February 2024 arXiv papers — page 87

Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem

Question answering (QA) can only make progress if we know if an answer is correct, but current answer correctness (AC) metrics struggle with verbose, free-form answers from large language models (LLMs). There are two challenges with current short-form QA evaluations: a lack of diverse styles of evaluation data and an over-reliance on expensive and slow LLMs.

Clayton Barnes, Leonid Mytnik, Zhenyao Sun

Consider the $[0,1]$-valued continuous random field solution $(u_t(x))_{t\geq 0, x\in \mathbb R}$ to the one-dimensional stochastic heat equation \[ \partial_t u_t = \frac{1}{2}\Delta u_t + b(u_t) + \sqrt{u_t(1-u_t)} \dot W, \] where $b(1)\leq 0\leq b(0)$ and $\dot W$ is space-time white noise. In this paper, we establish the weak existence and uniqueness of

Yejun Yoon, Seunghyun Yoon, Kunwoo Park

This paper addresses the critical challenge of assessing the representativeness of news thumbnail images, which often serve as the first visual engagement for readers when an article is disseminated on social media. We focus on whether a news image represents the actors discussed in the news text. To serve the challenge, we introduce NewsTT, a manually annot

Priyanka Pandey, Mrinal Arandhara, Paul L. Houston, Chen Qu

Here we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reported in 2004. We describe the details of both fitting methods and then compare the two PESs with respect to preci

Andrei Iakovlev, Annie Liang

Machine learning algorithms are now capable of performing evaluations previously conducted by human experts (e.g., medical diagnoses). How should we conceptualize the difference between evaluation by humans and by algorithms, and when should an individual prefer one over the other? We propose a framework to examine one key distinction between the two forms o

Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun

We study low-rank matrix trace regression and the related problem of low-rank matrix bandits. Assuming access to the distribution of the covariates, we propose a novel low-rank matrix estimation method called LowPopArt and provide its recovery guarantee that depends on a novel quantity denoted by B(Q) that characterizes the hardness of the problem, where Q i

Mary Hogan, Devon Loehr, John Sonchack, Shir Landau Feibish

Programmable data planes allow for sophisticated applications that give operators the power to customize the functionality of their networks. Deploying these applications, however, often requires tedious and burdensome optimization of their layout and design, in which programmers must manually write, compile, and test an implementation, adjust the design, an

Iddo Ben-Ari, Ningwei Jiang

This work provides complete description of Quasistationary Distributions (QSDs) for Markov chains with a unique absorbing state and an irreducible set of non-absorbing states. As is well-known, every QSD has an associated absorption parameter describing the exponential tail of the absorption time under the law of the process with the QSD as the initial distr

Shuhan Liu, Kaize Ding

Distribution shifts on graphs -- the data distribution discrepancies between training and testing a graph machine learning model, are often ubiquitous and unavoidable in real-world scenarios. Such shifts may severely deteriorate the performance of the model, posing significant challenges for reliable graph machine learning. Consequently, there has been a sur

Esther Capó, James C. McWilliams, Jonathan Gula, M. Jeroen Molemaker

Realistic computational simulations in different oceanic basins reveal prevalent prograde mean flows (i.e. in the direction of topographic Rossby wave propagation along isobaths; a.k.a. topostrophy) on topographic slopes in the deep ocean, consistent with the barotropic theory of eddy-driven mean flows. Attention is focused on the Western Mediterranean Sea w

Mehdi Mirakhorli, Derek Garcia, Schuyler Dillon, Kevin Laporte

Modern software applications heavily rely on diverse third-party components, libraries, and frameworks sourced from various vendors and open source repositories, presenting a complex challenge for securing the software supply chain. To address this complexity, the adoption of a Software Bill of Materials (SBOM) has emerged as a promising solution, offering a

