February 2024 arXiv papers — page 55

Young Woo Nam

The first order difference equation induced by the sequence of maps on $ \mathbb{C} $ has Hyers-Ulam stability where the limit of the geometric average of growth rate is convergent and not equal to one. %The average growth rate is a generalization of contracting or expanding constant of maps. We show no Hyers-Ulam stability where the average growth rate is (

Xuxi Chen, Zhendong Wang, Daouda Sow, Junjie Yang

In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets, with a specific focus on selective retention of samples that i

Jihyeok Jung, Chan-Oi Song, Deok-Joo Lee, Kiho Yoon

This study introduces an optimal mechanism in a dynamic stochastic knapsack environment. The model features a single seller who has a fixed quantity of a perfectly divisible item. Impatient buyers with a piece-wise linear utility function arrive randomly and they report the two-dimensional private information: marginal value and demanded quantity. We derive

Yupeng Cao, Aishwarya Muralidharan Nair, Nastaran Jamalipour Soofi, Elyon Eyimife

Scientific facts are often spun in the popular press with the intent to influence public opinion and action, as was evidenced during the COVID-19 pandemic. Automatic detection of misinformation in the scientific domain is challenging because of the distinct styles of writing in these two media types and is still in its nascence. Most research on the validity

Alessandro Bravetti, Miguel Ángel García Ariza, Pablo Padilla

We frame Newton's Law of Cooling as a gradient flow within the context of information geometry. This connects it to a thermodynamic uncertainty relation and the Horse-Carrot Theorem, and reveals novel instances of asymmetric relaxations in endoreversible processes. We present a general criterion for predicting asymmetries using the Amari-Chentsov tensor, app

Teng-Hui Huang, Hesham El Gamal

We propose two novel extensions of the Wyner common information optimization problem. Each relaxes one fundamental constraints in Wyner's formulation. The \textit{Variational Wyner Common Information} relaxes the matching constraint to the known distribution while imposing conditional independence to the feasible solution set. We derive a tight surrogate upp

Wen-Di Guo, Qin Tan

From a five-dimensional Einstein-Maxwell theory, Bah et al. constructed a singularity free topology star/black hole [Phys. Rev. Lett. 126, 151101 (2021)]. After the Klein-Kluza reduction, i.e., integrating the extra space dimension, it can obtain an effective four-dimensional static spherical charged black hole with scalar hair. In this paper, we study the q

Jikai Jin, Vasilis Syrgkanis

Average treatment effect estimation is the most central problem in causal inference with application to numerous disciplines. While many estimation strategies have been proposed in the literature, the statistical optimality of these methods has still remained an open area of investigation, especially in regimes where these methods do not achieve parametric r

Xiangyong Luo, Michael J. Kuby, Yudai Honma, Mouna Kchaou-Boujelben

This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics via continuous-time fluid queue models and discrete flow refueling location modeling, all in the context of innovation diffusion principles. We employ a continuous-time approximation based on Ordinary Differen

S. -H. Yang, C. -M. Pi

We investigate the structure and the tidal deformability of the color-flavor locked strange stars admixed with mirror dark matter. Assuming the stars in the GW170817 event have a mirror-dark-matter core or a mirror-dark-matter halo, the observations of the central compact object within the supernova remnant HESS J1731-347 and the compact objects in the GW190

Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson

Optimal trajectory design is computationally expensive for nonlinear and high-dimensional dynamical systems. The challenge arises from the non-convex nature of the optimization problem with multiple local optima, which usually requires a global search. Traditional numerical solvers struggle to find diverse solutions efficiently without appropriate initial gu

Anisha Agarwal, Aaron Chan, Shubham Chandel, Jinu Jang

The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code Llama offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, utilizing LLMs out of the box is

Xin Bing, Bingqing Li, Marten Wegkamp

Linear Discriminant Analysis (LDA) is a fundamental method for classification. Its simple linear structure facilitates interpretation, and it is naturally suited to multi-class settings. LDA is also closely connected to several classical multivariate techniques, including Fisher's discriminant analysis, canonical correlation analysis, and linear regression.

