October 2024 arXiv papers — page 19

Gennian Ge, Zixiang Xu, Xiaochen Zhao

Celebrated results often unfold like episodes in a long-running series. In the field of extremal set thoery, Erd\H{o}s, Ko, and Rado in 1961 established that any $k$-uniform intersecting family on $[n]$ has a maximum size of $\binom{n-1}{k-1}$, with the unique extremal structure being a star. In 1967, Hilton and Milner followed up with a pivotal result, show

Ziyi Song, Weining Shen, Marina Vannucci, Alexandria Baldizon

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identif

Jie Sun, Junyan Zhang, Qian Xia, Chuanfu Sun

Dynamic positron emission tomography (PET) images can reveal the distribution of tracers in the organism and the dynamic processes involved in biochemical reactions, and it is widely used in clinical practice. Despite the high effectiveness of dynamic PET imaging in studying the kinetics and metabolic processes of radiotracers. Pro-longed scan times can caus

Yuechun Gu, Keke Chen

Differential privacy quantifies privacy through the privacy budget $\epsilon$, yet its practical interpretation is complicated by variations across models and datasets. Recent research on differentially private machine learning and membership inference has highlighted that with the same theoretical $\epsilon$ setting, the likelihood-ratio-based membership in

Yunong Tian, Tuan Li, Haitao Jiang, Zhipeng Wang

Global Navigation Satellite System/Inertial Navigation System (GNSS/INS)/Vision integration based on factor graph optimization (FGO) has recently attracted extensive attention in navigation and robotics community. Integrity monitoring (IM) capability is required when FGO-based integrated navigation system is used for safety-critical applications. However, tr

Neelesh Amrutha, Christian Wolf, Christopher A. Onken, Wei Jeat Hon

Changing-Look Active Galactic Nuclei (CLAGN) are characterised by extreme variations in line emission over short timescales, mostly affecting broad H$\beta$ lines. While a few hundred CLAGN are known, a complete sample of turn-on CLAGN is still elusive. Here, we present a search for turn-on CLAGN in a complete sample of galaxies, using archival spectra and r

Qian Chao, Jiun-Cheng Chen, Hsian-Hua Tseng

We prove a crepant transformation correspondence in genus zero Gromov-Witten theory for toric stack bundles related by crepant wall-crossings of the toric fibers. Specifically, we construct a symplectic transformation that identifies $I$-functions toric stack bundles suitably analytically continued using Mellin-Barnes integral approach. We compare our symple

Ananya Tadepalli

Modern web traffic relies on 2048-bit RSA encryption to secure our data in transit. Rapid advances in Quantum Computing pose a grave challenge by allowing hackers to break this encryption in hours. In August of 2024, the National Institute of Standards and Technology published Quantum-Safe Cryptography (QSC) standards, including CRYSTALS-Kyber for general en

Mohammad Mahmudul Alam, Alexander Oberle, Edward Raff, Stella Biderman

Vector Symbolic Architectures (VSAs) are one approach to developing Neuro-symbolic AI, where two vectors in $\mathbb{R}^d$ are `bound' together to produce a new vector in the same space. VSAs support the commutativity and associativity of this binding operation, along with an inverse operation, allowing one to construct symbolic-style manipulations over real

Jiun-Cheng Chen, Hsian-Hua Tseng

We introduce a class of flops between projective varieties modelled on direct sums of universal subbundles of Grassmannians. We study basic properties of these flops.

Gaven Martin, Cong Yao

We consider minimisers of the $p$-exponential conformal energy for homeomorphisms $f:R \to S$ of finite distortion $\IK(z,f)$ between analytically finite Riemann surfaces in a fixed homotopy class $[f_0]$,\[ \mE_p(f:R,S)=\int_R \exp(p\IK(z,f))\; d\sigma(z). \] Homeomorphic minimisers exist should the barrier be a homeomorphism of finite energy, $\mE_p(f_0,R,

S. S. Nalawade, R. S. Kim, J. Mahl, S. Cherono

Ruthenium metal and its oxide stand out for their exceptional catalytic activity, stability in Oxygen Evolution Reactions (OER) and electrical conductivity, making them indispensable in electronics and electrocatalysis. In this study, atomic layer deposition (ALD) was used to synthesize ruthenium thin films, and the subsequent annealing of deposited rutheniu

