May 2024 arXiv papers — page 28

Vasileios Klimis, Alastair F. Donaldson, Viktor Vafeiadis, John Wickerson

Memory persistency models provide a foundation for persistent programming by specifying which (and when) writes to non-volatile memory (NVM) become persistent. Memory persistency models for the Intel-x86 and Arm architectures have been formalised, but not empirically validated against real machines. Traditional validation methods %such as %extensive litmus t

Vikram Nitin, Rahul Krishna, Baishakhi Ray

Large language models (LLMs) are increasingly being used for the task of automated code translation, which has important real-world applications. However, most existing approaches use only the source code of a program as an input to an LLM, and do not consider the different kinds of specifications that can be extracted from a program. In this paper, we propo

Robert Wallace, Aakash Bansal, Zachary Karas, Ningzhi Tang

Abridged: Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. We conducted an in-depth human study with 10 Java programmers, where each programmer generated summaries for 40 methods from five large Java projects over five one-hour sessions. We used eye-tracking equipment to map the visu

Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza

Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. Our findings reveal that there ar

Noam Shlomo, Eugene Frumker

We present an approach for in situ dynamic four-dimensional (4D=3D space + 1D time) laser induced strong field ionization tomography, particularly suited for measuring far-from-equilibrium systems, such as supersonic and hypersonic pulsed gas jets. The inherent physical nature of strong-field interaction leads to significantly enhanced spatial resolution, wi

Haipeng Luo, Spandan Senapati, Vatsal Sharan

We consider the problem of online multiclass U-calibration, where a forecaster aims to make sequential distributional predictions over $K$ classes with low U-calibration error, that is, low regret with respect to all bounded proper losses simultaneously. Kleinberg et al. (2023) developed an algorithm with U-calibration error $O(K\sqrt{T})$ after $T$ rounds a

Javier Alejandro Chávez-Domínguez, Verónica Dimant, Daniel Galicer

We continue our study of the mapping ideal of operator $p$-compact maps, previously introduced by the authors. Our approach embraces a more geometric perspective, delving into the interplay between operator $p$-compact mappings and matrix sets, specifically we provide a quantitative notion of operator $p$-compactness for the latter. In particular, we conside

Diana E. Gherman, Thorsten O. Zander

Moral judgement is a complex human reaction that engages cognitive and emotional dimensions. While some of the morality neural correlates are known, it is currently unclear if we can detect moral violation at a single-trial level. In a pilot study, here we explore the feasibility of moral judgement decoding from text stimuli with passive brain-computer inter

Abrar Fahim, Alex Murphy, Alona Fyshe

Multi-modal contrastive models such as CLIP achieve state-of-the-art performance in zero-shot classification by embedding input images and texts on a joint representational space. Recently, a modality gap has been reported in two-encoder contrastive models like CLIP, meaning that the image and text embeddings reside in disjoint areas of the latent space. Pre

Bubai Manna

Let G be a simple connected graph with vertex set V(G) and edge set E(G. Each vertex of V(G) is colored by a color from the set of colors {c_1, c_2,\dots, c_{\alpha}}. We take a subset S of V(G), such that for every vertex v in V(G)\S, at least one vertex of the same color is present in its set of nearest neighbors in S. We refer to such an S as a consistent

Sami Davies, Sergei Vassilvitskii, Yuyan Wang

Push-Relabel is one of the most celebrated network flow algorithms. Maintaining a pre-flow that saturates a cut, it enjoys better theoretical and empirical running time than other flow algorithms, such as Ford-Fulkerson. In practice, Push-Relabel is even faster than what theoretical guarantees can promise, in part because of the use of good heuristics for se

Bernhard Endtmayer

In this work, we apply multi-goal oriented error estimation to the finite element method. In particular, we use the dual weighted residual method and apply it to a model problem. This model problem consist of locally different coercive partial differential equations in a checkerboard pattern, where the solution is continuous across the interface. In addition

Keiichi Ihara, Kyzyl Monteiro, Mehrad Faridan, Rubaiat Habib Kazi

This paper introduces Video2MR, a mixed reality system that automatically generates 3D sports and exercise instructions from 2D videos. Mixed reality instructions have great potential for physical training, but existing works require substantial time and cost to create these 3D experiences. Video2MR overcomes this limitation by transforming arbitrary instruc

