October 2023 arXiv papers — page 25

Saeed A. Khan, Ryan Kaufman, Boris Mesits, Michael Hatridge

We develop and demonstrate a trainable temporal post-processor (TPP) harnessing a simple but versatile machine learning algorithm to provide optimal processing of quantum measurement data subject to arbitrary noise processes, for the readout of an arbitrary number of quantum states. We demonstrate the TPP on the essential task of qubit state readout, which h

Nicolas Bousquet, Lucas De Meyer, Théo Pierron, Alexandra Wesolek

A non-crossing spanning tree of a set of points in the plane is a spanning tree whose edges pairwise do not cross. Avis and Fukuda in 1996 proved that there always exists a flip sequence of length at most $2n-4$ between any pair of non-crossing spanning trees (where $n$ denotes the number of points). Hernando et al. proved that the length of a minimal flip s

Hasib Zunair, A. Ben Hamza

Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small. While promising, existing multi-label image recognition models do not explicitly learn context-based representations, and hence struggle to correctly recognize small and occluded objects. Intuitively, recognizing occluded objects re

Igor Mezić

Koopman operator based models emerged as the leading methodology for machine learning of dynamical systems. But their scope is much larger. In fact they present a new take on modeling of physical systems, and even language. In this article I present some of the underlying mathematical structures, applications, connections to other methodologies such as trans

Anton Bushuiev, Roman Bushuiev, Petr Kouba, Anatolii Filkin

Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we

Brais Vila

The analysis of cylindrical resonators is part of standard physics curricula but, unlike for their rectangular counterpart, their mode structure is hardly ever visualized. The aim of this work is to show a way of doing it, providing a set of interactive web applications and citing potential use cases in the form of both academic courses and published researc

Sara Anderton, Rilee Burden, McKenzie Fontenot, Noah Fredrickson

Zero forcing is a dynamic coloring process on graphs. Initially, each vertex of a graph is assigned a color of either blue or white, and then a process begins by which blue vertices force white vertices to become blue. The zero forcing number is the cardinality of the smallest set of initially blue vertices which can force the entire graph to become blue, an

Fabien Roger, Ryan Greenblatt

Large language models (LLMs) often benefit from intermediate steps of reasoning to generate answers to complex problems. When these intermediate steps of reasoning are used to monitor the activity of the model, it is essential that this explicit reasoning is faithful, i.e. that it reflects what the model is actually reasoning about. In this work, we focus on

Habib Slim, Xiang Li, Yuchen Li, Mahmoud Ahmed

In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks. 3DCoMPaT$^{++}$ covers 41 shape categories, 275 fine-grained part categorie

Krzysztof Kutak

I discuss the thermodynamics-based derivation of the formula for the entanglement entropy of a system of gluons. The derivation is based on an approach where saturation and the Unruh effect were used to obtain and discuss the entropy of gluons. The formula agrees, in the high-energy limit, up to a numerical factor, with more recent results, where arguments b

Franz A. Heinsen

We find a succinct expression for computing the sequence $x_t = a_t x_{t-1} + b_t$ in parallel with two prefix sums, given $t = (1, 2, \dots, n)$, $a_t \in \mathbb{R}^n$, $b_t \in \mathbb{R}^n$, and initial value $x_0 \in \mathbb{R}$. On $n$ parallel processors, the computation of $n$ elements incurs $\mathcal{O}(\log n)$ time and $\mathcal{O}(n)$ space. Seq

Brian Gaudet, Kris Drozd, Roberto Furfaro

We use deep reinforcement learning (RL) to optimize a weapons to target assignment (WTA) policy for multi-vehicle hypersonic strike against multiple targets. The objective is to maximize the total value of destroyed targets in each episode. Each randomly generated episode varies the number and initial conditions of the hypersonic strike weapons (HSW) and tar

Ian E. Ochs, Nathaniel J. Fisch

Recently, it has been shown that altering the natural collisional power flow of the proton-Boron 11 (pB11) fusion reaction can significantly reduce the Lawson product of ion density and confinement time required to achieve ignition. However, these products are still onerous - on the order of $7 \times 10^{15}$ cm$^{-3}$s under the most optimistic scenarios.

