February 2024 arXiv papers — page 109

Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty

Due to statistical lower bounds on the learnability of many function classes under privacy constraints, there has been recent interest in leveraging public data to improve the performance of private learning algorithms. In this model, algorithms must always guarantee differential privacy with respect to the private samples while also ensuring learning guaran

Jonghee Yang, Anton V. Ievlev, Anna N. Morozovska, Eugene Eliseev

Ferroelectric materials promise exceptional attributes including low power dissipation, fast operational speeds, enhanced endurance, and superior retention to revolutionize information technology. However, the practical application of ferroelectric-semiconductor memory devices has been significantly challenged by the incompatibility of traditional perovskite

Thorben Tröbst, Vijay V. Vazirani

Unlike ordinal-utility matching markets, which are well-developed from the viewpoint of both theory and practice, recent insights from a computer science perspective have left cardinal-utility matching markets in a state of flux. The celebrated pricing-based mechanism for one-sided cardinal-utility matching markets due to Hylland and Zeckhauser, which had lo

Bernard de Mathan

In a previous paper, we studied certain sequences of simultaneous rational approximations in ${\bf R}^2$ which present some analogy with the continued fractions. We got results around the Littlewood conjecture by using such approximations. Here we show that such results also hold when we add divisibility conditions.

Ann Mukuno, Jiro Soda

Chromo-natural inflation is a model where non-abelian gauge fields are sustained by the coupling of the axion with the gauge field through the Chern-Simons term. While minimal warm inflation is a model where the axion produces a thermal bath of non-abelian gauge particles through the Chern-Simons term. Since both axion inflation models are based on the same

Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell

The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations with more robustness to error compounding than behavioral cloning approaches. On the other hand, it requires that the learner repeatedly solve a computationally expensive reinforceme

Hamidreza Behjoo, Michael Chertkov

In this study, we introduce a novel method for generating new synthetic samples that are independent and identically distributed (i.i.d.) from high-dimensional real-valued probability distributions, as defined implicitly by a set of Ground Truth (GT) samples. Central to our method is the integration of space-time mixing strategies that extend across temporal

Ziyang Ma, Guanrou Yang, Yifan Yang, Zhifu Gao

In this paper, we focus on solving one of the most important tasks in the field of speech processing, i.e., automatic speech recognition (ASR), with speech foundation encoders and large language models (LLM). Recent works have complex designs such as compressing the output temporally for the speech encoder, tackling modal alignment for the projector, and uti

Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu

We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contributions of different features, when their prediction changes are similar after perturbation. To enhance FAMs' discri

Zhiyao Tian, Anthony Lee, Shunhua Zhou

Bayesian curve fitting plays an important role in inverse problems, and is often addressed using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. However, this algorithm can be computationally inefficient without appropriately tuned proposals. As a remedy, we present an adaptive RJMCMC algorithm for the curve fitting problems by extending the

Mauricio Cruz Reyes, Richard I. Anderson, Lucas Johansson, Henryka Netzel

We present a comprehensive catalog of 2824 RR Lyrae stars (RRLs) residing in 115 Galactic globular clusters (GCs). Our catalog includes 1594 fundamental-mode (RRab), 824 first-overtone (RRc), and 28 double-mode (RRd) RRLs, as well as 378 RRLs of an unknown pulsation mode. We cross-matched 481349 RRLs reported in the third data release (DR3) of the ESA missio

Felix Höfer, H. Mete Soner

We propose a new mean-field game model with two states to study synchronization phenomena, and we provide a comprehensive characterization of stationary and dynamic equilibria along with their stability properties. The game undergoes a phase transition with increasing interaction strength. In the subcritical regime, the uniform distribution, representing inc

Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd

We consider the problem of finding an informative path through a graph, given initial and terminal nodes and a given maximum path length. We assume that a linear noise corrupted measurement is taken at each node of an underlying unknown vector that we wish to estimate. The informativeness is measured by the reduction in uncertainty in our estimate, evaluated

