November 2024 arXiv papers — page 100

Fabian Zaiser, Andrzej S. Murawski, C. -H. Luke Ong

We study the problem of bounding the posterior distribution of discrete probabilistic programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in this setting: even if exact Bayesian inference is possible, the state of the art requires user-provided loop invariant templates. By contrast, we aim to find guaranteed bounds, which

Haochen Qiu

We prove a surgery formula for the ordinary Seiberg-Witten invariants, and surgery formulas for the families Seiberg-Witten invariants of families of $4$-manifolds obtained through fibrewise surgery. Our formula expresses the Seiberg-Witten invariants of the manifold after the surgery, in terms of the original Seiberg-Witten moduli space cut down by a cohomo

Viktor D. Zozulia, Anton A. Smirnov, Natalia Ya. Sotnikova, Alexander A. Marchuk

We study vertical resonant trapping and resonant heating of orbits. These two processes both lead to the growth of a boxy/peanut-shaped bulge in a typical $N$-body model. For the first time, we study this by means of the action variables and resonant angles of the actual orbits that compose the model itself. We used the resonant angle instead of the frequenc

D. A. Estyunin, T. P. Estyunina, I. I. Klimovskikh, K. A. Bokai

This study investigates methods for controlling the physical properties of the intrinsic magnetic topological insulator MnBi$_2$Te$_4$ (MBT) by substituting Mn with Pb in Mn$_{1-x}$Pb$_x$Bi$_2$Te$_4$ (MPBT) solid solutions. This substitution enables tunable magnetic and electronic properties. Using various angle-resolved photoemission spectroscopy (ARPES) te

Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar

Internal crack detection has been a subject of focus in structural health monitoring. By focusing on crack detection in structural datasets, it is demonstrated that deep learning (DL) methods can effectively analyze seismic wave fields interacting with micro-scale cracks, which are beyond the resolution of conventional visual inspection. This work explores a

Dominique Attali, Mattéo Clémot, Bianca B. Dornelas, André Lieutier

Given a finite set of points $P$ sampling an unknown smooth surface $\mathcal{M} \subseteq \mathbb{R}^3$, our goal is to triangulate $\mathcal{M}$ based solely on $P$. Assuming $\mathcal{M}$ is a smooth orientable submanifold of codimension 1 in $\mathbb{R}^d$, we introduce a simple algorithm, Naive Squash, which simplifies the $\alpha$-complex of $P$ by rep

Nina Kastendiek, Jakob Niehues, Robin Delabays, Thilo Gross

In adaptive dynamical networks, the dynamics of the nodes and the edges influence each other. We show that we can treat such systems as a closed feedback loop between edge and node dynamics. Using recent advances on the stability of feedback systems from control theory, we derive local, sufficient conditions for steady states of such systems to be linearly s

Kamil Fortuna, Janusz Szwabiński

The fragility of financial systems was starkly demonstrated in early 2023 through a cascade of major bank failures in the United States, including the second, third, and fourth largest collapses in the US history. The highly interdependent financial networks and the associated high systemic risk have been deemed the cause of the crashes. The goal of this pap

Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then transmitted over a wireless channel. At the receiver, a decoder performs a machine learning task, specifically for classifying the received signals. The deep neural networks correspondi

Lucas Tate, Rebecca Jones, Doug Dennis, Tatyana Benko

Bills of materials (BOMs) are quickly becoming an effective tool for managing supply chain risk. As more BOMs enter circulation, the ability to compare them will be crucial to understanding how products differ and in managing BOMs from different tools or sources. This paper will describe some of the challenges of comparing BOMs followed by a discussion of se

Suraj Racha, Shubh Gupta, Humaira Firdowse, Aastik Solanki

Federated Learning (FL) is a pioneering approach in distributed machine learning, enabling collaborative model training across multiple clients while retaining data privacy. However, the inherent heterogeneity due to imbalanced resource representations across multiple clients poses significant challenges, often introducing bias towards the majority class. Th

Hanzhang Mao, Thomas G. J. Chandler, Mark Han, Saverio E. Spagnolie

Bending the edge of a thin elastic material promotes rigidity far from its clamped boundary. However, this curvature-induced rigidity can be overwhelmed by gravity or other external loading, resulting in elastic buckling and large deformations. We consider the role of body geometry on this competition using experiments, numerical simulations, and reduced-ord