Neil Ashby, Bijunath Patla

As humanity aspires to explore the solar system and investigate distant worlds such as the Moon, Mars, and beyond, there is a growing need to establish and broaden coordinate time references that depend on the rate of standard clocks. According to Einstein's theory of relativity, the rate of a standard clock is influenced by the gravitational potential at th

Ruiqing He, Bjorn Paulsson

The Parkfield M6 earthquake predicted from 1985 by the USGS to happen by 1993 happened 11 years later in 2004 instead. Till today, satisfactory answers to why this earthquake was mis-predicted have not been found. Seven months after the earthquake, we deployed a seismic array in the SAFOD main hole to monitor the San Andreas Fault. During a 13 days period, w

Kaixiang Zheng, En-Hui Yang

As a technique to bridge logit matching and probability distribution matching, temperature scaling plays a pivotal role in knowledge distillation (KD). Conventionally, temperature scaling is applied to both teacher's logits and student's logits in KD. Motivated by some recent works, in this paper, we drop instead temperature scaling on the student side, and

Yuta Yano, Fumitaka Nakamura, Shinichi W. Kinoshita

Dense core collisions, previously regarded as minor in star formation, are proposed to play a significant role in structure formation around protostellar envelopes and binary formation. Using archival data of nearby star-forming regions, we determine the frequencies of core collisions. Our calculations reveal that a typical core is likely to undergo multiple

Eran Alouf

We first prove that if $\mathcal{Z}$ is a dp-minimal expansion of $\left(\mathbb{Z},+,0,1\right)$ which is not interdefinable with $\left(\mathbb{Z},+,0,1,<\right)$, then every infinite subset of $\mathbb{Z}$ definable in $\mathcal{Z}$ is generic in $\mathbb{Z}$. Using this, we prove that if $\mathcal{Z}$ is a dp-minimal expansion of $\left(\mathbb{Z},+,0,1\

Riku Arakawa, Kiyosu Maeda, Hiromu Yakura

Multimodal scene search of conversations is essential for unlocking valuable insights into social dynamics and enhancing our communication. While experts in conversational analysis have their own knowledge and skills to find key scenes, a lack of comprehensive, user-friendly tools that streamline the processing of diverse multimodal queries impedes efficienc

Jeaheang Bang, Zhuolun Yang

We prove two Liouville type theorems for the stationary Navier-Stokes equations in $\mathbb{R}^3$ under some assumptions on 1) the growth of the $L^s$ mean oscillation of a potential function of the velocity field, or 2) the relative decay of the head pressure and the square of the velocity field at infinity. The main idea is to use Saint-Venant type estimat

Danyang Li, Roberto Tron

Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars. The task of binary and multi-class classification for time-series data has become a prominent area of research. Neural networks represent a popular approach to classifying data; However, they lack interpretability, which poses a significant challenge in

Chengkai Huang, Tong Yu, Kaige Xie, Shuai Zhang

Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs). In this paper, we attempt to thoroughly examine FM-based recommendation systems (FM4RecSys). We start by reviewing the research background of FM4RecSys. Then, we provide a systemat

Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han

Relation extraction (RE) aims to identify semantic relationships between entities within text. Despite considerable advancements, existing models predominantly require extensive annotated training data, which is both costly and labor-intensive to collect. Moreover, these models often struggle to adapt to new or unseen relations. Few-shot learning, aiming to

Thang-Anh-Quan Nguyen, Amine Bourki, Mátyás Macudzinski, Anthony Brunel

This review thoroughly examines the role of semantically-aware Neural Radiance Fields (NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It explores how NeRFs adeptly infer 3D representations for both stationary and dynamic objects in a scene. This capability is pivotal for generating high-quality new viewpoints, comple

Sijia Chen, Baochun Li, Di Niu

The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work, e.g., Tree of Thoughts, has pointed out the importance of exploration and self-evaluation in reasoning step selection for co

Fedor Borisyuk, Shihai He, Yunbo Ouyang, Morteza Ramezani

In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework. We share our insight on developing and deployment of GNNs at large scale at LinkedIn. We present a set of algorithmic improvements to the quality of GNN representation learning including temporal graph architectures with long term losses, effective cold start solu