E. Aldo Arroyo

We investigate the transition from quantum to classical mechanics using a one-dimensional free particle model. In the classical analysis, we consider the initial positions and velocities of the particle drawn from Gaussian distributions. Since the final position of the particle depends on these initial conditions, convolving the Gaussian distributions associ

Xiangyu Sun, Raquel Aoki, Kevin H. Wilson

Machine learning (ML) methods have experienced significant growth in the past decade, yet their practical application in high-impact real-world domains has been hindered by their opacity. When ML methods are responsible for making critical decisions, stakeholders often require insights into how to alter these decisions. Counterfactual explanations (CFEs) hav

Sherry Ruan, Tian Zhao

LLMs have significantly advanced the e-commerce industry by powering applications such as personalized recommendations and customer service. However, most current efforts focus solely on monolithic LLMs and fall short in addressing the complexity and scale of real-world e-commerce scenarios. In this work, we present JungleGPT, the first compound AI system ta

Omar Al-Ghattas, Daniel Sanz-Alonso

This paper studies sparse covariance operator estimation for nonstationary processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance component. Building on recent results from empirical process the

Degang Zhang

Motivated by recent scanning tunneling microscopy experiments on Fe atomic line defect in iron-based high temperature superconductors, we explore the origin of the zero energy bound states near the endpoints of the line defect by employing the two-orbit four-band tight binding model. With increasing the strength of the Rashba spin-orbit coupling along the li

Marija Vučković, Ana Najev, Biqiong Yu, Takao Sasagawa

We present a single crystal Cu NMR study of the cuprate superconductor La$_{2-x}$Sr$_{x}$CuO$_4$ with hole doping levels between $x=2$\% and $8$\%. Measurements with short spin echo times enable us to systematically study the local properties of the electronic spin system in the region of the phase diagram where the material evolves from the insulating antif

Shubhang Bhatnagar, Narendra Ahuja

Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interactions within each tuplets. We present a novel, compositional DML model that instead of in tuples, represents the influence of each example (embedding) by a continuous potential fiel

Simon Foreman, Andrej Obuljen, Marko Simonović

High-number-density tracers of large-scale structure, such as the HI-rich galaxies measured by 21 cm intensity mapping, have low sampling noise, making them particularly promising as cosmological probes. At large scales, this sampling noise can be subdominant to other scale-independent contributions to the power spectrum; such contributions arise from nonlin

Vishrut Deshpande, Yogesh Phalak, Ziyang Zhou, Ian Walker

Yoshimura origami is a classical folding pattern that has inspired many deployable structure designs. Its applications span from space exploration, kinetic architectures, and soft robots to even everyday household items. However, despite its wide usage, Yoshimura has been fixated on a set of design constraints to ensure its flat-foldability. Through extensiv

Renaud Detcherry, Efstratia Kalfagianni, Adam S. Sikora

We show that the Kauffman bracket skein module of a closed Seifert fibered 3-manifold $M$ is finitely generated over $\mathbb Z[A^{\pm 1}]$ if and only if $M$ is irreducible and non-Haken. We analyze in detail the character varieties $X(M)$ of such manifolds and show that under mild conditions they are reduced. We compute the Kauffman bracket skein modules f

Zhiyao Luo, Yangchen Pan, Peter Watkinson, Tingting Zhu

In the rapidly changing healthcare landscape, the implementation of offline reinforcement learning (RL) in dynamic treatment regimes (DTRs) presents a mix of unprecedented opportunities and challenges. This position paper offers a critical examination of the current status of offline RL in the context of DTRs. We argue for a reassessment of applying RL in DT

Jean-François de Kemmeter, Timoteo Carletti

Over the last few years, network science has proved to be useful in modeling a variety of complex systems, composed of a large number of interconnected units. The intricate pattern of interactions often allows the system to achieve complex tasks, such as synchronization or collective motions. In this regard, the interplay between network structure and dynami

Feiyang Cai, Chuchu Fan, Stanley Bak

Verifying safety of neural network control systems that use images as input is a difficult problem because, from a given system state, there is no known way to mathematically model what images are possible in the real-world. We build on recent work that considers a surrogate verification approach, training a conditional generative adversarial network (cGAN)

