March 2024 arXiv papers — page 8

Tarig Abdelgadir, Shinnosuke Okawa, Kazushi Ueda

We introduce a compact moduli scheme of marked noncommutative cubic surfaces as the GIT moduli scheme of relations of a quiver associated with a full strong exceptional collection on a cubic surface. It is a toric variety containing the configuration space of six points on a plane in general position as a locally closed subvariety, and birationally parametri

Estelle Basset

We show the existence of Lipschitz-free spaces verifying the Point of Continuity Property with arbitrarily high weak-fragmentability index. For this purpose, we use a generalized construction of the countably branching diamond graphs. As a consequence, we deduce that to be Lipschitz-universal for countable complete metric spaces, a separable complete metric

David Valiente, Fernando Rodríguez-Mas, Juan V. Alegre-Requena, David Dalmau

This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar cells (OSCs) with a multilayer structure ITO/PEDOT:PSS/P3HT:PCBM/Al. To that aim, we generated a database with 996 entries, which includes up to 7 variables regarding both the manu

Asheesh Sharma, Lucy Randewich, William Andrew, Sion Hannuna

This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate reproducibility. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individua

Sharon Ferguson, Georgia Van de Zande, Alison Olechowski

The data created from virtual communication platforms presents the opportunity to explore automated measures for monitoring team performance. In this work, we explore one important characteristic of successful teams - Psychological Safety - or the belief that a team is safe for interpersonal risk-taking. To move towards an automated measure of this phenomeno

Tanapon Lilasathapornkit, Meead Saberi

Planning assessment of the urban walking infrastructure requires appropriate methodologies that can capture the time-dependent and unique microscopic characteristics of bidirectional pedestrian flow. In this paper, we develop a simulation-based dynamic pedestrian traffic assignment (DPTA) model specifically formulated for walking networks (e.g. sidewalks) wi

David Heyrovský, Michal Karamazov

The analysis of gravitational lensing by galaxies and galaxy clusters typically relies on ellipsoidal lens models to describe the deflection of light by the involved dark-matter halos. These models are most often based on the isothermal density profile -- not an optimal description of the halo, but easy to use because it leads to an analytic deflection-angle

Tobias Fischer, Lorenzo Porzi, Samuel Rota Bulò, Marc Pollefeys

We estimate the radiance field of large-scale dynamic areas from multiple vehicle captures under varying environmental conditions. Previous works in this domain are either restricted to static environments, do not scale to more than a single short video, or struggle to separately represent dynamic object instances. To this end, we present a novel, decomposab

Chunyi Zhang, Marcos Calegari Andrade, Zachary K. Goldsmith, Abhinav S. Raman

The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large

Phillip Howard, Anahita Bhiwandiwalla, Kathleen C. Fraser, Svetlana Kiritchenko

With the advent of Large Language Models (LLMs) possessing increasingly impressive capabilities, a number of Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. Such models condition generated text on both an input image and a text prompt, enabling a variety of use cases such as visual question answering and multimodal

Markus J. Hofmann, Markus T. Jansen, Christoph Wigbels, Benny Briesemeister

Here we examine whether the personality dimension of openness to experience can be predicted from the individual google search history. By web scraping, individual text corpora (ICs) were generated from 214 participants with a mean number of 5 million word tokens. We trained word2vec models and used the similarities of each IC to label words, which were deri

Dmitry Arkhangelsky, Aleksei Samkov

We propose the Sequential Synthetic Difference-in-Differences (Sequential SDiD) estimator for event studies with staggered treatment adoption, particularly when the parallel trends assumption fails. The method uses an iterative imputation procedure on aggregated data, where estimates for early-adopting cohorts are used to construct counterfactuals for later

Yi-Heng Cao, Vincent Bourbonne, François Lucia, Ulrike Schick

Objective: Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatmen

Yunwei Mao, Qi He, Ju Li

Networked computing power is a critical utility in the era of artificial intelligence. This paper presents a novel Physical Infrastructure Finance (PinFi) protocol designed to facilitate the distribution of computing power within networks in a decentralized manner. Addressing the core challenges of coordination, pricing, and liquidity in decentralized physic