Avner Ash

Let St denote the Steinberg module of $SL_n(Q)$ tensored with Q. Let Sh denote the sharbly resolution of St. By Borel-Serre duality, $H^{n(n-1)/2-i}(SL_n(Z),Q)$ is isomorphic to $H_i(SL_n(Z),St)$. The latter is isomorphic to the homology of the $SL_n(Z)$-coinvariants of Sh. We produce nonzero classes in $H_i(SL_n(Z),St)$ for certain small $i$ in terms of sha

Yoshihiro Nambu

Lechner, Hauke, and Zoller proposed a parity-encoded spin-embedding scheme for quantum annealing (QA) with all-to-all connectivity to avoid the issue of limited connectivity in near-term QA hardware and to enable the implementation thereof using only geometrically local interactions between spins fabricated on the planar substrate. Nevertheless, the redundan

Matthew Downing, Erik B. Johnson, Joe Campbell, Adam A. Dadey

The Geiger photodiodes, or single photon avalanche photodiodes, for a radiation tolerant solid-state photomultiplier (SSPM) are being designed using Aluminum Gallium Arsenide (AlGaAs) and are less than 1 micrometer thick. Studies on the changes in dark current compared to silicon from thermal neutron damage were conducted demonstrating that the design concep

Maneesh Bilalpur, Mert Inan, Dorsa Zeinali, Jeffrey F. Cohn

Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility and efficacy of therapeutic support. Embodied agents with advanced interactive capabilities emerge as a promisin

Boris Khesin

The non-transitivity without extra constraints in the Euler equation in any dimension is almost evident and can be derived, e.g., from Morse theory.

Delaney Farrell, Fridolin Weber, Milva G. Orsaria, Ignacio F. Ranea-Sandoval

This book chapter explores key aspects of neutron stars, pulsar glitches, tidal deformability, fast pulsars, the equation of state, and strange quark matter stars. Challenges in directly measuring neutron star radius have led to reliance on spectroscopic and timing techniques, with uncertainties addressed through careful source selection and theoretical mode

Zixiong Wei, Nongnuch Artrith

Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive anode materials for solid-state Li-ion batteries but face mechanical instabilities due to significant volume variations with changing Li content. A fundamental grasp of the mechanica

Fernando O. Méndez, Maria Vaz Pinto, Rafael H. Villarreal

The aim of this work is to compare symbolic and ordinary powers of monomial ideals using commutative algebra and combinatorics. Monomial ideals whose symbolic and ordinary powers coincide are called Simis ideals. Weighted monomial ideals are defined by assigning linear weights to monomials. We examine Simis and normally torsion-free ideals, relate some of th

Zhaoan Wang, Shaoping Xiao, Jun Wang, Ashwin Parab

This study examines how artificial intelligence (AI), especially Reinforcement Learning (RL), can be used in farming to boost crop yields, fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil. Facing climate change and limited agricultural knowledge, we use Partially Obser

Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun

With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products - a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in ge

Sammy Khalife, Yann Ponty, Laurent Bulteau

Several popular language models represent local contexts in an input text $x$ as bags of words. Such representations are naturally encoded by a sequence graph whose vertices are the distinct words occurring in $x$, with edges representing the (ordered) co-occurrence of two words within a sliding window of size $w$. However, this compressed representation is

Sébastien Gomé, Aliénor Rivière, Laurette S. Tuckerman, Dwight Barkley

Directed percolation (DP), a universality class of continuous phase transitions, has recently been established as a possible route to turbulence in subcritical wall-bounded flows. In canonical straight pipe or planar flows, the transition occurs via discrete large-scale turbulent structures, known as puffs in pipe flow or bands in planar flows, which either

Daiqi Gao, Yuanjia Wang, Donglin Zeng

An individualized treatment rule (ITR) is a decision rule that recommends treatments for patients based on their individual feature variables. In many practices, the ideal ITR for the primary outcome is also expected to cause minimal harm to other secondary outcomes. Therefore, our objective is to learn an ITR that not only maximizes the value function for t