Stephen Obadinma, Alia Lachana, Maia Norman, Jocelyn Rankin

The world's healthcare systems and mental health agencies face both a growing demand for youth mental health services, alongside a simultaneous challenge of limited resources. Here, we focus on frontline crisis support, where Crisis Responders (CRs) engage in conversations for youth mental health support and assign an issue tag to each conversation. In this

Trong-Tuan Nguyen, Van-Dat Thang, Nguyen Van Thin, Phuong T. Nguyen

The entropy error function has been widely used in neural networks. Nevertheless, the network training based on this error function generally leads to a slow convergence rate, and can easily be trapped in a local minimum or even with the incorrect saturation problem in practice. In fact, there are many results based on entropy error function in neural networ

Xingjian Li, Lirong Xiang

This work presents a new robotics simulation environment built upon Unreal Engine 5 (UE5) for agricultural image data generation. The simulation utilizes the state-of-the-art real-time rendering engine to provide realistic plant images which are often used in agricultural applications. This study showcases the rendering accuracy of UE5 in comparison to exist

Alvise Sommariva, Marco Vianello

We give a short proof of almost sure invertibility of unsymmetric random Kansa collocation matrices by a class of analytic RBF vanishing at infinity, for the Poisson equation with Dirichlet boundary conditions. Such a class includes popular Positive Definite instances such as Gaussians, Generalized Inverse MultiQuadrics and Matern RBF. The proof works on gen

Jiongli Zhu, Su Feng, Boris Glavic, Babak Salimi

We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive multiplicity. Our approach leverages abstract interpretation and zonotopes, a type of convex polytope, to compactly represent these dataset variations, enabling the symbolic execu

Marco Sälzer, Eric Alsmann, Martin Lange

We analyse the complexity of the satisfiability problem, or similarly feasibility problem, (trSAT) for transformer encoders (TE), which naturally occurs in formal verification or interpretation, collectively referred to as formal reasoning. We find that trSAT is undecidable when considering TE as they are commonly studied in the expressiveness community. Fur

Arun Reddy, Yuan Cheng

CAPTCHAs are commonly used to distinguish between human and bot users on the web. However, despite having various types of CAPTCHAs, there are still concerns about their security and usability. To address these concerns, we surveyed over 250 participants from a university campus and Amazon Mechanical Turk. Our goal was to gather user perceptions regarding th

Alireza Vahid

We introduce an information-theoretic framework to dynamically pair up different reconfigurable intelligent surfaces (RISs) with wireless users with goal of maximizing the fundamental network capacity. We focus on a double RIS-aided broadcast packet network with two users. We show using a dynamic RIS-user association and an opportunistic protocol, the networ

F. M. Guedes, M. S. Guimaraes, I. Roditi, S. P. Sorella

A study of the spin 1 Unruh-De Witt detectors interacting with a relativistic scalar quantum field is presented. After tracing out the field modes, the resulting density matrix for a bipartite qutrit system is employed to investigate the violation of the Bell-CHSH inequality. Unlike the case of spin $1/2$, for which the effects of the quantum field result in

K. Ammigan, G. Arora, S. Bidhar, A. Burleigh

As beam power continues to increase in next-generation accelerator facilities, high-power target systems face crucial challenges. Components like beam windows and particle-production targets must endure significantly higher levels of particle fluence. The primary beam's energy deposition causes rapid heating (thermal shock) and induces microstructural change

Morgan Redington, Eva Zurek

High-temperature high-pressure superconducting hydrides are typically characterized by cage-like hydrogenic lattices filled with electropositive metal atoms. Here, density functional theory based evolutionary crystal structure searches find two phases that possess these geometric features and are related to the Type-II clathrate structure. In these $Fd\overl

D. Condon, Yuxin Wang, E. Yang

We introduce the notion of de Bruijn polyominoes, which generalizes the notions of de Bruijn sequences and arrays. Given a polyomino $p$ and a positive integer $n$, a $(p,n)$-de Bruijn polyomino is a colored polyomino $P$ with cells colored from $\{1,...,n\}$ such that every possible coloring of $p$ from $\{1,...,n\}$ occurs within $P$, not counting rotation

