October 2023 arXiv papers — page 88

Alex Levchenko, Songci Li, A. V. Andreev

We develop a hydrodynamic description of electron magnetotransport in conductors without Galilean invariance in the presence of a weak long-range disorder potential. We show that magnetoresistance becomes strong (of order 100 %) at relatively small fields, at which the inverse square of the magnetic length becomes comparable to disorder-induced variations of

Jan-Nico Zaech, Martin Danelljan, Tolga Birdal, Luc Van Gool

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for many computer vision tasks. Despite requiring multiple measurements from the noisy AQC, current approaches only utilize

Lingchen Meng, Xiyang Dai, Jianwei Yang, Dongdong Chen

Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to explore extra data with image-level labels, yet it produces limited results due to (1) semantic ambiguity -- an image-level label only captures a salient part of the image, ignoring th

Madhuparna Karmakar, Rajesh Narayanan

We present the first instance of a disorder tuned Fermi-Bose crossover that could be realized in superconducting systems. More specifically, harnessing a non perturbative numerical technique we analyze the ground state behavior of a two-dimensional attractive Hubbard model subjected to spin selective disorder potential. In particular, using spectroscopic pro

Liwei Chen, Yuan Yuan

The Hardy spaces are defined on the quotient domain of a bounded complete Reinhardt domain by a finite subgroup of $U(n)$. The Szeg\H{o} projection on the quotient domain can be studied by lifting to the covering space. This setting builds on the solution of a boundary value problem for holomorphic functions. In particular, when the covering space is either

Hung-Ting Chen, Fangyuan Xu, Shane Arora, Eunsol Choi

How retrieved documents are used in language models (LMs) for long-form generation task is understudied. We present two controlled studies on retrieval-augmented LM for long-form question answering (LFQA): one fixing the LM and varying evidence documents and the other fixing evidence documents and varying the LMs. We study various attributes of generated ans

Zhen Yang, Ganggui Ding, Wen Wang, Hao Chen

By comparing the original and target prompts, we can obtain numerous editing pairs, each comprising an object and its corresponding editing target. To allow editability while maintaining fidelity to the input image, existing editing methods typically involve a fixed number of inversion steps that project the whole input image to its noisier latent representa

Ulf Danielsson, Suvendu Giri

In this paper, we study rotating horizon less black shells as an alternative to Kerr black holes. We make use of Ernst's potential to solve the Einstein equations perturbatively in the angular momentum $a$. Calculating to order $a^6$, we find accurate predictions up to about $a=0.45$, where the quadrupole moment is predicted to be around $1\%$ higher than it

Hanbo Zhang, Jie Xu, Yuchen Mo, Tao Kong

Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced performance in realistic and open-ended scenarios. To address these issues, we present a large-scale dataset, \invig, for interactive visual grounding under language ambiguity. Our data

Ken Xuan Wei, Isaac Lauer, Emily Pritchett, William Shanks

Fixed-frequency superconducting qubits demonstrate remarkable success as platforms for stable and scalable quantum computing. Cross-resonance gates have been the workhorse of fixed-coupling, fixed-frequency superconducting processors, leveraging the entanglement generated by driving one qubit resonantly with a neighbor's frequency to achieve high-fidelity, u

Richeek Das, Samuel Dooley

Making models algorithmically fairer in tabular data has been long studied, with techniques typically oriented towards fixes which usually take a neural model with an undesirable outcome and make changes to how the data are ingested, what the model weights are, or how outputs are processed. We employ an emergent and different strategy where we consider updat

Aditi Desai, Stephanie E. Sallum, Ravinder Banyal, Natalie Batalha

SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) is a 2 - 5 micron high-contrast lenslet-based integral field spectrograph (IFS) designed to characterize exoplanets and their atmospheres. The SCALES medium-spectral-resolution mode uses a lenslet subarray with a 0.34 x 0.36 arcsecond field of view which allows for exoplanet character