Criston Hyett, Laurent Pagnier, Jean Alisse, Igal Goldshtein

In many power systems, particularly those isolated from larger intercontinental grids, operational dependence on natural gas becomes pivotal, especially during fluctuations or unavailability of renewables coupled with uncertain consumption patterns. Efficient orchestration and inventive strategies are imperative for the smooth functioning of these standalone

Kunal Chawla, Inhyeok Choi, Vivian He, Kasra Rafi

In this paper, we study random walks on groups that contain superlinear divergent geodesics, in the line of thoughts of Goldsborough-Sisto. The existence of a superlinear divergent geodesic is a quasi-isometry invariant which allows us to execute Gou\"ezel's pivoting technique. We develop the theory of superlinear divergence and establish a central limit the

Jonathan Tammer Eweis-LaBolle, Chuanning Zhao, Yoonjin Won, Ramin Bostanabad

As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. Two-phase cooling methods enhanced by porous surfaces, which capitalize on thin-film evaporation atop structured porous surfaces, are emerging as potential solutions. In such porous structures, the optimum heat dis

Vamsi Borra, Osama Oudat, Daniel G. Georgiev, Victor G. Karpov

In this work, a very high, locally applied electric field was used to induce whisker nucleation on an Sn film. The field was generated by using a conductive AFM tip and applying a voltage bias between the sample and the conductive cantilever>The tip-sample separation distance was thus controllable, and any dielectric breakdown could be avoided. At locations

Yingying Dong, Ying-Ying Lee

Many empirical applications estimate causal effects of a continuous endogenous variable (treatment) using a binary instrument. Estimation is typically done through linear 2SLS. This approach requires a mean treatment change and causal interpretation requires the LATE-type monotonicity in the first stage. An alternative approach is to explore distributional c

Vamsi Borra, Srikanth Itapu, Daniel G. Georgiev

The potential of NiO sublayers for whisker growth mitigation has been examined. A thin NiO film was applied on a Cu-coated substrate before the deposition of a thicker Sn layer. The growth of Sn whiskers was then followed by optical and scanning electron microscopy and was compared with the whisker growth on a control sample without the NiO sublayer. No whis

Vamsi Borra, Daniel G. Georgiev, Corey R. Grice

The fabrication of optically smooth thin Sn films by vacuum or electrodeposition techniques is usually challenging. Little has been published on how to address this challenge mainly because very few applications require such smooth Sn surfaces. The excitation of surface plasmon polaritons on Sn surfaces by prism-based methods represents a case that requires

Hanaa Zitane, Delfim F. M. Torres

We prove a useful formula and new properties for the recently introduced power fractional calculus with non-local and non-singular kernels. In particular, we prove a new version of Gronwall's inequality involving the power fractional integral; and we establish existence and uniqueness results for nonlinear power fractional differential equations using fixed

Vamsi Borra, Daniel G. Georgiev, Victor G. Karpov

This work presents a preliminary experimental study on the possibility to initiate growth of whiskers on the surfaces of some technologically important metals utilizing the enhanced electric field of surface plasmon polaritons (SPPs). The results provide evidence that a relatively high concentration of what appear to be whisker nuclei form in the region wher

Maria Valentini, Jennifer Weber, Jesus Salcido, Téa Wright

With recent advances in large language models (LLMs), the concept of automatically generating children's educational materials has become increasingly realistic. Working toward the goal of age-appropriate simplicity in generated educational texts, we first examine the ability of several popular LLMs to generate stories with properly adjusted lexical and read

Artem Mukhamedyanov, Alexander A. Zyablovsky, Evgeny S. Andrianov

System with strong photon-phonon interaction and optomechanical instability are perspective for generation of coherent phonons and photons. Typically, above the threshold of optomechanical instability, the photon intensity increases linearly with pumping. We demonstrate that in such systems, it is possible to achieve hard mode of excitation when jump increas

Elizabeth Tipton, Michalis Mamakos

Typically, a randomized experiment is designed to test a hypothesis about the average treatment effect and sometimes hypotheses about treatment effect variation. The results of such a study may then be used to inform policy and practice for units not in the study. In this paper, we argue that given this use, randomized experiments should instead be designed

Pete Rigas

We study open boundary conditions for the $D^{(2)}_3$ spin chain, which shares connections with the six-vertex model, under staggering, and also to the antiferromagnetic Potts model. By formulating a suitable transfer matrix that is related to $K$ matrices and to the Jimbo $R$-matrix, we obtain an analytical expression for the Hamiltonian, as a logarithmic d

Ruibo Chen, Tianyi Xiong, Yihan Wu, Guodong Liu

This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes.