Alberto Del Pia

In this paper we consider the problem of minimizing a general quadratic function over the mixed integer points in an ellipsoid. This problem is strongly NP-hard, NP-hard to approximate within a constant factor, and optimal solutions can be irrational. In our main result we show that an arbitrarily good solution can be found in polynomial time, if we fix the

Diptangshu Sen, Jingyan Wang, Juba Ziani

We study a two-sided online data ecosystem comprised of an online platform, users on the platform, and downstream learners or data buyers. The learners can buy user data on the platform (to run a statistic or machine learning task). Potential users decide whether to join by looking at the trade-off between i) their benefit from joining the platform and inter

Cao Vien Phung, Andre Drummond, Admela Jukan

In the modern landscape of wireless communications, multi-hop, high-bandwidth, indoor Terahertz (THz) wireless communications are gaining significant attention. These systems couple Reconfigurable Intelligent Surface (RIS) and relay devices within the emerging 6G network framework, offering promising solutions for creating cell-less, indoor, and on-demand me

Tianxiang Zhao, Xiang Zhang, Suhang Wang

Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains. Despite their great successful, one critical challenge is often overlooked by existing works, i.e., the learning of message propagation that can generalize effectively to underrepresented graph regions. These minority regions ofte

Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip H. S. Torr

Continual learning has primarily focused on the issue of catastrophic forgetting and the associated stability-plasticity tradeoffs. However, little attention has been paid to the efficacy of continually learned representations, as representations are learned alongside classifiers throughout the learning process. Our primary contribution is empirically demons

Ke Li, Ruidong Zhang, Siyuan Chen, Boao Chen

In this paper, we introduce EyeEcho, a minimally-obtrusive acoustic sensing system designed to enable glasses to continuously monitor facial expressions. It utilizes two pairs of speakers and microphones mounted on glasses, to emit encoded inaudible acoustic signals directed towards the face, capturing subtle skin deformations associated with facial expressi

Serhii D. Koval, Roman O. Popovych

Within the class of (1+2)-dimensional ultraparabolic linear equations, we distinguish a fine Kolmogorov backward equation with a quadratic diffusivity. Modulo the point equivalence, it is a unique equation within the class whose essential Lie invariance algebra is five-dimensional and nonsolvable. Using the direct method, we compute the point symmetry pseudo

Jiaxiang Li, Xuxing Chen, Shiqian Ma, Mingyi Hong

Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually require the knowledge of Lipschitz constant of the global gradient or topological information of the communication networks, which are usually not accessible in practice. In this paper

A. Álvarez, E. Flapan, M. Hunnell, J. Hutchens

The topological symmetry group $\mathrm{TSG}(\Gamma)$ of an embedding $\Gamma$ of a graph in $S^3$ is the subgroup of the automorphism group of the graph which is induced by homeomorphisms of $(S^3,\Gamma)$. If we restrict to orientation preserving homeomorphisms then we obtain the orientation preserving topological symmetry group $\mathrm{TSG}_+(\Gamma)$. I

Federico Maria Vitrò, Marco Leonesio, Lorenzo Fagiano

This paper proposes a novel approach to improve the training efficiency and the generalization performance of Feed Forward Neural Networks (FFNNs) resorting to an optimal rescaling of input features (OFR) carried out by a Genetic Algorithm (GA). The OFR reshapes the input space improving the conditioning of the gradient-based algorithm used for the training.