Ankur Garg, Meenakshi Sarkar, S. M. Moorthi, Debajyoti Dhar

Resourcesat-3S/3SA, an upcoming Indian satellite, is designed with Aft and Fore payloads capturing stereo images at look angles of -5deg and 26deg, respectively. Operating at 632.6 km altitude, it features a panchromatic (PAN) band offering a Ground Sampling Distance (GSD) of 1.25 meters and a 60 km swath. To balance swath width and resolution, an Instantane

Hao Wen

In this note we describe a logarithmic version of mirror Landau-Ginzburg model for a semi-projective toric manifold and show the ring of state space of the Landau-Ginzburg model is isomorphic to the $\C$-valued cohomology of the toric manifold.

Bhuwanesh Rao Patil, Mohan

Let $A$ be a set of natural numbers. A set $B$, a set of natural numbers, is an additive complement of the set $A$ if all sufficiently large natural numbers can be represented in the form $x+y$, where $x\in A$ and $y\in B$. Erd\H{o}s proposed a conjecture that every infinite set of natural numbers has a sparse additive complement, and in 1954, Lorentz proved

Lam Nguyen Tung, Steven Cho, Xiaoning Du, Neelofar Neelofar

Machine learning (ML) for text classification has been widely used in various domains. These applications can significantly impact ethics, economics, and human behavior, raising serious concerns about trusting ML decisions. Studies indicate that conventional metrics are insufficient to build human trust in ML models. These models often learn spurious correla

Junting Chen, Checheng Yu, Xunzhe Zhou, Tianqi Xu

Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based multi-agent systems (LLM-based MAS) have shown success in areas like software development and operating systems, but applying these systems to robot control presents unique challeng

Pierre-Étienne H. Fiquet, Eero P. Simoncelli

Temporal prediction is inherently uncertain, but representing the ambiguity in natural image sequences is a challenging high-dimensional probabilistic inference problem. For natural scenes, the curse of dimensionality renders explicit density estimation statistically and computationally intractable. Here, we describe an implicit regression-based framework fo

Jiahui Huang, Hua Feng, Lian Tao

Black hole low-mass X-ray binaries undergo quiescence-outburst cycles. During the outbursts, they typically go through a q-shaped pattern in the hardness-intensity diagram (HID), known as the hysteresis q-diagram, while the physical nature is still unknown. We argue that the hysteresis q-diagram can be well explained with a recently proposed magnetized accre

Garry Kuwanto, Chaitanya Agarwal, Genta Indra Winata, Derry Tanti Wijaya

Code-switching, the phenomenon of alternating between two or more languages in a single conversation, presents unique challenges for Natural Language Processing (NLP). Most existing research focuses on either syntactic constraints or neural generation, with few efforts to integrate linguistic theory with large language models (LLMs) for generating natural co

Changbeom Shim, Youngho Kim, Craig Butterworth

The sufficiency of accurate data is a core element in data-centric geotechnics. However, geotechnical datasets are essentially uncertain, whereupon engineers have difficulty with obtaining precise information for making decisions. This challenge is more apparent when the performance of data-driven technologies solely relies on imperfect databases or even whe

Guo Chen, Chao-Jun Feng, Wei Fang, Chenggang Shu

In this study, we introduced a phenomenological dark energy model $H^{2}+H^{-2}$ model) that incorporates the first-order approximation of Kaniadakis holographic entropy dark energy and utilizes the Hubble horizon, $1/H$ as the infrared cutoff. The advantage of this model is that it can relieve the Hubble tension issue and cicumventing the potential issue of

Daehee Lee, Minjong Yoo, Woo Kyung Kim, Wonje Choi

Continual Imitation Learning (CiL) involves extracting and accumulating task knowledge from demonstrations across multiple stages and tasks to achieve a multi-task policy. With recent advancements in foundation models, there has been a growing interest in adapter-based CiL approaches, where adapters are established parameter-efficiently for tasks newly demon

Tian Li, Tianyi Zhou, Jeffrey A. Bilmes

Sharpness-Aware Minimization (SAM) has been demonstrated to improve the generalization performance of overparameterized models by seeking flat minima on the loss landscape through optimizing model parameters that incur the largest loss within a neighborhood. Nevertheless, such min-max formulations are computationally challenging especially when the problem i