Tianyi Lorena Yan, Fei Wang, James Y. Huang, Wenxuan Zhou

Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the same instruction is phrased with slightly varied forms or language styles. This behavior indicates LLMs' lack of

Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde

While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass. However, current PFNs have limitati

Valentina Macchiati, Piero Mazzarisi, Diego Garlaschelli

Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating

Jorge Guccione, Juan Jose Guccione, Christian Valqui

Assume that $P$ and $Q$ are elements of $A_1$ satisfying $[P,Q] = 1$. The Dixmier Conjecture for $A_1$ says that they always generate $A_1$. We show that if $P$ is a sum of not more than~$4$ homogeneous elements of $A_1$ then $P$ and $Q$ generate $A_1$, which generalizes the main result in arXiv:2210.00257.

Andrii Babii, Marine Carrasco, Idriss Tsafack

We study the functional linear regression model with a scalar response and a Hilbert space-valued predictor, a canonical example of an ill-posed inverse problem. We show that the functional partial least squares (PLS) estimator attains nearly minimax-optimal convergence rates over a class of ellipsoids and propose an adaptive early stopping procedure for sel

Xin Jin, Kit Chan, Ian Barnett, Riddhi Pratim Ghosh

Two-sample hypothesis testing for large graphs is popular in cognitive science, probabilistic machine learning and artificial intelligence. While numerous methods have been proposed in the literature to address this problem, less attention has been devoted to scenarios involving graphs of unequal size or situations where there are only one or a few samples o

Arindam Mitra, Hamed Khanpour, Corby Rosset, Ahmed Awadallah

Mathematical word problem-solving has long been recognized as a complex task for small language models (SLMs). A recent study hypothesized that the smallest model size, needed to achieve over 80% accuracy on the GSM8K benchmark, is 34 billion parameters. To reach this level of performance with smaller models, researcher often train SLMs to generate Python co

M. Alkhateeb, A. Matzkin

We investigate the construction of spin-1/2 fermionic and spin-0 bosonic wave-packets having compact spatial support in the framework of a computational quantum field theory (QFT) scheme offering space-time solutions of the relativistic wave equations in background fields. In order to construct perfectly localized wave-packets, we introduce a spatial density

Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason

Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As the number of downstream tasks grows, these draft models a

J. N. Graham, C. Mielke, D. Das, T. Morresi

The breaking of time-reversal symmetry (TRS) in the normal state of kagome superconductors $A$V$_{3}$Sb$_{5}$ stands out as a significant feature. Yet the extent to which this effect can be tuned remains uncertain, a crucial aspect to grasp in light of the varying details of TRS breaking observed through different techniques. Here, we employ the unique low-e

Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian

Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challenges with complex inputs and encounter difficulties due to noisy knowledge retrieval, notably hindering model effectiveness. To address this issue, we introduce BlendFilter, a novel

Oguzhan Yücel, Denis Yagodkin, Jan N. Kirchhof, Abhijeet Kumar

Single-photon emission centers generated by controlled atomic force microscopy (AFM) indentation in monolayer WSe\(_2\) on a flexible polymer substrate are explored for applications in quantum technologies. Here, we study the response of these emitters to the polymer substrate's strain state, which is controlled by selecting the indentation force and by grad

Avimita Chatterjee, Debarshi Kundu, Swaroop Ghosh

Quantum computing holds transformative potential for various fields, yet its practical application is hindered by the susceptibility to errors. This study makes a pioneering contribution by applying quantum error correction codes (QECCs) for complex, multi-qubit classification tasks. We implement 1-qubit and 2-qubit quantum classifiers with QECCs, specifical

Michael Penwarden, Houman Owhadi, Robert M. Kirby

Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDE) has garnered much attention in the Computational Science and Engineering (CS&E) world. This topic encompasses a broad array of methods and models aimed at solving a single or a collection of PDE problems, called multitask learning. PIML is characterized by the