Luke B. Handley, Erik A. Petigura, Velibor V. Misic

The size and complexity of modern astronomical surveys has grown to the point where, in many cases, traditional human scheduling of observations are tedious at best and impractical at worst. Automated scheduling algorithms present an opportunity to save human effort and increase scientific productivity. A common scheduling challenge involves determining the

Zachariah Carmichael, Walter J. Scheirer

Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led to an abundance of explanation algorithms for these black boxes. Such post hoc explainers produce human-comprehensible explanations, however, their fidelity with respect to the mod

Vamsi Borra, Srikanth Itapu, Victor G. Karpov, Daniel G. Georgiev

We report on the modification of tin (Sn) film surfaces under a laser beam irradiation that triggers surface plasmon polariton (SPP) excitations. The observed surface features in the form of small raised grains, with well-defined rooting, look similar to tin whisker nodules. We attribute the appearance of those features to the field-induced nucleation caused

Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma, Miguel Lago

To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available. We propose an evaluation approach for testing medical imaging AI models that relies on in silico imaging pipelines in which stochas

Ping-Hsuan Tsai, Seung Whan Chung, Debojyoti Ghosh, John Loffeld

Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. In this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equ

Jonas Bärgman, Malin Svärd, Simon Lundell, Erik Hartelius

Safety assessment of crash and conflict avoidance systems is important for both the automotive industry and other stakeholders. One type of system that needs such an assessment is a driver monitoring system (DMS) with some intervention (e.g., warning or nudging) when the driver looks off-road for too long. Although using computer simulation to assess safety

Jaiden Fairoze, Sanjam Garg, Somesh Jha, Saeed Mahloujifar

We present a publicly-detectable watermarking scheme for LMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable cryptographic signature into LM output using rejection sampling and prove that this produces unforgeable and distortion-free (i.e., undetectable without access to the public key)

Marcel Scherer

Let $\mathcal{B}(H)$ be the bounded, linear operators on a separable Hilbert space equipped with the norm topology. A property is called typical if the set of operators fulfilling the property is co-meager. We show that having non-empty continuous spectrum is a typical property and that the operators with non-empty point spectrum form a nowhere dense set. In

E. J. Kolmes, I. E. Ochs, J. -M. Rax, N. J. Fisch

Hot plasma is highly conductive in the direction parallel to a magnetic field. This often means that the electrical potential will be nearly constant along any given field line. When this is the case, the cross-field voltage drops in open-field-line magnetic confinement devices are limited by the tolerances of the solid materials wherever the field lines imp

John E. Darges, Alen Alexanderian, Pierre A. Gremaud

The formulation of Bayesian inverse problems involves choosing prior distributions; choices that seem equally reasonable may lead to significantly different conclusions. We develop a computational approach to better understand the impact of the hyperparameters defining the prior on the posterior statistics of the quantities of interest. Our approach relies o

Suman Devkota, Kwame A Nyako, Brendan Kuzior, Victor G. Karpov

With the ubiquitous acceptance of PVs, the number of devices manufactured annually is following an exponential trend. Yet, the manufacturing process of important brands of thin film solar cells involves a tedious and expensive step of laser scribing. The time-consuming and technologically involved laser scribing method remains widely used to contact the devi

Ethan Schreyer, James. E. Owen, R. O. Parke Loyd, Ruth Murray-Clay

Lyman-$\alpha$ transits provide an opportunity to test models of atmospheric escape directly. However, translating observations into constraints on the properties of the escaping atmosphere is challenging. The major reason for this is that the observable parts of the outflow often comes from material outside the planet's Hill sphere, where the interaction be