Shuai Wang, Zexian Li, Tianhui Song, Xubin Li

Arbitrary-resolution image generation still remains a challenging task in AIGC, as it requires handling varying resolutions and aspect ratios while maintaining high visual quality. Existing transformer-based diffusion methods suffer from quadratic computation cost and limited resolution extrapolation capabilities, making them less effective for this task. In

Martin Ostoja-Starzewski

Continuum-type constitutive relations of odd matter need to be formulated according to the second law of thermodynamics. Based on the primitive thermodynamics of Edelen, a procedure admitting most general relations, is outlined for heat-conducting fluids and solids. For viscous responses of odd matter, the theory accounts for the irreversible and non-dissipa

George Lyu, Fatemeh Nosrat, Andrew J. Schaefer

We explore the inverse of integer programs (IPs) by studying the inverse of their Gomory corner relaxations (GCRs). We show that solving a set of inverse GCR problems always yields an upper bound on the optimal value of the inverse IP that is at least as tight as the optimal value of the inverse of the linear program (LP) relaxation. We provide conditions un

Caleb Musfeldt

This thesis details a Python-based software designed to calculate the Jones polynomial, a vital mathematical tool from Knot Theory used for characterizing the topological and geometrical complexity of curves in \( \mathbb{R}^3 \), which is essential in understanding physical systems of filaments, including the behavior of polymers and biopolymers. The Jones

Yuechun Gu, Jiajie He, Keke Chen

Training data privacy has been a top concern in AI modeling. While methods like differentiated private learning allow data contributors to quantify acceptable privacy loss, model utility is often significantly damaged. In practice, controlled data access remains a mainstream method for protecting data privacy in many industrial and research environments. In

Charles Wiame, Ken R. Duffy, Muriel Médard

We introduce a novel approach to error correction decoding in the presence of additive alpha-stable noise, which serves as a model of interference-limited wireless systems. In the absence of modifications to decoding algorithms, treating alpha-stable distributions as Gaussian results in significant performance loss. Building on Guessing Random Additive Noise

Aobo Liang, Yan Sun, Nadra Guizani

In recent years, Transformer-based models (Transformers) have achieved significant success in multivariate time series forecasting (MTSF). However, previous works focus on extracting features either from the time domain or the frequency domain, which inadequately captures the trends and periodic characteristics. To address this issue, we propose a wavelet le

Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal, Imran Razzak

Visual Question Answering (VQA) has emerged as a promising area of research to develop AI-based systems for enabling interactive and immersive learning. Numerous VQA datasets have been introduced to facilitate various tasks, such as answering questions or identifying unanswerable ones. However, most of these datasets are constructed using real-world images,

Yuetian Luo, Chao Gao

This paper studies the construction of adaptive confidence intervals under Huber's contamination model when the contamination proportion is unknown. For the robust confidence interval of a Gaussian mean, we show that the optimal length of an adaptive interval must be exponentially wider than that of a non-adaptive one. An optimal construction is achieved thr

Shuzhen Li, Yuxin Chen, Xuesong Chen, Ruiyang Gao

Sleep monitoring plays a crucial role in maintaining good health, with sleep staging serving as an essential metric in the monitoring process. Traditional methods, utilizing medical sensors like EEG and ECG, can be effective but often present challenges such as unnatural user experience, complex deployment, and high costs. Ballistocardiography~(BCG), a type

Hernando Quevedo

We investigate how test particles absorbed by a black hole affect the properties of the event horizon. We consider particles that arrive from infinity with positive energy and cross the horizon. We also study the absorption of particles with negative energy, which are generated inside the ergosphere as the result of the decay of other particles, following th

Wen Chen, Ding Yuan, Xueshang Feng, Stefaan Poedts

Geomagnetically Induced Current (GIC) refers to the electromagnetic response of the Earth and its conductive modern infrastructures to space weather and would pose a significant threat to high-voltage power grids designed for the alternative current operation. To assess the impact of space weather on the power grid, one needs to calculate the GIC on a nation

Ruiyu Xiao, Lei Wu, Yuhang Gou, Weinan Zhang

Argumentative essay generation (AEG) aims to generate complete texts on specific controversial topics or debates. Although current AEG methods can generate individual opinions, they often overlook the high-level connections between these opinions. This often leads to the generated results being mired in logical confusion, unable to proof their own arguments

