December 2024 arXiv papers — page 74

Michael E. Glinsky

The theory of plasmas, that is collectives of charged particles, is developed using the coordinate-free and geometric methods of exterior calculus. This dramatically simplifies the algebra and gives a geometric physical interpretation. The fundamental foundation on which the theory is built is the conservation of phase space volume expressed by the Generaliz

Junyang Cai, Serdar Kadioglu, Bistra Dilkina

Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have shown a potential to speed up MIP solving via offline training that then guides important design decisions during the search. However, a significant drawback of these methods is their

S. S. Islam, V. Sazgari, C. Witteveen, J. N. Graham

Gaining a deeper understanding of the interplay between charge density wave (CDW) order and superconductivity in transition metal dichalcogenides (TMDs), particularly within the (4H/2H)-NbX$_{2}$ (X=Se,S) family, remains an open and intriguing challenge. A systematic microscopic study across various compounds in this family is therefore required to unravel t

Victor A. E. Farias, Felipe T. Brito, Cheryl Flynn, Javam C. Machado

Differentially private selection mechanisms are fundamental building blocks for privacy-preserving data analysis. While numerous mechanisms exist for single-objective selection, many real-world applications require optimizing multiple competing objectives simultaneously. We present two novel mechanisms for differentially private multi-objective selection: Pr

Phuc D. A. Nguyen

Oriented object detection in aerial images poses a significant challenge due to their varying sizes and orientations. Current state-of-the-art detectors typically rely on either two-stage or one-stage approaches, often employing Anchor-based strategies, which can result in computationally expensive operations due to the redundant number of generated anchors

Marjorie Decleir, Karl D. Gordon, Karl A. Misselt, Burcu Günay

We present the initial results of MEAD (Measuring Extinction and Abundances of Dust), with a focus on the dust extinction features observed in our JWST near- and mid-infrared spectra of nine diffuse Milky Way sightlines ($1.2 \leq A(V) \leq 2.5$). For the first time, we find strong correlations between the 10 $\mu$m silicate feature strength and the column d

Alexander Belles, Caryl Gronwall, Michael H. Siegel, Robin Ciardullo

We present Swift Ultraviolet Optical Telescope (UVOT) observations of the deep field GOODS-N in four near-UV filters. A catalog of detected galaxies is reported, which will be used to explore galaxy evolution using ultraviolet emission. Swift/UVOT observations probe galaxies at $z \lesssim 1.5$ and combine a wide field of view with moderate spatial resolutio

Ruhan Yi, Mihail Popescu, James M. Keller, Grant Scott

Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) can aid in tracking cardiovascular diseases (CVDs), sleep quality, sleep disorders, and reflect autonomic nervous system activity, stress levels, and overall well-being. These metrics are valuable in both clinical and everyday settings. In this paper, we present a transformer network

Vesa Kuikka, Lauri Pykälä, Tuomas Takko, Kimmo Kaski

In order to improve the resilience of computer infrastructure against cyber attacks and finding ways to mitigate their impact we need to understand their structure and dynamics. Here we propose a novel network-based influence spreading model to investigate event trajectories or paths in various types of attack and causal graphs, which can be directed, weight

Anxhelo Xhebraj, Sean Lee, Hanfeng Chen, Vinod Grover

We present JaxPP, a system for efficiently scaling the training of large deep learning models with flexible pipeline parallelism. We introduce a seamless programming model that allows implementing user-defined pipeline schedules for gradient accumulation. JaxPP automatically distributes tasks, corresponding to pipeline stages, over a cluster of nodes and aut

William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu

Large Language Models (LLMs) have demonstrated exceptional versatility across domains, including applications to electrocardiograms (ECGs). A growing body of work focuses on generating text from multi-channeled ECG signals and corresponding textual prompts. Existing approaches often involve a two-stage process: pretraining an ECG-specific encoder with a self

Izaias S. de Lima Neto, Marco A. A. de Aguiar Vieira, Anderson R. Tavares

Ludii is a Java general game system with a considerable number of board games, with an API for developing new agents and a game description language to create new games. To improve versatility and ease development, we provide Python interfaces for agent programming. This allows the use of Python modules to implement general game playing agents. As a means of