Zhiyuan Wang, Jinhao Duan, Chenxi Yuan, Qingyu Chen

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative i

Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin

Recent studies have demonstrated that large language models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content. The existing evaluation metrics and methods to address these ethical challenges use datasets intentionally created by instructing humans to create instances including ethical pro

Bo-Jie Deng, Rui Guo, Jian-Wen Zhang

We focus on the semi-discrete complex modified Korteweg-de Vries (DcmKdV) equation in this paper. The direct and inverse scattering theory is developed with zero and non-zero boundary conditions (BCs) of the potential. For direct problem, the properties of the eigenfunctions and the scattering matrix, including analyticity, asymptotics and symmetries, are in

Xin Jin, Zhu Cao, Yang Tang, Juergen Kurths

In this paper, we consider the partial quantum consensus problem of a qubit network in a distributed view. The local quantum operation is designed based on the Hamiltonian by using the local information of each quantum system in a network of qubits. We construct the unitary transformation for each quantum system to achieve the partial quantum consensus, i.e.

Yudong Ren, Kangpeng Ye, Qiaolu Chen, Fujia Chen

Topological phases have prevailed across diverse disciplines, spanning electronics, photonics, and acoustics. Hitherto, the understanding of these phases has centred on energy (frequency) bandstructures, showcasing topological boundary states at spatial interfaces. Recent strides have uncovered a unique category of bandstructures characterized by gaps in mom

Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy

Machine learning (ML) algorithms can often differ in performance across domains. Understanding $\textit{why}$ their performance differs is crucial for determining what types of interventions (e.g., algorithmic or operational) are most effective at closing the performance gaps. Existing methods focus on $\textit{aggregate decompositions}$ of the total perform

Xin-Yang Zheng, Hao Pan, Yu-Xiao Guo, Xin Tong

As a promising 3D generation technique, multiview diffusion (MVD) has received a lot of attention due to its advantages in terms of generalizability, quality, and efficiency. By finetuning pretrained large image diffusion models with 3D data, the MVD methods first generate multiple views of a 3D object based on an image or text prompt and then reconstruct 3D

Nur Yildirim, Hannah Richardson, Maria T. Wetscherek, Junaid Bajwa

Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient'

Qian Zhu, Zhuo Wang, Wei Zeng, Wai Tong

Situated visualization blends data into the real world to fulfill individuals' contextual information needs. However, interacting with situated visualization in public environments faces challenges posed by user acceptance and contextual constraints. To explore appropriate interaction design, we first conduct a formative study to identify user needs for data

Sumin Lee, Jihoon Kim, Namwoo Kang

Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies certain kinematic or quasi-static requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, while the quasi-static requirements of a mechanism may include its torque

Deepanshu Trivedi, Arjuna Madanayake, Alex Krasnok

In experimental physics, it is essential to understand electromagnetic (EM) wave scattering across EM spectrum, from radio waves to X-rays, and is pivotal in driving photonics innovations. Recent advancements have uncovered phenomena like bound states in the continuum (BICs) and parity-time (PT) symmetric systems, which are closely associated with the charac

Tyler J. Evans, Alice Fialowski, Yong Yang

We show that the Heisenberg Lie algebras over a field $\mathbb{F}$ of characteristic $p>0$ admit a family of restricted Lie algebras, and we classify all such non-isomorphic restricted Lie algebra structures. We use the ordinary 1- and 2-cohomology spaces with trivial coefficients to compute the restricted 1- and 2-cohomology spaces of these restricted Heise

Tofara Moyo, Panashe Chiurunge

We propose a neural coding framework harmonic toroidal codes in which abstract cognitive operations are implemented through dynamical activity on manifolds derived from music theoretic structures.

Li-Li Ye, Chen-Di Han, Ying-Cheng Lai

The phenomenon of spin-dependent quantum scattering in two-dimensional (2D) pseudospin-1/2 Dirac materials leading to a relativistic quantum chimera was recently uncovered. We investigate spin-dependent Dirac electron optics in 2D pseudospin-1 Dirac materials, where the energy-band structure consists of a pair of Dirac cones and a flat band. In particular, w

Lingzhong Zeng

In this paper, we consider the eigenvalue problem of Dirac operator on a compact Riemannian manifold isometrically immersed into Euclidean space and derive some extrinsic estimates for the sum of arbitrary consecutive $n$ eigenvalues of the square of the Dirac operator acting on some Dirac invariant subbundles. As some applications, we deduce some eigenvalue