Siyi Wang, Sandra Hirche

Emerging cyber-physical systems impel the development of communication protocols that optimize resource utilization. This article investigates infinite-horizon optimal scheduling for resource-aware networked control systems by addressing the rate-regulation tradeoff. Consider a scenario where the sensor and the controller communicate via a networked channel,

Benoit Dherin, Mihaela Rosca

We characterize regions of a loss surface as corridors when the continuous curves of steepest descent -- the solutions of the gradient flow -- become straight lines. We show that corridors provide insights into gradient-based optimization, since corridors are exactly the regions where gradient descent and the gradient flow follow the same trajectory, while t

Muhammad Farooq, Nima Afraz, Fatemeh Golpayegani

Multimodal intelligent transportation systems (M-ITS) encompass a range of transportation services that utilise various modes of transport and incorporate intelligent technologies for enhanced efficiency and user experience. There are several challenges in M-ITS including data integration, Interoperability, scalability, user experience, etc. To address these

Konrad Majewski, Michał Pilipczuk, Anna Zych-Pawlewicz

We design a randomized data structure that, for a fully dynamic graph $G$ updated by edge insertions and deletions and integers $k, d$ fixed upon initialization, maintains the answer to the Split Completion problem: whether one can add $k$ edges to $G$ to obtain a split graph. The data structure can be initialized on an edgeless $n$-vertex graph in time $n \

Gabriela Garcia-Arroyo, L. Arturo Ureña-López, J. Alberto Vázquez

The main aim of this work is to explore the possibility that cold dark matter (CDM) and early dark energy (EDE) can be described by canonical scalar fields that are coupled at the level of its conservation equations. The formalism covers dynamical aspects at the background and linear perturbation levels for an arbitrary coupling function, followed by an exam

Melissa van Beekveld, Wim Beenakker, Jochem Kip, Marrit Schutten

In this paper we examine the effect of adding CP-violating phases to the pMSSM on the Dark-Matter (DM) direct-detection cross sections, the velocity-weighted annihilation cross section, and the DM relic density. We show that $\varphi_{M_2}$ and $\varphi_{\mu}$, the phases of the wino and higgsino mass parameters, are sufficiently constrained by electron elec

Carlota Andrés, Liliana Apolinário, Néstor Armesto, André Cordeiro

The theoretical treatment of jet quenching lacks a full description of the interplay between vacuum-like emissions, usually formulated in momentum space, and medium induced ones that demand an interface with a space-time picture of the expanding medium and thus must be formulated in position space. In this work we build a toy Monte-Carlo parton shower ordere

Berk Bozkurt, Aditya Mahajan, Ashutosh Nayyar, Yi Ouyang

We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov decision process $\mathcal{M}$ when we only have access to an approximate model $\hat{\mathcal{M}}$. How well does an optimal policy $\hat{\pi}^{\star}$ of the approximate model perform when used in the original model $\mathcal{M}$? We answer this question by

Zijian Ding, Joel Chan

Complex data analysis inherently seeks unexpected insights through exploratory visual analysis methods, transcending logical, step-by-step processing. However, existing interfaces such as notebooks and dashboards have limitations in exploration and comparison for visual data analysis. Addressing these limitations, we introduce a "design-like" intelligent can

Gal Mishne, Adam Charles

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically

Doyeon A. Kim, Yong Zheng, Mary E. Putman

We present a new technique to identify associations of HI emission in the Magellanic Stream (MS) and ultraviolet (UV) absorbers from 92 QSO sight lines near the MS. We quantify the level of associations of individual HI elements to the main HI body of the Stream using Wasserstein distance-based models, and derive characteristic spatial and kinematic distance

Shuo Liu, Nitin H. Vaidya

In this report, we study the problem of Byzantine fault-tolerant distributed set intersection and the importance of redundancy in solving this problem. Specifically, consider a distributed system with $n$ agents, each of which has a local set. There are up to $f$ agents that are Byzantine faulty. The goal is to find the intersection of the sets of the non-fa

Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir

We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared $\ell_2$-norm of the weights (sum of squares of all weights in the network). Whereas previous depth separation results focused on separation in terms of width, such results do not give insight into whether depth determines if it is possible to l

Ndivhuwo Nyase, Lebohang Mashatola, Aviwe Kohlakala, Kahn Rhrissorrakrai

Advancing the discovery of prognostic cancer biomarkers is crucial for comprehending disease mechanisms, refining treatment plans, and improving patient outcomes. This study introduces Weighted Gene Topological Data Analysis (WGTDA), an innovative framework utilizing topological principles to identify gene interactions and distinctive biomarker features. WGT