Arthur Josi, Luiz Gustavo Hafemann, Abdallah Dib, Emeline Got

Monocular facial performance capture in-the-wild is challenging due to varied capture conditions, face shapes, and expressions. Most current methods rely on linear 3D Morphable Models, which represent facial expressions independently of identity at the vertex displacement level. We propose SEREP (Semantic Expression Representation), a model that disentangles

Gaetan de Rassenfosse

This article examines the complex trade-offs inherent in the patent system, exploring whether patents truly incentivize innovation or inadvertently hinder progress. It traces the historical evolution of patent rights from their origins in Renaissance Venice to the modern framework enshrined in constitutional and international law. By balancing the exclusivit

Shixiong Wang, Wei Dai, Jianyong Sun, Zongben Xu

Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception. Typical applications range from mobile networks and the Internet of Things to radar and sensor grids. Despite transformative capabilities, wireless systems often face diverse uncertainties in design and operation, such as modeling error

Yuxuan Jiang, Francis Ferraro

Recently, Large Language Models (LLMs) have shown impressive performance in character understanding tasks, such as analyzing the roles, personalities, and relationships of fictional characters. However, the extensive pre-training corpora used by LLMs raise concerns that they may rely on memorizing popular fictional works rather than genuinely understanding a

Patrick Thomas, Kevin Schroeder, Jonathan Black

Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradients to train a neural network to act as a velocity controller for a quadcopter. The quadcopter's objective is to quickly fly through a gate wh

Elif Ayten, Shuai Wang, Hjalmar Snoep

Recent advances in generative AI make it convenient to create different types of content, including text, images, and code. In this paper, we explore the generation of images in the style of paintings in the surrealism movement using vision-language generative models, including DALL-E, Deep Dream Generator, and DreamStudio. Our investigation starts with the

Laura Cañadillas-Delgado, Lidia Mazzuca, Sanliang Ling, Matthew J. Cliffe

The hybrid perovskites [CH$_3$NH$_3$]Co$_x$Ni$_{x-1}$(HCOO)$_3$ with $x$ = 0, 0.25, 0.5, 0.75 and 1.0 possess multiple phase transitions including incommensurate structures. [CH$_3$NH$_3$]Ni(HCOO)$_3$ has also been found to have a proper magnetic incommensurate structure in its ground state. We have carried out a detailed structural characterization of the i

Michael Krivelevich, Alan Lew, Peleg Michaeli

We study minimum degree conditions that guarantee that an $n$-vertex graph is rigid in $\mathbb{R}^d$. For small values of $d$, we obtain a tight bound: for $d = O(\sqrt{n})$, every $n$-vertex graph with minimum degree at least $(n+d)/2 - 1$ is rigid in $\mathbb{R}^d$. For larger values of $d$, we achieve an approximate result: for $d = O(n/{\log^2}{n})$, ev

Utkarsh Saxena, Sayeh Sharify, Kaushik Roy, Xin Wang

Post-training quantization (PTQ) of large language models (LLMs) holds the promise in reducing the prohibitive computational cost at inference time. Quantization of all weight, activation and key-value (KV) cache tensors to 4-bit without significantly degrading generalizability is challenging, due to the high quantization error caused by extreme outliers in

Shreyas Ekanathan, Oscar Smith, Christopher Rackauckas

Radau IIA methods, specifically the adaptive order Radau method in Fortran due to Hairer, are known to be state-of-the-art for the high-accuracy solution of highly stiff ordinary differential equations (ODEs). However, the traditional implementation was specialized to a specific range of tolerance, in particular only supporting 5th, 9th, and 13th order versi

Alessandro Massaad, Rene Moawad, Oumaima Nijad Fares, Sahaphon Vairungroj

We revisit the long-only trend-following strategy presented in A Century of Profitable Industry Trends by Zarattini and Antonacci, which achieved exceptional historical performance with an 18.2% annualized return and a Sharpe Ratio of 1.39. While the results outperformed benchmarks, practical implementation raises concerns about robustness and evolving marke

Alec Eickbusch, Matt McEwen, Volodymyr Sivak, Alexandre Bourassa

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error corre

Mohamed R Ibrahim

Generating a bird's eye view of road users is beneficial for a variety of applications, including navigation, detecting agent conflicts, and measuring space occupancy, as well as the ability to utilise the metric system to measure distances between different objects. In this research, we introduce a simple approach for estimating a bird's eye view from image