Meead Saberi, Tanapon Lilasathapornkit

Walking and cycling are known to bring substantial health, environmental, and economic advantages. However, the development of evidence-based active transportation planning and policies has been impeded by significant data limitations, such as biases in crowdsourced data and representativeness issues of mobile phone data. In this study, we develop and apply

Sharon Ferguson, Michael Massimi

While distributed workers rely on scheduled meetings for coordination and collaboration, these meetings can also challenge their ability to focus. Protecting worker focus has been addressed from a technical perspective, but companies are now attempting organizational interventions, such as meeting-free weeks. Recognizing distributed collaboration as a sociot

Isaac Pincus, Alison Rodger, J. Ravi Prakash

We demonstrate the use of multiscale polymer modelling to quantitatively predict DNA linear dichroism (LD) in shear flow. LD is the difference in absorption of light polarised along two perpendicular axes, and has long been applied to study biopolymer structure and drug-biopolymer interactions. As LD is orientation-dependent, the sample must be aligned in or

Musashi Hinck, Matthew L. Olson, David Cobbley, Shao-Yen Tseng

We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs). Of particular interest is the 2B parameter Gemma model, which provides opportunities to construct capable small-scale MMFMs. In line with findings from other papers in this space, we test the effec

Anne-Catherine de la Hamette, Viktoria Kabel, Časlav Brukner

We explore the notion of events at the intersection between quantum physics and gravity, inspired by recent research on superpositions of semiclassical spacetimes. By going through various experiments and thought experiments -- from a decaying atom, to the double-slit experiment, to the quantum switch -- we analyse which properties can and cannot be used to

Alireza Aghasi, Saeed Ghadimi

In this paper, we study and analyze zeroth-order stochastic approximation algorithms for solving bilvel problems, when neither the upper/lower objective values, nor their unbiased gradient estimates are available. In particular, exploiting Stein's identity, we first use Gaussian smoothing to estimate first- and second-order partial derivatives of functions w

Fabienne Comte, Nicolas Marie

We assume that we observe $N$ independent copies of a diffusion process on a time-interval $[0,2T]$. For a given time $t$, we estimate the transition density $p_t(x,y)$, namely the conditional density of $X_{t + s}$ given $X_s = x$, under conditions on the diffusion coefficients ensuring that this quantity exists. We use a least squares projection method on

Pavel Mnev, Konstantin Wernli

We state and prove two gluing formulae for the heat kernel of the Laplacian on a Riemannian manifold of the form $M_1 \cup_\gamma M_2$. We present several examples.

Daisuke Kuroshima, Michael Kilgour, Mark E. Tuckerman, Jutta Rogal

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the f

Bosu Choi, Yoonsang Lee

In data assimilation, an ensemble provides a way to propagate the probability density of a system described by a nonlinear prediction model. Although a large ensemble size is required for statistical accuracy, the ensemble size is typically limited to a small number due to the computational cost of running the prediction model, which leads to a sampling erro

Elie Eshoa, Ali R. Zomorrodi

Nash equilibrium is a key concept in game theory fundamental for elucidating the equilibrium state of strategic interactions, finding applications in diverse fields such as economics, political science, and biology. However, the Nash equilibrium may not always align with the optimal or desired outcomes within a system. This article introduces a novel game en

Monica Munnangi, Sergey Feldman, Byron C Wallace, Silvio Amir

Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In this work we set out to improve LLM performance on biomedical NER in limited data settings via a new knowledge augmentation approach which incorporates definitions of relevant concep

Eldar Knar

An aggregated recursive K-index is proposed as a new scientometric indicator of added value and scientific research output of individual publications. This index can be used instead of or in addition to the H-index (J.E. Hirsch. An index to quantify an individual's scientific research output, arXiv:physics/0508025). In particular, it is proposed to switch fr

I. Soszyński, D. M. Skowron, A. Udalski, P. Pietrukowicz

We report the discovery of the classical Cepheid OGLE-GD-CEP-1884 (= GDS_J1535467-555656) with the longest pulsation period known in our Galaxy. The period of 78.14 d is nearly 10 d longer than that of the previous record-holding Cepheid, S Vulpeculae, and thus, OGLE-GD-CEP-1884 can be categorized as the first ultra long period Cepheid in the Milky Way. This