Po-han Li, Yunhao Yang, Mohammad Omama, Sandeep Chinchali

Autonomous agents perceive and interpret their surroundings by integrating multimodal inputs, such as vision, audio, and LiDAR. These perceptual modalities support retrieval tasks, such as place recognition in robotics. However, current multimodal retrieval systems encounter difficulties when parts of the data are missing due to sensor failures or inaccessib

Mainak Bhowmik, Poornendu Kumar

The Herglotz representation theorem for holomorphic functions with non-negative real part is a fundamental result in the theory of holomorphic functions. In this paper, we reinterpret the Herglotz representation in the context of modern techniques, specifically realization formula. This reinterpretation is then extended to operator-valued functions on arbitr

Sophie M. Woodward, Mauricio Tec, Francesca Dominici

Studies investigating the causal effects of spatially varying exposures on outcomes often rely on observational and spatially indexed data. A prevalent challenge is unmeasured spatial confounding, where an unobserved spatially varying variable affects both exposure and outcome, leading to biased estimates and invalid confidence intervals. There is a very lar

Nathan Kleeorin, Kirill Kuzanyan, Nikolai Safiullin, Igor Rogachevskii

We discuss a prediction of the solar activity on a short time-scale applying the method based on a combination of a nonlinear mean-field dynamo model and the artificial neural network. The artificial neural network which serves as a correction scheme for the forecast, uses the currently available observational data (e.g., the 13 month running average of the

Camilo Rojas P., Diogo Boito, Cristiane Y. London, Pere Masjuan

In the context of the anomalous magnetic moment of the muon, the hadronic contribution plays a crucial role, especially given its large contribution to the final error. Currently, lattice QCD simulations are in disagreement with dispersive calculations based on $e^+e^-$ hadronic cross sections. The new MUonE experiment intends to shed light on this situation

Albert S. Berahas, Raghu Bollapragada, Wanping Dong

In this paper, we propose algorithms that exploit negative curvature for solving noisy nonlinear nonconvex unconstrained optimization problems. We consider both deterministic and stochastic inexact settings, and develop two-step algorithms that combine directions of negative curvature and descent directions to update the iterates. Under reasonable assumption

Klervi Le Gall, Lise Bellanger, David Laplaud, Aymeric Stamm

Multiple sclerosis (MS) is the leading cause of severe non-traumatic disability in young adults and its incidence is increasing worldwide. The variability of gait impairment in MS necessitates the development of a non-invasive, sensitive, and cost-effective tool for quantitative gait evaluation. The eGait movement sensor, designed to characterize human gait

Guillaume Ricard, Eric Falcon

We report on the dynamics of a soliton propagating on the surface of a fluid in a 4-m-long canal with a random or periodic bottom topography. Using a full space-and-time resolved wavefield measurement, we evidence, for the first time experimentally, how the soliton is affected by the disorder, in the context of Anderson localization, and how localization dep

Vincent Martin, Isaac Engel, Lorenzo Picinali

Different methods can be employed to render virtual reverberation, often requiring substantial information about the room's geometry and the acoustic characteristics of the surfaces. However, fully comprehensive approaches that account for all aspects of a given environment may be computationally costly and redundant from a perceptual standpoint. For these m

Miguel A. Cascales Sandoval, J. Jurczyk, L. Skoric, D. Sanz-Hernández

In-operando techniques enable real-time measurement of intricate physical properties at the micro- and nano-scale under external stimuli, allowing the study of a wide range of materials and functionalities. In nanomagnetism, in-operando techniques greatly benefit from precise three-dimensional (3D) magnetic field control, enabling access to complex magnetic

Fei Li, Mubdi Rahman, Norman Murray, Dušan Kereš

Simulations and observations suggest that galaxy interactions may enhance the star formation rate (SFR) in merging galaxies. One proposed mechanism is the torque exerted on the gas and stars in the larger galaxy by the smaller galaxy. We analyze the interaction torques and star formation activity on six galaxies from the FIRE-2 simulation suite with masses c

