of this area, SOD in RGB-D videos remains som

Pedro Alonso, Jun Zhang, Xiao-Dong Li

Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large scale galaxy

Miao Chen, Hui-Huang Chen

In this paper, we consider the R\'enyi entanglement asymmetry of excited states in the 1+1 dimensional free compact boson conformal field theory (CFT) at equilibrium. We obtain a universal CFT expression written by correlation functions for the charged moments via the replica trick. We provide detailed analytic computations of the second R\'enyi entanglement

Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Merhdad Honarkhah

Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis. In this paper, we leverage the power of diffusion model for generating synthetic tabular data. The heterogeneous features in tabular data have been main obstacles in tabular dat

Oswin So, Zachary Serlin, Makai Mann, Jake Gonzales

Control barrier functions (CBF) have become popular as a safety filter to guarantee the safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct functions that satisfy the CBF constraints for high relative degree systems with input constraints. To address these challenges, recent work has explored learning CBFs using

Kaiyan Zhang, Ning Ding, Biqing Qi, Xuekai Zhu

Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However, when tuning publicly accessible, centralized LLMs with private instruction data, privacy concerns are inevitable. While direct transfer of parameterized modules between models is a

Yuanfeng Song, Yuanqin He, Xuefang Zhao, Hanlin Gu

The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm. Along this line of research endeavors in the area, LLM-based prompting methods have attracted much attention, partially due to the technological advantages bro

Bingyu Hu, Ming-Jing Zhao

Quantum coherence is an important quantum resource and it is intimately related to various research fields. The geometric coherence is a coherence measure both operationally and geometrically. We study the trade-off relation of geometric coherence in qubit systems. We first derive an upper bound for the geometric coherence by the purity of quantum states. Ba

Tokuji Araya, Kei-ichiro Iima, Ryo Takahashi

Let R be a commutative noetherian ring. In this paper, we study, for the singularity category of R, the vanishing of the complexity $\delta_t(X,Y)$ in the sense of Dimitrov, Haiden, Katzarkov and Kontsevich. We prove that the set of real numbers t such that $\delta_t(X,Y)$ does not vanish is bounded in various cases. We do it by building the high syzygy modu

Viktoriia Borovik, Bernd Sturmfels, Svala Sverrisdóttir

We determine the number of complex solutions to a nonlinear eigenvalue problem on the Grassmannian in its Pl\"ucker embedding. This is motivated by quantum chemistry, where it represents the truncation to single electrons in coupled cluster theory. We prove the formula for the Grassmannian of lines which was conjectured in earlier work with Fabian Faulstich.

Peng Du, Hui Tang, Jun Zhang, Wenxian Zhang

Benchmarking a high-precision quantum operation is a big challenge for many quantum systems in the presence of various noises as well as control errors. Here we propose an $O(1)$ benchmarking of a dynamically corrected rotation by taking the quantum advantage of a squeezed spin state in a spin-1 Bose-Einstein condensate. Our analytical and numerical results

Mike Van Ness, Tomas Bosschieter, Natasha Din, Andrew Ambrosy

Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assume a log-linear hazard function as well as proportional hazards over time, and can perform poorly when these as

Qingqiang He, Nan Guan, Shuai Zhao, Mingsong Lv

This paper studies the response time bound of a DAG (directed acyclic graph) task. Recently, the idea of using multiple paths to bound the response time of a DAG task, instead of using a single longest path in previous results, was proposed and leads to the so-called multi-path bound. Multi-path bounds can greatly reduce the response time bound and significa

Zitao Wang, Xinyi Wang, Wei Hu

We study continual event extraction, which aims to extract incessantly emerging event information while avoiding forgetting. We observe that the semantic confusion on event types stems from the annotations of the same text being updated over time. The imbalance between event types even aggravates this issue. This paper proposes a novel continual event extrac