Sanghyoup Gu, Ratnesh Kumar

Traditional Visual Simultaneous Localization and Mapping (VSLAM) systems assume a static environment, which makes them ineffective in highly dynamic settings. To overcome this, many approaches integrate semantic information from deep learning models to identify dynamic regions within images. However, these methods face a significant limitation as a supervise

A. G. Nikiforov, A. N. Baushev, M. V. Barkov

We consider the gravitational collapse of a homogeneous pressureless ellipsoid. We have shown that the minimal size $r$ that the ellipsoid can reach during collapse depends on its initial eccentricity $e_0$ as $r\propto e_0^\nu$, where $\nu \approx 15/8$, and this dependence is very universal. We have estimated the parameters (in particular, the initial ecce

Lily Lee, Julian Fontes, Andrew Weinert, Laura Schomacker

Machine learning (ML) is a powerful tool for efficiently analyzing data, detecting patterns, and forecasting trends across various domains such as text, audio, and images. The availability of annotation tools to generate reliably annotated data is crucial for advances in ML applications. In the domain of geospatial tracks, the lack of such tools to annotate

Prem Talwai, David Simchi-Levi

In this paper, we consider nonparametric estimation over general Dirichlet metric measure spaces. Unlike the more commonly studied reproducing kernel Hilbert space, whose elements may be defined pointwise, a Dirichlet space typically only contain equivalence classes, i.e. its elements are only unique almost everywhere. This lack of pointwise definition prese

Jaromír Fiurášek

The stellar hierarchy of quantum states of light classifies the states according to the Fock-state resources that are required for their generation together with unitary Gaussian operations. States with stellar rank n can be also equivalently referred to as genuinely n-photon quantum non-Gaussian states. Here we present an efficient method for construction o

Matthew Riemer, Gopeshh Subbaraj, Glen Berseth, Irina Rish

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectively minimize regret. However, recent advances in machine learning involve larger neural networks with longer inference times, raising questions about their applicability in realtime systems where reaction time is crucial.

Zhichao Xu, Jinghua Yan, Ashim Gupta, Vivek Srikumar

Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly $O(1)$ time complexity in inference. Despite their potential, SSMs' effectiveness at text r

Xiangjue Dong, Maria Teleki, James Caverlee

Techniques that enhance inference through increased computation at test-time have recently gained attention. In this survey, we investigate the current state of LLM Inference-Time Self-Improvement from three different perspectives: Independent Self-improvement, focusing on enhancements via decoding or sampling methods; Context-Aware Self-Improvement, leverag

Kenneth Church, Raman Chandrasekar, John E. Ortega, Ibrahim Said Ahmad

How effective is peer-reviewing in identifying important papers? We treat this question as a forecasting task. Can we predict which papers will be highly cited in the future based on venue and "early returns" (citations soon after publication)? We show early returns are more predictive than venue. Finally, we end with constructive suggestions to address scal

Alexandre G. Urzhumtsev, Vladimir Y. Lunin

We develop a class of functions Omega_N(x; mu, nu) in N-dimensional space concentrated around a spherical shell of the radius mu and such that, being convoluted with an isotropic Gaussian function, these functions do not change their expression but only a value of its 'width' parameter, nu. Isotropic Gaussian functions are a particular case of Omega_N(x; mu,

Mahmoud Zaher, Emil Björnson, Marina Petrova

In many wireless communication applications, it is desirable to transmit the same data to multiple user equipments (UEs). Physical layer multicasting presents an efficient transmission topology to exploit the beamforming capabilities at the transmitting nodes and broadcast nature of the wireless channel to satisfy the demand for the same content from several

S. Alekhin, M. V. Garzelli, J. Mazzitelli, S. -O. Moch

We describe the fits of the top-quark mass value at NNLO using as input the double-differential distributions in rapidity and invariant mass of $t\bar{t}$ pairs obtained by the ATLAS and CMS collaborations from unfolding of their experimental data to the parton level, compared to NNLO theory predictions. We consider different state-of-the-art PDF sets, findi

Matias Bundgaard-Nielsen, Marco Saldutti, Benjamin Falkenberg Gøtzsche, Emil Grovn

Nanolasers operating at low power levels are strongly affected by intrinsic quantum noise, influencing both intensity fluctuations and laser coherence. Starting from semiclassical rate equations and making a simple hypothesis for the phase of the laser field, a simple stochastic model for the laser quantum noise is suggested. The model is shown to agree quan

Andreea Musulan, Veronica Xia, Ethan Kosak-Hine, Tom Gibbs

In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, societal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target.

