February 2024 arXiv papers — page 116

Bohdan Khromets, Zach D. Merino, Jonathan Baugh

We present a unitary control pulse design method for a scalable quantum computer architecture based on electron spins in lateral quantum dots. We employ simultaneous control of spin interactions and derive the functional forms of spin Hamiltonian parameter pulses for a universal set of 1- and 2-qubit logic gates. This includes selective spin rotations with t

Rushang Karia, Pulkit Verma, Alberto Speranzon, Siddharth Srivastava

This paper introduces a new approach for continual planning and model learning in relational, non-stationary stochastic environments. Such capabilities are essential for the deployment of sequential decision-making systems in the uncertain and constantly evolving real world. Working in such practical settings with unknown (and non-stationary) transition syst

Joshua Kavner, Lirong Xia

Iterative voting is a natural model of repeated strategic decision-making in social choice theory when agents have the opportunity to update their votes prior to finalizing the group decision. Prior work has analyzed the efficacy of iterative plurality on the welfare of the chosen outcome at equilibrium, relative to the truthful vote profile, via an adaptati

Evangelos Katsamakas, Oleg V. Pavlov, Ryan Saklad

Artificial intelligence (AI) advances and the rapid adoption of generative AI tools like ChatGPT present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systemic approach that captures a holistic view of the AI transformation of higher education institutions (HEIs). To

Esa Hyytiä, Rhonda Righter

This technical report explains how optimal size-aware dispatching policies can be determined numerically using value iteration. It also contains some numerical examples that shed light to the nature of the optimal policies itself. The report complements our ``Towards the Optimal Dynamic Size-aware Dispatching'' article that will appear in Elsevier's Performa

Maria Jose Colmenares, Michiel Lambrechts, Elishevah van Kooten, Anders Johansen

During protoplanetary disk formation, dust grains located in the outer disk retain their pristine icy composition, while solids in the inner stellar-heated disk undergo volatile loss. This process may have left a fossil record in Solar System material showing different nucelosynthetic imprints that have been attributed to different degrees of thermal process

Kyu-Hwan Lee, Se-jin Oh

In this paper, we study the quantum virtual Grothendieck ring, denoted by $\frakK_q(\g)$, which was introduced in [39], and further investigated in [26, 25]. Our approach involves examining this ring from two perspectives: first, by considering its connection to quantum cluster algebras of non-skew-symmetric types; and second, by exploring its relevance to c

Jay Damask

The algorithm derived in this article, which builds upon the original paper, takes a holistic view of the handedness of an orthonormal eigenvector matrix so as to transfer what would have been labeled as a reflection in the original algorithm into a rotation through a major arc in the new algorithm. In so doing, the angular wrap-around on the interval {\pi}

Minyoung Park, Mirae Do, YeonJae Shin, Jaeseok Yoo

Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction. We introduce a novel two-phase learning approach, H2O-SDF, that discriminates between object and non-object regions within indoor environments. This method achieves a nuanced balanc

Isaac Cheng, Tyrone E. Woods, Patrick Côté, Jennifer Glover

The Cosmological Advanced Survey Telescope for Optical and ultraviolet Research (CASTOR) is a proposed Canadian-led 1m-class space telescope that will carry out ultraviolet and blue-optical wide-field imaging, spectroscopy, and photometry. CASTOR will provide an essential bridge in the post-Hubble era, preventing a protracted UV-optical gap in space astronom

Muqing Zheng, Yousu Chen, Xiu Yang, Ang Li

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, h

Thomas F. Varley

Since its introduction in 2011, the partial information decomposition (PID) has triggered an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order ("synergistic") interactions in complex systems. Despite its power, however, the PID has a number of limitations that restrict its general applicability: it

Koby Hayashi, Sinan G. Aksoy, Grey Ballard, Haesun Park

Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-rank matrix and its transpose. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation. The first algorithm uses randomized