Xiaoyi Chen, Siyuan Tang, Rui Zhu, Shijun Yan

The rapid advancements of large language models (LLMs) have raised public concerns about the privacy leakage of personally identifiable information (PII) within their extensive training datasets. Recent studies have demonstrated that an adversary could extract highly sensitive privacy data from the training data of LLMs with carefully designed prompts. Howev

Mohammad Mohammadi, Ali Mohammadi

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consum

Haoran Li, Yuan-Hua Ni

This paper is on learning the Kalman gain by policy optimization method. Firstly, we reformulate the finite-horizon Kalman filter as a policy optimization problem of the dual system. Secondly, we obtain the global linear convergence of exact gradient descent method in the setting of known parameters. Thirdly, the gradient estimation and stochastic gradient d

Benyamin Haghi, Lin Ma, Sahin Lale, Anima Anandkumar

We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption. The proposed architecture leverages the energy efficiency of transistor

Ming Xie, Xiu-Xiu Zhan, Chuang Liu, Zi-Ke Zhang

Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in ordinary networks, higher-order interactions between entities in various real-world systems are not usually taken into account. In this p

Shivam Mathur, Keun Hee Park, Dhivya Chinnappa, Saketh Kotamraju

Interpreting answers to yes-no questions in social media is difficult. Yes and no keywords are uncommon, and the few answers that include them are rarely to be interpreted what the keywords suggest. In this paper, we present a new corpus of 4,442 yes-no question-answer pairs from Twitter. We discuss linguistic characteristics of answers whose interpretation

Saeid Bayat, Yong Hoon Lee, James T. Allison

Floating offshore wind turbine (FOWT) systems involve several coupled physical analysis disciplines, including aeroelasticity, multi-body structural dynamics, hydrodynamics, and controls. Conventionally, physical structure (plant) and control design decisions are treated as two separate problems, and generally, control design is performed after the plant des

Shuyang Bai, Jiemiao Chen

We consider empirical measures in a triangular array setup with underlying distributions varying as sample size grows. We study asymptotic properties of multiple integrals with respect to normalized empirical measures. Limit theorems involving series of multiple Wiener-It\^o integrals are established.

Ashish Sharma, Kevin Rushton, Inna Wanyin Lin, Theresa Nguyen

Self-guided mental health interventions, such as "do-it-yourself" tools to learn and practice coping strategies, show great promise to improve access to mental health care. However, these interventions are often cognitively demanding and emotionally triggering, creating accessibility barriers that limit their wide-scale implementation and adoption. In this p

Yang Li, Chunhe Xia, Chunyan Li, Yuan Zhao

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice d

Hannah L Weeks, Matthew S Shotwell

Opportunistic pharmacokinetic (PK) studies have sparse and imbalanced clinical measurement data, and the impact of sample time errors is an important concern when seeking accurate estimates of treatment response. We evaluated an approximate Bayesian model for individualized pharmacokinetics in the presence of time recording errors (TREs), considering both a

Tianyu Liang, Chao Chen, Per-Gunnar Martinsson, George Biros

Boundary value problems involving elliptic PDEs such as the Laplace and the Helmholtz equations are ubiquitous in mathematical physics and engineering. Many such problems can be alternatively formulated as integral equations that are mathematically more tractable. However, an integral-equation formulation poses a significant computational challenge: solving

Mingchao Cai, Meng Lei, Jingzhi Li, Jiaao Sun

In this work, we present an iteratively decoupled algorithm for solving the quasi-static multiple-network poroelastic model. Our approach employs a total-pressure-based formulation with solid displacement, total pressure, and network pressures as primary unknowns. This reformulation decomposes the original problem into a generalized Stokes problem and a para

Leonardino A. Digma MD, Joseph R. Winer PhD, Michael D. Greicius MD

Alzheimer's disease (AD) is a prevalent, progressive, and ultimately fatal neurodegenerative disorder that is defined pathologically by the accumulation of amyloid plaques and tau neurofibrillary tangles in the brain. There remains an unmet need for therapies that can halt or slow the course of AD. To address this need, the FDA has provided a mechanism, unde