Harshit Tiwari, Lekha Sharma, Mahendra K. Verma

Heat transport in highly turbulent convection is not well understood. In this paper, we simulate compressible convection in a box of aspect ratio 4 using computationally-efficient MacCormack-TVD finite difference method on single and multi-GPUs, and reach very high Rayleigh number ($\mathrm{Ra}$) -- $10^{15}$ in two dimensions and $10^{11}$ in three dimensio

Qing Cheng, Zefan Zeng, Xingchen Hu, Yuehang Si

Event Causality Identification (ECI) has become an essential task in Natural Language Processing (NLP), focused on automatically detecting causal relationships between events within texts. This comprehensive survey systematically investigates fundamental concepts and models, developing a systematic taxonomy and critically evaluating diverse models. We begin

Yin Chen, Runxuan Zhang

We develop a deformation theory for finite-dimensional left-symmetric color algebras, which can be used to construct new algebraic structures and interpret left-symmetric color cohomology spaces of lower degrees. We explore equivalence classes and extendability of deformations for a fixed left-symmetric color algebra, demonstrating that each infinitesimal de

Haoran Wei, Wencheng Han, Xingping Dong, Jianbing Shen

Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then distilled into 3D representations. However, these methods usually struggle to produce high-fidelity 3D models, frequently yielding excessively blurred textures. We attribute this issue to the insufficient cons

Guangzong Chen, Mingui Sun, Zhi-Hong Mao, Kangni Liu

Generative Adversarial Networks (GANs) are a class of neural networks that have been widely used in the field of image-to-image translation. In this paper, we propose a streamlined image-to-image translation network with a simpler architecture compared to existing models. We investigate the relationship between GANs and autoencoders and provide an explanatio

Pooja Aslami, Kejun Chen, Timothy M. Hansen, Malik Hassanaly

False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also actively developed, we show that they remain vulnerable to impactful and stealthy adversarial examples that can be crafted using Reinforcement Learning (RL). We propose to include such

Jeremy M. Wachter, Ken D. Olum, Jose J. Blanco-Pillado, Vishnu R. Gade

We report on the results of performing computational gravitational backreaction on cosmic string loops taken from a network simulation. The principal effect of backreaction is to smooth out small-scale structure on loops, which we demonstrate by various measures including the average loop power spectrum and the distribution of kink angles on the loops. Backr

Demet Taylan

We provide lower bounds on the connectivity of the independence complexes of hypergraphs. Additionally, we compute the homotopy types of the independence complexes of $d$-uniform properly-connected triangulated hypergraphs.

Tianhao Ma, Han Chen, Juncheng Hu, Yungang Zhu

Learning from label proportions (LLP), i.e., a challenging weakly-supervised learning task, aims to train a classifier by using bags of instances and the proportions of classes within bags, rather than annotated labels for each instance. Beyond the traditional bag-level loss, the mainstream methodology of LLP is to incorporate an auxiliary instance-level los

Christian Weiß

We establish the existence of $N$-point sets in dimension $d$ whose star-discrepancy is bounded above by $2.4631832 \sqrt{\frac{d}{N}}$, where the numerical constant improves upon all previously known bounds. This improvement is obtained by combining a recent result by Gnewuch on bracketing numbers in high dimensions with discrepancy bounds for Hammersley po

Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot

Large language models (LLMs) are excellent few-shot learners. They can perform a wide variety of tasks purely based on natural language prompts provided to them. These prompts contain data of a specific downstream task -- often the private dataset of a party, e.g., a company that wants to leverage the LLM for their purposes. We show that deploying prompted m

Libo Wang

This research proposes the interaction loop model "ASR-LLMs-Smart Glasses", which model combines automatic speech recognition, large language model and smart glasses to facilitate seamless human-computer interaction. And the methodology of this research involves decomposing the interaction process into different stages and elements. Speech is captured and pr

Erik Jansson, Volker Scheuer, Elena Jordan, Konstantina Kostourou

The transparent and conductive properties of indium tin oxide (ITO) thin films, make them an attractive coating for optically integrated ion traps. However, the relatively low transmittance for wavelengths $<$ 400 nm, high scattering and high production temperature limits the usability in trapped-ion-based quantum technologies. Here we present ITO coatings a

David Gaspard, Arthur Goetschy

We present a field-theoretic framework to characterize the distribution of transmission eigenvalues for coherent wave propagation through disordered media. The central outcome is a transport equation for a matrix-valued radiance, analogous to the classical radiative transport equation but capable of capturing coherent effects encoded in the transmission matr