Frédéric Hérau, David Krejcirik, Nicolas Raymond

The Bloch--Torrey operator $-h^2Δ+e^{iα}x_1$ on a bounded smooth planar domain, subject to Dirichlet boundary conditions, is analyzed. Assuming $α\in\left[0,\frac{3π}{5}\right)$ and a non-degeneracy assumption on the left-hand side of the domain, asymptotics of the eigenvalues with the smallest real part in the limit $h \to 0$ are derived. The strategy is a

Tommaso Puccetti, Simone Nardi, Cosimo Cinquilli, Tommaso Zoppi

Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer. Additionally, often the attacks are generated in dedicated attack sessions, without reproducing the realistic alternation and overlap of normal and atta

Ludovick Bouthat

In a recent article, Chávez, Garcia and Hurley introduced a new family of norms $\|\cdot\|_{\mathbf{X},d}$ on the space of $n \times n$ complex matrices which are induced by random vectors $\mathbf{X}$ having finite $d$-moments. Therein, the authors asked under which conditions the norms induced by a scalar multiple of $\mathbf{X}$ are submultiplicative. In

Brittany Terese Fasy, Samuel Micka, David L. Millman, Anna Schenfisch

The persistent homology transform (PHT) represents a shape with a multiset of persistence diagrams parameterized by the sphere of directions in the ambient space. In this work, we describe a finite set of diagrams that discretize the PHT such that it faithfully represents the underlying shape. We provide a discretization that is exponential in the dimension

Anindya De, Huan Li, Shivam Nadimpalli, Rocco A. Servedio

We consider the following basic, and very broad, statistical problem: Given a known high-dimensional distribution ${\cal D}$ over $\mathbb{R}^n$ and a collection of data points in $\mathbb{R}^n$, distinguish between the two possibilities that (i) the data was drawn from ${\cal D}$, versus (ii) the data was drawn from ${\cal D}|_S$, i.e. from ${\cal D}$ subje

Matteo Tiezzi, Michele Casoni, Alessandro Betti, Tommaso Guidi

A longstanding challenge for the Machine Learning community is the one of developing models that are capable of processing and learning from very long sequences of data. The outstanding results of Transformers-based networks (e.g., Large Language Models) promotes the idea of parallel attention as the key to succeed in such a challenge, obfuscating the role o

Michael Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer

Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly untruthful reports of participating agents. Optimizing dynamic mechanisms for welfare is relatively well understood. However, there has been less work on optimizing for other goal

Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro

Bandits with feedback graphs are powerful online learning models that interpolate between the full information and classic bandit problems, capturing many real-life applications. A recent work by Zhang et al. (2023) studies the contextual version of this problem and proposes an efficient and optimal algorithm via a reduction to online regression. However, th

Mengxiao Zhang, Haipeng Luo

Contextual multinomial logit (MNL) bandits capture many real-world assortment recommendation problems such as online retailing/advertising. However, prior work has only considered (generalized) linear value functions, which greatly limits its applicability. Motivated by this fact, in this work, we consider contextual MNL bandits with a general value function

Xiaohao Xu, Tianyi Zhang, Sibo Wang, Xiang Li

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllab

Antoine Desrier, Morgan Berkane, Camille Lévêque, Richard Taïeb

We investigate how vibration affects molecular photoemission dynamics, through simulations on two-dimension asymmetric model molecules including the electronic and nuclear motions in a fully correlated way. We show that a slight anisotropy in the electron-ion momentum sharing is sufficient to prevent one from unambigously characterizing the vibrationnaly ave

G. P. Donnelly, J. D. T. Smith, B. T. Draine, A. Togi

We present a focused study of radially-resolved varying PAH emission in the low-luminosity AGN-host NGC 4138 using deep Spitzer/IRS spectral maps. Using new model PAH spectra, we investigate whether these variations could be associated with changes to the PAH grain size distribution due to photodestruction by the AGN. Separately, we model the effects of the