Yuqing Wang, Libo Liang, Qinpei Zheng, Qi Huang

The thermalization of an isolated quantum system is described by quantum mechanics and thermodynamics, while these two subjects are still not fully consistent with each other. This leaves a less-explored region where both quantum and thermal effects cannot be neglected, and the ultracold atom platform provides a suitable and versatile testbed to experimental

Ly V. Nguyen, Junil Choi, Bjorn Ottersten, A. Lee Swindlehurst

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging th

Karina Livramento, Francesco Noseda

We exhibit infinite lists of ramification indices $\delta$ for which the classical Lie groups over the ring of integers of $p$-adic fields admit a faithful self-similar action on a regular rooted $\delta$-ary tree in such a way that the action is transitive on the first level. These results follow from the study of virtual endomorphisms of the classical Lie

Edric, Ueta Daisuke, Kurokawa Yukimasa, Karlekar Jayashree

In this paper, we introduce a novel unbiased regression loss for DETR-based detectors. The conventional $L_{1}$ regression loss tends to bias towards larger boxes, as they disproportionately contribute more towards the overall loss compared to smaller boxes. Consequently, the detection performance for small objects suffers. To alleviate this bias, the propos

Guande He, Kaiwen Zheng, Jianfei Chen, Fan Bao

Diffusion models (DMs) have become the dominant paradigm of generative modeling in a variety of domains by learning stochastic processes from noise to data. Recently, diffusion denoising bridge models (DDBMs), a new formulation of generative modeling that builds stochastic processes between fixed data endpoints based on a reference diffusion process, have ac

Quanyong Zhu, Xiaobin Luo, A. A. Shanenko, Yajiang Chen

Recently, interference-induced surface superconductivity (SC) has been predicted within an attractive Hubbard model with $s$-wave pairing, prompting intensive studies of its properties. The most notable finding is that the surface critical temperature $T_{cs}$ can be significantly enhanced relative to the bulk critical temperature $T_{cb}$. In this work, con

Nazanin Dehghan, Alessio D'Errico, Yingwen Zhang, Benjamin Sussman

Two-photon states generated through degenerate spontaneous parametric down-conversion (SPDC) can exhibit sharp correlations in the transverse spatial coordinates. This property leads to unique free-space propagation features. Here, we show that a phase object placed in the image plane of the source affects the free space propagation of the SPDC in a way that

Chunhe Li, Hongyi Bian, Yateng Qiao, Jin Zhu

The physical mechanism of microbial motion near solid-liquid interfaces is crucial for understanding various biological phenomena and developing ecological applications. However, limited works have been conducted on the swimming behavior of C. reinhardtii, a typical "puller" type cell, near solid surfaces, particularly with varying and conflicting experiment

Deng-Shan Wang, Dinghao Zhu, Xiaodong Zhu

This paper employs the Riemann-Hilbert problem to provide a comprehensive analysis of the asymptotic behavior of the high-genus Korteweg-de Vries soliton gases. It is demonstrated that the two-genus soliton gas is related to the two-phase Riemann-Theta function as \(x \to +\infty\), and approaches to zero as \(x \to -\infty\). Additionally, the long-time asy

Ian M. Newsome, Paul R. Anderson, Eric M. Grotzke

An investigation of the validity of the semiclassical approximation to quantum electrodynamics in 1+1 dimensions is given. The criterion for validity used here involves the impact of quantum fluctuations introduced through a two-point function which emerges naturally when considering the stability of the backreaction equation to linear order perturbations, r

Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang

Transformer-based autoregressive sampling has been the major bottleneck for slowing down large language model inferences. One effective way to accelerate inference is \emph{Speculative Decoding}, which employs a small model to sample a sequence of draft tokens and a large model to validate. Given its empirical effectiveness, the theoretical understanding of

Huiqiu Lin, Lianping Liu, Zhe You, Da Zhao

Let $\Delta$ and $B$ be the maximum vertex degree and a subset of vertices in a graph $G$ respectively. In this paper, we study the first (non-trivial) Steklov eigenvalue $\sigma_2$ of $G$ with boundary $B$. Using metrical deformation via flows, we first show that $\sigma_2 = \mathcal{O}\left(\frac{\Delta(g+1)^3}{|B|}\right)$ for graphs of orientable genus $

Qian Chen, Ling Chen

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a Deep Evolutionary Clustering jointed temporal kno