Gioele Barabucci, Victor Shia, Eugene Chu, Benjamin Harack

Background: Large language models (LLMs) such as OpenAI's GPT-4 or Google's PaLM 2 are proposed as viable diagnostic support tools or even spoken of as replacements for "curbside consults". However, even LLMs specifically trained on medical topics may lack sufficient diagnostic accuracy for real-life applications. Methods: Using collective intelligence metho

Mir Mehedi Faruk

In this article, we point out that to solve the null Raychaudhuri equation for higher dimensional spacetime with accelerating FRW solution in external directions and static compact internal directions, it is necessary to violate the Strong Energy condition in higher dimensions. This constraint is well-known in obtaining accelerating cosmological solutions in

Patrick Jaillet, Chara Podimata, Andrew Vakhutinsky, Zijie Zhou

In this work, we study an upgrading scheme for online resource allocation problems. We work in a sequential setting, where at each round a request for a resource arrives and the decision-maker has to decide whether to accept it (and thus, offer the resource) or reject it. The resources are ordered in terms of their value. If the decision-maker decides to acc

Rui Chen, Virat Tara, Jayita Dutta, Zhuoran Fang

Chalcogenide phase-change materials (PCMs) offer new paradigms for programmable photonic integrated circuits (PICs) thanks to their zero static energy and significant refractive index contrast. However, prototypical PCMs, such as GeSbTe (GST), are lossy in their crystalline phase, albeit transparent in the amorphous state. Moreover, electrically switching PC

Jiaying Gong, Hoda Eldardiry

E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values, large quantities of labeled training data are needed to train a traditional supervised learning model. Typic

Leuson Da Silva, Jordan Samhi, Foutse Khomh

Since its release in November 2022, ChatGPT has shaken up Stack Overflow, the premier platform for developers queries on programming and software development. Demonstrating an ability to generate instant, human-like responses to technical questions, ChatGPT has ignited debates within the developer community about the evolving role of human-driven platforms i

Adam Nowak, Peter Sjögren, Tomasz Z. Szarek

We obtain sharp estimates for the Jacobi heat kernel in a range of parameters where the result has not been established before. This extends and completes an earlier result due to the authors. The proof is based on a generalization of the Dijksma-Koornwinder product formula for Jacobi polynomials.

Dan Garber, Ben Kretzu

We consider the setting of online convex optimization with adversarial time-varying constraints in which actions must be feasible w.r.t. a fixed constraint set, and are also required on average to approximately satisfy additional time-varying constraints. Motivated by scenarios in which the fixed feasible set (hard constraint) is difficult to project on, we

Alexander I. Bobenko, Nikolai Bobenko, Yuri B. Suris

In this paper we develop a general approach to dimer models analogous to Krichever's scheme in the theory of integrable systems. We start with a Riemann surface and the simplest generic meromorphic functions on it and demonstrate how to obtain integrable dimer models. These are dimer models on doubly periodic bipartite graphs with quasi-periodic positive wei

Girish Sastry, Lennart Heim, Haydn Belfield, Markus Anderljung

Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing

Tatiana Chakravorti, Sai Dileep Koneru, Sarah Rajtmajer

In the past decade, open science and science of science communities have initiated innovative efforts to address concerns about the reproducibility and replicability of published scientific research. In some respects, these efforts have been successful, yet there are still many pockets of researchers with little to no familiarity with these concerns, subsequ

Kevin J Zahnle

Noble gases provide tracers of cosmic provenance that are accessible to a future Uranus Atmospheric Probe. Argon and krypton are expected to be well-mixed on Uranus with respect to H$_2$ and He, although condensation at the winter pole may be possible. The Ar/H$_2$ and Ar/Kr ratios address whether the materials accreted by Uranus resembled the extremely cold