Fabian Mies

One reason why standard formulations of the central limit theorems are not applicable in high-dimensional and non-stationary regimes is the lack of a suitable limit object. Instead, suitable distributional approximations can be used, where the approximating object is not constant, but a sequence as well. We extend Gaussian approximation results for the parti

Renato Diniz, Oscar Ocampo, Paulo Cesar Cerqueira dos Santos Júnior

Let $M$ be a closed surface, $q\geq 2$ and $n\geq 2$. In this paper, we analyze the Coxeter-type quotient group $B_n(M)(q)$ of the surface braid group $B_{n}(M)$ by the normal closure of the element $\sigma_1^q$, where $\sigma_1$ is the classic Artin generator of the Artin braid group $B_n$. Also, we study the Coxeter-type quotient groups obtained by taking

Tapas Bhowmik, Wei-Lun Tsai, Dongxi Ye

We give Euler-like recursive formulas for the $t$-colored partition function when $t=2$ or $t=3,$ as well as for all $t$-regular partition functions. In particular, we derive an infinite family of ``triangular number" recurrences for the $3$-colored partition function. Our proofs are inspired by the recent work of Gomez, Ono, Saad, and Singh on the ordinary

Krzysztof Bartoszek

One of the first fully quantitative distance matrix visualization methods was proposed by Jan Czekanowski at the beginning of the previous century. Recently, a software package, RMaCzek, was made available that allows for producing such diagrams in R. Here we reanalyze the original data that Czekanowski used for introducing his method, and in the accompanyin

Hannah Banks, Stephanie Beram, Rashaad Reid, Aaron C. Vincent

Dark matter captured in stars can act as an additional heat transport mechanism, modifying fusion rates and asteroseismoloigcal observables. Calculations of heat transport rates rely on approximate solutions to the Boltzmann equation, which have never been verified in realistic stars. Here, we simulate heat transport in the Sun, the Earth, and a brown dwarf

Ezequiel A. Galpern, Ernesto A. Roman, Diego U. Ferreiro

Protein sequences serve as a natural record of the evolutionary constraints that shape their functional structures. We show that it is possible to use only sequence information to go beyond predicting native structures and global stability to infer the folding mechanisms of globular proteins. The one- and two-body evolutionary energy fields at the amino-acid

Alexis Fox, Samarth Swarup, Abhijin Adiga

Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluation metrics. Several recent approaches utilize "precision" and "recall," borrowed from the classification domain, to individually quantify the output fidelity (realism) and output diversity (representation of the

Spencer Wadsworth, Jarad Niemi

The annual influenza outbreak leads to significant public health and economic burdens making it desirable to have prompt and accurate probabilistic forecasts of the disease spread. The United States Centers for Disease Control and Prevention (CDC) hosts annually a national flu forecasting competition which has led to the development of a variety of flu forec

Moez Dawood, Ben Heavner, Marsha M. Wheeler, Rachel A. Ungar

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and ge

Salam Afiouni, Jakub Černý, Chun Kai Ling, Christian Kroer

While Nash equilibria are guaranteed to exist, they may exhibit dense support, making them difficult to understand and execute in some applications. In this paper, we study $k$-sparse commitments in games where one player is restricted to mixed strategies with support size at most $k$. Finding $k$-sparse commitments is known to be computationally hard. We st

Yoonkyeong Lee

We study the operator-valued partial derivative associated with covariance matrices on a von Neumann algebra B. We provide a cumulant characterization for the existence of conjugate variables and study some structure implications of their existence. Namely, we show that the center of the von Neumann algebra generated by B and its relative commutant is the ce

Anirudha Agrawal, Shaizeen Aga, Suchita Pati, Mahzabeen Islam

Concurrent computation and communication (C3) is a pervasive paradigm in ML and other domains, making its performance optimization crucial. In this paper, we carefully characterize C3 in ML on GPUs, which are most widely deployed for ML training and inference. We observe that while C3 leads to performance uplifts, the uplifts are far lower than ideal speedup