Avrim Blum, Melissa Dutz

Gameplay under various forms of uncertainty has been widely studied. Feldman et al. (2010) studied a particularly low-information setting in which one observes the opponent's actions but no payoffs, not even one's own, and introduced an algorithm which guarantees one's payoff nonetheless approaches the minimax optimal value (i.e., zero) in a symmetric zero-s

Zihua Liu, Hiroki Sakuma, Masatoshi Okutomi

Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abundant 3D labels, typically obtained through expensive and labor-intensive annotation on LiDAR point clouds. To tackle this problem, we propose

Patrick C. Ford, Andrew A. Voitiv, Chuanzhou Zhu, Mark T. Lusk

We observe and measure the nonequilibrium dynamics of optical vortices as a function of propagation distance through a nonlinear medium. The precession of a tilted-core vortex is quantified as is vortex-core sharpening, where the infinite width of a linear core subsequently shrinks and approaches the healing length of this nonlinear optical fluid. Experiment

Hang Yu, Phil Arras, Nevin N. Weinberg

We investigate the dynamical tide in a gravitational wave (GW)-driven coalescing binary involving a neutron star (NS). The NS is assumed to spin rapidly, with its spin axis anti-aligned with the orbit. Such an NS may exist if the binary forms dynamically in a dense environment, and it can lead to a strong tide because the f-mode can be resonantly excited dur

Huiyuan Yu, Jia He, Maggie Cheng

Orthogonal Matching Pursuit (OMP) has been a powerful method in sparse signal recovery and approximation. However, OMP suffers computational issues when the signal has a large number of non-zeros. This paper advances OMP and its extension called generalized OMP (gOMP) by offering fast algorithms for the orthogonal projection of the input signal at each itera

Paweł Teisseyre, Konrad Furmańczyk, Jan Mielniczuk

The goal of positive-unlabeled (PU) learning is to train a binary classifier on the basis of training data containing positive and unlabeled instances, where unlabeled observations can belong either to the positive class or to the negative class. Modeling PU data requires certain assumptions on the labeling mechanism that describes which positive observation

Ziyu Zhou, Anton Orlichenko, Gang Qu, Zening Fu

Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder. However, combining complementary information from these two modalities is challenging due to their heterogeneity. Many existing methods fall short of capturing the interaction between these modalities, frequently defaulting to a sim

Yorai Shaoul, Itamar Mishani, Maxim Likhachev, Jiaoyang Li

An exciting frontier in robotic manipulation is the use of multiple arms at once. However, planning concurrent motions is a challenging task using current methods. The high-dimensional composite state space renders many well-known motion planning algorithms intractable. Recently, Multi-Agent Path-Finding (MAPF) algorithms have shown promise in discrete 2D do

Ahmad Diab, Rr. Nefriana, Yu-Ru Lin

Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them. However, not all discussions surrounding conspiracy theories promote them, as some are intended to debunk them. Existing research has relied on simple proxies or focused on a constrained set of signals to identify conspiracy theories, which

Xiaolei Lu, Jianghong Ma

Explainability algorithms aimed at interpreting decision-making AI systems usually consider balancing two critical dimensions: 1) \textit{faithfulness}, where explanations accurately reflect the model's inference process. 2) \textit{plausibility}, where explanations are consistent with domain experts. However, the question arises: do faithfulness and plausib

Honghui Xu, Yingshu Li, Olusesi Balogun, Shaoen Wu

In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration. We explore how generative AI drives innovation in IoT and we analyze the potential for data breaches when using generative AI and the misuse of generative AI tech

Ioannis Kiorpelidis, Fotios K. Diakonos, Georgios Theocharis, Vincent Pagneux

The Mathieu equation occurs naturally in the description of vibrations or in the propagation of waves in media with time-periodic refractive index. It is known to lead to exponential parametric instability in some regions of the parameter space. However, even in the stable region the matrix that propagates the initial conditions forward in time is non-normal