John C. Duchi, Saminul Haque

We present an information-theoretic lower bound for the problem of parameter estimation with time-uniform coverage guarantees. Via a new a reduction to sequential testing, we obtain stronger lower bounds that capture the hardness of the time-uniform setting. In the case of location model estimation, logistic regression, and exponential family models, our $\O

Omid Nejati Manzari, Javad Mirzapour Kaleybar, Hooman Saadat, Shahin Maleki

The accurate segmentation of medical images is critical for various healthcare applications. Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) like U-Net, have shown remarkable success in medical image segmentation tasks. However, they have limitations in capturing global context and long-range relations, especially for obj

Daniel Xiang, Nikolaos Ignatiadis, Peter McCullagh

In large-scale studies with parallel signal-plus-noise observations, the local false discovery rate is a summary statistic that is often presumed to be equal to the posterior probability that the signal is null. We prefer to call the latter quantity the local null-signal rate to emphasize our view that a null signal and a false discovery are not identical ev

N. Panchal, L. Balogh, J. -F. Caron, G. Giroux

In this paper, we report on a tabletop experiment studying neutron scattering in a Spherical Proportional Counter using an Am-Be source. Systematic studies were carried out to investigate the effect of gas mixture, pressure, operating voltage, and sphere size on the drift time-rise time relationship of the signal in a spherical proportional counter. Our expe

Aditya Malusare, Vaneet Aggarwal

Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative models and the utilization of extensive biomedical knowledge, often systematized within knowledge graphs, whose potential to inform and enhance g

Alexander Philip, Sanya Chawla, Lola Jover, George P. Kafentzis

Chest X-ray is a commonly used tool during triage, diagnosis and management of respiratory diseases. In resource-constricted settings, optimizing this resource can lead to valuable cost savings for the health care system and the patients as well as to and improvement in consult time. We used prospectively-collected data from 137 patients referred for chest X

Timothy Wong, Claire Li, Sam Lam, Billy Chiu

This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid dyslexic students who have cognitive deficiency in writing skills but have no problem expressing their ideas through speech. For Cantonese speech recognition, the basic unit of ac

Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper

We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities, while maintaining the integrity of essential knowledge generation and not affecting causally unrelated inf

K. Fushimi, D. Chernyak, H. Ejiri, K. Hata

The existence of cosmic dark matter and neutrino properties are long-standing problems in cosmology and particle physics. These problems have been investigated by using radiation detectors. We will discuss the application of inorganic crystal scintillators to studies on dark matter and neutrino properties. A large volume and high-purity inorganic crystal is

Jianing Wang, Junda Wu, Yupeng Hou, Yao Liu

Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment. Specifically, we first propose a structured format verbalizer to unify al

Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey

Neural fields encode continuous multidimensional signals as neural networks, enabling diverse applications in computer vision, robotics, and geometry. While Adam is effective for stochastic optimization, it often requires long training times. To address this, we explore alternative optimization techniques to accelerate training without sacrificing accuracy.

Ken Ono, Ajit Singh

In his important 1920 paper on partitions, MacMahon defined the partition generating functions \begin{align*} A_k(q)=\sum_{n=1}^{\infty}\mathfrak{m}(k;n)q^n&:=\sum_{0< s_1<s_2<\cdots<s_k} \frac{q^{s_1+s_2+\cdots+s_k}}{(1-q^{s_1})^2(1-q^{s_2})^2\cdots(1-q^{s_k})^2},\\ C_k(q)=\sum_{n=1}^{\infty} \mathfrak{m}_{odd}(k;n)q^n&:=\sum_{0< s_1<s_2<\cdots<s_k} \frac{q

Doha Kattan, David Singerman

The modular group $\Gamma$ (which is the Hecke group $H_3$) can be used to study triangular maps. Here we use the Hecke group $H_4$ to study the regular map that underlies Bring's surface of genus 4. Our main result is the determination of the 20-sided hyperbolic polygon that gives Bring's surface.