Smriti Singh, Shuvam Keshari, Vinija Jain, Aman Chadha

Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds. This pervasive issue perpetuates systemic inequalities, hindering the pursuit of inclusive progress as a society. In this paper, we investigate the presence of socioeconomic bias, if any, in large lan

Rayna Hata, Narit Trikasemsak, Andrea Giudice, Stacy A. Doore

While there is no replacement for the learned expertise, devotion, and social benefits of a guide dog, there are cases in which a robot navigation assistant could be helpful for individuals with blindness or low vision (BLV). This study investigated the potential for an industrial agile robot to perform guided navigation tasks. We developed two interface pro

Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley

Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning. Implicit learning of causal mechanisms typically involves two categories of interventional data: hard and soft interventions. In real-world scenarios, soft interventions

Yong Huang, Charles A. Downs, Amir M. Rahmani

Warfarin, an anticoagulant medication, is formulated to prevent and address conditions associated with abnormal blood clotting, making it one of the most prescribed drugs globally. However, determining the suitable dosage remains challenging due to individual response variations, and prescribing an incorrect dosage may lead to severe consequences. Contextual

Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou

Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-modifying ME and parameter-preserving ME (integrating extra modules while preserving original parameters). Regrettably, previous studies on ME evaluation have two critical limitatio

Ahab Isaac, Wesley Armour, Karel Adámek

Accurate modelling of spectra produced by X-ray sources requires the use of Monte-Carlo simulations. These simulations need to evaluate physical processes, such as those occurring in accretion processes around compact objects by sampling a number of different probability distributions. This is computationally time-consuming and could be sped up if replaced b

Heba Bou KaedBey, Mark van Hoeij, Man Cheung Tsui

We present two algorithms for computing what we call the absolute factorization of a difference operator. We also give an algorithm to solve third order difference equations in terms of second order equations, together with applications to OEIS sequences. The latter algorithm is similar to existing algorithms for differential equations.

Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva, Alexey Kurakin

Distribution shifts and adversarial examples are two major challenges for deploying machine learning models. While these challenges have been studied individually, their combination is an important topic that remains relatively under-explored. In this work, we study the problem of adversarial robustness under a common setting of distribution shift - unsuperv

Nikolos Gurney, Fred Morstatter, David V. Pynadath, Adam Russell

We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (thos

Mark Bun, Aloni Cohen, Rathin Desai

We continue the study of the computational complexity of differentially private PAC learning and how it is situated within the foundations of machine learning. A recent line of work uncovered a qualitative equivalence between the private PAC model and Littlestone's mistake-bounded model of online learning, in particular, showing that any concept class of Lit

Peter Danziger, Melissa A. Huggan, Rehan Malik, Trent G. Marbach

We consider playing the game of Tic-Tac-Toe on block designs BIBD($v, k, \lambda$) and transversal designs TD($k, n$). Players take turns choosing points and the first player to complete a block wins the game. We show that triple systems, BIBD($v, 3, \lambda$), are a first player win if and only if $v \geq 5$. Further, we show that for $k = 2, 3$, TD($k, n$)

Laura Nelsone, Guntars Kitenbergs

Phase-separated magnetic fluids provide a very strong magnetic response ($\mu>25$) in a liquid state material. Even small fields can cause a notable material response, but this depends on its properties, which are often difficult to control. Here, we investigate how temperature affects the properties of the system, where phase separation is induced by an inc

Azeddine Zaidni, Philip J Morrison, Saad Benjelloun

Cahn-Hilliard-Navier-Stokes (CHNS) systems describes flows with two-phases, e.g., a liquid with bubbles. Obtaining constitutive relations for general dissipative processes for such a systems, which are thermodynamically consistent, can be a challenge. We show how the metriplectic 4-bracket formalism achieves this in a straightforward, in fact algorithmic, ma