Seok Hyung Lie, James Fullwood

The theory of quantum states over time extends the density operator formalism into the temporal domain, providing a unified of treatment of timelike and spacelike separated systems in quantum theory. Although recent results have characterized quantum states over time involving two timelike separated systems, it remains unclear how to consistently extend the

Ziyang Gong, Zhixiang Wei, Di Wang, Xiaoxing Hu

The field of Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios. Despite the substantial domain gaps in RS images that are characterized by variabilities such as location, wavelength, and sensor type, research in this area rem

Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta

Mobile devices such as smartphones, laptops, and tablets can often connect to multiple access networks (e.g., Wi-Fi, LTE, and 5G) simultaneously. Recent advancements facilitate seamless integration of these connections below the transport layer, enhancing the experience for apps that lack inherent multi-path support. This optimization hinges on dynamically d

Kohei Yoshimura, Yoh Maekawa, Ryuna Nagayama, Sosuke Ito

Thermodynamic force and irreversible current are the foundational concepts of classical nonequilibrium thermodynamics. Entropy production rate is provided by their product in classical systems, ranging from mesoscopic to macroscopic systems. However, there is no complete quantum extension of such a structure that respects quantum mechanics. In this paper, we

Ryan Hasler, Konstantin Läufer, George K. Thiruvathukal, Huiyun Peng

Computing systems are consuming an increasing and unsustainable fraction of society's energy footprint, notably in data centers. Meanwhile, energy-efficient software engineering techniques are often absent from undergraduate curricula. We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate soft

Sunhwa Hwang, Hansub Hwang, Kangjin Kim, Andrew Byun

Movable single atoms have drawn significant attention for their potentials as flying quantum memory in non-local, dynamic quantum computing architectures. However, when dynamic optical tweezers are employed to control atoms opto-mechanically, conventional methods such as adiabatic controls and constant jerk controls are either inherently slow or induce mecha

FNU Aryan, Simon Stepputtis, Sarthak Bhagat, Joseph Campbell

Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn varying compositions of the same scene, our method reasons over their constituent objects and analyzes their arrangement to infer a scene's meaning. We propose a novel approach tha

Timothée Leleu, Samuel Reifenstein

We propose a general framework for a hybrid continuous-discrete algorithm that integrates continuous-time deterministic dynamics with Metropolis-Hastings steps to combine search dynamics with and without detailed balance. Our purpose is to study the non-equilibrium dynamics that leads to the ground state of rugged energy landscapes in this general setting. O

Vahid Kamali, Rudnei O. Ramos

We explore a model of a pseudo-Nambu-Goldstone boson inflaton field coupled to a non-Abelian $SU(2)$ gauge field. This model naturally leads to a warm inflation scenario, where the inflationary dynamics is dominated by thermal dissipation. In this work, we consider a scenario where the inflaton, an axion-like field, is coupled to the $SU(2)$ gauge field, sim

Ryozo Masukawa, Sanggeon Yun, Yoshiki Yamaguchi, Mohsen Imani

Video crime detection is a significant application of computer vision and artificial intelligence. However, existing datasets primarily focus on detecting severe crimes by analyzing entire video clips, often neglecting the precursor activities (i.e., privacy violations) that could potentially prevent these crimes. To address this limitation, we present PV-VT

Dung Thuy Nguyen, Taylor T. Johnson, Kevin Leach

Federated Learning (FL) shows promise in preserving privacy and enabling collaborative learning. However, most current solutions focus on private data collected from a single domain. A significant challenge arises when client data comes from diverse domains (i.e., domain shift), leading to poor performance on unseen domains. Existing Federated Domain General

Masataka Mogi, Dongsung Choi, Louis Primeau, Baiqing Lv

Ultrafast photoexcitation offers a novel approach to manipulating quantum materials. One of the long-standing goals in this field is to achieve optical control over topological properties. However, the impact on their electronic structures, which host gapless surface states, has yet to be directly observed. Here, using time- and angle-resolved photoemission

E. Brodsky, P. Dangwal, S. Hamlin, L. Chekhov

We consider the symplectic groupoid of pairs $(B, A)$ with $A$ real unipotent upper-triangular matrix and $B\in GL_n$ being such that $\tilde A=BAB^T$ is also a unipotent upper-triangular matrix. Fock and Chekhov defined a Poisson map of Teichm\"uller space ${\mathcal T_{g,s}$ of genus $g$ surfaces with $s$ holes into the space of unipotent upper-triangular