Cheng Qian, Xiaoxian Lao, Chunguang Li

Anomaly localization, which involves localizing anomalous regions within images, is a significant industrial task. Reconstruction-based methods are widely adopted for anomaly localization because of their low complexity and high interpretability. Most existing reconstruction-based methods only use normal samples to construct model. If anomalous samples are a

Jinyi Liu, Yifu Yuan, Jianye Hao, Fei Ni

Recently, there has been considerable attention towards leveraging large language models (LLMs) to enhance decision-making processes. However, aligning the natural language text instructions generated by LLMs with the vectorized operations required for execution presents a significant challenge, often necessitating task-specific details. To circumvent the ne

Xinglin Zhou, Yifu Yuan, Shaofu Yang, Jianye Hao

Hierarchical reinforcement learning (HRL) provides a promising solution for complex tasks with sparse rewards of intelligent agents, which uses a hierarchical framework that divides tasks into subgoals and completes them sequentially. However, current methods struggle to find suitable subgoals for ensuring a stable learning process. Without additional guidan

Li-Li Ye, Ying-Cheng Lai

When a static electrical field is applied to a two-dimensional (2D) Dirac material, Landau-Zener transition (LZT) and Bloch-Zener oscillations can occur. Employing alpha-T3 lattices as a paradigm for a broad class of 2D Dirac materials, we uncover two phenomena. First, due to the arbitrarily small energy gaps near a Dirac point that make it more likely for L

R. Peredo-Ortiz, O. Joaquín-Jaime, L. López-Flores, M. Medina-Noyola

We propose a first-principles theoretical approach for the description of the aging of the linear viscoelastic properties of a colloidal liquid after a sudden quench into a dynamically arrested (glass or gel) state. Specifically, we couple a general expression for the time-evolving shear-stress relaxation function $\eta(\tau;t)$ (whose $\tau$-integral is the

Chengzhang Yu, Xianjun Yang, Wenxia Bao, Shaonan Wang

In environments where RGB images are inadequate, pressure maps is a viable alternative, garnering scholarly attention. This study introduces a novel self-supervised pressure map keypoint detection (SPMKD) method, addressing the current gap in specialized designs for human keypoint extraction from pressure maps. Central to our contribution is the Encoder-Fuse

Gui-Qiang G. Chen, James Glimm, Hamid Said

A principle of maximum entropy is proposed in the context of viscous incompressible flow in Eulerian coordinates. The relative entropy functional, defined over the space of $L^2$ divergence-free velocity fields, is maximized relative to alternate measures supported over the energy--enstrophy surface. Since thermodynamic equilibrium distributions are characte

Hideki Tanimura, Albert Bonnefous, Jia Liu, Sanmay Ganguly

In this work, we seek to improve the velocity reconstruction of clusters by using Graph Neural Networks -- a type of deep neural network designed to analyze sparse, unstructured data. In comparison to the Convolutional Neural Network (CNN) which is built for structured data such as regular grids, GNN is particularly suitable for analyzing galaxy catalogs. In

Xiao-Dan Zhu, Di Wu, Dan Wen

In this paper, we investigate the topological numbers of rotating charged AdS black holes in both four- and five-dimensional gauged supergravity theories. Our analysis is conducted within the framework of the thermodynamical topological approach to black holes, utilizing the generalized off-shell Helmholtz free energy. We demonstrate that the number of rotat

Li-Li Ye, Chen-Di Han, Ying-Cheng Lai

The emergence of a flat band in Dirac-Weyl materials offers new possibilities for electronic transitions, leading to stronger interaction with light. As a result, the optical conductivity can be significantly enhanced in these flat-band materials as compared with graphene, making them potentially better candidates for optical sensing and modulation. Recently

Jiaqian Liu, Shengyu Tang

This article delves into the $L_p$ Minkowski problem within the framework of generalized Gaussian probability space. This type of probability space was initially introduced in information theory through the seminal works of Lutwak, Yang, and Zhang [49,50], as well as by Lutwak, Lv, Yang, and Zhang [45]. The primary focus of this article lies in examining the