Fredy Vides, Idelfonso B. R. Nogueira, Gabriela Lopez Gutierrez, Lendy Banegas

In this document, we present key findings in structured matrix approximation theory, with applications to the regressive representation of dynamic financial processes. Initially, we explore a comprehensive approach involving generic nonlinear time delay embedding for time series data extracted from a financial or economic system under examination. Subsequent

Yuanzhi Li, Raghu Meka, Rina Panigrahy, Kulin Shah

Supervised and unsupervised learning using deep neural networks typically aims to exploit the underlying structure in the training data; this structure is often explained using a latent generative process that produces the data, and the generative process is often hierarchical, involving latent concepts. Despite the significant work on understanding the lear

Md. Abid Al Morshed, Md. Mustakim Hayder, Tayfur Rahman Maruf

Self-balancing robot is based on the principle of Inverted pendulum, which is a two-wheel vehicle balances itself up in the vertical position with reference to the ground. It consists of both hardware and software implementation. Mechanical model based on the state space design of the cart, pendulum system. To find its stable inverted position, we used a gen

Jian Ding, Fenglin Huang, Aoteng Xia

We study the random field Ising model in a two-dimensional box with side length $N$ where the external field is given by independent normal variables with mean $0$ and variance $\epsilon^2$. Our primary result is the following phase transition at $T = T_c$: for $\epsilon \ll N^{-7/8}$ the boundary influence (i.e., the difference between the spin averages at

Tianyu Zhang, Jing Lei

Online nonparametric estimators are gaining popularity due to their efficient computation and competitive generalization abilities. An important example includes variants of stochastic gradient descent. These algorithms often take one sample point at a time and incrementally update the parameter estimate of interest. In this work, we consider model selection

Guanghui Lan, Yuyuan Ouyang, Zhe Zhang

We propose novel optimal and parameter-free algorithms for computing an approximate solution with small (projected) gradient norm. Specifically, for computing an approximate solution such that the norm of its (projected) gradient does not exceed $\varepsilon$, we obtain the following results: a) for the convex case, the total number of gradient evaluations i

Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian

Recently we found compelling evidence for a gravitational wave background with Hellings and Downs (HD) correlations in our 15-year data set. These correlations describe gravitational waves as predicted by general relativity, which has two transverse polarization modes. However, more general metric theories of gravity can have additional polarization modes wh

Gabriela Araujo-Pardo, György Kiss, Tamás Szönyi

In this paper, we obtain new lower and upper bounds for the problem of bipartite biregular cages. Moreover, for girth $6$, we give the exact parameters of the $(m,n;6)$-bipartite biregular cages when $n\equiv -1$ $\pmod m$ using the existence of Steiner System system $S(2,k=m,v=1+n(m-1)+m)$. For girth $g=2r$ and $r=\{4,6,8\}$, we use results on $t$-good stru

Miroslava Poláková, Vladimír Špitalský

Recurrence rate, determinism, average line length, and entropy of line lengths are measures of complexity in recurrence quantification analysis, that help to understand the structure, predictability and complexity of dynamical systems. In this paper, we provide explicit formulas for these characteristics for all binary substitutions of constant length. Moreo

Shikhar Murty, Orr Paradise, Pratyusha Sharma

With large language models surpassing human performance on an increasing number of benchmarks, we must take a principled approach for targeted evaluation of model capabilities. Inspired by pseudorandomness, we propose pseudointelligence, which captures the maxim that "(perceived) intelligence lies in the eye of the beholder". That is, that claims of intellig

Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng

Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the similarity between the representations formed by these diverse systems? Do similarities in representations then translate into similar behavior? If so, then how can a sy

Nina Bizien, Thierry Dudok de Wit, Clara Froment, Marco Velli

Switchbacks are sudden and large deflections in the magnetic field that Parker Solar Probe frequently observes in the inner heliosphere. Their ubiquitous occurrence has prompted numerous studies to determine their nature and origin. Our goal is to describe the boundary of these switchbacks using a series of events detected during the spacecraft's first encou

Ingrid Marçal, Rogério Eduardo Garcia

The correctness of software systems is vital for their effective operation. It makes discovering and fixing software bugs an important development task. The increasing use of Artificial Intelligence (AI) techniques in Software Engineering led to the development of a number of techniques that can assist software developers in identifying potential bugs in cod