Alessio Squarcini, José M. Romero-Enrique, Andrew O. Parry

For a long time, the study of thermal effects at three-dimensional (3D) short-ranged wetting transitions considered only the effect of interfacial fluctuations. We show that an entropic Casimir contribution, missed in previous treatments, produces significant effects when it is included; in particular, mean-field predictions are no longer obtained when inter

Steven Hogue, Chenxu Zhang, Yapeng Tian, Xiaohu Guo

Recent advances in co-speech gesture and talking head generation have been impressive, yet most methods focus on only one of the two tasks. Those that attempt to generate both often rely on separate models or network modules, increasing training complexity and ignoring the inherent relationship between face and body movements. To address the challenges, in t

Luca Delle Monache, Daniel F. Steinhoff, Rachel Weihs, Matthew Simpson

A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on the Weather Research and Forecast atmospheric model tailored for the prediction of atmospheric rivers and associated heavy-to-extreme precipitation events over the Western US (West-WRF) is presented. The ensemble (WW200En) is generated with initial and boundary cond

Nicole R. Crumpler, Vedant Chandra, Nadia L. Zakamska, Gautham Adamane Pallathadka

Models predict that the well-studied mass-radius relation of white dwarf stars depends on the temperature of the star, with hotter white dwarfs having larger masses at a given radius than cooler stars. In this paper, we use a catalog of 26,041 DA white dwarfs observed in Sloan Digital Sky Survey Data Releases 1-19. We measure the radial velocity, effective t

Marcel S. Pawlowski, Mariana P. Júlio, Kosuke Jamie Kanehisa, Oliver Müller

The existence of planes of satellite galaxies has been identified as a long-standing challenge to $\Lambda$CDM cosmology, due to the rarity of satellite systems in cosmological simulations that are as extremely flattened and as strongly kinematically correlated as observed structures. Here we investigate a recently proposed new metric to measure the overall

Armin Moradi, Nicola Neophytou, Florian Carichon, Golnoosh Farnadi

Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing underrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic b

Adam Meyers, Advait Pravin Savant, John E. Ortega

This article is about Semantic Role Labeling for English partitive nouns (5%/REL of the price/ARG1; The price/ARG1 rose 5 percent/REL) in the NomBank annotated corpus. Several systems are described using traditional and transformer-based machine learning, as well as ensembling. Our highest scoring system achieves an F1 of 91.74% using "gold" parses from the

Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang

Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large language models (LLMs). In this work, we propose a novel inference-aware fine-tuning paradigm, in which the model is fine-tuned in a manner that directly optimizes the performance of the inference-time strategy. We study this

Xijun Wang, Prateek Chennuri, Dilshan Godaliyadda, Yu Yuan

Modern cameras' performance in low-light conditions remains suboptimal due to fundamental limitations in photon shot noise and sensor read noise. Generative image restoration methods have shown promising results compared to traditional approaches, but they suffer from hallucinatory content generation when the signal-to-noise ratio (SNR) is low. Leveraging th

Vijay Goyal, Mustafa Khan, Aprameya Tirupati, Harveer Saini

Large language models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing (NLP) tasks. However, these models are often difficult to deploy due to significant computational requirements and resource constraints. Knowledge distillation (KD) is an effective technique for transferring the performance of larger LLMs

Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski

Using pre-trained models has been found to reduce the effect of data heterogeneity and speed up federated learning algorithms. Recent works have explored training-free methods using first- and second-order statistics to aggregate local client data distributions at the server and achieve high performance without any training. In this work, we propose a traini

Diego Mondéjar

Adapting a homotopy reconstruction theorem for general metric compacta, we show that every countable metric or ultrametric compact space can be topologically reconstructed as the inverse limit of a sequence of finite $T_0$ spaces which are finer approximations of the space.