Robin Strässer, Julian Berberich, Manuel Schaller, Karl Worthmann

In this paper, we provide a tutorial overview and an extension of a recently developed framework for data-driven control of unknown nonlinear systems with rigorous closed-loop guarantees. The proposed approach relies on the Koopman operator representation of the nonlinear system, for which a bilinear surrogate model is estimated based on data. In contrast to

Martin Mendez, Federico M. Pont

The dynamics and processes involved in particle-molecule scattering, including nuclear dynamics, are described and analyzed using various quantum information quantities throughout the different stages of the scattering. The main process studied and characterized with the information quantities is the interatomic coulombic electronic capture (ICEC), an inelas

Xumin Gao, Mark Stevens, Grzegorz Cielniak

The current vision-based aphid counting methods in water traps suffer from undercounts caused by occlusions and low visibility arising from dense aggregation of insects and other objects. To address this problem, we propose a novel aphid counting method through interactive stirring actions. We use interactive stirring to alter the distribution of aphids in t

Andrea Agostini, Daphné Chopard, Yang Meng, Norbert Fortin

Multimodal data integration and label scarcity pose significant challenges for machine learning in medical settings. To address these issues, we conduct an in-depth evaluation of the newly proposed Multimodal Variational Mixture-of-Experts (MMVM) VAE on the challenging MIMIC-CXR dataset. Our analysis demonstrates that the MMVM VAE consistently outperforms ot

David Gaspard, Arthur Goetschy

We develop a field-theoretic framework, called radiant field theory, to calculate the distribution of transmission eigenvalues for coherent wave propagation in disordered media. At its core is a self-consistent transport equation for a $2\times 2$ matrix radiance, reminiscent of the radiative transfer equation but capable of capturing coherent interference e

Enrique Ruiz Arriola, Wojciech Broniowski

We analyze the recent MIT lattice data for the gravitational form factors (GFFs) of the pion which extend up to $Q^2= 2~{\rm GeV}^2$ for $m_\pi=170$~MeV~\cite{Hackett:2023nkr}. We show that simple monopole fits comply with the old idea of meson dominance. We use Chiral Perturbation theory ($\chi$PT) to next-to-leading order (NLO) to transform the MIT data to

Andrea Gnarini, Ruben Farinelli, Francesco Ursini, Stefano Bianchi

We report the first simultaneous X-ray spectropolarimetric observation of the bright atoll neutron star low-mass X-ray binary GX 3+1, performed by the Imaging X-ray Polarimetry Explorer (IXPE) joint with NICER and NuSTAR. The source does not exhibit significant polarization in the 2-8 keV energy band, with an upper limit of 1.3% at a 99% confidence level on

Alex Moriani, Enrico Trebeschi

We study in this article the curvature of complete maximal spacelike submanifolds in pseudo-hyperbolic spaces. We show that the scalar curvature of these submanifolds is nonpositive in every signature. This gives, together with a result of Ishihara, a sharp bound on the scalar curvature of complete maximal spacelike submanifolds in pseudo-hyperbolic spaces o

Lin Ling, Fazle Rabbi, Song Wang, Jinqiu Yang

Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be present in the code produced by LLMs. To solve this issue, we propose a novel fairness framework, i.e., Solar, to assess and mitigate the social biases of LLM-generated code. Specifica

Xinren Chen, William Gonçalves, Yi Hu, Yipeng Gao

Grain boundaries (GBs) trigger structure-specific chemical segregation of solute atoms. According to the three-dimensional (3D) topology of grains, GBs - although defined as planar defects - cannot be free of curvature. This implies formation of topologically-necessary arrays of secondary GB dislocations. We report here that these pattern-forming secondary G

Shiksha Pandey, Ish Gupta, Koustav Chandra, Bangalore S. Sathyaprakash

We examine the role of LIGO-India in facilitating multi-messenger astronomy in the era of next generation observatories. A network with two L-shaped Cosmic Explorer (CE) detectors and one triangular Einstein Telescope (ET) would precisely localize nearly the entire annual binary neutron star merger population up to a redshift of 0.5--over 10,000 events would