André Grossi Fonseca, Sachin Vaidya, Thomas Christensen, Mikael C. Rechtsman

Weyl fermions are hypothetical chiral particles that can also manifest as excitations near three-dimensional band crossing points in lattice systems. These quasiparticles are subject to the Nielsen-Ninomiya "no-go" theorem when placed on a lattice, requiring the total chirality across the Brillouin zone to vanish. This constraint results from the topology of

Marcelo Barraza-Alfaro, Mario Flock, Thomas Henning

Context. Planets are thought to form inside weakly ionized regions of protoplanetary disks, where turbulence creates ideal conditions for solid growth. However, the nature of this turbulence is still uncertain. In this zone, vertical shear instability (VSI) can operate, inducing a low level of gas turbulence and large-scale motions. Resolving kinematic signa

Juhi Jang, Jiaqi Liu, Matthew Schrecker

In this paper, we rigorously prove the existence of self-similar converging shock wave solutions for the non-isentropic Euler equations for $\gamma\in (1,3]$. These solutions are analytic away from the shock interface before collapse, and the shock wave reaches the origin at the time of collapse. The region behind the shock undergoes a sonic degeneracy, whic

B. Bolliet, A. Kusiak, F. McCarthy, A. Sabyr

class_sz is a versatile and robust code in C and Python that can compute theoretical predictions for a wide range of observables relevant to cross-survey science in the Stage IV era. The code is public at https://github.com/CLASS-SZ/class_sz along with a series of tutorial notebooks (https://github.com/CLASS-SZ/notebooks). It will be presented in full detail

Bodun Hu, Le Xu, Jeongyoon Moon, Neeraja J. Yadwadkar

Rapid advancements over the years have helped machine learning models reach previously hard-to-achieve goals, sometimes even exceeding human capabilities. However, to attain the desired accuracy, the model sizes and in turn their computational requirements have increased drastically. Thus, serving predictions from these models to meet any target latency and

Haitian Zhong, David Sankoff

Capacity restrictions in stores, maintained by mechanisms like spacing customer intake, became familiar features of retailing in the time of the pandemic. Shopping rates in a crowded store under a social distance regime is prone to considerable slowdown. Inspired by the random particle collision concepts of statistical mechanics, we introduce a dynamical mod

Manish Osti, Aashray Thakuri, Basheer Qolomany, Aos Mulahuwaish

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization o

Ayodeji Ibitoye, Wei-Ming Dai, Yin-Zhe Ma, Patricio Vielva

We present a joint cosmological analysis of the power spectra measurement of the Planck Compton parameter and the integrated Sachs-Wolfe (ISW) maps. We detect the statistical correlation between the Planck Thermal Sunyaev-Zeldovich (tSZ) map and ISW data with a significance of a $3.6\sigma$ confidence level~(CL), with the autocorrelation of the Planck tSZ da

Yian Deng, Tingting Mu

The strategy of ensemble has become popular in adversarial defense, which trains multiple base classifiers to defend against adversarial attacks in a cooperative manner. Despite the empirical success, theoretical explanations on why an ensemble of adversarially trained classifiers is more robust than single ones remain unclear. To fill in this gap, we develo

Mohammed Srati

In this paper, we introduce the fractional anisotropic Orlicz-Sobolev spaces, and by using some variational methods, we establish the existence or non-existence of eigenvalues of fractional anisotropic problems involving a nonlocal integro-differential operator of elliptic type. In each case, the competition between the growth rates of the anisotropic coeffi

Zifeng Luo, M. Barbui, J. Bishop, G. Chubarian

Background: The triple-alpha process is a vital reaction in nuclear astrophysics, characterized by two consecutive reactions [$2\alpha\leftrightarrows{^{8}\rm{Be}}(\alpha,\gamma){^{12}\rm{C}}$] that drive carbon formation. The second reaction occurs through the Hoyle state, a 7.65 MeV excited state in ${^{12}\rm{C}}$ with $J^{\pi}=0^{+}$. The rate of the pro