Denini Silva, Martin Gruber, Satyajit Gokhale, Ellen Arteca

Flaky tests are tests that nondeterministically pass and fail in unchanged code. These tests can be detrimental to developers' productivity. Particularly when tests run in continuous integration environments, the tests may be competing for access to limited computational resources (CPUs, memory etc.), and we hypothesize that resource (in)availability may be

Subinay Adhikary, Sagnik Das, Sagnik Saha, Procheta Sen

The escalating number of pending cases is a growing concern world-wide. Recent advancements in digitization have opened up possibilities for leveraging artificial intelligence (AI) tools in the processing of legal documents. Adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly

Nick Early

A detailed understanding of the moduli spaces $X(k,n)$ of $n$ points in projective $k-1$ space is essential to the investigation of generalized biadjoint scalar amplitudes, as discovered by Cachazo, Early, Guevara and Mizera (CEGM) in 2019. But in math, conventional wisdom says that it is completely hopeless due to the arbitrarily high complexity of realizat

Sateesh Kandukuri, Felipe Garcia-Sanchez, P K Thiruvikraman, V Satya Narayana Murthy

Controlled creation of stable chiral spin textures is required to use them as an energy-efficient information carrier in spintronics. Here we have studied the stable creation of isolated chiral spin texture (skyrmion and antiskyrmion) and its pair through the magnetization reversal of a rectangular nanostructure using spin-polarized currents. An isolated spi

Abhay Zala, Han Lin, Jaemin Cho, Mohit Bansal

Text-to-image (T2I) generation has seen significant growth over the past few years. Despite this, there has been little work on generating diagrams with T2I models. A diagram is a symbolic/schematic representation that explains information using structurally rich and spatially complex visualizations (e.g., a dense combination of related objects, text labels,

Giuseppe Attanasio, Flor Miriam Plaza-del-Arco, Debora Nozza, Anne Lauscher

Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often focuses on standard performance benchmarks, leaving compelling fairness and ethical considerations behind. In MT, this might lead to misgendered translations, resulting, among othe

Mohammadreza Salehi, Sachin Mehta, Aditya Kusupati, Ali Farhadi

We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct different samples to sub-networks with varying widths. Our experiments demonstrate that: (1) SHARCS outperforms or complements existing per-sample adaptive inference methods acros

Farbod Jahandar, René Doyon, Étienne Artigau, Neil J. Cook

Determination of fundamental parameters of stars impacts all fields of astrophysics, from galaxy evolution to constraining the internal structure of exoplanets. This paper presents a detailed spectroscopic analysis of Barnard's star that compares an exceptionally high-quality (an average signal-to-noise ratio of $\sim$1000 in the entire domain), high-resolut

Giuseppe Gaetano Luciano, Luca Smaldone

Time-energy uncertainty relation (TEUR) plays a fundamental role in quantum mechanics, as it allows to grasp peculiar aspects of a variety of phenomena based on very general principles and symmetries of the theory. Using the Mandelstam-Tamm method, TEUR has been recently derived for neutrino oscillations by connecting the uncertainty on neutrino energy with

Nathanael Skrepek, Marcus Waurick

We regard anisotropic Maxwell's equations as a boundary control and observation system on a bounded Lipschitz domain. The boundary is split into two parts: one part with perfect conductor boundary conditions and the other where the control and observation takes place. We apply a feedback control law that stabilizes the system in a semi-uniform manner without

Martín Monteiro, Cecilia Stari, Arturo C. Marti

The Atwood Machine, a classic apparatus in physics education, has historically been pivotal in demonstrating Newtonian mechanics, specifically Newton's Second Law. This study introduces an innovative adaptation, the circular Atwood machine, aimed at exploring circular motion and angular dynamics and integrating emerging technologies, specifically smartphone

Sean Kim, Samuel Kim

Mental disorders impact the lives of millions of people globally, not only impeding their day-to-day lives but also markedly reducing life expectancy. This paper addresses the persistent challenge of predicting mortality in patients with mental diagnoses using predictive machine-learning models with electronic health records (EHR). Data from patients with me