Frederik F. Flöther, Daniel Blankenberg, Maria Demidik, Karl Jansen

Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, for example for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from ad

Mark Hamilton, Yael Karshon, Takahiko Yoshida

We give a simple proof that, for a pre-quantized compact symplectic manifold with a Lagrangian torus fibration, its Riemann-Roch number coincides with its number of Bohr-Sommerfeld fibres. This can be viewed as an instance of the "independence of polarization" phenomenon of geometric quantization. The base space for such a fibration acquires a so-called inte

Muchen He, Jizhe Lai

In this work, we propose a simple but effective experiment for probing the boundary in which a wave-function collapses. Using a quantum optics system interacting with a photomultiplier tube (PMT), one is able to determine the number of electrons needed to interfere with the "which-path" information to cause the collapse of a quantum state.

Rui Yin, Haotong Qin, Yulun Zhang, Wenbo Li

Dense prediction is a critical task in computer vision. However, previous methods often require extensive computational resources, which hinders their real-world application. In this paper, we propose BiDense, a generalized binary neural network (BNN) designed for efficient and accurate dense prediction tasks. BiDense incorporates two key techniques: the Dis

Nandavardhan R., Somanathan R., Vikram Suresh, Savaridassan P

Radiologists and doctors make use of X-ray images of the non-dominant hands of children and infants to assess the possibility of genetic conditions and growth abnormalities. This is done by assessing the difference between the actual extent of growth found using the X-rays and the chronological age of the subject. The assessment was done conventionally using

Muhammad Hamza, Vy Kauppinen, Muhammad Azeem Akbar, Wardah Naeem Awan

Enterprises are increasingly adopting serverless computing to enhance scalability, reduce costs, and improve efficiency. However, this shift introduces new responsibilities and necessitates a distinct set of skills for practitioners. This study aims to identify and organize the industry requirements for serverless practitioners by conducting a qualitative an

Abbas B. Ziad, Ankit Zalawadiya, Canberk Topal, Joan Camps

To avoid prohibitive overheads in performing fault-tolerant quantum computation, the decoding problem needs to be solved accurately and at speeds sufficient for fast feedback. Existing decoding systems fail to satisfy both of these requirements, meaning they either slow down the quantum computer or reduce the number of operations that can be performed before

Arian Aminoleslami, Geoffrey M. Anderson, Davide Chicco

Electronic health records (EHRs) contain important longitudinal information on individuals who have received medical care. Traditionally, EHRs have been used to support a wide range of administrative activities such as billing and clinical workflow, but, given the depth and breadth of clinical and demographic data they contain, they are increasingly being us

Jiacheng Ma, Matthis Thorade

Cyclic frosting and defrosting operations constitute a common characteristic of air-source heat pumps in cold climates during winter. Simulation models that can capture simultaneous heat and mass transfer phenomena associated with frost/defrost behaviors and their impact on the overall heat pump system performance are of critical importance to improved contr

Michele Trabucchi, Giada Pastorelli

We use one-dimensional hydrodynamic calculations combined with synthetic stellar population models of the Magellanic Clouds to study the onset of self-excited pulsation in luminous red giants. By comparing the results with OGLE observations in the period-luminosity diagram we are able to link the transition from small-amplitude red giants to semi-regular var

Yanzhi Wang, Jinhong Wu, Chu Wang, Qi Zhou

Fault diagnosis of mechanical equipment provides robust support for industrial production. It is worth noting that, the operation of mechanical equipment is accompanied by changes in factors such as speed and load, leading to significant differences in data distribution, which pose challenges for fault diagnosis. Additionally, in terms of application deploym

Isabel Cardoso Barbosa, Tim Hochdörffer, Juliusz A. Wolny, Dennis Lönard

Fe$^{\mathrm{II}}$ spin-crossover (SCO) complexes are materials that change their magnetic properties upon temperature variation, exhibiting a thermal hysteresis. Particularly interesting for magnetic-memory applications are thin layers of SCO complexes, where practical magnetic probing techniques are required. While conventional magnetometry on SCO complexe

Florian Schott, Benjamin Dollet, Stéphane Santucci, Christian Matthias Schlepütz

Rheology aims at quantifying the response of materials to mechanical forcing. However, standard rheometers provide only global macroscopic quantities, such as viscoelastic moduli. They fail to capture the heterogeneous flow of soft amorphous materials at the mesoscopic scale, arising from the rearrangements of the microstructural elements, that must be accou