Francisco de Arriba-Pérez, Silvia García-Méndez, Francisco J. González-Castaño, Enrique Costa-Montenegro

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for enterta

Maxime Zanella, Ismail Ben Ayed

Recent progress in the few-shot adaptation of Vision-Language Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task. However, this promising, already quite abundant few-shot literature has focused principally on prompt learning and, to a lesser extent, on adapters, o

Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre

Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker l

Lola Burgueño, Davide Di Ruscio, Houari Sahraoui, Manuel Wimmer

Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved

Ramsey Rossmann, Mihai Anitescu, Julie Bessac, Michael Ferris

Power grid expansion planning requires making large investment decisions in the present that will impact the future cost and reliability of a system exposed to wide-ranging uncertainties. Extreme temperatures can pose significant challenges to providing power by increasing demand and decreasing supply and have contributed to recent major power outages. We pr

Shivesh Jadon, Mehrad Faridan, Edward Mah, Rajan Vaish

This paper introduces the concept of augmented conversation, which aims to support co-located in-person conversations via embedded speech-driven on-the-fly referencing in augmented reality (AR). Today computing technologies like smartphones allow quick access to a variety of references during the conversation. However, these tools often create distractions,

Sophia Sun, Wenyuan Chen, Zihao Zhou, Sonia Fereidooni

Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model. Existing mechanical simulators for MCS rely on oversimplifying assumptions and are insensitive to patient-specific behavior, limiting their applicability to real-world treatment scenarios. To address these shortcomings, our model Domain Adversarial Neural Proces

Mykyta Onizhuk, Giulia Galli

The usefulness of solid-state spins in quantum technologies depends on how long they can remain in a coherent superposition of quantum states. This Colloquium discusses how first-principles simulations can predict spin dynamics for different types of solid-state electron spins, helping design novel and improved platforms for quantum computing, networking, an

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

In this work, we give a new technique for analyzing individualized privacy accounting via the following simple observation: if an algorithm is one-sided add-DP, then its subsampled variant satisfies two-sided DP. From this, we obtain several improved algorithms for private combinatorial optimization problems, including decomposable submodular maximization an

Zefan Yang, Jiajin Zhang, Ge Wang, Mannudeep K. Kalra

Accurate prediction of Cardiovascular disease (CVD) risk in medical imaging is central to effective patient health management. Previous studies have demonstrated that imaging features in computed tomography (CT) can help predict CVD risk. However, CT entails notable radiation exposure, which may result in adverse health effects for patients. In contrast, che

Robert M. Raddi, Tim Marshall, Vincent A. Voelz

To quantify how well theoretical predictions of structural ensembles agree with experimental measurements, we depend on the accuracy of forward models. These models are computational frameworks that generate observable quantities from molecular configurations based on empirical relationships linking specific molecular properties to experimental measurements.

Pedro Picchetti, Cristine C. X. Pinto, Stephanie T. Shinoki

This paper provides a formal econometric framework behind the newly developed difference-in-discontinuities design (DiDC). Despite its increasing use in applied research, there are currently limited studies of its properties. We formalize the theory behind the difference-in-discontinuity approach by stating the identification assumptions, proposing a nonpara

M. Baldini, G. Ambrosio, G. Apollinari, J. Blowers

The Large Hadron Collider will soon undergo an upgrade to increase its luminosity by a factor of ~10 [1]. A crucial part of this upgrade will be replacement of the NbTi focusing magnets with Nb3Sn magnets that achieve a ~50% increase in the field strength. This will be the first ever large-scale implementation of Nb3Sn magnets in a particle accelerator. The

Jonathan W. Arenberg, Leon K. Harding, Bob Chang, Steve Kuehn

The Single Aperture Large Telescope for Universe Studies (SALTUS) is a mission concept for a far-infrared observatory developed under the recent Astrophysics Probe Explorer opportunity from NASA. The enabling element of the program is a 14 m diameter inflatable primary mirror, M1. Due to its importance to SALTUS and potentially other space observatories, thi