Wentao Wu, Chi Wang

Modern database systems rely on cost-based query optimizers to come up with good execution plans for input queries. Such query optimizers rely on cost models to estimate the costs of candidate query execution plans. A cost model represents a function from a set of cost units to query execution cost, where each cost unit specifies the unit cost of executing a

Igor Kudelin, Pedram Shirmohammadi, William Groman, Samin Hanifi

Modern communication, navigation, and radar systems rely on low noise and frequency-agile microwave sources. In this application space, photonic systems provide an attractive alternative to conventional microwave synthesis by leveraging high spectral purity lasers and optical frequency combs to generate microwaves with exceedingly low phase noise. However, t

V. Vasanth

This paper presents a detailed study of the type II solar radio burst that occurred on 06 March 2014 using combined data analysis. It is a classical radio event consisting of type III radio burst and a following type II radio burst in the dynamic spectrum. The type II radio burst is observed between 235 - 130 MHz (120 - 60 MHz) in harmonic (fundamental) band

Alireza Khalilian, Bowen Yu, Yasha Yi

We explore the cutting-edge application of silicon-rich nitride (SRN) in the realm of high numerical aperture (NA) metalens design, focusing on the crucial role of pitch size optimization in amplifying lens efficiency through advanced simulations. Our investigation unveils how the exceptional tunable high refractive index of SRN can be harnessed to achieve s

Chuyuan Tao, Sheng Cheng, Yang Zhao, Fanxin Wang

For the cascaded planning and control modules implemented for robot navigation, the frequency gap between the planner and controller has received limited attention. In this study, we introduce a novel B-spline parameterized optimization-based planner (BSPOP) designed to address the frequency gap challenge with limited onboard computational power in robots. T

Molin Zhang, Polina Golland, Patricia Ellen Grant, Elfar Adalsteinsson

The quality of fetal MRI is significantly affected by unpredictable and substantial fetal motion, leading to the introduction of artifacts even when fast acquisition sequences are employed. The development of 3D real-time fetal pose estimation approaches on volumetric EPI fetal MRI opens up a promising avenue for fetal motion monitoring and prediction. Chall

Chase Stokes, Marti A. Hearst

Visualization research tends to de-emphasize consideration of the textual context in which its images are placed. We argue that visualization research should consider textual representations as a primary alternative to visual options when assessing designs, and when assessing designs, equal attention should be given to the construction of the language as to

Lisa Mais, Peter Hirsch, Claire Managan, Ramya Kandarpa

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmenta

Jingyao Zhu, Stephanie Tonnesen, Greg L. Bryan, Mary E. Putman

The circumgalactic medium (CGM) of star-forming dwarf galaxies plays a key role in regulating the galactic baryonic cycle. We investigate how susceptible the CGM of dwarf satellite galaxies is to ram pressure stripping (RPS) in Milky Way-like environments. In a suite of hydrodynamical wind tunnel simulations, we model an intermediate-mass dwarf satellite gal

Ugo Lomoio, Pierangelo Veltri, Pietro Hiram Guzzi, Pietro Lio'

Electrocardiogram (ECG) signals play a pivotal role in cardiovascular diagnostics, providing essential information on the electrical activity of the heart. However, the inherent noise and limited resolution in ECG recordings can hinder accurate interpretation and diagnosis. In this paper, we propose a novel model for ECG super resolution (SR) that uses a DNA

Aakash Yadav, Daniel Hedman, Hongsik Jeong

Electronic Structure Theory (EST) describes the behavior of electrons in matter and is used to predict material properties. Conventionally, this involves forming a Hamiltonian and solving the Schr\"odinger equation through discrete computation. Here, a new perspective to EST is provided by treating a perfectly crystalline material as a Linear Translation Inv

C. A. Breu, H. Peter, S. K. Solanki, R. Cameron

Observed spectral profiles of emission lines from the corona are found to have widths exceeding the thermal line width. To investigate the physical mechanism, we run a 3D MHD model of a single, straightened loop in which we partially resolve turbulent motions that form in response to the driving by self-consistently evolving magneto-convection in the photosp

Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang

We show that any pseudoentangled state ensemble with a gap of $t$ bits of entropy requires $\Omega(t)$ non-Clifford gates to prepare. This bound is tight up to polylogarithmic factors if linear-time quantum-secure pseudorandom functions exist. Our result follows from a polynomial-time algorithm to estimate the entanglement entropy of a quantum state across a

Muhammad Ali Siddiqi, Jan Andrés Galvan Hernández, Anteneh Gebregiorgis, Rajendra Bishnoi

Next-generation personalized healthcare devices are undergoing extreme miniaturization in order to improve user acceptability. However, such developments make it difficult to incorporate cryptographic primitives using available target technologies since these algorithms are notorious for their energy consumption. Besides, strengthening these schemes against

Sana Isam, Hossein Hassani

Classifying Sorani Kurdish subdialects poses a challenge due to the need for publicly available datasets or reliable resources like social media or websites for data collection. We conducted field visits to various cities and villages to address this issue, connecting with native speakers from different age groups, genders, academic backgrounds, and professi

Nikhil U. Shinde, Zih-Yun Chiu, Florian Richter, Jason Lim

Inaccurate tool localization is one of the main reasons for failures in automating surgical tasks. Imprecise robot kinematics and noisy observations caused by the poor visual acuity of an endoscopic camera make tool tracking challenging. Previous works in surgical automation adopt environment-specific setups or hard-coded strategies instead of explicitly con

Peijie Qiu, Jin Yang, Sayantan Kumar, Soumyendu Sekhar Ghosh

In the past decades, deep neural networks, particularly convolutional neural networks, have achieved state-of-the-art performance in a variety of medical image segmentation tasks. Recently, the introduction of the vision transformer (ViT) has significantly altered the landscape of deep segmentation models. There has been a growing focus on ViTs, driven by th

Adam Chapman, Ilan Levin

We associate an $(n_1+\dots+n_t-k(t-1))$-fold Pfister form to any $t$-tuple of $k$-linked Pfister forms of dimensions $2^{n_1},\dots,2^{n_t}$, and prove its invariance under the different symbol presentations of the forms with a common $k$-fold sub-symbol. We then show that it vanishes when the forms are actually $(k+1)$-linked or when the characteristic is

Sudipta Gupta, Rasangi M. Perera, Christopher J. Van Leeuwen, Tianyu Li

Micelles and vesicles are promising candidates in targeted drug/gene delivery, bioreactors, and templates for nanoparticle synthesis. We investigated the morphology and dynamics of PEG-PDMS-PEG triblock copolymer nano-scale assemblies regarding the membrane dynamics because the molecular dynamics of the membrane govern mechanical properties like the stabilit

Yifan Guo

We establish a local Harnack inequality in a neighborhood of an indecomposable singular point of a stationary integral varifold. Extending the method of Gr\"uter and Widman \cite{gruter1982green}, we construct the Green function on a stationary integral varifold with Euclidean volume growth, allowing the pole to be any point in the support. Using the local H

Jiayi Liu, Javier Boix-Campos, Jonathan E. Ron, Johan M. Kux

Migratory and tissue resident cells exhibit highly branched morphologies to perform their function and to adapt to the microenvironment. Immune cells, for example, display transient branched shapes while exploring the surrounding tissues. In another example, to properly irrigate the tissues, blood vessels bifurcate thereby forcing the branching of cells movi

Natasha Cowley, Christopher K. Revell, Emma Johns, Sarah Woolner

We investigate the viscoelastic relaxation to equilibrium of a disordered planar epithelium described using the cell vertex model. In its standard form, the model is formulated as coupled evolution equations for the locations of vertices of confluent polygonal cells. Exploiting the model's gradient-flow structure, we use singular-value decomposition to proje

Duilio De Santis, Bernardo Spagnolo, Angelo Carollo, Davide Valenti

In the recent work "Non-reciprocal topological solitons in active metamaterials" (see arXiv:2312.03544v1), for an analytical understanding of the system under consideration, the authors derive an ordinary differential equation for the sine-Gordon (anti)soliton velocity, with the perturbation theory in the adiabatic approximation, via the inverse scattering t