Cristian Jardon, Brian Sheppard, Veet Zaveri

We design a motion planning algorithm to coordinate the movements of two robots along a figure eight track, in such a way that no collisions occur. We use a topological approach to robot motion planning that relates instabilities in motion planning algorithms to topological features of configuration spaces. The topological complexity of a configuration space

Peng Gao, Tao Yu, Fei Wang, Ru-Yue Yuan

Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are n

Mengqi Lu, Robert B. Mann

We evaluate the quantum gravity partition function that counts the dimension of the Hilbert space of a simply connected spatial region of fixed proper volume in the context of Lovelock gravity, generalizing the results for Einstein gravity [1]. We find that there exists sphere saddle metrics for a partition function at a fixed spatial volume in Lovelock theo

Joshua Zelinsky

Write $T(n)$ as the sum of the reciprocals of the primes which divide $n$. Write $H(n) = \prod_{p|n}p/(p-1)$ where the product is over the prime divisors of $n$. We prove new bounds for $T(n)$ and $H(n)$ in terms of the smallest prime factor of $n$, under the assumption that $n$ is an odd perfect number. Some of the results also apply under the weaker assump

Quentin Bramas, Sayaka Kamei, Anissa Lamani, Sébastien Tixeuil

We consider a collection of $k \geq 2$ robots that evolve in a ring-shaped network without common orientation, and address a variant of the crash-tolerant gathering problem called the \emph{Stand-Up Indulgent Gathering} (SUIG): given a collection of robots, if no robot crashes, robots have to meet at the same arbitrary location, not known beforehand, in fini

Tong Li, Zhuoni Qian, Michael A. Schmidt, Man Yuan

The axion-like particle (ALP) may induce flavor-changing neutral currents (FCNCs) when their Peccei-Quinn charges are not generation universal. The search for flavor-violating ALP couplings with a bottom quark so far focused on FCNC processes of $B$ mesons at low energies. The recent measurements of $B\to K +X$ rare decays place stringent bounds on the quark

Zi-Kui Liu

During his time at Royal Institute of Technology (Kungliga Tekniska Hogskolan, KTH) in Sweden, the present author learned nonequilibrium thermodynamics from Mats Hillert. The key concepts are the separation of internal and external variables of a system and the definitions of potentials and molar quantities. In equilibrium thermodynamics derived by Gibbs, th

Lichi Li, Zainul Abi Din, Zhen Tan, Sam London

In the evolving e-commerce field, recommendation systems crucially shape user experience and engagement. The rise of Consumer-to-Consumer (C2C) recommendation systems, noted for their flexibility and ease of access for customer vendors, marks a significant trend. However, the academic focus remains largely on Business-to-Consumer (B2C) models, leaving a gap

Jason Gaitonde, Elchanan Mossel

We consider the problem of linear regression with self-selection bias in the unknown-index setting, as introduced in recent work by Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis [STOC 2023]. In this model, one observes $m$ i.i.d. samples $(\mathbf{x}_{\ell},z_{\ell})_{\ell=1}^m$ where $z_{\ell}=\max_{i\in [k]}\{\mathbf{x}_{\ell}^T\mathbf{w}_i+\eta_{i,\el

Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai

Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences. Given the evolving nature of human preferences, continual alignment becomes more crucial and practical in comparison to traditional static alignment. Nevertheless, making RLHF compatible with Continual Learni

Pauline Bourigault, Dongpo Xu, Danilo P. Mandic

We develop a robust quaternion recurrent neural network (QRNN) for real-time processing of 3D and 4D data with outliers. This is achieved by combining the real-time recurrent learning (RTRL) algorithm and the maximum correntropy criterion (MCC) as a loss function. While both the mean square error and maximum correntropy criterion are viable cost functions, i

P. P. Li, L. Tao, R. C. Ma, M. Y. Ge

We present a comprehensive timing analysis on the Be/X-ray binary pulsar RX J0440.9+4431 using observations from \textit{NICER} and \textit{Insight}-HXMT during the 2022--2023 outburst. The power density spectrum (PDS) of RX J0440.9+4431 exhibits typical aperiodic variability in X-ray flux across a wide frequency range. During a super-critical accretion stat