Plabon Paul, Md. Nazmul Islam, Fazle Rafsani, Pegah Khorasani

Uncontrolled cell division in the brain is what gives rise to brain tumors. If the tumor size increases by more than half, there is little hope for the patient's recovery. This emphasizes the need of rapid and precise brain tumor diagnosis. When it comes to analyzing, diagnosing, and planning therapy for brain tumors, MRI imaging plays a crucial role. A brai

Jean-Lou De Carufel, Paola Flocchini, Nicola Santoro, Frédéric Simard

We consider the Cops and Robber pursuit-evasion game when the edge-set of the graph is allowed to change in time, possibly at every round. Specifically, the game is played on an infinite periodic sequence $\mathcal{G} = (G_0, \dots, G_{p-1})^*$ of graphs on the same set $V$ of $n$ vertices: in round $t$, the topology of $\mathcal{G}$ is $G_i=(V,E_i)$ where $

Arkaprava Roy, Anindya Roy, Subhashis Ghosal

Under a high-dimensional vector autoregressive (VAR) model, we propose a way of efficiently estimating both the stationary graph structure between the nodal time series and their temporal dynamics. The framework is then used to make inferences on the change in interdependencies between several economic indicators due to the impact of the Great Recession, the

Sopam Dasgupta, Joaquín Arias, Elmer Salazar, Gopal Gupta

Machine learning models are increasingly used in critical areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transparency is crucial, as individuals need explanations to understand decisions, primarily if the decisions result in an undesired outcome. Our work introduces CoGS (Counterfac

Zhijie Chen, Hanqing Zhao

We study the weakly coupled nonlinear Schr\"odinger system \begin{equation*} \begin{cases} -\Delta u_1 = \mu_1 u_1^{p} +\beta u_1^{\frac{p-1}{2}} u_2^{\frac{p+1}{2}}\text{ in } \Omega,\\ -\Delta u_2 = \mu_2 u_2^{p} +\beta u_2^{\frac{p-1}{2}}u_1^{\frac{p+1}{2}} \text{ in } \Omega,\\ u_1,u_2>0\quad\text{in }\;\Omega;\quad u_1=u_2=0 \quad\text { on } \;\partial

Saul D. Freedman, Hong Yi Huang, Melissa Lee, Kamilla Rekvényi

A base for a finite permutation group $G \le \mathrm{Sym}(\Omega)$ is a subset of $\Omega$ with trivial pointwise stabiliser in $G$, and the base size of $G$ is the smallest size of a base for $G$. Motivated by the interest in groups of base size two, Burness and Giudici introduced the notion of the Saxl graph. This graph has vertex set $\Omega$, with edges

Keiichiro Takeda, Naoki Sato

We present the noncanonical Hamiltonian structure of the relativistic Euler equations for a perfect fluid in Minkowski spacetime. By identifying the system's noncanonical Poisson bracket and Hamiltonian, we show that relativistic fluid flows preserve helicity and enstrophy as conserved quantities in three-dimensional and two-dimensional cases, respectively.

Yuliy Baryshnikov, Efe Onaran

We introduce a dynamic model where the state space is the set of contractible cubical sets in the Euclidian space. The permissible state transitions, that is addition and removal of a cube to/from the set, are closest to Eden model with topological constraints, and, we show, are locally decidable. We prove that in the planar special case the state space is c

A. A. Ovchinnikov

We present simple derivation of the Luttinger liquid relation for the 1D Hubbard model both for finite $U$ and in the $U=\infty$ limit. We describe the simple solution of the Hubbard model in the infinite repulsion limit and use it to calculate the correlators of the model in this limit in a simple and a physical way using the Bosonization technique. We then

Cristina Sgattoni, Luca Sgheri, Matthias Chung

FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) was selected in 2019 as the ninth Earth Explorer mission by the European Space Agency (ESA). Its primary objective is to collect interferometric measurements in the Far-InfraRed (FIR) spectral range, which accounts for 50\% of Earth's outgoing longwave radiation emitted into space, and will

Xin Li, Changhao Li, Xiangui Chen, Zaixin Wang

A multiple-image method is developed to accurately calculate the electrostatic interaction between neutral dielectric particles and a uniformly charged dielectric substrate. The difference in dielectric constants between the particle and the solvent medium leads to a reversal of positive and negative polarizations in the particle. The variance in dielectric