Yuwen Lu, Ziang Tong, Qinyi Zhao, Chengzhi Zhang

The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mobile user interfaces (UIs). This position paper investigates the use of LLMs for UI layout generation. Central to our exploration is the introduction of UI grammar -- a novel approa

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song

We study a class of private learning problems in which the data is a join of private and public features. This is often the case in private personalization tasks such as recommendation or ad prediction, in which features related to individuals are sensitive, while features related to items (the movies or songs to be recommended, or the ads to be shown to use

Thai Duong Do, Van Thien Nguyen

We investigate the finite $p$-energy classes $E_p$ of quaternionic plurisubharmonic functions of Cegrell type. We also construct an example to show that the optimal constant in the energy estimate is strictly bigger than $1$ for $p>0$, $p\neq 1$. This leads us to the fact that we can not use the variational method to solve the quaternionic Monge-Amp\`ere equ

Shaolin Chen, Manzi Huang, Xiantao Wang, Jie Xiao

One of the celebrated results by Riesz \cite{Rie} is the Riesz conjugate functions theorem for analytic functions in the complex plane $\mathbb{C}$. The study on the Riesz conjugate functions theorem for functions in higher dimensional spaces has attracted much attention. Fefferman and Stein \cite{FS-1972} established the Riesz conjugate functions theorem fo

Chunguang Tang, Preetham Permude, Shunxin Fei, Terry J. Frankcombe

Understanding of the critical atomistic steps during the dehydrogenation process of liquid organic hydrogen carriers (LOHCs) is important to the design of cost-efficient, high-performance LOHC catalysts. Based on the density functional theory (DFT) we studied the thermodynamics and kinetics of the complete dehydrogenation path of perhydro-N-ethylcarbazole (1

Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer

This paper focuses on causal representation learning (CRL) under a general nonparametric latent causal model and a general transformation model that maps the latent data to the observational data. It establishes identifiability and achievability results using two hard uncoupled interventions per node in the latent causal graph. Notably, one does not know whi

Qian-Qian Chen, Ji-Ming Guo

Let $G$ be a graph with an adjacent matrix $A(G)$. The multiplicity of an arbitrary eigenvalue $\lambda$ of $A(G)$ is denoted by $m_\lambda(G)$. In \cite{Wong}, the author apply the Pater-Wiener Theorem to prove that if the diameter of $T$ at least $4$, then $m_\lambda(T)\leq \beta'(T)-1$ for any $\lambda\neq0$. Moreover, they characterized all trees with $m

Huiling Zhang, Zi Xu

Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose an accelerated first-order regularized momentum descent ascent algorithm (FORMDA) for solving stochastic nonconvex-concave minimax problems. The iteration complexity of the algorithm is pr

Hyun-Song Kwon, Sung-Hee Lee

Realistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single image with pose. Assuming that 3D garments are modeled by stitching 2D garment sewing patterns, our specific goal is to generate a textur

Sara Ayhan

It has been argued that reduction procedures are closely connected to the question about identity of proofs and that accepting certain reductions would lead to a trivialization of identity of proofs in the sense that every derivation of the same conclusion would have to be identified. In this paper it will be shown that the question, which reductions we acce

Dusan Guller

The propositional product logic is one of the basic fuzzy logics with continuous t-norms, exploiting the multiplication t-norm on the unit interval [0,1]. Our aim is to combine well-established automated deduction (theorem proving) with fuzzy inference. As a first step, we devise a modification of the procedure of Davis, Putnam, Logemann, and Loveland (DPLL)

Xiaojun Jia, Jianshu Li, Jindong Gu, Yang Bai

Adversarial training has shown promise in building robust models against adversarial examples. A major drawback of adversarial training is the computational overhead introduced by the generation of adversarial examples. To overcome this limitation, adversarial training based on single-step attacks has been explored. Previous work improves the single-step adv

Yang Li

We construct special Lagrangian pair of pants in general dimensions, inside the cotangent bundle of $T^n$ with the Euclidean structure.