Rajesh Asapanna, Rabih El Sokhen, Albert F. Adiyatullin, Clément Hainaut

Discrete-step walks describe the dynamics of particles in a lattice subject to hopping or splitting events at discrete times. Despite being of primordial interest to the physics of quantum walks, the topological properties arising from their discrete-step nature have been hardly explored. Here we report the observation of topological phases unique to discret

Lisa Wang, Adam Meyers, John E. Ortega, Rodolfo Zevallos

Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation. By manually inserting the

Andrew J. Long, Moira Venegas

In models of warm dark matter, there is an appreciable population of high momentum particles in the early universe, which free stream out of primordial over/under densities, thereby prohibiting the growth of structure on small length scales. The distance that a dark matter particle travels without obstruction, known as the free streaming length, depends on t

Thijs G. I. van Wijk, E. Aylin Melan, Rani Mary Joy, Emerick Y. Guillaume

Color centers in diamond, such as the GeV center, are promising candidates for quantum-based applications. Here, we investigate the impact of strain on the zero-phonon line (ZPL) position of GeV$^0$. Both hydrostatic and linear strain are modeled using density functional theory for GeV$^0$ concentrations of $1.61$ \% down to $0.10$ \%. We present qualitative

Evan Wickenden, Marvin Qi, Arpit Dua, Michael Hermele

There are now many examples of gapped fracton models, which are defined by the presence of restricted-mobility excitations above the quantum ground state. However, the theory of fracton orders remains in its early stages, and the complex landscape of examples is far from being mapped out. Here we introduce the class of planon-modular (p-modular) fracton orde

Cy Maor

In this note, we define material-uniform hyperelastic bodies (in the sense of Noll) containing discrete disclinations and dislocations, and study their properties. We show in a rigorous way that the size of a disclination is limited by the symmetries of the constitutive relation; in particular, if the symmetry group of the body is discrete, it cannot admit a

Daniel Sanz-Alonso, Nathan Waniorek

Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state is high dimensional, ensemble Kalman filters are often the method of choice. This paper establishes long-time accuracy of ensemble Kalman filters. We introduce conditions on the dynamics and the observations un

Akash nag Oruganti

Multipartite entangled states are fundamental resources for multi-user quantum cryptographic tasks. Despite significant advancements in generating large-scale continuous-variable (CV) cluster states, particularly the dual-rail cluster state because of its utility in measurement-based quantum computation, its application in quantum cryptography has remained l

Jerome Droniou, Kim-Ngan Le, Jörn Wichmann

We propose and analyse a novel, fully discrete numerical algorithm for the approximation of the generalised Stokes system forced by transport noise -- a prototype model for non-Newtonian fluids including turbulence. Utilising the Gradient Discretisation Method, we show that the algorithm is long-term stable for a broad class of particular Gradient Discretisa

Aditya Bhaskara, Agastya Vibhuti Jha, Michael Kapralov, Naren Sarayu Manoj

In a graph bisection problem, we are given a graph $G$ with two equally-sized unlabeled communities, and the goal is to recover the vertices in these communities. A popular heuristic, known as spectral clustering, is to output an estimated community assignment based on the eigenvector corresponding to the second smallest eigenvalue of the Laplacian of $G$. S

Wahei Hara

The aim of this article is to prove the derived equivalence for a local model of the simple flop of type $G_2^{\dagger}$, which was found by Kanemitsu. This flop is the only known simple flop that comes from a non-homogeneous roof. The proof of the derived equivalence is done by using tilting bundles, and also produces a noncommutative crepant resolution of

Mar Curcó-Iranzo

For a prime $\mathfrak{p} \subseteq \mathbb{F}_{q}[T]$ and a positive integer $r$, we consider the generalised Jacobian $J_{0}(\mathfrak{n})_{\mathbf{m}}$ of the Drinfeld modular curve $X_{0}(\mathfrak{n})$ of level $\mathfrak{n}=\mathfrak{p}^r$, with respect to the modulus~$\mathbf{m}$ consisting of all cusps on the modular curve. We show that the $\ell$-pr

Brett Barkley, David Fridovich-Keil

Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms are a family of techniques for generating synthetic state transition data and thereby enhancing the sample efficiency of off-policy RL algorithms. This paper identifies and investigates a surprising performance gap observed when applying DMBRL algorithms across different benchmark en