Nikolos Gurney, David V. Pynadath, Volkan Ustun

Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM -- reas

Frédéric Rousset, Changzhen Sun

In this paper, we establish the transverse linear asymptotic stability of one-dimensional small-amplitude solitary waves of the gravity water-waves system. More precisely, we show that the semigroup of the linearized operator about the solitary wave decays exponentially within a spectral subspace supplementary to the space generated by the spectral projectio

Zihao He, Siyi Guo, Ashwin Rao, Kristina Lerman

Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs represent different perspectives, existing research focused on positional alignment, i.e., how closely the models mi

Alvaro Alvarez-Candal

Context. Small bodies change their brightness due to different motives: Rotation along their axis or axes, combined with irregular shapes and/or changing surface properties, or changes in the geometry of observations. In this work, we tackle the problem of Phase curves, which show the change in brightness due to changes in the fraction of illuminated surface

Xingyi He, S. Sandeep Pradhan

In this work, we propose a soft covering problem for fully quantum channels using relative entropy as a criterion for operator closeness. We establish covering lemmas by deriving one-shot bounds on the achievable rates in terms of smooth min-entropies. In the asymptotic regime, we show that the infimum of the rate, defined as the logarithm of the minimum ran

Alexis Chevalier, Jiayi Geng, Alexander Wettig, Howard Chen

NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life use-cases of LMs for science, including applications in education that require processing long scientific documents. To address this, we introduce TutorEval and TutorChat. TutorEv

Christopher A. Johnson, Piyashi Biswas, Rubi Tapia, Jill See

Ankle proprioceptive deficits are common after stroke and occur independently of ankle motor impairments. Despite this independence, some studies have found that ankle proprioceptive deficits predict gait function, consistent with the concept that somatosensory input plays a key role in gait control. Other studies, however, have not found a relationship, pos

Gruia Calinescu, Sami Davies, Samir Khuller, Shirley Zhang

We study the online busy time scheduling model on heterogeneous machines. In our setting, jobs with uniform length arrive online with a deadline that becomes known to the algorithm at the job's arrival time. An algorithm has access to machines, each with different associated capacities and costs. The goal is to schedule jobs on machines by their deadline, so

Nir Avni, Itay Glazer, Michael Larsen

To a non-trivial word $w(x_{1},...,x_{r})$ in a free group $F_{r}$ on $r$ elements and a group $G$, one can associate the word map $w_{G}:G^{r}\rightarrow G$ that takes an $r$-tuple $(g_{1},...,g_{r})$ in $G^{r}$ to $w(g_{1},...,g_{r})$. If $G$ is compact, we further associate the word measure $\tau_{w,G}$, defined as the distribution of $w_{G}(\mathsf{X}_{1

Malcolm Jones, Hannah Chorley, Flynn Owen, Tamsyn Hilder

As efforts to mitigate the effects of climate change grow, reliable and thorough reporting of greenhouse gas emissions are essential for measuring progress towards international and domestic emissions reductions targets. New Zealand's national emissions inventories are currently reported between 15 to 27 months out-of-date. We present a machine learning appr

Vlad Roman

We are considering the commuting variety of the Lie algebra $\mathfrak{pgl}_n$ over an algebraically closed field of characteristic $p >0$, namely the set of pairs $ \{ (A,B) \in \mathfrak{pgl}_n \times \mathfrak{pgl}_n \mid [A,B]=0 \} $. We prove that if $n=pr$, then there are precisely two irreducible components, of dimensions $n^2+r-1$ and $n^2+n-2$. We a

Avimita Chatterjee, Swaroop Ghosh

Quantum Error Correction Codes (QECCs) are pivotal in advancing quantum computing by protecting quantum states against the adverse effects of noise and errors. With a variety of QECCs developed, including new developments and modifications of existing ones, selecting an appropriate QECC tailored to specific conditions is crucial. Despite significant improvem