Ilias Boulbarj, Bouklouze Abdelaziz, Yousra El Alami, Douzi Samira

Honey, a natural product generated from organic sources, is widely recognized for its revered reputation. Nevertheless, honey is susceptible to adulteration, a situation that has substantial consequences for both the well-being of the general population and the financial well-being of a country. Conventional approaches for detecting honey adulteration are of

Alexandru Chirvasitu

Let $\mathbb{G}$ be a compact Hausdorff group acting on a compact Hausdorff space $X$, $\alpha$ an irreducible $\mathbb{G}$-representation, and $C(X)$ the $C^*$-algebra of complex-valued continuous functions on $X$. We prove that the isotypic component $C(X)_{\alpha}$ is finitely generated as a module over the invariant subalgebra $C(X/\mathbb{G})\subseteq C

Giorgio Visentin, Harry Ramanantoanina, Anastasia Borschevsky, Larry Viehland

We propose a theoretically designed laser resonance chromatography (LRC) experiment on Rf$^+$ (Z = 104) drifting in He buffer gas. To this end, we first developed a four-level rate equation model that simulates the optical pumping of Rf$^+$ from its ground state, $^2$D$_{3/2}$ (7s$^2$6d$^1$), to the metastable $^4$F$_{3/2}$ (7s$^1$6d$^2$) state via laser res

Noah Lebowitz-Lockard, Joseph Vandehey

Let $p_{\textrm{dsd}} (n)$ be the number of partitions of $n$ into distinct squarefree divisors of $n$. In this note, we find a lower bound for $p_{\textrm{dsd}} (n)$, as well as a sequence of $n$ for which $p_{\textrm{dsd}} (n)$ is unusually large.

Arpana Saboo, Soumyadeep Halder, Subrata Das, Sonjoy Majumder

We explore topological excitations in a spin-1 Bose-Einstein condensate subjected to an in-plane sinusoidally varying magnetic field and Rashba spin-orbit coupling (SOC). In the absence of SOC, the periodic magnetic field induces vortex-anti-vortex structures in the $\ket{F=1, m_F=\pm1}$ condensates at saddle-points, such that the net topological charge rema

Sarwan Ali, Tamkanat E Ali, Prakash Chourasia, Murray Patterson

In the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. The classification of molecular sequences has seen widespread use of neural network-based techniques. Despite their astounding accuracy, these models often require a substantial number of parameters and more data collection. In this wo

Cheongho Han, Andrzej Udalski, Youn Kil Jung, Andrew Gould

Light curves of microlensing events occasionally deviate from the smooth and symmetric form of a single-lens single-source event. While most of these anomalous events can be accounted for by employing a binary-lens single-source (2L1S) or a single-lens binary-source (1L2S) framework, it is established that a small fraction of events remain unexplained by eit

Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati

There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamples--ranging from multiplication to simple planning--there persists a wide spread belief that LLMs can self-critique and

William Muldrew, Peter Hayes, Mingtian Zhang, David Barber

As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important. A key consideration for aligning these models is how to most effectively use human resources, or model resources in the case where LLMs themselves are used as oracles. Reinforcement learning from Human or AI preferences (RLHF

Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang

There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While promising, exam questions do not reflect the complexity of real patient-doctor interactions. In reality, physicians' decisions are shaped by many complex factors, such as patient c

Scott Goodfriend

Scripted agents have predominantly won the five previous iterations of the IEEE microRTS ($\mu$RTS) competitions hosted at CIG and CoG. Despite Deep Reinforcement Learning (DRL) algorithms making significant strides in real-time strategy (RTS) games, their adoption in this primarily academic competition has been limited due to the considerable training resou

Athoumane Niang, Ameth Ndiaye, Adama Thiandoum

In this paper we generalized a result of Soley Ersoy and Kemal Eren [10] about Bonnet timelike surface in Minkowski 3-space. We give a necessary and sufficient condition for a surface M in a Lorentzian 3-space to be timelike Bonnet surface. At the end, a theorem of classification of timelike Bonnet surface in a Lorentzian 3-space is given.