Filip Tokarski

A designer distributes goods while considering the perceived equity of the resulting allocation. Such concerns are modeled through an equity constraint requiring that equally deserving agents receive equal allocations. I ask what forms of screening are compatible with equity and show that while the designer cannot equitably screen with a single instrument (e

Sagar Pathak, Bidhya Shrestha

This research project presents the implementation of a Deep Q-Learning Network (DQN) for a self-driving car on a 2-dimensional (2D) custom track, with the objective of enhancing the DQN network's performance. It encompasses the development of a custom driving environment using Pygame on a track surrounding the University of Memphis map, as well as the design

Akhil Jalan, Deepayan Chakrabarti

In network formation games, agents form edges with each other to maximize their utility. Each agent's utility depends on its private beliefs and its edges in the network. Strategic agents can misrepresent their beliefs to get a better resulting network. Most prior works in this area consider honest agents or a single strategic agent. Instead, we propose a mo

Ali Shirinzad, Kecheng Xu, Pierre Edward Sullivan

An array of twelve circular-orifice synthetic jet actuators (SJAs) was used to provide the unsteady forcing required for flow separation control over a National Advisory Committee for Aeronautics (NACA) 0025 airfoil at a chord-based Reynolds number of 100000 and an angle of attack of 10{\deg}. Two distinct high- and low-forcing frequencies corresponding to t

Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e., DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for refere

Zachary W. Windom, AV Lam, Ajith Perera, Rodney J. Bartlett

Frozen natural orbitals (FNOs) are used to augment IP/EA-EOM-CCSD calculations targeting the band gap of trans-polyacetylene and polyacene. We show the resulting electron affinities (EAs), ionization potentials (IPs), and extrapolated band gaps incur errors that are largely tunable to a desired accuracy, yet require many orders of magnitude fewer core-hours

Thorben Tröbst, Rajan Udwani

In the online hypergraph matching problem, hyperedges of size $k$ over a common ground set arrive online in adversarial order. The goal is to obtain a maximum matching (disjoint set of hyperedges). A na\"ive greedy algorithm for this problem achieves a competitive ratio of $\frac{1}{k}$. We show that no (randomized) online algorithm has competitive ratio bet

Angus Fung, Beno Benhabib, Goldie Nejat

Tracking of dynamic people in cluttered and crowded human-centered environments is a challenging robotics problem due to the presence of intraclass variations including occlusions, pose deformations, and lighting variations. This paper introduces a novel deep learning architecture, using conditional latent diffusion models, the Latent Diffusion Track (LDTrac

Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, Jane Dwivedi-Yu

State-of-the-art language models can exhibit impressive reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best models struggle to identify \textit{when and where to refine} without access to external feedback. Outcome-based Reward Models (\textbf{ORMs}), trained to predict correctness of the f

Kenneth Li, Tianle Liu, Naomi Bashkansky, David Bau

System-prompting is a standard tool for customizing language-model chatbots, enabling them to follow a specific instruction. An implicit assumption in the use of system prompts is that they will be stable, so the chatbot will continue to generate text according to the stipulated instructions for the duration of a conversation. We propose a quantitative bench

Ander Aguirre, Hoi H. Nguyen, Jingheng Wang

Many statistics of roots of random polynomials have been studied in the literature, but not much is known on the concentration aspect. In this note we present a systematic study of this question, aiming towards nearly optimal bounds to some extent. Our method is elementary and works well for many models of random polynomials, with gaussian or non-gaussian co

Yu Quan Chong, Jiaoyang Li, Katia Sycara

The Multi-Agent Path Finding (MAPF) problem entails finding collision-free paths for a set of agents, guiding them from their start to goal locations. However, MAPF does not account for several practical task-related constraints. For example, agents may need to perform actions at goal locations with specific execution times, adhering to predetermined orders