Jeffrey Wen, Rizwan Ahmad, Philip Schniter

In imaging inverse problems, one seeks to recover an image from missing/corrupted measurements. Because such problems are ill-posed, there is great motivation to quantify the uncertainty induced by the measurement-and-recovery process. Motivated by applications where the recovered image is used for a downstream task, such as soft-output classification, we pr

Bilge Acun, Brent Morgan, Henry Richardson, Nat Steinsultz

A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would improve renewable utilization. The core of this work is to enable the machine learning community to help decarbonize ele

Haonan Han, Rui Yang, Huan Liao, Jiankai Xing

Traditional image-to-3D models often struggle with scenes containing multiple objects due to biases and occlusion complexities. To address this challenge, we present REPARO, a novel approach for compositional 3D asset generation from single images. REPARO employs a two-step process: first, it extracts individual objects from the scene and reconstructs their

Hongjun Wu, Li Xiao, Xingkuo Zhang, Yining Miao

Knowledge distillation is commonly employed to compress neural networks, reducing the inference costs and memory footprint. In the scenario of homogenous architecture, feature-based methods have been widely validated for their effectiveness. However, in scenarios where the teacher and student models are of heterogeneous architectures, the inherent difference

Jiaze Wang, Yi Wang, Ziyu Guo, Renrui Zhang

We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversity and improving alignment across modalities. Our proposed two-stage training pipeline combines feature-level and input-level mixing to opti

Raymond G. Carlberg, Adrian Jenkins, Carlos S. Frenk, Andrew P. Cooper

The dark matter subhalos orbiting in a galactic halo perturb the orbits of stars in thin stellar streams. Over time the random velocities in the streams develop non-Gaussian wings. The rate of velocity increase is approximately a random walk at a rate proportional to the number of subhalos, primarily those in the mass range $\approx 10^{6-7} M_\odot$. The di

Kimia Hamidieh, Haoran Zhang, Swami Sankaranarayanan, Marzyeh Ghassemi

Supervised learning methods have been found to exhibit inductive biases favoring simpler features. When such features are spuriously correlated with the label, this can result in suboptimal performance on minority subgroups. Despite the growing popularity of methods which learn from unlabeled data, the extent to which these representations rely on spurious f

Yingkai Li, Boli Xu

A principal and an agent can launch a project under unanimous consent. Their individual payoffs from the project depend on an underlying state, and the agent privately knows his own preference. The principal can conduct a test to learn about the state and then communicate with the agent, but has limited commitment, as she may misreport her findings. We show

Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang

Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously collected data for policy learning. However, most existing off-policy RL algorithms fail to maximally exploit the information in the replay buffer, limiting sample efficiency and policy performance. In this work, we discover

Tonica Valla, Asish K. Kundu, Petar Pervan, Ivo Pletikosić

Determining the doping level in high-temperature cuprate superconductors is crucial for understanding the origin of superconductivity in these materials and for unlocking their full potential. However, accurately determining the doping level remains a significant challenge due to a complex interplay of factors and limitations in various measurement technique

Runquan Zhang, Jiawen Jiang, Xiaoping Shi

Traditional survival analysis methods often struggle with complex time-dependent data,failing to capture and interpret dynamic characteristics adequately.This study aims to evaluate the performance of three long-sequence models,LSTM,Transformer,and Mamba,in analyzing recurrence event data and integrating them with the Cox proportional hazards model.This stud

Joaquín Moraga

We study the birational complexity of log Calabi-Yau $3$-folds. For such a pair $(X,B)$ of index one and coregularity zero, we show that $c_{\rm bir}(X,B)\in \{0,2,3\}$. Further, we prove that $(X,B)$ has a log Calabi-Yau crepant birational model that admits a crepant contraction to $(\mathbb{P}^{3-c},H_0+\dots+H_{3-c})$, where $c=c_{\rm bir}(X,B)$. To prove

Xinyi Liu, Pinxin Liu, Hangfeng He

In this study, we investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 in the context of debate evaluation. We discover that LLM's performance exceeds humans and surpasses the performance of state-of-the-art methods fine-tuned on extensive datasets in debate evaluation. We additionally explore an

Yunuo Chen, Tianyi Xie, Zeshun Zong, Xuan Li

Existing diffusion-based text-to-3D generation methods primarily focus on producing visually realistic shapes and appearances, often neglecting the physical constraints necessary for downstream tasks. Generated models frequently fail to maintain balance when placed in physics-based simulations or 3D printed. This balance is crucial for satisfying user design