Andrea C. Burgess, Peter Danziger, Daniel Horsley, Muhammad Tariq Javed

Given positive integers $v$, $k$, $t$ and $\lambda$ with $v \geq k \geq t$, a packing design PD$_{\lambda}(v,k,t)$ is a pair $(V,\mathcal{B})$, where $V$ is a $v$-set and $\mathcal{B}$ is a collection of $k$-subsets of $V$ such that each $t$-subset of $V$ appears in at most $\lambda$ elements of $\mathcal{B}$. When $\lambda=1$, a PD$_1(v,k,t)$ is equivalent

Sumegha Garg, Madhu Sudan, Gabriel Wu

Motivated by recent advances in locally testable codes and quantum LDPCs based on robust testability of tensor product codes, we explore the local testability of tensor products of (an abstraction of) algebraic geometry codes. Such codes are parameterized by, in addition to standard parameters such as block length $n$ and dimension $k$, their genus $g$. We s

Michał Bobula, Tomasz Pawłowski

The loop quantum gravitational collapse of the dust ball in presence of positive cosmological constant is investigated within the Oppenheimer-Snyder collapse scenario. The dust ball interior is described within the framework of loop quantum cosmology, while its exterior geometry is determined by the differentiability of the spacetime metric at the dust ball

Yuya Haraguchi, Yuto Yoshida, Akira Matsuo, Koichi Kindo

We report the magnetic properties of CaCo$_2$TeO$_6$ as a Kitaev candidate. CaCo$_2$TeO$_6$ was synthesized through a topochemical process, wherein all Na$^+$ ions in Na$_2$Co$_2$TeO$_6$ were replaced with half the amount of Ca$^{2+}$ ions. This substitution brings the CoO$_6$ octahedra closer to an approximate cubic symmetry. CaCo$_2$TeO$_6$ exhibits antife

Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann

This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-grade sensors. Gradient Boosting Models (XGBoost, LightGBM, CatBoost) and sequential models (LSTM, GRU) were t

Jinhao Cheng, Chen Wang, Wenxue He, Jiaojiao Wang

Anderson localization transitions are a universal quantum phenomenon sensitive to the disorder and dimensionality of electronic systems. Over the past decades, this intriguing topic has inspired overwhelmingly more theoretical studies than experimental verifications due to the difficulty of controlling a material's disorder or dimensionality without modi

Hyun-Young Park, Shahab Asoodeh, Si-Hyeon Lee

The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the f-divergence between original and sampling distr

Charleston Dale M. Ambatali, Shinichi Nakasuka

To achieve efficient long-range wireless power transfer (WPT), large antenna systems are necessary spanning lengths of tens to thousands of meters in one dimension. This creates an array in the order of at least hundreds of thousands to billions of elements. This makes the implementation of beamforming control a challenge. Various works focus on iterative op

James W. A. Strachan, Oriana Pansardi, Eugenio Scaliti, Marco Celotto

Large Language Models (LLMs) are capable of reproducing human-like inferences, including inferences about emotions and mental states, from text. Whether this capability extends beyond text to other modalities remains unclear. Humans possess a sophisticated ability to read the mind in the eyes of other people. Here we tested whether this ability is also prese

Marco Minici, Luca Luceri, Federico Cinus, Emilio Ferrara

Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on $\mathbb{X}$ (formerly Twitter). Using our machine learning

Zhaoting Chen, Alkistis Pourtsidou

We study the viability of using power spectrum clustering wedges as summary statistics of 21cm surveys during the Epoch of Reionization (EoR). For observations in a wide redshift range $z\sim 7-9$ corresponding to a line-of-sight scale of $\sim 500$Mpc, the power spectrum is subject to anisotropic effects due to the evolution along the light-of-sight. Inform

Dan Abramovich, Ming Hao Quek

We first introduce and study the notion of multi-weighted blow-ups, which is later used to systematically construct an explicit yet efficient algorithm for functorial logarithmic resolution in characteristic zero, in the sense of Hironaka. Specifically, for a singular, reduced closed subscheme $X$ of a smooth scheme $Y$ over a field of characteristic zero, w

Elie Genois, Noah J. Stevenson, Noah Goss, Irfan Siddiqi

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we

Yi-Ting Huang, Ying-Ren Guo, Guo-Wei Wong, Meng Chang Chen

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. Our approach assumes that real-world syste