Igor V. Moskalenko

The last two decades have brought spectacular advances in astrophysics of cosmic rays (CRs) and space- and ground-based astronomy. Launches of missions that employ forefront detector technologies enabled measurements with large effective areas, wide fields of view, and precision that we recently could not even dream of. Meanwhile, interpretation of the indiv

V. Shalgin, S. Tikhomirov

We consider various iterative algorithms for solving the linear equation $ax=b$ using a quantum computer operating on the principle of quantum annealing. Assuming that the computer's output is described by the Boltzmann distribution, it is shown under which conditions the equation-solving algorithms converge, and an estimate of their convergence rate is prov

Yuma Ichikawa, Koji Hukushima

Variational autoencoders (VAEs) face a notorious problem wherein the variational posterior often aligns closely with the prior, a phenomenon known as posterior collapse, which hinders the quality of representation learning. To mitigate this problem, an adjustable hyperparameter $\beta$ and a strategy for annealing this parameter, called KL annealing, are pro

Chaewon Park, Soohwan Kim, Kyubyong Park, Kunwoo Park

Numerous datasets have been proposed to combat the spread of online hate. Despite these efforts, a majority of these resources are English-centric, primarily focusing on overt forms of hate. This research gap calls for developing high-quality corpora in diverse languages that also encapsulate more subtle hate expressions. This study introduces K-HATERS, a ne

Olena Blumberg, Ben Morris, Hans Oberschelp

In each step of the overlapping cycles shuffle on $n$ cards, a fair coin is flipped which determines whether the $m$th card or the $n$th card is moved to the top of the deck. Angel, Peres, and Wilson showed the following interesting fact: If $m = \lfloor \alpha n \rfloor$ where $\alpha$ is rational, then the relaxation time of a single card in the overlappin

Jiewei Xiang, Meiting Song, Yi Zhang, Jennifer Kruschwitz

Multispectral imagers that capture spatial and spectral information are of growing importance in various fields, particularly in remote sensing and metrology. To enable integrated snapshot multispectral imagers and eliminate the drawbacks of traditional systems, such as bulkiness and slow scanning mechanisms, miniature, broadband multispectral filter arrays

Yu Nong, Richard Fang, Guangbei Yi, Kunsong Zhao

Accompanying the successes of learning-based defensive software vulnerability analyses is the lack of large and quality sets of labeled vulnerable program samples, which impedes further advancement of those defenses. Existing automated sample generation approaches have shown potentials yet still fall short of practical expectations due to the high noise in t

Savvas Petridis, Michael Terry, Carrie J. Cai

Prototyping AI applications is notoriously difficult. While large language model (LLM) prompting has dramatically lowered the barriers to AI prototyping, designers are still prototyping AI functionality and UI separately. We investigate how coupling prompt and UI design affects designers' workflows. Grounding this research, we developed PromptInfuser, a Figm

Minoo Mohebbifar, Mohammad Panahazari, Omid Mirzapour

Multi-port DC-DC converters are gaining more significance in modern power system environments by enabling the connection of multiple renewable energy sources, so the efficient operation of these converters is paramount. Soft switching methods increase efficiency in DC-DC converters and increase the reliability and lifespan of devices by relieving stress on c

Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus

Developing accurate off-policy estimators is crucial for both evaluating and optimizing for new policies. The main challenge in off-policy estimation is the distribution shift between the logging policy that generates data and the target policy that we aim to evaluate. Typically, techniques for correcting distribution shift involve some form of importance sa

Xinyang Rui, Omid Mirzapour, Brittany Pruneau, Mostafa Sahraei-Ardakani

The growing penetration of renewable energy requires upgrades to the transmission network to ensure the deliverability of renewable generation. As an efficient alternative to transmission expansion, flexible transmission technologies, whose benefits have been widely studied, can alleviate transmission system congestion and enhance renewable energy integratio