Fabio Ancona, Luca Talamini

Consider a scalar conservation law with a spatially discontinuous flux at a single point x=0, and assume that the flux is uniformly convex when x\neq 0. Given an interface connection (A,B), we define a backward solution operator consistent with the concept of AB-entropy solution [4,13,16]. We then analyze the family A^{[AB]}(T) of profiles that can be attain

Yifan Guo

We are interested in finding a nonlinear polynomial $P$ on $\mathbb{R}^n$ that solves the minimal surface equation. Even though no explicit solution is found in this article, we investigate constraints that a polynomial solution must obey. We first prove a structure theorem on such polynomials. We show that the highest degree term $P_m$ must factor as $p^kQ_

Liviu-Daniel Ştefan, Dan-Cristian Stanciu, Mihai Dogariu, Mihai Gabriel Constantin

Recent advancements in Generative Adversarial Networks (GANs) have enabled photorealistic image generation with high quality. However, the malicious use of such generated media has raised concerns regarding visual misinformation. Although deepfake detection research has demonstrated high accuracy, it is vulnerable to advances in generation techniques and adv

Bruno José Olivieri de Souza, markus Endler

In the research on FANETs (Flying Ad-Hoc Networks) and distributed coordination of UAVs (Unmanned Aerial Vehicles), also known as drones, there are many studies that validate their proposals through simulations. Simulations are important, but beyond them, there is also a need for real-world tests to validate the proposals and enhance results. However, field

Brian Charles Brown, Michael King, Sean Warnick, Enoch Yeung

The Singular Value Decomposition (SVD) of linear functions facilitates the calculation of their 2-induced norm and row and null spaces, hallmarks of linear control theory. In this work, we present a function representation that, similar to SVD, provides an upper bound on the 2-induced norm of bounded-input bounded-output functions, as well as facilitates the

Zhuojun Yu, Peter J. Thomas

Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke-recovery systems. We augment two previously

Sílvia O. Sequeira, Ekaterina Pasnak, Carla Viegas, Bianca Gomes

Microbial contamination poses a threat to both the preservation of library and archival collections and the health of staff and users. This study investigated the microbial communities and potential health risks associated with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection exhibiting visible microbial colonization and staff health conce

Menglin Zhou, Natalia Nolde

As an important tool in financial risk management, stress testing aims to evaluate the stability of financial portfolios under some potential large shocks from extreme yet plausible scenarios of risk factors. The effectiveness of a stress test crucially depends on the choice of stress scenarios. In this paper we consider a pragmatic approach to stress scenar

Yiyong Liu, Rui Wen, Michael Backes, Yang Zhang

Machine learning as a Service (MLaaS) allows users to query the machine learning model in an API manner, which provides an opportunity for users to enjoy the benefits brought by the high-performance model trained on valuable data. This interface boosts the proliferation of machine learning based applications, while on the other hand, it introduces the attack

Syeda Nyma Ferdous, Xin Li

Occlusion remains one of the major challenges in person reidentification (ReID) as a result of the diversity of poses and the variation of appearances. Developing novel architectures to improve the robustness of occlusion-aware person Re-ID requires new insights, especially on low-resolution edge cameras. We propose a deep ensemble model that harnesses both

Jiyun Zhang, Anastasia Barabash, Tian Du, Jianchang Wu

Achieving high-performance perovskite photovoltaics, especially in ambient air relies heavily on optimizing process parameters. However, traditional manual methods often struggle to effectively control the key variables. This inherent challenge requires a paradigm shift toward automated platforms capable of precise and reproducible experiments. Herein, we us

Valery M. Nakariakov, Sihui Zhong, Dmitrii Y. Kolotkov, Rebecca L. Meadowcroft

Macroscopic wave and oscillatory phenomena ubiquitously detected in the plasma of the corona of the Sun are interpreted in terms of magnetohydrodynamic theory. Fast and slow magnetoacoustic waves are clearly distinguished in observations. Properties of coronal magnetohydrodynamic waves are determined by local parameters of the plasma, including the field-ali