Daniel Halpern, Safwan Hossain, Jamie Tucker-Foltz

Motivated by the difficulty of specifying complete ordinal preferences over a large set of $m$ candidates, we study voting rules that are computable by querying voters about $t < m$ candidates. Generalizing prior works that focused on specific instances of this problem, our paper fully characterizes the set of positional scoring rules that can be computed fo

Sayed Sajad Hashemi, Michael Guerzhoy, Noah H. Paulson

In Materials Science, material development involves evaluating and optimizing the internal structures of the material, generically referred to as microstructures. Microstructures structure is stochastic, analogously to image textures. A particular microstructure can be well characterized by its spatial statistics, analogously to image texture being character

Mohammad Saber Hashemi, Khiem Nguyen, Levi Kirby, Xuan Song

We devised a general heterogeneous microstructural design methodology applied to a specific material system, elasto-electro-active piezoelectric ceramic embedded plastics, which has great potential in sensing, 5G communication, and energy harvesting. Due to the multiphysics interactions of the studied material system, we have developed an accurate and effici

Hualin Zhan, Viqar Ahmad, Azul Mayon, Grace Tabi

The ability to extract material parameters of perovskite from quantitative experimental analysis is essential for rational design of photovoltaic and optoelectronic applications. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters for perovskite. Here we use Bayes

Selime Beyza Özçevik, Abdullah Dertli

In this study, novel Hyperbolic spinor sequences of Jacobsthal, Jacobsthal-Lucas and Jacobsthal polynomial, which have not been studied before, are defined by investigating the relationship between spinors, which are important mathematical objects used in physics and mathematics, and split Jacobsthal and split Jacobsthal-Lucas quaternions, which are extensio

Yinghui Li, Qingyu Zhou, Yuanzhen Luo, Shirong Ma

Recently, Large Language Models (LLMs) make remarkable evolutions in language understanding and generation. Following this, various benchmarks for measuring all kinds of capabilities of LLMs have sprung up. In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning te

Ishmael Nii Amartei Amartey

Breast Cancer is a major disease affecting women's health in the United States with incidence and prevalence dominant among younger women and the Black race. We analyzed the association between breast cancer characteristics with age and race and how survival months and age differ in racial groups. Using the Surveillance, Epidemiology, and End Results (SEER)

Colin Campbell, Edward D Dahl

This paper examines QAOA in the context of parity network synthesis. We propose a pair of algorithms for parity network synthesis and linear circuit inversion. Together, these algorithms can build the diagonal component of the QAOA circuit, generally the most expensive in terms of two qubit gates. We compare the CNOT count of our strategy to off-the-shelf co

CMS Collaboration

A search for beyond the standard model spin-0 bosons, $\phi$, that decay into pairs of electrons, muons, or tau leptons is presented. The search targets the associated production of such bosons with a W or Z gauge boson, or a top quark-antiquark pair, and uses events with three or four charged leptons, including hadronically decaying tau leptons. The proton-

Yannic Rath, George H. Booth

While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a pract

Gabriel Nicholas, Paul Friedl

On July 20, 2023, a group of 27 scholars and digital rights advocates with expertise in law, computer science, political science, and other disciplines gathered for the Large Language Models, Law and Policy Roundtable, co-hosted by the NYU School of Law's Information Law Institute and the Center for Democracy & Technology. The roundtable convened to discuss

Liudmyla Kryvonos, Edward B. Saff

We study the problem originally communicated by E. Meckes on the asymptotics for the eigenvalues of the kernel of the unitary eigenvalue process of a random $n \times n$ matrix. The eigenvalues $p_{j}$ of the kernel are, in turn, associated with the discrete prolate spheroidal wave functions. We consider the eigenvalue counting function $|G(x,n)|:=\#\{j:p_j>

Xuelun Shen, Zhipeng Cai, Wei Yin, Matthias Müller

Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types and are impractical when the scene type is unknown in advance. One of the underlying pro