John A. Schneeloch, Shunshun Liu, Prasanna V. Balachandran, Qiang Zhang

CrI$_{2}$ is a quasi-one dimensional (1D) van der Waals (vdW) system that exhibits helimagnetism that propagates along the ribbons. This was determined from neutron time-of-flight diffraction measurements. Below $T_N=17$ K, a screw-like helimagnetic order develops with an incommensurate wavevector of $\mathbf{q} \approx (0.2492,0,0)$ at 8 K. Using density fu

Alexander Giessing

We derive, up to a constant factor, matching lower and upper bounds on the concentration functions of suprema of separable centered Gaussian processes and order statistics of Gaussian random fields. These bounds reveal that suprema of separable centered Gaussian processes $\{X_u : u \in U\}$ exhibit the same anti-concentration properties as a single Gaussian

Osman Batur İnce, Tanin Zeraati, Semih Yagcioglu, Yadollah Yaghoobzadeh

Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization comparable to human cognitive abilities remains a topic of debate. While existing approaches in the field have mainly focused on novel architectures and alternative learning paradigms,

Filippo Pascucci, Sara Conti, Andrea Perali, Jacques Tempere

We investigate the correlations acting within the layers in a superfluid system of electron-hole spatially separated layers. In this system of quasi-dipoles, the dominant correlations are Hartree--Fock. We find in the BEC regime of the superfluid where screening is negligible, that the effect of the correlations on superfluid properties is also negligible. H

Chuan-Chi Lai, Hsuan-Yu Lin, Chuan-Ming Liu

Skyline queries typically search a Pareto-optimal set from a given data set to solve the corresponding multiobjective optimization problem. As the number of criteria increases, the skyline presumes excessive data items, which yield a meaningless result. To address this curse of dimensionality, we proposed a k-dominant skyline in which the number of skyline m

Clément Bénard, Jeffrey Näf, Julie Josse

Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In this article, we introduce a variable importance algorithm for DRFs, based on the well-established drop and relearn principle and MMD distance. While traditional importance measures

Pierfrancesco Di Cintio

Context. Deur (2014) and Winters et al. (2023) proposed an empirical relation between the dark to total mass ratio and ellipticity in elliptical galaxies from their observed total dynamical mass-to-light ratio data M/L = (14.1 +/- 5.4){\epsilon}. In other words, the larger is the content of dark matter in the galaxy, the more the stellar component would be f

Zehang Weng, Haofei Lu, Jens Lundell, Danica Kragic

We propose CAPGrasp, an $\mathbb{R}^3\times \text{SO(2)-equivariant}$ 6-DoF continuous approach-constrained generative grasp sampler. It includes a novel learning strategy for training CAPGrasp that eliminates the need to curate massive conditionally labeled datasets and a constrained grasp refinement technique that improves grasp poses while respecting the

Caelin G. Kaplan, Chuan Xu, Othmane Marfoq, Giovanni Neglia

Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility. Most existing defenses against membership inference attacks assume access to reference data, defined as an additional dataset coming from the

Yuanyuan Wang, Yang Zhang, Zhiyong Wu, Zhihan Yang

Data augmentation is vital to the generalization ability and robustness of deep neural networks (DNNs) models. Existing augmentation methods for speaker verification manipulate the raw signal, which are time-consuming and the augmented samples lack diversity. In this paper, we present a novel difficulty-aware semantic augmentation (DASA) approach for speaker

Michael Meth, Jan F. Haase, Jinglei Zhang, Claire Edmunds

Particle physics underpins our understanding of the world at a fundamental level by describing the interplay of matter and forces through gauge theories. Yet, despite their unmatched success, the intrinsic quantum mechanical nature of gauge theories makes important problem classes notoriously difficult to address with classical computational techniques. A pr