Lance Labun, Miguel Gracia-Linares, Ou Z. Labun, Stephen V. Milton

Laser wakefield accelerator experiments have made enormous progress over the past $\sim 20$ years, but their promise to revolutionize high-energy particle sources is only beginning to be realized. To make the next step toward engineering LWFAs for different accelerator outcomes, we need more reliable and quantitative models to predict performance. Using the

Tara S. Holm, Liat Kessler, Susan Tolman

For manifolds equipped with group actions, we have the following natural question: To what extent does the equivariant cohomology determine the equivariant diffeotype? We resolve this question for Hamiltonian circle actions on compact, connected symplectic four-manifolds. They are equivariantly diffeomorphic if and only if their equivariant cohomology rings

Maram Sakr, H. F. Machiel Van der Loos, Dana Kulic, Elizabeth Croft

Learning from Demonstration (LfD) empowers robots to acquire new skills through human demonstrations, making it feasible for everyday users to teach robots. However, the success of learning and generalization heavily depends on the quality of these demonstrations. Consistency is often used to indicate quality in LfD, yet the factors that define this consiste

Benjamin Steenhoek, Michele Tufano, Neel Sundaresan, Alexey Svyatkovskiy

Software testing is a crucial but time-consuming aspect of software development, and recently, Large Language Models (LLMs) have gained popularity for automated test case generation. However, because LLMs are trained on vast amounts of open-source code, they often generate test cases that do not adhere to best practices and may even contain test smells (anti

Neil K. R. Sehgal, Dan Svirsky

We present the results of an experiment documenting racial bias on Meta's Advertising Platform in Brazil and the United States. We find that darker skin complexions are penalized, leading to real economic consequences. For every \$1,000 an advertiser spends on ads with models with light-skin complexions, that advertiser would have to spend \$1,159 to achieve

Benjamin Steenhoek, Kalpathy Sivaraman, Renata Saldivar Gonzalez, Yevhen Mohylevskyy

This paper presents the first empirical study of a vulnerability detection and fix tool with professional software developers on real projects that they own. We implemented DeepVulGuard, an IDE-integrated tool based on state-of-the-art detection and fix models, and show that it has promising performance on benchmarks of historic vulnerability data. DeepVulGu

Enrico Lunghi, Nathaniel Sherrill

We examine nonrenormalizable Lorentz- and CPT-violating effective operators applied to the quark sector of the Standard Model. Using Drell-Yan events collected by the ATLAS and CMS Collaborations, several constraints are extracted from time-independent modifications of the cross section on the $Z$-boson pole. The sensitivity to time-dependent modifications a

David Restrepo, Chenwei Wu, Zhengxu Tang, Zitao Shuai

Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising solution to automate various procedures such as triaging, preliminary tests like visual acuity assessment, and report summaries. However, LLMs have demonstrated significantly varie

David Marckx

Top quark pair production in association with a W boson is a rare standard model process that has proven to be an intriguing puzzle for theorists and experimentalists alike. Recent measurements, performed at $\sqrt{s}$ = 13 TeV, by both the ATLAS and CMS Collaborations at the CERN LHC, find cross section values that are consistently higher than the latest st

Oleg Lashinin, Denis Krasilnikov, Aleksandr Milogradskii, Marina Ananyeva

Transformer-based approaches such as BERT4Rec and SASRec demonstrate strong performance in Next Item Recommendation (NIR) tasks. However, applying these architectures to Next-Basket Recommendation (NBR) tasks, which often involve highly repetitive interactions, is challenging due to the vast number of possible item combinations in a basket. Moreover, frequen

Jing Wang, Wonho Bae, Jiahong Chen, Kuangen Zhang

Source-free domain adaptation (SFDA) involves adapting a model originally trained using a labeled dataset ({\em source domain}) to perform effectively on an unlabeled dataset ({\em target domain}) without relying on any source data during adaptation. This adaptation is especially crucial when significant disparities in data distributions exist between the tw

Alberto S. Cattaneo, Nima Moshayedi, Alberto Smailovic Funcasta

Three-dimensional supergravity in the Batalin-Vilkovisky formalism is constructed by showing that the theory including the Rarita-Schwinger term is equivalent to an AKSZ theory.