Jianghong Zhou, Bo Liu, Jhalak Nilesh Acharya Yao Hong, Kuang-chih Lee

In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptio

Kavel Rao, Liwei Jiang, Valentina Pyatkin, Yuling Gu

Moral or ethical judgments rely heavily on the specific contexts in which they occur. Understanding varying shades of defeasible contextualizations (i.e., additional information that strengthens or attenuates the moral acceptability of an action) is critical to accurately represent the subtlety and intricacy of grounded human moral judgment in real-life scen

Bin B. Ren, Chen Xie, Myriam Benisty, Ruobing Dong

While there have been nearly two dozen of spiral arms detected from planet-forming disks in near-infrared scattered light, none of their substellar drivers have been confirmed. By observing spiral systems in at least two epochs spanning multiple years, and measuring the motion of the spirals, we can distinguish the cause of the spirals, and locate the orbits

Weihong Qi

Sentiment analysis, widely critiqued for capturing merely the overall tone of a corpus, falls short in accurately reflecting the latent structures and political stances within texts. This study introduces topic metrics, dummy variables converted from extracted topics, as both an alternative and complement to sentiment metrics in stance classification. By emp

Savvas Petridis, Ben Wedin, James Wexler, Aaron Donsbach

Large language model (LLM) prompting is a promising new approach for users to create and customize their own chatbots. However, current methods for steering a chatbot's outputs, such as prompt engineering and fine-tuning, do not support users in converting their natural feedback on the model's outputs to changes in the prompt or model. In this work, we explo

Tianyi Chen, Tianyu Ding, Badal Yadav, Ilya Zharkov

Large Language Models (LLMs) have transformed the landscape of artificial intelligence, while their enormous size presents significant challenges in terms of computational costs. We introduce LoRAShear, a novel efficient approach to structurally prune LLMs and recover knowledge. Given general LLMs, LoRAShear at first creates the dependency graphs over LoRA m

Luca Ciambelli, Luis Lehner

The fluid-gravity correspondence is a duality between anti-de Sitter Einstein gravity and a relativistic fluid living at the conformal boundary. We show that one can accommodate the causal first-order viscous hydrodynamics recently developed by Bemfica, Disconzi, Noronha, and Kovtun in this framework, by requiring a set of natural conditions for the geometri

Justin Koeln, Trevor J. Bird, Jacob Siefert, Justin Ruths

This paper introduces zonoLAB, a MATLAB-based toolbox for set-based control system analysis using the hybrid zonotope set representation. Hybrid zonotopes have proven to be an expressive set representation that can exactly represent the reachable sets of mixed-logical dynamical systems and tightly approximate the reachable sets of nonlinear dynamic systems.

Matthew C. Kelley, Scott James Perry, Benjamin V. Tucker

Forced alignment systems automatically determine boundaries between segments in speech data, given an orthographic transcription. These tools are commonplace in phonetics to facilitate the use of speech data that would be infeasible to manually transcribe and segment. In the present paper, we describe a new neural network-based forced alignment system, the M

Joel Yuen-Zhou, Arghadip Koner

In this article, we show that the collective light-matter strong coupling regime, where $N$ molecular emitters couple to the photon mode of an optical cavity, can be mapped to a quantum impurity model where the photon is the impurity that is coupled to a bath of anharmonic transitions. In the thermodynamic limit where $N\gg1$, we argue that the bath can be r

Youngjun Ahn, Xiaoyu Guo, Rui Xue, Kejian Qu

Broken symmetries and electronic topology are nicely manifested together in the second order nonlinear optical responses from topologically nontrivial materials. While second order nonlinear optical effects from the electric dipole (ED) contribution have been extensively explored in polar Weyl semimetals (WSMs) with broken spatial inversion (SI) symmetry, th

Haotian Wang, Meng Yang, Nanning Zheng

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and

Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras

Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering. Our benchmark draws upon important theoretical requisites from psychology and necessary e

Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang

Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence information to be effective. However, in short texts, co-occurrence information is minimal, which results in feature sparsity in document representation. Therefore, existing topic models (p