Adyasha Mohanty, Grace Gao

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based and they utilize satellite geometry and the known properties of satellite signals. However, model-based methods have l

Luca Belluzzi, Simone Riva, Gioele Janett, Nuno Guerreiro

Measurable linear scattering polarization signals have been predicted and detected at the solar disk center in the core of chromospheric lines. These forward-scattering polarization signals, which are of high interest for magnetic field diagnostics, have always been modeled either under the assumption of complete frequency redistribution (CRD), or taking par

Marina Neseem, Conor McCullough, Randy Hsin, Chas Leichner

Low-precision quantization is recognized for its efficacy in neural network optimization. Our analysis reveals that non-quantized elementwise operations which are prevalent in layers such as parameterized activation functions, batch normalization, and quantization scaling dominate the inference cost of low-precision models. These non-quantized elementwise op

Hyosun Park, Yongsik Jo, Seokun Kang, Taehwan Kim

The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, time series forecasting, etc. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We con

Mason Cai, Sam Nelson

Quandle Coloring Quivers are directed graph-valued invariants of classical and virtual knots and links associated to finite quandles. Quandle action quivers are subquivers of the full quandle coloring quiver associated to quandle actions by elements of the coloring quandle. These quivers provide a categorification of the quandle counting invariant associated

Matías G. Gonzalez, Anna Fancelli, Han Yan, Johannes Reuther

Spiral spin liquids possess a subextensively degenerate ground-state manifold, represented by a continuum of energy minima in reciprocal space. Since a small change of the spiral state wavevector requires a global change of the spin configuration in real space, it is a priori unclear how such systems can fluctuate within the degenerate ground state manifold.

Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun

We study the evaluation of a policy under best- and worst-case perturbations to a Markov decision process (MDP), using transition observations from the original MDP, whether they are generated under the same or a different policy. This is an important problem when there is the possibility of a shift between historical and future environments, $\textit{e.g.}$

Mohammed Brahimi, Bjoern Haefner, Zhenzhang Ye, Bastian Goldluecke

Neural approaches have shown a significant progress on camera-based reconstruction. But they require either a fairly dense sampling of the viewing sphere, or pre-training on an existing dataset, thereby limiting their generalizability. In contrast, photometric stereo (PS) approaches have shown great potential for achieving high-quality reconstruction under s

Fan Zou, Zhibo Yu, W. N. Brandt, Hyungsuk Tak

The growth of supermassive black holes is strongly linked to their galaxies. It has been shown that the population mean black-hole accretion rate ($\overline{\mathrm{BHAR}}$) primarily correlates with the galaxy stellar mass ($M_\star$) and redshift for the general galaxy population. This work aims to provide the best measurements of $\overline{\mathrm{BHAR}

Dobromir A. Kalchevski, Dimitar V. Trifonov, Stefan K. Kolev, Valentin N. Popov

We present a computational study of assembling carbon clusters and nanophases in space from carbon aggregations. Geometry optimizations and Density-functional-based tight-binding (SCC-DFTB) dynamics methods are employed to predict carbon clusters, their time evolution, and their stability. The initial density of the aggregates is found to be of primary impor

Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang

Machine learning models struggle with generalization when encountering out-of-distribution (OOD) samples with unexpected distribution shifts. For vision tasks, recent studies have shown that test-time adaptation employing diffusion models can achieve state-of-the-art accuracy improvements on OOD samples by generating new samples that align with the model's d

Hoang Ky Nguyen, Bertrand Chauvineau

We provide a concrete example exhibiting marked deviation from the PPN approximation in a modified theory of gravity. Specifically, we derive the exact formula for the Robertson parameter $\gamma$ in Brans-Dicke gravity for compact mass sources, explicitly incorporating the pressure content of these sources. We achieve this by exploiting the $\textit integra

Lili Alderson, Natasha E. Batalha, Hannah R. Wakeford, Nicole L. Wallack

We present two transit observations of the ~870K, 1.7R$_E$ super-Earth TOI-836b with JWST NIRSpec/G395H, resulting in a 2.8-5.2$\mu$m transmission spectrum. Using two different reduction pipelines, we obtain a median transit depth precision of 34ppm for Visit 1 and 36ppm for Visit 2, leading to a combined precision of 25ppm in spectroscopic channels 30 pixel