Changjiang Zhao, Shulin He, Xueliang Zhang

Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals. Currently, deep learning methods achieve great success in speech enhancement, e.g. the representative convolutional recurrent neural network (CRN) and its variants. However, CRN typically employs consecutive

Alexandria Leto, Elliot Pickens, Coen D. Needell, David Rothschild

The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics

Rui An, Kimberly K. Boddy, Vera Gluscevic

We analyze cosmic microwave background (CMB) data to constrain the mass and interaction strengths of thermally-produced dark matter (DM) in a self-consistent manner, simultaneously taking into account the cosmological effects of its mass and interactions. The presence of a light thermal-relic particle contributes non-negligibly to the radiation density durin

Masato Fujita

Thamrongthanyalak demonstrated a definable version of Michael's selection theorem in d-minimal expansions of the real field. We generalize this result to the case in which the structures are d-minimal expansions of ordered fields $\mathcal F=(F,<,+,\cdot,0,1,\ldots)$. We also show that we can choose a definable continuous selection $f$ of a lower semi-contin

Hongyan Ji, Sriram V. Pemmaraju

The Knowledge Till rho CONGEST model is a variant of the classical CONGEST model of distributed computing in which each vertex v has initial knowledge of the radius-rho ball centered at v. The most commonly studied variants of the CONGEST model are KT0 CONGEST in which nodes initially know nothing about their neighbors and KT1 CONGEST in which nodes initiall

Liam Hodgson, Danilo Bzdok

The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the sizes of its constituent categories are unknown. Here, we prop

Amirhossein Taherpour, Abbas Taherpour, Tamer Khattab

We investigate the problem of spectrum sensing in cognitive radios (CRs) when the receivers are equipped with a large array of antennas. We propose and derive three detectors based on the concept of linear spectral statistics (LSS) in the field of random matrix theory (RMT). These detectors correspond to the generalized likelihood ratio (GLR), Frobenius norm

Jubee Sohn, Margaret J. Geller, Josh Borrow, Mark Vogelsberger

We examine the central stellar velocity dispersion of subhalos based on IllustrisTNG cosmological hydrodynamic simulations. The central velocity dispersion is a fundamental observable that links galaxies with their dark matter subhalos. We carefully explore simulated stellar velocity dispersions derived with different definitions to assess possible systemati

Darij Grinberg, Nazar Korniichuk, Kostiantyn Molokanov, Severyn Khomych

We prove a formula for the image of a skew Schur polynomial $s_{\lambda/\mu}\left( x_{1}, x_{2}, \ldots, x_{N}\right) $ under the differential operator $\nabla:= \dfrac{\partial}{\partial x_{1}} +\dfrac{\partial}{\partial x_{2}}+\cdots+\dfrac{\partial}{\partial x_{N}}$. This generalizes a formula of Weigandt for $\nabla\left( s_{\lambda}\right) $.

Hirotaka Akatsuka, Yuya Murakami

In 2013 Bettin and Conrey have introduced a cotangent sum $c \colon \mathbb{Q}_{>0}\to \mathbb{R}$, which can be regarded as a variant of the Dedekind sum. They have discovered that the cotangent sum satisfies a kind of reciprocity laws. Roughly speaking, the reciprocity law for $c(x)$ means that there is a relation between $c(x)$ and $c(1/x)$ modulo holomor

Yu-Qi Yang, Yu-Xiao Guo, Yang Liu

Data diversity and abundance are essential for improving the performance and generalization of models in natural language processing and 2D vision. However, 3D vision domain suffers from the lack of 3D data, and simply combining multiple 3D datasets for pretraining a 3D backbone does not yield significant improvement, due to the domain discrepancies among di

Philippe Di Francesco, Rinat Kedem, Sergey Khoroshkin, Gus Schrader

We give a description of the Halln\"as--Ruijsenaars eigenfunctions of the 2-particle hyperbolic Ruijsenaars system as matrix coefficients for the order 4 element $S\in SL(2,\mathbb{Z})$ acting on the Hilbert space of $GL(2)$ quantum Teichm\"uller theory on the punctured torus. The $GL(2)$ Macdonald polynomials are then obtained as special values of the analy

Xinke Shen, Lingyi Tao, Xuyang Chen, Sen Song

Neural representations induced by naturalistic stimuli offer insights into how humans respond to stimuli in daily life. Understanding neural mechanisms underlying naturalistic stimuli processing hinges on the precise identification and extraction of the shared neural patterns that are consistently present across individuals. Targeting the Electroencephalogra