Mauro Nievas Offidani, Facundo Roffet, Claudio Augusto Delrieux, Maria Carolina Gonzalez Galtier

Classification is a fundamental task in machine learning. While conventional methods-such as binary, multiclass, and multi-label classification-are effective for simpler problems, they may not adequately address the complexities of some real-world scenarios. This paper introduces the Multiplex Classification Framework, a novel approach developed to tackle th

S. Bernal, P. Sánchez-Sáez, P. Arévalo, F. E. Bauer

We used random forest algorithms to classify all objects in a large portion of the sky, using optical light curves obtained, or built from images provided, by the Zwicky Transient Facility (ZTF). We compare different selection sets based on alerts or complete light curves derived from different photometric selection algorithms. The AGN candidates thus select

Jingyuan Wang, Zhimei Ren, Ruohan Zhan, Zhengyuan Zhou

Distributionally robust policy learning aims to find a policy that performs well under the worst-case distributional shift, and yet most existing methods for robust policy learning consider the worst-case joint distribution of the covariate and the outcome. The joint-modeling strategy can be unnecessarily conservative when we have more information on the sou

Ammon Fischer, Lennart Klebl, Valentin Crépel, Siheon Ryee

The recent observation of superconductivity in the vicinity of Fermi surface reconstructed insulating or metallic states has established twisted bilayers of WSe\textsubscript{2} as an exciting platform to study the interplay of strong electron-electron interactions, broken symmetries and topology. In this work, we use a first-principles, material-specific th

Anna Manasyan, Maximilian Seitzer, Filip Radovic, Georg Martius

Unsupervised object-centric learning from videos is a promising approach to extract structured representations from large, unlabeled collections of videos. To support downstream tasks like autonomous control, these representations must be both compositional and temporally consistent. Existing approaches based on recurrent processing often lack long-term stab

Viorica Pătrăucean, Xu Owen He, Joseph Heyward, Chuhan Zhang

We propose a novel block for \emph{causal} video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gated linear recurrent units (LRUs) perform information mixing over time, self-attention layers perform mixing over space, and MLPs over channels. The resulting architecture \emph{TRecViT} is causal and shows s

Jiayin Gu, Fan Zhang

We conjecture that the current fluctuations in one-dimensional driven transport systems obey an upper bound determined by the mean current and the driving force. This inequality originates from repulsive interactions between transporting particles, and the bound is approached both in near-equilibrium systems and in far-from-equilibrium systems with weak inte

Patrick Erik Bradley

The transcendent part of the Drinfeld p-adic upper half plane is shown to be a Polish space. Using Radon measures associated with regular differential 1-forms invariant under Schottky groups allows to construct self-adjoint diffusion operators as Laplacian integral operators with kernel functions determined by the p-adic absolute value on the complex p-adic

Simony Santos da Costa

In this work, I investigate the impact of Dark Energy Spectroscopic Instrument (DESI) Baryonic Acoustic Oscillations (BAO) data on cosmological parameters, focusing on the inflationary spectral index $n_s$, the amplitude of scalar perturbations $A_s$, and the matter density parameter $\omega_m$. By examining different models of late-time new physics, the inf

Neil Dummigan, Gonzalo Tornaría

We prove congruences of Hecke eigenvalues between cuspidal Hilbert newforms $f_{79}$ and $h_{79}$ over $F=\mathbb Q(\sqrt{5})$, of weights (2,2) and (2,4) respectively, level of norm 79. In the main example, the modulus is a divisor of 5 in some coefficient field, in the secondary example a divisor of 2. The former allows us to prove that the 4-dimensional m

Oliver Hart, David T. Stephen, Dominic J. Williamson, Rahul Nandkishore

Nonlocal quantum games provide proof of principle that quantum resources can confer advantage at certain tasks. They also provide a compelling way to explore the computational utility of phases of matter on quantum hardware. In a recent manuscript [Hart et al., arXiv:2403.04829] we demonstrated that a toric code resource state conferred advantage at a certai

József Balogh, Felix Christian Clemen, Adrian Dumitrescu, Dingyuan Liu

Given a finite point set satisfying condition $\mathcal{A}$, the subset selection problem asks, how large of a subset satisfying condition $\mathcal{B}$ can be extracted? In this paper, we make progress on three instances of subset selection problems in planar point sets. Let $n,s\in\mathbb{N}$ with $n\geq s$, and let $P\subseteq\mathbb{R}^2$ be a set of $n$