Wenxiang Pei, Qi Guo, Ming Li, Qiao Wang

We investigate emission line galaxies across cosmic time by combining the modified L-Galaxies semi-analytical galaxy formation model with the JiuTian cosmological simulation. We improve the tidal disruption model of satellite galaxies in L-Galaxies to address the time dependence problem. We utilise the public code CLOUDY to compute emission line ratios for a

Shibo Xu, Zheng-Zhi Sun, Ke Wang, Hekang Li

Non-Abelian topological orders offer an intriguing path towards fault-tolerant quantum computation, where information can be encoded and manipulated in a topologically protected manner immune to arbitrary local noises and perturbations. However, realizing non-Abelian topologically ordered states is notoriously challenging in both condensed matter and program

Veome Kapil, Luca Reali, Roberto Cotesta, Emanuele Berti

Next-generation gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will have increased sensitivity and observing volumes, enabling unprecedented precision in parameter estimation. However, this enhanced precision could also reveal systematic biases arising from waveform modeling, which may impact astrophysical inference. We inves

Nikolaos Kidonakis, Alberto Tonero

We calculate the complete soft-gluon corrections for the production of colorless final states through N$^3$LO in single-particle-inclusive kinematics. We present explicit analytical results and use them to study higher-order QCD corrections for the production of a heavy charged Higgs pair ($H^+ H^-$) via quark-antiquark annihilation in the Two-Higgs-Doublet

Sean D. Johnson, Zhuoqi Will Liu, Jennifer I. Li, Joop Schaye

We report the discovery of large ionized, [O II] emitting circumgalactic nebulae around the majority of thirty UV luminous quasars at $z=0.4-1.4$ observed with deep, wide-field integral field spectroscopy (IFS) with the Multi-Unit Spectroscopy Explorer (MUSE) by the Cosmic Ultraviolet Baryon Survey (CUBS) and MUSE Quasar Blind Emitters Survey (MUSEQuBES). Am

Adrien Florio, David Frenklakh, Kazuki Ikeda, Dmitri E. Kharzeev

The possible link between entanglement and thermalization, and the dynamics of hadronization are addressed by studying the real-time response of the massive Schwinger model coupled to external sources. This setup mimics the production and fragmentation of quark jets, as the Schwinger model and QCD share the properties of confinement and chiral symmetry break

Jorge E. Santos, Yoav Zigdon

In the context of the black hole/string transition, it is useful to produce Euclidean string backgrounds representing hot and self-gravitating strings. We utilise analytical and numerical methods to find a smooth, stationary rotating solution in the heterotic string theory at high temperatures. The solution describes a spinning winding-momentum condensate li

Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming

This paper introduces a novel task to evaluate the robust understanding capability of Large Multimodal Models (LMMs), termed $\textbf{Unsolvable Problem Detection (UPD)}$. Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of LMMs, but it does not guarantee that LMMs truly comprehend the answer. UPD assesses the L

Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang

Large vision-language models (LVLMs) have recently achieved rapid progress, sparking numerous studies to evaluate their multi-modal capabilities. However, we dig into current evaluation works and identify two primary issues: 1) Visual content is unnecessary for many samples. The answers can be directly inferred from the questions and options, or the world kn

Joel Ruben Antony Moniz, Soundarya Krishnan, Melis Ozyildirim, Prathamesh Saraf

Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful

Zhengmao He, Kun Lei, Yanjie Ze, Koushil Sreenath

Quadruped robots are progressively being integrated into human environments. Despite the growing locomotion capabilities of quadrupedal robots, their interaction with objects in realistic scenes is still limited. While additional robotic arms on quadrupedal robots enable manipulating objects, they are sometimes redundant given that a quadruped robot is essen

Yikang Zhou, Tao Zhang, Shunping Ji, Shuicheng Yan

Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despite large-scale motion and transient occlusion. However, they all underperform on newly emerging and disappearing objects that are common in the real world because they attempt to mo