Guoxing Lin

The general theoretical description of spin self-diffusion under nonlinear gradient is proposed, which extends the effective phase diffusion method for linear gradient field. Based on the phase diffusion, the proposed method reveals the general features of phase evolutions in non-nonlinear gradient fields. There are three types of phase evolutions: phase dif

Utkarsh Khandelwal, Harshvardhan Jog, Shupeng Xu, Yicong Chen

The interaction of light with correlated Weyl semimetals (WSMs) provides a unique platform for exploring non-equilibrium phases and fundamental properties such as chirality. Here, we investigate the structural chirality of (TaSe4)2I, a correlated WSM, under weak optical pumping using Circular Photogalvanic Effect (CPGE) measurements and Raman spectroscopy. S

Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin

Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based neural networks, a theoretical understanding of their algorithmic reasoning capabilities in realistic parameter regimes is lacking. We investigate this question in terms of the ne

Germain Tobar, Igor Pikovski, Michael Edmund Tobar

We propose a multi-mode bar consisting of mass elements of decreasing size for the implementation of a gravitational version of the photo-electric effect through the stimulated absorption of up to kHz gravitons from a binary neutron star merger and post-merger. We find that the multi-mode detector has normal modes that retain the coupling strength to the gra

Wentian Xu, Matthew Moffat, Thalia Seale, Ziyun Liang

Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determined by a protocol for the specific disease. This work explores the following open questions: Is it feasible to train a model using multiple databases that contain varying sets of MRI

James Derek Lomas, Willem van der Maden, Sohhom Bandyopadhyay, Giovanni Lion

Generative AI systems are increasingly capable of expressing emotions via text and imagery. Effective emotional expression will likely play a major role in the efficacy of AI systems -- particularly those designed to support human mental health and wellbeing. This motivates our present research to better understand the alignment of AI expressed emotions with

Aarya A. Patil, Gwendolyn M. Eadie, Joshua S. Speagle, David J. Thomson

In Patil et. al 2024a, we developed a multitaper power spectrum estimation method, mtNUFFT, for analyzing time-series with quasi-regular spacing, and showed that it not only improves upon the statistical issues of the Lomb-Scargle periodogram, but also provides a factor of three speed up in some applications. In this paper, we combine mtNUFFT with the harmon

Shailesh Kumar, Rishabh Kumar Singh, Abhishek Chowdhuri, Arpan Bhattacharyya

The fundamental process of detecting and examining the polarization modes of gravitational waves plays a pivotal role in enhancing our grasp on the precise mechanisms behind their generation. A thorough investigation is essential for delving deeper into the essence of gravitational waves and rigorously evaluating and validating the range of modified gravity

Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet

In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents profound challenges. This work explores injecting hierarchical prior knowledge into graph neural networks (GNNs) for single-cell multi-class classification of tabular cellular data. By representing the

Antika Sinha, Sanjoy Kumar Saha, Partha Basuchowdhuri

In this work, we study methodical decomposition of an undirected, unweighted complete graph ($K_n$ of order $n$, size $m$) into minimum number of edge-disjoint trees. We find that $x$, a positive integer, is minimum and $x=\lceil\frac{n}{2}\rceil$ as the edge set of $K_n$ is decomposed into edge-disjoint trees of size sequence $M = \{m_1,m_2,...,m_x\}$ where

Matthew Spong

Following an outline of Rezk, we give a construction of complex-analytic $G$-equivariant elliptic cohomology for an arbitrary compact Lie group $G$ and we prove some of its fundamental properties. The construction is parametrised over the orbit category of the groupoid of principal $G$-bundles over 2-dimensional tori and generalises Grojnowski's construction

Matteo M. Wauters, Edoardo Ballini, Alberto Biella, Philipp Hauke

Quantum measurements profoundly influence system dynamics. They lead to complex nonequilibrium phenomena like the quantum Zeno effect, and they can be used for mitigating errors in quantum simulations. Such an ability is particularly valuable for lattice gauge theories (LGTs), which require the challenging preservation of an extensive number of local conserv