Sebastian Kühnert

Estimating parameters of functional ARMA, GARCH and invertible processes requires estimating lagged covariance and cross-covariance operators of Cartesian product Hilbert space-valued processes. Asymptotic results have been derived in recent years, either less generally or under a strict condition. This article derives upper bounds of the estimation errors f

Kapilya Gangadharan, K. Malathi, Anoop Purandaran, Barathi Subramanian

This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing,

Kasper Johansson, Thomas Schmelzer, Stephen Boyd

We propose a new method for finding statistical arbitrages that can contain more assets than just the traditional pair. We formulate the problem as seeking a portfolio with the highest volatility, subject to its price remaining in a band and a leverage limit. This optimization problem is not convex, but can be approximately solved using the convex-concave pr

Mishaal Kazmi, Hadrien Lautraite, Alireza Akbari, Qiaoyue Tang

We present PANORAMIA, a privacy leakage measurement framework for machine learning models that relies on membership inference attacks using generated data as non-members. By relying on generated non-member data, PANORAMIA eliminates the common dependency of privacy measurement tools on in-distribution non-member data. As a result, PANORAMIA does not modify t

Majid Zahedian, Vadim Vorobyov, Jörg Wrachtrup

Nuclear spins in solids offer a promising avenue for developing scalable quantum hardware. Leveraging nearby single-color centers, these spins can be efficiently addressed at the single-site level through spin resonance. However, characterising individual nuclear spins is quite cumbersome since the characterisation protocols may differ depending on the stren

Razvan-Andrei Lascu, Mateusz B. Majka, Łukasz Szpruch

We study two variants of the mirror descent-ascent (MDA) algorithm for solving min-max problems on the space of measures: simultaneous and alternating. We work under assumptions of convexity-concavity and relative smoothness of the payoff function with respect to a suitable Bregman divergence, defined on the space of measures via flat derivatives. We establi

Changhao Shi, Gal Mishne

Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amounts to endowing covariance selection with the Laplacian structure. In graph signal processing (GSP), it is essential to infer the unobserved graph from the outputs of a filtering sys

Yueqin Yin, Zhendong Wang, Yi Gu, Hai Huang

In the field of large language models (LLMs), aligning models with the diverse preferences of users is a critical challenge. Direct Preference Optimization (DPO) has played a key role in this area. It works by using pairs of preferences derived from the same prompts, and it functions without needing an additional reward model. However, DPO does not fully ref

David C. Oluigboa, Bikash Santra, Tejas Sudharshan Mathai, Pritam Mukherjee

Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize. For the management of patients with PPGLs, CT is the preferred modality of choice for precise localization and estimation of their progression. However, due to the myriad variations in size, morphology, and appearance of the tumors

Brenden W. Hamilton, Timothy C. Germann

High pressure shear band formation is a critical phenomenon in energetic materials due to its influence on both mechanical strength and mechanochemical activation. While shear banding is know to occur in a variety of these materials, the governing dynamics of the mechanisms is not well defined for molecular crystals. We conduct molecular dynamics simulations

Aiswarya R., Rasheed Shaik, Jobin Jose, Hari R. Varma

Plane-wave electrons undergo momentum transfer as they scatter off a target in overlapping spherical waves. The transferred momentum leads to target structural information to be encoded in angle and energy differential scattering. For symmetric, periodic or structured targets this can engender diffraction in the electron intensity both in real (angular) and

Lara Groves, Jacob Metcalf, Alayna Kennedy, Briana Vecchione

In July 2023, New York City (NYC) initiated the first algorithm auditing system for commercial machine-learning systems. Local Law 144 (LL 144) mandates NYC-based employers using automated employment decision-making tools (AEDTs) in hiring to undergo annual bias audits conducted by an independent auditor. This paper examines lessons from LL 144 for other nat

Federico Ranaldi, Elena Sofia Ruzzetti, Dario Onorati, Leonardo Ranaldi

Understanding textual description to generate code seems to be an achieved capability of instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there is a severe possibility that this translation ability may be influenced by having seen target textual descriptions and the related code. This effect is known as Data Contamination. I

Gabriel Galeote-Checa, Gabriella Panuccio, Angel Canal-Alonso, Teresa Serrano-Gotarredona

Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Around 30-40% of patients do not respond to pharmacological treatment, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy.

Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee

Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocols and associated sequences does not exist due to wide variations in imaging practice at institutions and myriad MRI scanners from various manufacturers being used for imaging. The

Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani, Aryan Mokhtari

In this paper, we focus on simple bilevel optimization problems, where we minimize a convex smooth objective function over the optimal solution set of another convex smooth constrained optimization problem. We present a novel bilevel optimization method that locally approximates the solution set of the lower-level problem using a cutting plane approach and e

Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly

Incrementally fine-tuning foundational models on new tasks or domains is now the de facto approach in NLP. A known pitfall of this approach is the \emph{catastrophic forgetting} of prior knowledge that happens during fine-tuning. A common approach to alleviate such forgetting is to rehearse samples from prior tasks during fine-tuning. Several existing works

Hongrui Chen, Lexing Ying

Diffusion models have achieved huge empirical success in data generation tasks. Recently, some efforts have been made to adapt the framework of diffusion models to discrete state space, providing a more natural approach for modeling intrinsically discrete data, such as language and graphs. This is achieved by formulating both the forward noising process and

Joseph Hart, Bart van Bloemen Waanders

Optimization constrained by high-fidelity computational models has potential for transformative impact. However, such optimization is frequently unattainable in practice due to the complexity and computational intensity of the model. An alternative is to optimize a low-fidelity model and use limited evaluations of the high-fidelity model to assess the qualit

John Franklin Crenshaw, Andrew J. Connolly, Joshua E. Meyers, J. Bryce Kalmbach

The Vera C. Rubin Observatory will, over a period of 10 years, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an Active Optics System (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the

Mateusz Łajszczak, Guillermo Cámbara, Yang Li, Fatih Beyhan

We introduce a text-to-speech (TTS) model called BASE TTS, which stands for $\textbf{B}$ig $\textbf{A}$daptive $\textbf{S}$treamable TTS with $\textbf{E}$mergent abilities. BASE TTS is the largest TTS model to-date, trained on 100K hours of public domain speech data, achieving a new state-of-the-art in speech naturalness. It deploys a 1-billion-parameter aut

Michel Osca Engelbrecht, Jonathan Jenderny, Henrik Hylla, Dominik Filla

Controlling fluxes of vacuum ultraviolet (VUV) radiation is important in a number of industrial and biomedical applications of low pressure plasma sources because, depending on the process, VUV radiation may be desired, required to a certain degree, or unwanted. In this work, the emission of VUV radiation from O atoms is investigated in low-pressure Ar/O$_2$

Li-Jen Chen, Daniel Gershman, Brandon Burkholder, Yuxi Chen

We report a rare regime of Earth's magnetosphere interaction with sub-Alfv\'enic solar wind in which the windsock-like magnetosphere transforms into one with Alfv\'en wings. In the magnetic cloud of a Coronal Mass Ejection (CME) on April 24, 2023, NASA's Magnetospheric Multiscale mission distinguishes the following features: (1) unshocked and accelerated col

Sean Jaffe, Alexander Davydov, Deniz Lapsekili, Ambuj Singh

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametr

Massimo Giovannini

We observe that the energy and the enthalpy densities can be smeared by two fudge factors that are constrained by the contracted Bianchi identities. Depending on the analytic properties of the smearing functions the underlying cosmological solutions belong to two physically different classes, namely the bounces of the scale factor and the curvature bounces.

Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson, Smriti Prathapan

Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical settings, where data drift may cause unexpected performance that jeopardizes patient safety. Method: We propose a ML-enabled Statistical Process Control (SPC) framework for out-of-dist

Mahtab Darvish, Ryan Trask, Patrick Tallon, Mélina Khansari

Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximate

Dipti Jasrasaria, Timothy C. Berkelbach

We use vibrational dynamical mean-field theory (VDMFT) to study the vibrational structure of type-I clathrate solids, specifically X$_8$Ga$_{16}$Ge$_{30}$, where X=Ba,Sr. These materials are cage-like chemical structures hosting loosely bound guest atoms, resulting in strong anharmonicity, short phonon lifetimes, and ultra-low thermal conductivities. Present

Yun-Da Tsai, Ting-Yu Yen, Pei-Fu Guo, Zhe-Yan Li

This research paper addresses the challenge of modality mismatch in multimodal learning, where the modalities available during inference differ from those available at training. We propose the Text-centric Alignment for Multi-Modality Learning (TAMML) approach, an innovative method that utilizes Large Language Models (LLMs) with in-context learning and found

Ziquan Wei, Tingting Dan, Guorong Wu

Graph learning is crucial in the fields of bioinformatics, social networks, and chemicals. Although high-order graphlets, such as cycles, are critical to achieving an informative graph representation for node classification, edge prediction, and graph recognition, modeling high-order topological characteristics poses significant computational challenges, res

Michael Dominguez, Amin Rezaei

Physical Unclonable Functions (PUFs) leverage manufacturing process imperfections that cause propagation delay discrepancies for the signals traveling along these paths. While PUFs can be used for device authentication and chip-specific key generation, strong PUFs have been shown to be vulnerable to machine learning modeling attacks. Although there is an imp

Thomas Halloran, Yishu Wang, K. W. Plumb, M. B. Stone

Inelastic neutron scattering (INS) measurements of powder D$_3(^{7}$Li)($^{193}$Ir)$_2$O$_6$ reveal low energy magnetic excitations with a scattering cross section that is broad in $|Q|$ and consistent with a Kitaev spin-liquid (KSL) state. The magnetic nature of the excitation spectrum is demonstrated by longitudinally polarized neutron studies. The total m

Frank Cole, Yulong Lu

While score-based generative models (SGMs) have achieved remarkable success in enormous image generation tasks, their mathematical foundations are still limited. In this paper, we analyze the approximation and generalization of SGMs in learning a family of sub-Gaussian probability distributions. We introduce a notion of complexity for probability distributio

Marina Fernández Galán, Enrique Conejero Jarque, Julio San Roman

The countless applications of ultrashort laser pulses in very different scientific areas explain the ongoing efforts to develop new strategies for the generation of light pulses with increasingly better characteristics. In this work, we theoretically study the application of the nonlinear reverse propagation method to produce few-cycle pulses with clean temp

Lamiya Mowla, Kartheik Iyer, Yoshihisa Asada, Guillaume Desprez

The most distant galaxies detected by JWST are assembling in a Universe that is less than 5\% of its present age. At these times, the progenitors of galaxies like the Milky Way are expected to be about 10,000 times less massive than they are now, with masses quite comparable to that of massive globular clusters seen in the local Universe. Composed today prim

Lucas Elbert Suryana, Sina Nordhoff, Simeon C. Calvert, Arkady Zgonnikov

The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this

Daksitha Withanage Don, Philipp Müller, Fabrizio Nunnari, Elisabeth André

Flexible and natural nonverbal reactions to human behavior remain a challenge for socially interactive agents (SIAs) that are predominantly animated using hand-crafted rules. While recently proposed machine learning based approaches to conversational behavior generation are a promising way to address this challenge, they have not yet been employed in SIAs. T

Yang Liu, Peng Sun, Hang Li

By formally defining the training processes of large language models (LLMs), which usually encompasses pre-training, supervised fine-tuning, and reinforcement learning with human feedback, within a single and unified machine learning paradigm, we can glean pivotal insights for advancing LLM technologies. This position paper delineates the parallels between t

Biraj Pandey, Bamdad Hosseini, Pau Batlle, Houman Owhadi

This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration. A theoretical analysis of the proposed method is prese