Yash Mahajan, Matthew Freestone, Naman Bansal, Sathyanarayanan Aakur

Large Language Models (LLMs) have recently shown remarkable advancement in various NLP tasks. As such, a popular trend has emerged lately where NLP researchers extract word/sentence/document embeddings from these large decoder-only models and use them for various inference tasks with promising results. However, it is still unclear whether the performance imp

Johannes Bayer, Leo van Waveren, Andreas Dengel

As digitization in engineering progressed, circuit diagrams (also referred to as schematics) are typically developed and maintained in computer-aided engineering (CAE) systems, thus allowing for automated verification, simulation and further processing in downstream engineering steps. However, apart from printed legacy schematics, hand-drawn circuit diagrams

Chang Wang, Lu Wang

To promote precision medicine, individualized treatment regimes (ITRs) are crucial for optimizing the expected clinical outcome based on patient-specific characteristics. However, existing ITR research has primarily focused on scenarios with categorical treatment options and a single outcome. In reality, clinicians often encounter scenarios with continuous t

Pingping Zhu, Chang Liu, Peter Estephan

This paper addresses the path-planning challenge for very large-scale robotic systems (VLSR) operating in complex and cluttered environments. VLSR systems consist of numerous cooperative agents or robots working together autonomously. Traditionally, many approaches for VLSR systems are developed based on Gaussian mixture models (GMMs), where the GMMs represe

Patricia Vindel-Zandbergen, Jesús González-Vázquez

For years, theoretical calculations and scalable computer simulations have complemented ultrafast experiments as they offer the advantage to overcome experimental restrictions and have access to the whole dynamics. This synergy between theory and experiment promises to yield a deeper understanding of photochemical processes, offering valuable insights into t

Yixin Wan, Kai-Wei Chang

Recent large-scale T2I models like DALLE-3 have made progress in reducing gender stereotypes when generating single-person images. However, significant biases remain when generating images with more than one person. To systematically evaluate this, we propose the Paired Stereotype Test (PST) framework, which queries T2I models to depict two individuals assig

Massimiliano Bonamente, Dale Zimmerman

This paper presents likelihood-based inference methods for the family of univariate gamma-normal distributions GN({\alpha}, r, {\mu}, {\sigma}^2 ) that result from summing independent gamma({\alpha}, r) and N({\mu}, {\sigma}^2 ) random variables. First, the probability density function of a gamma-normal variable is provided in compact form with the use of pa

Md Rafique Un Nabi, Yue Li, Suzanne G E te Velthuis, Santosh Karki Chhetri

Spintronics, an evolving interdisciplinary field at the intersection of magnetism and electronics, explores innovative applications of electron charge and spin properties for advanced electronic devices. The topological Hall effect, a key component in spintronics, has gained significance due to emerging theories surrounding noncoplanar chiral spin textures.

Camilo Sanabria Malagón

In this paper we revisit the following inverse problem: given a curve invariant under an irreducible finite linear algebraic group, can we construct an ordinary linear differential equation whose Schwarz map parametrizes it? We present an algorithmic solution to this problem under the assumption that we are given the function field of the quotient curve. The

Z. Hao, T. Shaw, M. Hatefipour, W. M. Strickland

Nearly quantum limited Josephson parametric amplifiers (JPAs) are essential components in superconducting quantum circuits. However, higher order nonlinearities of the Josephson cosine potential are known to cause gain compression, therefore limiting scalability. In an effort to reduce the fourth order, or Kerr nonlinearity, we realize a parametric amplifier

Johannes Brustle, José Correa, Paul Dütting, Tomer Ezra

We study the classic single-choice prophet inequality problem through a resource augmentation lens. Our goal is to bound the $(1-\varepsilon)$-competition complexity of different types of online algorithms. This metric asks for the smallest $k$ such that the expected value of the online algorithm on $k$ copies of the original instance, is at least a $(1-\var

Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang

Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. Although the ``pre-training & finetuning'' learning paradigm significantly improves the VQA performance, the adversarial robustness of such a learning paradigm has not been explored. In this paper, we delve into a new problem: using a pre-trained mul

Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman

We introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats. Starting at the base, the pyramid emphasizes Data Integrity, which is essential for the accuracy a

William Y. Chen, Nick Salter

We show that the configuration space of four unordered points in $\mathbb{C}$ with barycenter 0 is isomorphic to the space of triples $(E,Q,\omega)$, where $E$ is an elliptic curve, $Q\in E^\circ$ a nonzero point, and $\omega$ a nonzero holomorphic differential on $E$. At the level of fundamental groups, our construction unifies two classical exceptional exa

Brendan J. Mahoney, Craig S. Lent

The Lieb-Robinson correlation function is the norm of a commutator between local operators acting on separate subsystems at different times. This provides a useful state-independent measure for characterizing the specifically quantum interaction between spatially separated qubits. The finite propagation velocity for this correlator defines a "light-cone" of

Federico Corberi, Luca Smaldone

We investigate the aging properties of the one-dimensional voter model with long-range interactions in its ordering kinetics. In this system, an agent $S_i=\pm 1$ positioned at a lattice vertex $i$, copies the state of another one located at a distance $r$, selected randomly with a probability $P(r) \propto r^{-\alpha}$. Employing both analytical and numeric

Govind Gangadhar, Karl Stratos

Standard fine-tuning is considered not as effective as specialized methods for model editing due to its comparatively poor performance. However, it is simple, agnostic to the architectural details of the model being edited, and able to leverage advances in standard training techniques with no additional work (e.g., black-box PEFT for computational efficiency

D. Di Croce, M. Giovannozzi, E. Krymova, T. Pieloni

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the features of dynamic aperture is crucial for the design and operation of such accelerators, as it provides insig

Wael Bahsoun, Carlangelo Liverani

We develop a bifurcation theory for infinite dimensional systems satisfying abstract hypotheses that are tailored for applications to mean field coupled chaotic maps. Our abstract theory can be applied to many cases, from globally coupled expanding maps to globally coupled Axiom A diffeomorphisms. To illustrate the range of applicability, we analyze an expli

Athanasios Paralikis, Claudia Piccinini, Abdulmalik A. Madigawa, Pietro Metuh

Quantum emitters in transition metal dichalcogenides (TMDs) have recently emerged as a promising platform for generating single photons for optical quantum information processing. In this work, we present an approach for deterministically controlling the polarization of fabricated quantum emitters in a tungsten diselenide (WSe$_2$) monolayer. We employ novel

Makoto Araya, Masaaki Harada, Hadi Kharaghani, Ali Mohammadian

Two skew Hadamard matrices are considered {\sf SH}-equivalent if they are similar by a signed permutation matrix. This paper determines the number of {\sf SH}-inequivalent skew Hadamard matrices of order $36$ for some types. We also study ternary self-dual codes and association schemes constructed from the skew Hadamard matrices of order $36$.

Jingwei Ni, Minjing Shi, Dominik Stammbach, Mrinmaya Sachan

With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a fact-checking pipeline, suffers from two key issues that limit its scalability and generalizability: (1) inconsistency in definitions of the task and what a claim is, and (2) the

Mohammad Mehdi Mousavi, Mahdi Kohan Sefidi, Shirin Allahyarkhani

Economists modeled self-control problems in decisions of people with the time-inconsistence preferences model. They argued that the source of self-control problems could be uncertainty and temptation. This paper uses an experimental test offered to individuals instantaneous reward and future rewards to measure awareness of self-control problems in a tempting

Hugo Aimar, Aníbal Chicco Ruiz, Ivana Gómez

In this article we aim to obtain the Fisher Riemann geodesics for nonparametric families of probability densities as a weak limit of the parametric case with increasing number of parameters.