Tsung-Hung Yao, Yang Ni, Anindya Bhadra, Jian Kang

Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical assumptions: (i) a homogeneous graph with a common network for all subjects; or (ii) an assumption of normality especially in the context of Gaussian graphical models. Both assumpt

Pedro Piacenza, Daewon Lee, Volkan Isler

As service robots begin to be deployed to assist humans, it is important for them to be able to perform a skill as ubiquitous as pouring. Specifically, we focus on the task of pouring an exact amount of water without any environmental instrumentation, that is, using only the robot's own sensors to perform this task in a general way robustly. In our approach

Maede Ashofteh Barabadi, Xiaodan Zhu, Wai Yip Chan, Amber L. Simpson

Understanding the progression of cancer is crucial for defining treatments for patients. The objective of this study is to automate the detection of metastatic liver disease from free-style computed tomography (CT) radiology reports. Our research demonstrates that transferring knowledge using three approaches can improve model performance. First, we utilize

Elise Walker, Jonas A. Actor, Carianne Martinez, Nathaniel Trask

Causal representation learning algorithms discover lower-dimensional representations of data that admit a decipherable interpretation of cause and effect; as achieving such interpretable representations is challenging, many causal learning algorithms utilize elements indicating prior information, such as (linear) structural causal models, interventional data

Daniele Bartoli, Nicola Durante, Giovanni Giuseppe Grimaldi

Ovoids of the Klein quadric $Q^+(5,q)$ of $\mathrm{PG}(5,q)$ have been studied in the last 40 year, also because of their connection with spreads of $\mathrm{PG}(3,q)$ and hence translation planes. Beside the classical example given by a three dimensional elliptic quadric (corresponding to the regular spread of $\mathrm{PG}(3,q)$) many other classes of examp

Cedric Leblond-Menard, Gabriel Picard-Krashevski, Sofiane Achiche

Although the number of gaze estimation datasets is growing, the application of appearance-based gaze estimation methods is mostly limited to estimating the point of gaze on a screen. This is in part because most datasets are generated in a similar fashion, where the gaze target is on a screen close to camera's origin. In other applications such as assistive

Amy Thomas, Jai Grover, Dario Izzo, Dominik Dold

We propose to use a recently introduced Totimorphic metamaterial for constructing morphable space structures. As a first step to investigate the feasibility of this concept, we present a method for morphing such structures autonomously between different shapes using physically plausible actuations, guaranteeing that the material traverses through valid confi

Tuan Anh Dao, Murtazo Nazarov, Ignacio Tomas

We introduce a novel structure-preserving method in order to approximate the compressible ideal Magnetohydrodynamics (MHD) equations. This technique addresses the MHD equations using a non-divergence formulation, where the contributions of the magnetic field to the momentum and total mechanical energy are treated as source terms. Our approach uses the Marchu

Boris Putievskiy

This article investigates integer sequences that partition the sequence into blocks of various lengths - irregular arrays. The main result of the article is explicit formulas for numbering of irregular arrays. A generalization of Cantor diagonal method is proposed. We also define and describe intra-block permutations of natural numbers. Generalizations of re

Artin Tajdini, Lalit Jain, Kevin Jamieson

We consider maximizing an unknown monotonic, submodular set function $f: 2^{[n]} \rightarrow [0,1]$ with cardinality constraint under stochastic bandit feedback. At each time $t=1,\dots,T$ the learner chooses a set $S_t \subset [n]$ with $|S_t| \leq k$ and receives reward $f(S_t) + \eta_t$ where $\eta_t$ is mean-zero sub-Gaussian noise. The objective is to m

Charity Woodrum, Marcia Rieke, Zhiyuan Ji, William M. Baker

The detection of numerous and relatively bright galaxies at redshifts z > 9 has prompted new investigations into the star-forming properties of high-redshift galaxies. Using local forms of the initial mass function (IMF) to estimate stellar masses of these galaxies from their light output leads to galaxy masses that are at the limit allowed for the state of

Mingchen Li, Huixue Zhou, Rui Zhang

Biomedical triple extraction systems aim to automatically extract biomedical entities and relations between entities. The exploration of applying large language models (LLM) to triple extraction is still relatively unexplored. In this work, we mainly focus on sentence-level biomedical triple extraction. Furthermore, the absence of a high-quality biomedical t