Koichi Saito, Dongjun Kim, Takashi Shibuya, Chieh-Hsin Lai

Sound content creation, essential for multimedia works such as video games and films, often involves extensive trial-and-error, enabling creators to semantically reflect their artistic ideas and inspirations, which evolve throughout the creation process, into the sound. Recent high-quality diffusion-based Text-to-Sound (T2S) generative models provide valuabl

Jarmo Mäkelä

Beginning from the standard Arnowitt-Deser-Misner (ADM) formulation of general relativity we construct a tentative model of quantum gravity from the point of view of an observer with constant proper acceleration, just outside of a horizon of spacetime. In addition of producing the standard results of black-hole thermodynamics, our model makes an entirely new

Andrii Arman, Andriy Bondarenko, Fedor Nazarov, Andriy Prymak

For every large enough $n$, we explicitly construct a body of constant width $2$ that has volume less than $0.9^n \text{Vol}(\mathbb{B}^{n}$), where $\mathbb{B}^{n}$ is the unit ball in $\mathbb{R}^{n}$. This answers a question of O.~Schramm.

Karamveer Kaur, Nicholas C. Stone

Advances in time domain astronomy have produced a growing population of flares from galactic nuclei, including both tidal disruption events (TDEs) and flares in active galactic nuclei (AGN). Because TDEs are uncommon and AGN variability is abundant, large-amplitude AGN flares are usually not categorized as TDEs. While TDEs are normally channelled by the coll

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi

Robustness of deep neural networks to input noise remains a critical challenge, as naive noise injection often degrades accuracy on clean (uncorrupted) data. We propose a novel training framework that addresses this trade-off through two complementary objectives. First, we introduce a loss function applied at the penultimate layer that explicitly enforces in

Gongyue Zhang, Honghai Liu

We have identified a potential method for unifying first-order optimizers through the use of variable Second-Moment Exponential Scaling(SMES). We begin with back propagation, addressing classic phenomena such as gradient vanishing and explosion, as well as issues related to dataset sparsity, and introduce the theory of balance in optimization. Through this t

Alireza Vahid, Shih-Chun Lin

Movable antennas and reconfigurable intelligent surfaces enable a new paradigm in which channel statistics can be controlled and altered. Further, the known trajectory and operation protocol of communication satellites results in networks with predictable statistics. The predictability of future changes results in a non-ergodic model for which the fundamenta

Maxence Corman, William E. East

Gravitational wave observations of black hole-neutron star binaries, particularly those where the black hole has a lower mass compared to other observed systems, have the potential to place strong constraints on modifications to general relativity that arise at small curvature length scales. Here we study the dynamics of black hole-neutron star mergers in sh

Atreyee Das, Tyler J. Slade, Rustem Khasanov, Sergey L. Bud'ko

$\text{La}_{5}\text{Co}_{2}\text{Ge}_{3}$ is an itinerant ferromagnet with a Curie temperature, $T_C$, of $\sim$ 3.8 K and a remarkably small saturated moment of 0.1 $\mu_{B}/\text{Co}$. Here we present the growth and characterization of single crystals of the ${\text{La}_{5}\text{(Co}_{1-x}\text {Ni}_{x})_2\text {Ge}_{3}}$ series for 0.00 $\leq x \leq$ 0.18

Yuping Gao, Songling Shan

In 1980, Akiyama, Exoo, and Harary conjectured that any graph $G$ can be decomposed into at most $\lceil(\Delta(G)+1)/2\rceil$ linear forests. We confirm the conjecture for robust expanders of linear minimum degree. As a consequence, the conjecture holds for dense quasirandom graphs of linear minimum degree as well as for large $n$-vertex graphs with minimum

Andrea Boccia, Fabio Iocco, Luca Visinelli

We investigate the scenario in which primordial black holes (PBHs) with masses Mpbh < 10^9 g undergo Hawking evaporation, around the Big-Bang nucleosynthesis (BBN) epoch. The evaporation process modifies the Universe's expansion rate and the baryon-to-photon ratio, leading to an alteration of the primordial abundance of light nuclei. We present numerical sol