Maria Chudnovsky, Peter Gartland, Sepehr Hajebi, Daniel Lokshtanov

We prove that for every integer $t\geq 1$ there exists an integer $c_t\geq 1$ such that every $n$-vertex even-hole-free graph with no clique of size $t$ has treewidth at most $c_t\log{n}$. This resolves a conjecture of Sintiari and Trotignon, who also proved that the logarithmic bound is asymptotically best possible. It follows that several \textsf{NP}-hard

Shrushti Tapar, Bhaskaran Muralidharan

Generating discernible valley contrasts and segregating valley-indexed fermions in real space within graphene poses considerable challenges due to the isotropic transport within the continuum energy range for degenerate valleys. This study unveils an interesting finding: introducing valley contrast through anisotropic chiral transport in isotropic Dirac syst

Z. Zheng, Q. Hao, Y. Qiu, J. Hong

Studies on the dynamics of solar filaments have significant implications for understanding their formation, evolution, and eruption, which are of great importance for space weather warning and forecasting. The H$\alpha$ Imaging Spectrograph (HIS) onboard the recently launched Chinese H$\alpha$ Solar Explorer (CHASE) can provide full-disk solar H$\alpha$ spec

Wenlong Deng, Blair Chen, Beidi Zhao, Chiyu Zhang

Mitigating biases in machine learning models has become an increasing concern in Natural Language Processing (NLP), particularly in developing fair text embeddings, which are crucial yet challenging for real-world applications like search engines. In response, this paper proposes a novel method for learning fair text embeddings. First, we define a novel cont

Yijia Shao, Yucheng Jiang, Theodore A. Kanell, Peter Xu

We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. This underexplored problem poses new challenges at the pre-writing stage, including how to research the topic and prepare an outline prior to writing. We propose STORM, a writing system for the Syn

Vahidin Jeleskovic, Yinan Wan

This study delves into the intra-industry effects following a firm-specific scandal, with a particular focus on the Facebook data leakage scandal and its associated events within the U.S. tech industry and two additional relevant groups. We employ various metrics including daily spread, volatility, volume-weighted return, and CAPM-beta for the pre-analysis c

Amit Kumar Singh Yadav, Ziyue Xiang, Kratika Bhagtani, Paolo Bestagini

Many deep learning synthetic speech generation tools are readily available. The use of synthetic speech has caused financial fraud, impersonation of people, and misinformation to spread. For this reason forensic methods that can detect synthetic speech have been proposed. Existing methods often overfit on one dataset and their performance reduces substantial

Kaijian Cao

This paper makes some modifications to the warped product space. Based on Alias,Impera and Rigoli, a warping function is added to the warped product space. This new function affects the Riemannian metric of the warped product space. In this new warped product space, we continue to discuss the sufficient condition for calculating the height of the immersed su

Lynnette Hui Xian Ng, Ian Kloo, Kathleen M. Carley

The COVID-19 pandemic of 2021 led to a worldwide health crisis that was accompanied by an infodemic. A group of 12 social media personalities, dubbed the ``Disinformation Dozen", were identified as key in spreading disinformation regarding the COVID-19 virus, treatments, and vaccines. This study focuses on the spread of disinformation propagated by this grou

Mitchell Black, Zhengchao Wan, Gal Mishne, Amir Nayyeri

The distinguishing power of graph transformers is closely tied to the choice of positional encoding: features used to augment the base transformer with information about the graph. There are two primary types of positional encoding: absolute positional encodings (APEs) and relative positional encodings (RPEs). APEs assign features to each node and are given

Mohit Garg, Debajyoti Kar, Arindam Khan

In the Maximum Independent Set of Hyperrectangles problem, we are given a set of $n$ (possibly overlapping) $d$-dimensional axis-aligned hyperrectangles, and the goal is to find a subset of non-overlapping hyperrectangles of maximum cardinality. For $d=1$, this corresponds to the classical Interval Scheduling problem, where a simple greedy algorithm returns