Shweta Jain, Sandro Tacchella, Moein Mosleh

The estimation of galaxy stellar masses depends on the assumed prior of the star-formation history (SFH) and spatial scale of the analysis (spatially resolved versus integrated scales). In this paper, we connect the prescription of the SFH in the Spectral Energy Distribution (SED) fitting to spatially resolved scales ($\sim\mathrm{kpc}$) to shed light on the

Toni Hirvonen, Mahmoud Namazi

In this paper, techniques for improving multichannel lossless coding are examined. A method is proposed for the simultaneous coding of two or more different renderings (mixes) of the same content. The signal model uses both past samples of the upmix, and the current time samples of downmix samples to predict the upmix. Model parameters are optimized via a ge

Tuan Anh Le, Xin-She Yang

This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamformin

Pedro Piacenza, Jiacheng Yuan, Jinwook Huh, Volkan Isler

We consider the problem of closed-loop robotic grasping and present a novel planner which uses Visual Feedback and an uncertainty-aware Adaptive Sampling strategy (VFAS) to close the loop. At each iteration, our method VFAS-Grasp builds a set of candidate grasps by generating random perturbations of a seed grasp. The candidates are then scored using a novel

Chloe Qinyu Zhu, Rickard Stureborg, Brandon Fain

Language Representation Models (LRMs) trained with real-world data may capture and exacerbate undesired bias and cause unfair treatment of people in various demographic groups. Several techniques have been investigated for applying interventions to LRMs to remove bias in benchmark evaluations on, for example, word embeddings. However, the negative side effec

Sean Welleck, Rahul Saha

We present LLMSTEP, a tool for integrating a language model into the Lean proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to a server hosting a language model. The language model generates suggestions, which are checked in Lean and displayed to a user in their development environment. We provide a baseline language model, along wi

John Fullerton, Arthur R. C. McCray, Amanda K. Petford-Long, Charudatta Phatak

Comprehending the interaction between geometry and magnetism in three-dimensional (3D) nanostructures is of importance to understand the fundamental physics of domain wall (DW) formation and pinning. Here, we use focused electron beam-induced deposition to fabricate magnetic nanohelices with increasing helical curvature with height. Using electron tomography

Krunoslav Lehman Pavasovic, Alain Durmus, Umut Simsekli

A recent line of empirical studies has demonstrated that SGD might exhibit a heavy-tailed behavior in practical settings, and the heaviness of the tails might correlate with the overall performance. In this paper, we investigate the emergence of such heavy tails. Previous works on this problem only considered, up to our knowledge, online (also called single-

Rebecca M. M. Hicke, David Mimno

Large language models have shown breakthrough potential in many NLP domains. Here we consider their use for stylometry, specifically authorship identification in Early Modern English drama. We find both promising and concerning results; LLMs are able to accurately predict the author of surprisingly short passages but are also prone to confidently misattribut

Irene Drelichman

We characterize the real interpolation space between weighted $L^1$ and $W^{1,1}$ spaces on arbitrary domains different from $\mathbb{R}^n$, when the weights are positive powers of the distance to the boundary multiplied by an $A_1$ weight. As an application of this result we obtain weighted fractional Poincar\'e inequalities with sharp dependence on the fra

António Girão, Zach Hunter

In this paper we prove that for every $s\geq 2$ and every graph $H$ the following holds. Let $G$ be a graph with average degree $\Omega_H(s^{C|H|^2})$, for some absolute constant $C>0$, then $G$ either contains a $K_{s,s}$ or an induced subdivision of $H$. This is essentially tight and confirms a conjecture of Bonamy, Bousquet, Pilipczuk, Rz\k{a}\.zewski, Th

Cong Qi, Qin Liu, Kan Liu

Influence prediction plays a crucial role in the academic community. The amount of scholars' influence determines whether their work will be accepted by others. Most existing research focuses on predicting one paper's citation count after a period or identifying the most influential papers among the massive candidates, without concentrating on an individual