Andrey V. Shanin, Raphael C. Assier, Andrey I. Korolkov, Oleg I. Makarov

We propose a general procedure to study double integrals arising when considering wave propagation in periodic structures. This method, based on a complex deformation of the integration surface to bypass the integrands' singularities, is particularly efficient to estimate the Green's functions of such structures in the far field. We provide several illustrat

Huixin Zhan, Ying Nian Wu, Zijun Zhang

Although DNA foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. This limitation starkly contrasts with the success of natural language foundation models, which thrive on substantially larger scales. Furthermore, genome understanding involves numerous downstr

Behrooz Mirzaii, Elvis Torres Pérez

In this article we study the low dimensional homology groups of the special linear group $\textrm{SL}_2(A)$ and the projective special linear group $\textrm{PSL}_2(A)$, $A$ a domain, through the natural surjective map $\textrm{SL}_2(A) \to \textrm{PSL}_2(A)$. In particular, we study the connection of the first, the second and the third homology groups of the

Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem

Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspe

Yeming Wen, Pengcheng Yin, Kensen Shi, Henryk Michalewski

Large language models (LLMs) have recently demonstrated a remarkable ability to generate code from natural language (NL) prompts. However, in the real world, NL is often too ambiguous to capture the true intent behind programming problems, requiring additional input-output (I/O) specifications. Unfortunately, LLMs can have difficulty aligning their outputs w

Bailey Kimmel, Austin Geisert, Lily Yaro, Brendan Gipson

Generative AI is changing the way that many disciplines are taught, including computer science. Researchers have shown that generative AI tools are capable of solving programming problems, writing extensive blocks of code, and explaining complex code in simple terms. Particular promise has been shown in using generative AI to enhance programming error messag

Miguel C. N. Fiolhais, Andrea Ferroglia

This paper presents a double spatio-temporal localized Dirac-delta solution for the linear wave equation. The solution arises from the interference of sinusoidal waves with frequencies that vary as a function of the time of emission. It is shown that the time-dependent frequency function required to produce a localized Dirac-delta wave is exclusively determi

Sunday Akukodi Ugwu

This mini-project models propagation of shocks, in time point, through links in connected banks. In particular, financial network of 100 banks out of which 15 are shocked to default (that is, 85.00% of the banks are solvent) is modelled using Erdos and Renyi network -- directed, weighted and randomly generated network. Shocking some banks in a financial netw

Hanan Gani, Nada Saadi, Noor Hussein, Karthik Nandakumar

Since their inception, Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) across a wide spectrum of tasks. ViTs exhibit notable characteristics, including global attention, resilience against occlusions, and adaptability to distribution shifts. One underexplored aspect of ViTs is their potential for mu

Zizhong Tian, Vernon M. Chinchilli, Chan Shen, Shouhao Zhou

Measurement of the interrater agreement (IRA) is critical in various disciplines. To correct for potential confounding chance agreement in IRA, Cohen's kappa and many other methods have been proposed. However, owing to the varied strategies and assumptions across these methods, there is a lack of practical guidelines on how these methods should be preferred

Paulo Sérgio Almeida, Ehud Shapiro

Conflict-free Replicated Data Types (CRDTs) are designed for replica convergence without global coordination or consensus. Recent work has achieved the same in a Byzantine environment, through DAG-like structures based on cryptographic hashes of content. The blocklace is a partially-ordered generalization of the blockchain in which each block has any finite

Jiongzhi Zheng, Lukasz Komza, Yihuang Xiong, Natalya Sheremetyeva

Photoluminescence (PL) spectra are a versatile tool for exploring the electronic and optical properties of quantum defect systems. In this work, we investigate the PL spectra of the G center in silicon by combining first-principles computations with a machine-learned compressive-sensing technique and experiment. We show that the compressive-sensing technique

Laytimi Fatima Nahm Werner

For a partition $a$ and a vector bundle $E$ on a projective variety $X$ let $\mathcal{F}l_s(E)$ be the corresponding flag manifold. There is a line bundle $\it Q_a^s$ on $\mathcal{F}l_s(E)$ with $p:\mathcal{F}l_s(E)\to X $ and $\it p_*Q_a^s = \mathcal{S}_aE$. We prove, if $\mathcal{S}_aE $ is $k$-ample (in the sense of Sommese) then $\it Q_a^s$ is $k$-ample.