Frederik Ritter, Andrej Rode, Laurent Schmalen

In this work, we present an open-source implementation of the enumerative sphere shaping (ESS) algorithm used for probabilistic constellation shaping (PCS). PCS aims at closing the shaping gap caused by using uniformly distributed modulation symbols in channels for which information theory shows non-uniformly distributed signaling to be optimal. ESS is one s

Michał Buchała

We study unitarily equivalent bilateral weighted shifts with operator weights. We establish a general characterization of unitary equivalence of such shifts under the assumption that the weights are quasi-invertible. We prove that under certain assumptions unitary equivalence of bilateral weighted shifts with operator weights defined on $ \mathbb{C}^{2} $ ca

Xiangyu Chang, Sk Miraj Ahmed, Srikanth V. Krishnamurthy, Basak Guler

The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in data distribution, but also in data quality, as well as compute/communication latency. An integrated view of t

Degan Hao, Dooman Arefan, Margarita Zuley, Wendie Berg

Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data. In this study, we proposed a novel adversarially robust feature learning (ARFL) method for a real-world application of breast cancer diagnosis. ARFL facilitates adv

Gabriel J. Grell, Maurice A. Leutenegger, Pedro Amaro, José R. Crespo López-Urrutia

We calculated cross sections for the dielectronic recombination (DR) satellite lines of Fe XVII and benchmarked our predictions with experimental cross sections of Fe XVII resonances that were mono-energetically excited in an electron beam ion trap. We extend the benchmark to all resolved DR and direct electron-impact excitation (DE) channels in the experime

Uddipan Banik

In the standard cosmological paradigm, structure formation occurs via gravitational encounters and mergers between galaxies and dark matter halos. These collisionless self-gravitating systems therefore prevail in a state of non-equilibrium or quasi-equilibrium at best. Recent observations show that even our own Milky Way galaxy harbors non-equilibrium featur

Sukankana Chakraborty, Leonardo Castro-Gonzalez, Helen Margetts, Hardik Rajpal

With the rise of social media, political conversations now take place in more diffuse environments. In this context, it is not always clear why some actors, more than others, have greater influence on how discussions are shaped. To investigate the factors behind such influence, we build on nodality, a concept in political science which describes the capacity

Paul Engelmann, Peter Brunsgaard Trolle, Christian Hardmeier

Dehumanization is a mental process that enables the exclusion and ill treatment of a group of people. In this paper, we present two data sets of dehumanizing text, a large, automatically collected corpus and a smaller, manually annotated data set. Both data sets include a combination of political discourse and dialogue from movie subtitles. Our methods give

Qiyuan An, Christos Sevastopoulos, Fillia Makedon

Endeavors in indoor robotic navigation rely on the accuracy of segmentation models to identify free space in RGB images. However, deep learning models are vulnerable to adversarial attacks, posing a significant challenge to their real-world deployment. In this study, we identify vulnerabilities within the hidden layers of neural networks and introduce a prac

Hannes Gernandt, Timo Reis

In this paper, we extend a classical approach to linear quadratic (LQ) optimal control via Popov operators to abstract linear differential-algebraic equations (ADAEs) in Hilbert spaces. To ensure existence of solutions, we assume that the underlying differential-algebraic equation has index one in the pseudo-resolvent sense. This leads to the existence of a

Jillian Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang

The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship when needed, e.g., blind reviews for scientific papers, anonymous online reviews, or anonymous interactions in the mental health forums. In this paper, we propose an

Alberto Enciso, Daniel Peralta-Salas

In 1988, Nelson proposed that neighboring vortex lines in high-temperature superconductors may become entangled with each other. In this article we construct solutions to the Ginzburg--Landau equations which indeed have this property, as they exhibit entangled vortex lines of arbitrary topological complexity.

Xijun Deng, Stéphane Lafortune

In this paper, we are concerned with a one-parameter family of peakon equations with cubic nonlinearity parametrized by a parameter usually denoted by the letter $b$. This family is called the ``$b$-Novikov'' since it reduces to the integrable Novikov equation in the case $b=3$. By extending the corresponding linearized operator defined on functions in $H^1(