Jiamin Xie, John H. L. Hansen

In this paper, we present MixRep, a simple and effective data augmentation strategy based on mixup for low-resource ASR. MixRep interpolates the feature dimensions of hidden representations in the neural network that can be applied to both the acoustic feature input and the output of each layer, which generalizes the previous MixSpeech method. Further, we pr

Wenqian Xing, JungHo Lee, Chong Liu, Shixiang Zhu

Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police redistricting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces a nove

Jiannan Jiang, Noel J. Walkington, Yukun Yue

Convergence and compactness properties of approximate solutions to elliptic partial differential computed with the hybridized discontinuous Galerkin (HDG) are established. While it is known that solutions computed using the HDG scheme converge at optimal rates to smooth solutions, this does not establish the stability of the method or convergence to solution

Malik Wagih, Yannick Naunheim, Tianjiao Lei, Christopher A. Schuh

In alloys, solute segregation at grain boundaries is classically attributed to three driving forces: a high solution enthalpy, a high size mismatch, and a high difference in interfacial energy. These effects are generally cast into a single scalar segregation energy and used to predict grain boundary solute enrichment or depletion. This approach neglects the

Marvin Morgan, Brendan P. Bowler, Quang H. Tran, Erik Petigura

Transiting giant planets provide a natural opportunity to examine stellar obliquities, which offer clues about the origin and dynamical histories of close-in planets. Hot Jupiters orbiting Sun-like stars show a tendency for obliquity alignment, which suggests that obliquities are rarely excited or that tidal realignment is common. However, the stellar obliqu

Jiaxin Lu, Zetian Jiang, Tianzhe Wang, Junchi Yan

Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable. However, these assumptions do not align with real-world applications. This work addresses a more realistic scenario where graphs exhibit diverse modes, requiring graph grouping before or along with matching, a task termed mixture graph ma

Biagio La Rosa, Leilani H. Gilpin, Roberto Capobianco

Compositional Explanations is a method for identifying logical formulas of concepts that approximate the neurons' behavior. However, these explanations are linked to the small spectrum of neuron activations (i.e., the highest ones) used to check the alignment, thus lacking completeness. In this paper, we propose a generalization, called Clustered Composition

Kristen Michaelson, Andrey A. Popov, Renato Zanetti

Few real-world systems are amenable to truly Bayesian filtering; nonlinearities and non-Gaussian noises can wreak havoc on filters that rely on linearization and Gaussian uncertainty approximations. This article presents the Bayesian Recursive Update Filter (BRUF), a Kalman filter that uses a recursive approach to incorporate information from nonlinear measu

C. M. Hull

Symmetries and conserved charges are investigated for linearised gravity and its dual formulation in terms of the dual graviton field. Conserved charges are constructed for the dual graviton theory as Noether charges associated with invariances of the dual graviton. These invariances arise for gauge parameters that are certain generalised Killing tensors. Th

Rosamond Thalken, Edward H. Stiglitz, David Mimno, Matthew Wilkens

Generative language models (LMs) are increasingly used for document class-prediction tasks and promise enormous improvements in cost and efficiency. Existing research often examines simple classification tasks, but the capability of LMs to classify on complex or specialized tasks is less well understood. We consider a highly complex task that is challenging

Mouyang Cheng, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Mingda Li

Majorana zero modes (MZMs), emerging as exotic quasiparticles that carry non-Abelian statistics, hold great promise for achieving fault-tolerant topological quantum computation. A key signature of the presence of MZMs is the zero-bias peaks (ZBPs) from tunneling differential conductance. However, the identification of MZMs from ZBPs has faced tremendous chal

Yubin Wang, Huimin Yu, Yuming Yan, Shuyi Song

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps

B. D. Lailey, T. A. A. Sigut

We test the viability of training machine learning algorithms with synthetic H alpha line profiles to determine the inclination angles of Be stars (the angle between the central B star's rotation axis and the observer's line of sight) from a single observed medium-resolution, moderate S/N, spectrum. The performance of three different machine learning algorit

S. E. Chorfi, G. El Guermai, L. Maniar, W. Zouhair

This work presents a comparative study to numerically compute impulse approximate controls for parabolic equations with various boundary conditions. Theoretical controllability results have been recently investigated using a logarithmic convexity estimate at a single time based on a Carleman commutator approach. We propose a numerical algorithm for computing