Stephan G. Heinemann, Chaitanya Sishtla, Simon Good, Maxime Grandin

High-speed solar wind streams (HSSs) interact with the preceding ambient solar wind to form Stream Interaction Regions (SIRs), which are a primary source of recurrent geomagnetic storms. However, HSSs may also encounter and subsequently interact with Interplanetary Coronal Mass Ejections (ICMEs). In particular, the impact of the interaction between slower IC

Jennifer Chu-Carroll, Andrew Beck, Greg Burnham, David OS Melville

Since the advent of Large Language Models a few years ago, they have often been considered the de facto solution for many AI problems. However, in addition to the many deficiencies of LLMs that prevent them from broad industry adoption, such as reliability, cost, and speed, there is a whole class of common real world problems that Large Language Models perfo

Nowfel Mashnoor, Jay Thom, Abdur Rouf, Shamik Sengupta

The advent of the Internet of Things (IoT) has brought forth additional intricacies and difficulties to computer networks. These gadgets are particularly susceptible to cyber-attacks because of their simplistic design. Therefore, it is crucial to recognise these devices inside a network for the purpose of network administration and to identify any harmful ac

Benjamin Plaut, Hanlin Zhu, Stuart Russell

Most learning algorithms with formal regret guarantees assume that all mistakes are recoverable and essentially rely on trying all possible behaviors. This approach is problematic when some mistakes are "catastrophic", i.e., irreparable. We propose an online learning problem where the goal is to minimize the chance of catastrophe. Specifically, we assume tha

Fanjun Bu, Stacey Li, David Goedicke, Mark Colley

In automotive user interface design, testing often starts with lab-based driving simulators and migrates toward on-road studies to mitigate risks. Mixed reality (XR) helps translate virtual study designs to the real road to increase ecological validity. However, researchers rarely run the same study in both in-lab and on-road simulators due to the challenges

Damien Fournier, Janosch Preuss, Thorsten Hohage, Laurent Gizon

Context. Acoustic waves in the Sun are affected by the atmospheric layers, but this region is often ignored in forward models due to the increase in computational cost. Aims. The purpose of this work is to take into account the solar atmosphere without increasing significantly the computational cost. Methods. We solve a scalar wave equation that describes th

Rodrigo Nicolau Almeida

In this note we generalize the construction, due to Ghilardi, of the free Heyting algebra generated by a finite distributive lattice, to the case of arbitrary distributive lattices. Categorically, this provides an explicit construction of a left adjoint to the inclusion of Heyting algebras in the category of distributive lattices This is shown to have severa

Oscar Braun-White

In this thesis, the infrared structure of squared matrix elements in quantum chromodynamics (QCD) is scrutinised. Specifically, the triple-collinear splitting functions are decomposed and improvements to antenna subtraction are sought through the construction of idealised antenna functions. The antenna-subtraction technique has demonstrated remarkable effect

Álvaro Belmonte, Amelia Zafra, Eva Gibaja

MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of r

Abhijeet Ghoshal, Yan Li, Syam Menon, Sumit Sarkar

Quantum devices use qubits to represent information, which allows them to exploit important properties from quantum physics, specifically superposition and entanglement. As a result, quantum computers have the potential to outperform the most advanced classical computers. In recent years, quantum algorithms have shown hints of this promise, and many algorith

Jun Zhuang, Kun-Peng Wang, Peng-Xiang Wang, Ming-Rui Wei

The inherent polarization gradients in tight optical tweezers can be used to couple the atomic spins to the two-body motion under the action of a microwave spin-flip transition, so that such a spin-motion coupling offers an important control knob on the motional states of optically trapped two colliding atoms. Here, after preparing two elastically scattering