Daniel Y. Fu, Simran Arora, Jessica Grogan, Isys Johnson

Machine learning models are increasingly being scaled in both sequence length and model dimension to reach longer contexts and better performance. However, existing architectures such as Transformers scale quadratically along both these axes. We ask: are there performant architectures that can scale sub-quadratically along sequence length and model dimension

Ruth A. Daly, Megan Donahue, Christopher P. O'Dea, Biny Sebastian

Six archival Chandra observations are matched with eight sets of radio data and studied in the context of the outflow method to measure and study the spin properties of $\rm{Sgr ~A^*}$. Three radio and X-ray data sets obtained simultaneously, or partially simultaneously, are identified as preferred for the purpose of measuring the spin properties of $\rm{Sgr

Nataša Bolić, Tommaso Cesari, Roberto Colomboni

We investigate brokerage between traders from an online learning perspective. At any round $t$, two traders arrive with their private valuations, and the broker proposes a trading price. Unlike other bilateral trade problems already studied in the online learning literature, we focus on the case where there are no designated buyer and seller roles: each trad

Natalie C. Brown, John Peter Campora, Cassandra Granade, Bettina Heim

Fault-tolerant protocols enable large and precise quantum algorithms. Many such protocols rely on a feed-forward processing of data, enabled by a hybrid of quantum and classical logic. Representing the control structure of such programs can be a challenge. Here we explore two such fault-tolerant subroutines and analyze the performance of the subroutines usin

Lennart Meier, XiaoLin Danny Shi, Mingcong Zeng

In this paper, we construct a stratification tower for the equivariant slice filtration. This tower stratifies the slice spectral sequence of a $G$-spectrum $X$ into distinct regions. Within each of these regions, the differentials are determined by the localized slice spectral sequences, which compute the geometric fixed points along with their associated r

Jiru Liu, Wenchao Ge, M. Suhail Zubairy

Gaussian states with nonclassical properties such as squeezing and entanglement serve as crucial resources for quantum information processing. Accurately quantifying these properties within multi-mode Gaussian states has posed some challenges. To address this, we introduce a unified quantification: the 'classical-nonclassical polarity', represented by $\math

Li Ding, Jenny Zhang, Jeff Clune, Lee Spector

Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when RLHF is commonly used to optimize for average human preferences, especially in generative tasks that demand diverse model responses. Meanwhile, Quality Diversity (QD) algorithms ex

Zhong-Bo Kang, Emilie Li, Farid Salazar

We compute the differential cross-section for direct quarkonium production accompanied by a gluon in high-energy deep inelastic scattering (DIS) at small-$x$. We employ the Non-Relativistic QCD factorization framework, focusing on the $S$-wave contribution to the formation of the quarkonium, and including both color singlet and octet contributions. Our short

Miguel De La Cruz, Paolo Luzzatto-Fegiz

Wind tunnels are a key source of data collection, but their cost and size can be a significant obstacle to their acquisition and usage, especially for applications such as instrument calibration, instruction, or in-class demonstrations. Here we propose a design for a cost-effective, desktop wind tunnel. This design takes advantage of readily available, inexp

Yaqing Wang, Jialin Wu, Tanmaya Dabral, Jiageng Zhang

Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks by scaling up parameter counts from O(10^9) to O(10^{12}) levels and further beyond. These large scales make it impossible to adapt and deploy fully specialized models given a task of interest. Parameter-efficient fine-tuning (PEFT) emerg

Faisal Al-Khateeb, Nolan Dey, Daria Soboleva, Joel Hestness

Linear position interpolation helps pre-trained models using rotary position embeddings (RoPE) to extrapolate to longer sequence lengths. We propose using linear position interpolation to extend the extrapolation range of models using Attention with Linear Biases (ALiBi). We find position interpolation significantly improves extrapolation capability on upstr

J. M. P. Carmelo, P. D. Sacramento, T. Stauber, D. K. Campbell

The coexistence in the low-temperature spin-conducting phases of the zigzag materials BaCo2V2O8 and SrCo2V2O8 of one-dimensional (1D) physics with important deviations from it is not well understood. The studies of this paper account for an important selection rule that follows from interchain spin states being coupled more strongly within the spin dynamical