Younghun Lee, Dan Goldwasser, Laura Schwab Reese

Understanding the dynamics of counseling conversations is an important task, yet it is a challenging NLP problem regardless of the recent advance of Transformer-based pre-trained language models. This paper proposes a systematic approach to examine the efficacy of domain knowledge and large language models (LLMs) in better representing conversations between

Simone Biondini, Nora Brambilla, Gramos Qerimi, Antonio Vairo

For a quantitative investigation on the time evolution of heavy thermal dark matter at and after thermal freeze-out, near-threshold processes need to be taken into account which have a large impact on the observed dark matter relic abundance. In this conference paper, we study the recoil effect of heavy dark matter pairs in a thermal bath and compute the ann

Jérémi Do Dinh, Alexandros Hollender

We study public goods games, a type of game where every player has to decide whether or not to produce a good which is public, i.e., neighboring players can also benefit from it. Specifically, we consider a setting where the good is indivisible and where the neighborhood structure is represented by a directed graph, with the players being the nodes. Papadimi

Gabriel Currier, Kenneth Moore, Chi Hoi Yip

A conjecture of Erd\H{o}s, Graham, Montgomery, Rothschild, Spencer and Straus states that, with the exception of equilateral triangles, any two-coloring of the plane will have a monochromatic congruent copy of every three-point configuration. This conjecture is known only for special classes of configurations. In this manuscript, we confirm one of the most n

Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park

Despite the remarkable achievements of neural radiance fields (NeRF) in representing 3D scenes and generating novel view images, the aliasing issue, rendering "jaggies" or "blurry" images at varying camera distances, remains unresolved in most existing approaches. The recently proposed mip-NeRF has addressed this challenge by rendering conical frustums inste

Younghun Lee, Sungchul Kim, Tong Yu, Ryan A. Rossi

Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is challenging for LLMs to integrate structured data (e.g. KG, tables, DBs) into their prompts; LLMs need to either understand long text data or select the most relevant evidence prior t

Catherine Weaver, Chen Tang, Ce Hao, Kenta Kawamoto

Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must model complex environment dynamics and human decision-making. Sequence modeling is highly effective in capturing intricate patterns of motion sequences but struggles to adapt to new

Abel C. H. Chen

With the development of Shor's algorithm, some nondeterministic polynomial (NP) time problems (e.g. prime factorization problems and discrete logarithm problems) may be solved in polynomial time. In recent years, although some homomorphic encryption algorithms have been proposed based on prime factorization problems, the algorithms may be cracked by quantum

Masaaki Homma, Seon Jeong Kim

We introduce the notion of a relative of the Hermitian curve of degree $\sqrt{q}+1$ over $\mathbb{F}_q$, which is a plane curve defined by \[(x^{\sqrt{q}}, y^{\sqrt{q}}, z^{\sqrt{q}})A {}^t \!(x,y,z) =0\] with $A \in GL(3, \mathbb{F}_q)$, and study their basic properties, one of which is that the number of $\mathbb{F}_q$-points of any relative of the Hermiti

Junxu Li

Here we revisit the quantum phase estimation (QPE) algorithm, and devise an iterative method to improve the precision of QPE with propagators over a variety of time spans. For a given propagator and a certain eigenstate as input, QPE with propagator is introduced to estimate the phase corresponding to an eigenenergy. Due to the periodicity of the complex exp

Ali Övgün, Reggie C. Pantig, Ángel Rincón

In this manuscript, we explore the shadow and the greybody bounding characteristics of a regular black hole within 4-dimensional space-time, employing the context of gravity that is scale-dependent. Our focus lies in determining limitations on the parameter denoted as $\tilde{\epsilon}$, which serves as a descriptor for the scale-dependent solution with resp

Marco Aldi, Andrew Butler, Jordan Gardiner, Daniele Grandini

We describe a canonical decomposition of the cohomology of the Dani-Mainkar metabelian Lie algebras associated with graphs. As applications, we obtain explicit formulas for the third cohomology of any Dani-Mainkar Lie algebra and for the cohomology in all degrees of Lie algebras associated with arbitrary star graphs. We also describe a procedure to reduce th

Yuhang Zhou, Xuan Lu, Wei Ai

In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution. Analyzing a large dataset of sampled English tweets, we examine how newly released emojis gain traction and evolve in meaning. We find that community size of early adopters and