S. M. Seals, Valerie L. Shalin

Despite considerable performance improvements, current conversational AI systems often fail to meet user expectations. We discuss several pragmatic limitations of current conversational AI systems. We illustrate pragmatic limitations with examples that are syntactically appropriate, but have clear pragmatic deficiencies. We label our complaints as "Turing Te

Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh

The goal of an offline reinforcement learning (RL) algorithm is to learn optimal polices using historical (offline) data, without access to the environment for online exploration. One of the main challenges in offline RL is the distribution shift which refers to the difference between the state-action visitation distribution of the data generating policy and

Alvi Jawad

The Internet of Things (IoT) is becoming an integral part of our modern lives as we converge towards a world surrounded by ubiquitous connectivity. The inherent complexity presented by the vast IoT ecosystem ends up in an insufficient understanding of individual system components and their interactions, leading to numerous security challenges. In order to cr

Pierre Kawak, Harshad Bhapkar, David S. Simmons

Nanoparticles can greatly enhance the mechanical response of elastomeric polymers essential to a wide range of applications, yet their precise molecular mechanisms of high-strain reinforcement remain largely unresolved. Here we show, based on molecular dynamics simulations, that high-strain reinforcement emerges from an interplay between granular nanoparticu

T. Rick Perche, José Polo-Gómez, Bruno de S. L. Torres, Eduardo Martín-Martínez

We study the protocol of entanglement harvesting when the particle detectors that harvest entanglement from the field are replaced by fully relativistic quantum field theories. We show that two localized modes of the quantum field theories are able to harvest the same amount of leading order entanglement as two non-relativistic particle detectors, thus imply

Adam D. Lelkes, Eric Loreaux, Tal Schuster, Ming-Jun Chen

Social and behavioral determinants of health (SDOH) play a significant role in shaping health outcomes, and extracting these determinants from clinical notes is a first step to help healthcare providers systematically identify opportunities to provide appropriate care and address disparities. Progress on using NLP methods for this task has been hindered by t

Zhaoxin Chang, Fusang Zhang, Daqing Zhang

Internet of Things (IoT) technologies are the foundation of a fully connected world. Currently, IoT devices (or nodes) primarily use dedicated sensors to sense and collect data at large scales, and then transmit the data to target nodes or gateways through wireless communication for further processing and analytics. In recent years, research efforts have bee

Keira Behal, Jiayi Chen, Caleb Fikes, Sophia Xiao

In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of healthcare resources. However, medical datasets are often imbalanced in terms of sensitive attributes such as race/ethnicity, gender, and age. Machine learning models trained on clas

Anton Krieger, Sebastian Wolf

Monte Carlo radiative transfer (MCRT) simulations are a powerful tool for determining the appearance of astrophysical objects, analyzing the prevalent physical conditions within them, and inferring their properties on the basis of real observations. Consequently, a broad variety of codes has been implemented and optimized with the goal of solving this task e

Shay Moran, Hilla Schefler, Jonathan Shafer

We show that many definitions of stability found in the learning theory literature are equivalent to one another. We distinguish between two families of definitions of stability: distribution-dependent and distribution-independent Bayesian stability. Within each family, we establish equivalences between various definitions, encompassing approximate different

William A. Ingram, Jian Wu, Edward A. Fox

This poster addresses accessibility issues of electronic theses and dissertations (ETDs) in digital libraries (DLs). ETDs are available primarily as PDF files, which present barriers to equitable access, especially for users with visual impairments, cognitive or learning disabilities, or for anyone needing more efficient and effective ways of finding relevan

D. J. Fritzewski, T. Van Reeth, C. Aerts, J. Van Beeck

UBC 1 is an open cluster discovered in Gaia data and located near the edge of the Transiting Exoplanet Survey Satellite's (TESS) continuous viewing zone. We aim to provide age constraints for this poorly studied open cluster from the combination of gravity-mode (g-mode) asteroseismology, gyrochronology, and isochrone fitting. We established the members of UB