Jae-Mo Lihm, Johannes Halbinger, Jeongmin Shim, Jan von Delft

Multipoint vertex functions, and the four-point vertex in particular, are crucial ingredients in many-body theory. Recent years have seen significant algorithmic progress toward numerically computing their dependence on multiple frequency arguments. However, such computations remain challenging and are prone to suffer from numerical artifacts, especially in

Rodrigo Tenorio

We use the latest all-sky continuous gravitational-wave (CW) searches to estimate constraints on the sub-kiloparsec population of unknown neutron stars (NS). We then extend this analysis to the forthcoming LIGO-Virgo-KAGRA observing runs and the third generation (3G) of ground-based interferometric detectors (Einstein Telescope and Cosmic Explorer). We find

Surender Baswana, Koustav Bhanja

Let G be a directed weighted graph (DiGraph) on n vertices and m edges with source s and sink t. An edge in G is vital if its removal reduces the capacity of (s,t)-mincut. Since the seminal work of Ford and Fulkerson, a long line of work has been done on computing the most vital edge and all vital edges of G. Unfortunately, after 60 years, the existing resul

Nicola Rares Franco, Daniel Fraulin, Andrea Manzoni, Paolo Zunino

Deep Learning is having a remarkable impact on the design of Reduced Order Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for tackling complex problems for which classical methods might fail. In this respect, deep autoencoders play a fundamental role, as they provide an extremely flexible tool for reducing t

Muhammad Zubair, Igor Evangelista, Shoaib Khalid, Bharat Medasani

Two-dimensional materials with Rashba split bands near the Fermi level are key to developing upcoming next-generation spintronics. They enable generating, detecting, and manipulating spin currents without an external magnetic field. Here, we propose BiAs as a novel layered semiconductor with large Rashba splitting in bulk and monolayer forms. Using first-pri

Vladimir Krasnoselskikh, Arnaud Zaslavsky, Anton Artemyev, Clara Froment

The exploration of the inner heliosphere by Parker Solar Probe has revealed a highly structured solar wind with ubiquitous deflections from the Parker spiral, known as switchbacks. Interchange reconnection (IR) may play an important role in generating these switchbacks by forming unstable particle distributions that generate wave activity that in turn may ev

H. Umut Suluhan, Abdullah Enes Doruk, Hasan F. Ates, Bahadir K. Gunturk

High-spatio-temporal resolution (HSTR) video recording plays a crucial role in enhancing various imagery tasks that require fine-detailed information. State-of-the-art cameras provide this required high frame-rate and high spatial resolution together, albeit at a high cost. To alleviate this issue, this paper proposes a dual camera system for the generation

K. B. Webber, T. T. Hansen, J. L. Marshall, J. D. Simon

We present a detailed chemical abundance analysis of the brightest star in the ultra-faint dwarf (UFD) galaxy candidate Cetus II from high-resolution Magellan/MIKE spectra. For this star, DES J011740.53-173053, abundances or upper limits of 18 elements from Carbon to Europium are derived. Its chemical abundances generally follow those of other UFD galaxy sta

Theodore Kareta, Cristina Thomas, Jian-Yang Li, Matthew M. Knight

The impact of the DART spacecraft into Dimorphos, moon of the asteroid Didymos, changed Dimorphos' orbit substantially, largely from the ejection of material. We present results from twelve Earth-based facilities involved in a world-wide campaign to monitor the brightness and morphology of the ejecta in the first 35 days after impact. After an initial bright

Roberta Baggio, Jean-François Muzy

We consider the problem of short-term forecasting of surface wind speed probability distribution. Our approach consists in predicting the parameters of a given probability density function by training a neural network model whose loss function is the corresponding log-likelihood. We compare different possibilities among a set of distributions that have been

Filippo Emanuele Zadra, Aleksandr Petrosyan, Alessio Zaccone

The relativistic Langevin equation poses a number of technical and conceptual problems related to its derivation and underlying physical assumptions. Recently, a method has been proposed in [A. Petrosyan and A. Zaccone, J. Phys. A: Math. Theor. 55 015001 (2022)] to derive the relativistic Langevin equation from a first-principles particle-bath Lagrangian. As