Felix B Mueller, Rebekka Görge, Anna K Bernzen, Janna C Pirk

Memorization in large language models (LLMs) is a growing concern. LLMs have been shown to easily reproduce parts of their training data, including copyrighted work. This is an important problem to solve, as it may violate existing copyright laws as well as the European AI Act. In this work, we propose a systematic analysis to quantify the extent of potentia

Ben Forrest, Brian C. Lemaux, Ekta A. Shah, Priti Staab

We present an analysis of the number density of galaxies as a function of stellar mass (i.e., the stellar mass function, SMF) in the COSMOS field at z~3.3, making a comparison between the SMF in overdense environments and the SMF in the coeval field. In particular, this region contains the Elent\'ari proto-supercluster, a system of 6 extended overdensities s

Manisha Shrestha, K. Azalee Bostroem, David J. Sand, Griffin Hosseinzadeh

We present high-cadence photometric and spectroscopic observations of supernova (SN) 2024ggi, a Type II SN with flash spectroscopy features which exploded in the nearby galaxy NGC 3621 at $\sim$7 Mpc. The light-curve evolution over the first 30 hours can be fit by two power law indices with a break after 22 hours, rising from $M_V \approx -12.95$ mag at +0.6

Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya, Devdatt Dubhashi

A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, [Huang et al. Science 2022], introduced an approach for learning properties of the ground state of an $n$-qubit gapped local Hami

Ashutosh Kumar Alok, Neetu Raj Singh Chundawat, Jitendra Kumar, Arindam Mandal

It was previously shown that unlike the ratios $R_K^{\mu e} \equiv R_K \equiv \Gamma(B \to K \mu^+ \mu^-)/\Gamma(B \to K e^+ e^-)$ and $R_{K^*}^{\mu e} \equiv R_{K^*} \equiv \Gamma(B \to K^* \mu^+ \mu^-)/\Gamma(B \to K^* e^+ e^-)$, the ratios $R_K^{\tau \mu}$ and $R_{K^*}^{\tau \mu}$ can deviate from their Standard Model (SM) predictions even with universal

Robert Gabriel Popescu, Nantheera Anantrasirichai, Juliet Biggs

Satellite images have the potential to detect volcanic deformation prior to eruptions, but while a vast number of images are routinely acquired, only a small percentage contain volcanic deformation events. Manual inspection could miss these anomalies, and an automatic system modelled with supervised learning requires suitably labelled datasets. To tackle the

Anurag Kaushal, Naveen S. Prabhakar, Spenta R. Wadia

In this paper we introduce a definition of time that emerges in terms of the geometry of the configuration space of a dynamical system. We illustrate this, using the Hamilton-Jacobi equation, in various examples: particle mechanics on a fixed energy surface; non-Abelian gauge theories for compact semi-simple Lie groups where the Gauss law presents new featur

Stefano Carniani, Kevin Hainline, Francesco D'Eugenio, Daniel J. Eisenstein

The first observations of JWST have revolutionized our understanding of the Universe by identifying for the first time galaxies at $z\sim13$. In addition, the discovery of many luminous galaxies at Cosmic Dawn ($z>10$) has suggested that galaxies developed rapidly, in apparent tension with many standard models. However, most of these galaxies lack spectrosco

Zhenjiu Wang, Paul McClarty, Dobromila Dankova, Andreas Honecker

Tensor network states have enjoyed great success at capturing aspects of strong correlation physics. However, obtaining dynamical correlators at non-zero temperatures is generically hard even using these methods. Here, we introduce a practical approach to computing such correlators using minimally entangled typical thermal states (METTS). While our primary m

Mengyi Shan, Lu Dong, Yutao Han, Yuan Yao

This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While single-person text-to-motion generation is extensively studied, it remains challenging to synthesize motions for more than one or two subjects from in-the-wild prompts, mainly due to the lack of available datasets. In this work, we curate human po

Oriol Rubies-Bigorda, Raphael Holzinger, Ana Asenjo-Garcia, Oriol Romero-Isart

Subwavelength atomic arrays feature strong light-induced dipole-dipole interactions, resulting in subradiant collective resonances characterized by narrowed linewidths. In this work, we present a sideband cooling scheme for atoms trapped in subwavelength arrays that utilizes these narrow collective resonances. Working in the Lamb-Dicke regime, we derive an e