Xin Li

Just as the arrow of time structures physics, the arrow of inference organizes cognition, directing the flow of information in perception, action, and memory. The Context-Content Uncertainty Principle (CCUP) formalizes this asymmetry, between high-entropy context and low-entropy content, and frames inference as a cycle that aligns the two through selective,

Shuang Chen, Amir Atapour-Abarghouei, Hubert P. H. Shum

Existing image inpainting methods leverage convolution-based downsampling approaches to reduce spatial dimensions. This may result in information loss from corrupted images where the available information is inherently sparse, especially for the scenario of large missing regions. Recent advances in self-attention mechanisms within transformers have led to si

Tran Duy Anh Le, Michael P. Reidy, Eliot J. Smith

In this paper, we examine Yangjun Chen's technical report titled ``The 2-MAXSAT Problem Can Be Solved in Polynomial Time'' [Che23], which revises and expands upon their conference paper of the same name [Che22]. Chen's paper purports to build a polynomial-time algorithm for the ${\rm NP}$-complete problem 2-MAXSAT by converting a 2-CNF formula into a graph t

Polina Proskura, Alexey Zaytsev

In machine learning, ensembles are important tools for improving the model performance. In natural language processing specifically, ensembles boost the performance of a method due to multiple large models available in open source. However, existing approaches mostly rely on simple averaging of predictions by ensembles with equal weights for each model, igno

Xu Shen, Yongkeun Choi, Alex Wong, Francesco Borrelli

This paper introduces a comprehensive approach to optimize parking efficiency for connected and Automated vehicle (CAVs) fleets. We present a multi-vehicle parking simulator, equipped with hierarchical path planning and collision avoidance capabilities for individual CAVs. The simulator is designed to capture the key decision-making processes in parking, fro

Jiliang Li, Yifan Zhang, Zachary Karas, Collin McMillan

Recent language models have demonstrated proficiency in summarizing source code. However, as in many other domains of machine learning, language models of code lack sufficient explainability. Informally, we lack a formulaic or intuitive understanding of what and how models learn from code. Explainability of language models can be partially provided if, as th

Yi-Ting Chuang, Liang-Yan Hsu

In this study, we unveil an eccentric superradiance phenomenon in molecular aggregates coupled to surface plasmon polaritons. Through the quantization of electromagnetic fields in media, we demonstrate that superradiance can be significantly enhanced by polaritons and its behavior distinguishably surpasses the Dick&#39;s $N$ scaling law. To understand the me

Christina Schenk, Aditya Vasudevan, Maciej Haranczyk, Ignacio Romero

Mathematical and computational tools have proven to be reliable in decision-making processes. In recent times, in particular, machine learning-based methods are becoming increasingly popular as advanced support tools. When dealing with control problems, reinforcement learning has been applied to decision-making in several applications, most notably in games.

Quentin Gazda, Andreas Maurischat

Let $E$ be an Anderson $A$-module over $\mathbb{C}_{\infty}$. The period lattice of $E$ is related to its module of special functions by means of a non-canonical isomorphism introduced by the authors in [GM21]. In this paper, we explain how a modification of the inverse map is canonical by interpreting it as a residue morphism along the characteristic graph.

Leon Bungert, Jeff Calder, Tim Roith

In this paper we prove the first quantitative convergence rates for the graph infinity Laplace equation for length scales at the connectivity threshold. In the graph-based semi-supervised learning community this equation is also known as Lipschitz learning. The graph infinity Laplace equation is characterized by the metric on the underlying space, and conver

Quentin Gazda

The deepest arithmetic invariants attached to an algebraic variety defined over a number field $F$ are conjecturally captured by the integral part of its motivic cohomology. There are essentially two ways of defining it when $X$ is a smooth projective variety: one is via the $K$-theory of a regular model, the other is through its $\ell$-adic realization. Bot

Vida Dujmović, Pat Morin, David R. Wood, David Worley

Motivated by recent developments regarding the product structure of planar graphs, we study relationships between treewidth, grid minors, and graph products. We show that the Cartesian product of any two connected $n$-vertex graphs contains an $\Omega(\sqrt{n})\times\Omega(\sqrt{n})$ grid minor. This result is tight: The lexicographic product (which includes