Matt Landreman, Siena Hurwitz, Thomas M Antonsen

For designing high-field electromagnets, the Lorentz force on coils must be computed to ensure a support structure is feasible, and the inductance should be computed to evaluate the stored energy. Also, the magnetic field and its variation inside the conductor is of interest for computing stress and strain, and due to superconducting quench limits. For these

Xiang Chen, Duanzheng Song, Honghao Gui, Chenxi Wang

Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios, is a relatively unexplored area. To address this gap, we present FactCHD, a dedicated benchmark designed for t

Zikang Leng, Hyeokhyen Kwon, Thomas Plötz

In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge. Drawing inspiration from the latest advancements in generative AI, including Large Language Models (LLMs) and motion synthesis models, we believe that generative AI can address this data scarcity by autonomously generating virtual IMU data from t

Thomas Brunet, Emma Tolley, Stefano Corda, Roman Ilic

We explore applications of quantum computing for radio interferometry and astronomy using recent developments in quantum image processing. We evaluate the suitability of different quantum image representations using a toy quantum computing image reconstruction pipeline, and compare its performance to the classical computing counterpart. For identifying and l

Markus Gambietz, Marlies Nitschke, Jörg Miehling, Anne Koelewijn

Objective: As metabolic cost is a primary factor influencing humans' gait, we want to deepen our understanding of metabolic energy expenditure models. Therefore, this paper identifies the parameters and input variables, such as muscle or joint states, that contribute to accurate metabolic cost estimations. Methods: We explored the parameters of four metaboli

Jiří Jechumtál, Reza Rouzegar, Oliver Gueckstock, Christian Denker

We study the spatiotemporal dynamics of ultrafast electron spin transport across nanometer-thick copper layers using broadband terahertz spectroscopy. Our analysis of temporal delays, broadening and attenuation of the spin-current pulse revealed ballistic-like propagation of the pulse peak, approaching the Fermi velocity, and diffusive features including a s

Haoran Cheng, Dixin Luo, Hongteng Xu

Graph matching is one of the most significant graph analytic tasks, which aims to find the node correspondence across different graphs. Most existing graph matching approaches mainly rely on topological information, whose performances are often sub-optimal and sensitive to data noise because of not fully leveraging the multi-modal information hidden in graph

Kirandeep Kaur, Jonathan J Horsley, Csaba Kozma, Gerard R Hall

Aperiodic activity is a physiologically distinct component of the electrophysiological power spectrum. It is suggested to reflect the balance of excitation and inhibition in the brain, within selected frequency bands. However, the impact of recurrent seizures on aperiodic activity remains unknown, particularly in patients with severe bilateral seizures. Here

Mufan Bill Li, Mihai Nica

Recent analyses of neural networks with shaped activations (i.e. the activation function is scaled as the network size grows) have led to scaling limits described by differential equations. However, these results do not a priori tell us anything about "ordinary" unshaped networks, where the activation is unchanged as the network size grows. In this article,

Novin Rasooli, Wei Chen, Matthew Daly

The complex atomic scale structure of high entropy alloys presents new opportunities to expand the deformation theories of mechanical metallurgy. In this regard, solute-defect interactions have emerged as critical piece in elucidating the operation of deformation mechanisms. While notable progress has been made in understanding solute-defect interactions for

Pietro Vitiello, Kamil Dreczkowski, Edward Johns

In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge. We show how, with these constraints, imitation learning can be formulated as a combination of trajectory transfer and unseen object pose estimation. To explore this idea, we p

Manjary P. Gangan, Anoop Kadan, Lajish V L

Image forensics research has recently witnessed a lot of advancements towards developing computational models capable of accurately detecting natural images captured by cameras and GAN generated images. However, it is also important to ensure whether these computational models are fair enough and do not produce biased outcomes that could eventually harm cert