Gerard Ben Arous, Pax Kivimae

This is the second of a series of three papers about the Elastic Manifold model. This classical model proposes a rich picture due to the competition between the inherent disorder and the smoothing effect of elasticity. In this paper, we analyze our variational formula for the free energy obtained in our first companion paper [16]. We show that this variation

Oswin So, Cheng Ge, Chuchu Fan

Current reinforcement-learning methods are unable to directly learn policies that solve the minimum cost reach-avoid problem to minimize cumulative costs subject to the constraints of reaching the goal and avoiding unsafe states, as the structure of this new optimization problem is incompatible with current methods. Instead, a surrogate problem is solved whe

Sabyasachi Bandyopadhyay, Jiaqing Zhang, Ronald L. Ison, David J. Libon

The association between preoperative cognitive status and surgical outcomes is a critical, yet scarcely explored area of research. Linking intraoperative data with postoperative outcomes is a promising and low-cost way of evaluating long-term impacts of surgical interventions. In this study, we evaluated how preoperative cognitive status as measured by the c

Yeeka Yau

In this article, we establish some new combinatorial properties of cone types in Coxeter groups. Firstly, we show that for any element $x$ in a Coxeter group $W$ and root $\beta$ in its inversion set $\Phi(x)$, the set of elements $y \in W$ satisfying $\Phi(x) \cap \Phi(y) = \{ \beta \}$ is convex in the weak order and admits a unique minimal representative.

Harry Cheon, Anneke Wernerfelt, Sorelle A. Friedler, Berk Ustun

Consumer protection rules require companies that deploy models to automate decisions in high-stakes settings to explain predictions to decision subjects. These rules are motivated, in part, by the belief that explanations can promote recourse by revealing information that decision subjects can use to contest or overturn their predictions. In practice, compan

Alexander K Taylor, Anthony Cuturrufo, Vishal Yathish, Mingyu Derek Ma

We seek to address a core challenge facing current Large Language Models (LLMs). LLMs have demonstrated superior performance in many tasks, yet continue to struggle with reasoning problems on explicit graphs that require multiple steps. To address this gap, we introduce a novel benchmark designed to evaluate LLM performance on classical algorithmic reasoning

Christopher R. Hayner, John M. Carson, Behçet Açıkmeşe, Karen Leung

Perception algorithms are ubiquitous in modern autonomy stacks, providing necessary environmental information to operate in the real world. Many of these algorithms depend on the visibility of keypoints, which must remain within the robot's line-of-sight (LoS), for reliable operation. This paper tackles the challenge of maintaining LoS on such keypoints duri

Tejas Raja

The paper discusses how Systolic Arrays can improve matrix multiplication for deep neural networks (DNNs). With AI models like OpenAI's GPT now containing trillions of parameters, the need for efficient matrix multiplication is more critical than ever. In this paper, the three main systolic array data flows: Weight Stationary (WS), Input Stationary (IS), and

Hao Zhao, Rong Pan

An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for assessing system health to predict imminent system breakdowns. First, the Gaussian Derivative Change-Point Detection (GDCPD) algorithm is proposed for detecting changes in the high-di

Kwanghee Han, Minhyun Cho, Taehyung Kim, Seung Tae Kim

Moir\'e superlattice of twisted hexagonal boron nitride (hBN) has emerged as an advanced atomically thin van der Waals interfacial ferroelectricity platform. Nanoscale periodic ferroelectric moir\'e domains with out-of-plane potentials in twisted hBN allow the hosting of remote Coulomb superlattice potentials to adjacent two-dimensional materials for tailori

Royi Rassin, Aviv Slobodkin, Shauli Ravfogel, Yanai Elazar

We introduce GRADE, an automatic method for quantifying sample diversity in text-to-image models. Our method leverages the world knowledge embedded in large language models and visual question-answering systems to identify relevant concept-specific axes of diversity (e.g., ``shape'' for the concept ``cookie''). It then estimates frequency distributions of co

Christos Fragkathoulas, Vasiliki Papanikou, Evaggelia Pitoura, Evimaria Terzi

Counterfactual explanations assess unfairness by revealing how inputs must change to achieve a desired outcome. This paper introduces the first graph-based framework for generating group counterfactual explanations to audit group fairness, a key aspect of trustworthy machine learning. Our framework, FACEGroup (Feasible and Actionable Counterfactual Explanati