Qianfeng Wen, Zhongyi Gong, Lifeng Zhou, Zhongshun Zhang

The integration of autonomous vehicles into urban and highway environments necessitates the development of robust and adaptable behavior planning systems. This study presents an innovative approach to address this challenge by utilizing a Monte-Carlo Tree Search (MCTS) based algorithm for autonomous driving behavior planning. The core objective is to leverag

Shumpei Inoue, Minh-Tien Nguyen, Hiroki Mizokuchi, Tuan-Anh D. Nguyen

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure

Carla Farsi, Emily Proctor, Christopher Seaton

We introduce the universal Euler characteristic of orbit space definable groupoids, a class of groupoids containing cocompact proper Lie groupoids as well as translation groupoids associated to proper definable group actions. We show that every additive and multiplicative invariant of orbit space definable groupoids with an additional local triviality hypoth

Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Hasan Genc

Generative Large Language Models (LLMs) based on the Transformer architecture have recently emerged as a dominant foundation model for a wide range of Natural Language Processing tasks. Nevertheless, their application in real-time scenarios has been highly restricted due to the significant inference latency associated with these models. This is particularly

Clare Burrage, Peter Millington

We consider the theory of a light conformally coupled scalar field, i.e., one that is coupled directly to the Ricci scalar of the gravitational sector. This theory can be written equivalently as one of a light scalar that is coupled to the Standard Model of particle physics with a particular combination of Higgs-portal couplings. When the conformal coupling

Alexander L. Lewanski, Michael C. Grundler, Gideon S. Bradburd

In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(

Dimitrios Tanoglidis, Bhuvnesh Jain, Helen Qu

The deep learning architecture associated with ChatGPT and related generative AI products is known as transformers. Initially applied to Natural Language Processing, transformers and the self-attention mechanism they exploit have gained widespread interest across the natural sciences. The goal of this pedagogical and informal review is to introduce transform

Luca Reggio

We define Quillen model structures on a family of presheaf toposes arising from tree unravellings of Kripke models, leading to a homotopy theory for modal logic. Modal preservation theorems and the Hennessy-Milner property are revisited from a homotopical perspective.

Xian-Hui Ge, Zhaojie Xu

We study the thermo-electric transport coefficients of an extended version of the Gubser-Rocha model. After reviewing the two relaxation time model from holography and studying the effect of the magnetic field on thermo-electric transports from hydrodynamic theory, we present a new dilatonic dyonic asymptotically AdS black hole solution. Notice that S-dualit

Tatyana Dergunova, Andrew Lyden

Future pathways for Great Britain's energy system decarbonisation have highlighted the importance of low-carbon hydrogen as an energy carrier and demand flexibility support. However, the potential application within various sectors (heating, industry, transport) and production capacity through different technologies (methane reformation with carbon capture,

Safwan Hossain, Andjela Mladenovic, Yiling Chen, Gauthier Gidel

Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change u

Jakob Vogel

This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential r

Lukman Olagoke, Salil Vadhan, Seth Neel

Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if it is possible for an attacker to efficiently identify if a given point is a member of the G

Tianyang Xue, Mingdong Wu, Lin Lu, Haoxuan Wang

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing probl

Cristina Bustos, Carles Civit, Brian Du, Albert Sole-Ribalta

This work presents a study on how to exploit the CLIP embedding space to perform Visual Sentiment Analysis. We experiment with two architectures built on top of the CLIP embedding space, which we denote by CLIP-E. We train the CLIP-E models with WEBEmo, the largest publicly available and manually labeled benchmark for Visual Sentiment Analysis, and perform t

Benedikt Jahnel, Anh Duc Vu

We study survival and extinction of a long-range infection process on a diluted one-dimensional lattice in discrete time. The infection can spread to distant vertices according to a Pareto distribution, however spreading is also prohibited at random times. We prove a phase transition in the recovery parameter via block arguments. This contributes to a line o

Sandipan Choudhuri, Arunabha Sen

Unwanted samples from private source categories in the learning objective of a partial domain adaptation setup can lead to negative transfer and reduce classification performance. Existing methods, such as re-weighting or aggregating target predictions, are vulnerable to this issue, especially during initial training stages, and do not adequately address cla