Pasquale De Rosa, Yérom-David Bromberg, Pascal Felber, Djob Mvondo

In machine learning (ML), the inference phase is the process of applying pre-trained models to new, unseen data with the objective of making predictions. During the inference phase, end-users interact with ML services to gain insights, recommendations, or actions based on the input data. For this reason, serving strategies are nowadays crucial for deploying

Weiran Liao, Ge Li, Hongyi Zhou, Rudolf Lioutikov

This work introduces B-spline Movement Primitives (BMPs), a new Movement Primitive (MP) variant that leverages B-splines for motion representation. B-splines are a well-known concept in motion planning due to their ability to generate complex, smooth trajectories with only a few control points while satisfying boundary conditions, i.e., passing through a spe

Kiryl Piasotski, Omri Lesser, Adrian Reich, Pavel Ostrovsky

Recent experiments on planar superconductor-topological insulator-superconductor (S-TI-S) junctions, e.g., in Corbino geometry, have reported low-temperature nonzero Josephson currents in states with integer fluxoid (flux) induced in the junction by a perpendicular magnetic field. This effect was discussed in connection with Majorana zero modes localized in

Ammar Qammaz, Nikolaos Vasilikopoulos, Iason Oikonomidis, Antonis A. Argyros

We present Y-MAP-Net, a Y-shaped neural network architecture designed for real-time multi-task learning on RGB images. Y-MAP-Net, simultaneously predicts depth, surface normals, human pose, semantic segmentation and generates multi-label captions, all from a single network evaluation. To achieve this, we adopt a multi-teacher, single-student training paradig

Emmanuel Jacob, Benedikt Jahnel, Lukas Lüchtrath

We consider general continuum percolation models obeying sparseness, translation invariance, and spatial decorrelation. In particular, this includes models constructed on general point sets other than the standard Poisson point process or the Bernoulli-percolated lattice. Moreover, in our setting the existence of an edge may depend not only on the two end ve

Yongliang Wu, Xinting Hu, Yuyang Sun, Yizhou Zhou

Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue. However, they struggle to extend this visual understanding to tasks requiring precise temporal localization, known as Video Temporal Grounding (VTG). To address this gap, we introduce Number-Prompt (NumPro), a novel method that empowers Vi

Runteng Chen, Jun Zhang, Zelong Wang, Ke Lu

In this work, the long-sought cg-N with N-N single bond has been synthesized for the first time by a thermal-driven-only chemical route at ambient conditions. The successful synthesis of cg-N was achieved by first creating a solution of azides, which was then pretreated under vacuum conditions. Following the pretreatment, the resultant concentrated azide was

Mojtaba Shahi, Roozbeh Rajabi, Farnaz Masoumzadeh

This article addresses the gap in computational painting analysis focused on Persian miniature painting, a rich cultural and artistic heritage. It introduces a novel approach using Convolutional Neural Networks (CNN) to classify Persian miniatures from five schools: Herat, Tabriz-e Avval, Shiraz-e Avval, Tabriz-e Dovvom, and Qajar. The method achieves an ave

Huming Qiu, Guanxu Chen, Mi Zhang, Xiaohan Zhang

In recent years, text-to-image (T2I) generation models have made significant progress in generating high-quality images that align with text descriptions. However, these models also face the risk of unsafe generation, potentially producing harmful content that violates usage policies, such as explicit material. Existing safe generation methods typically focu

Maliheh Alaeddini

Emotion detection is pivotal in human communication, as it significantly influences behavior, relationships, and decision-making processes. This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset, which annotates Reddit comments with 27 distinct emotions. These emotions are subsequently mapped to Ekman's six basic categor

Arturs Mozers, Linda Serzane-Sadovska, Florian Gahbauer, Marcis Auzinsh

We have observed the Autler-Townes effect in single- and suspected double-photon magnetic-dipole transitions in the Cesium ground-state magnetic-sublevel manifold. Experiments were performed in a Cesium vapor cell. The D$_1$ line was excited by laser radiation to create ground-state optical polarization, and transitions between the ground-state magnetic subl

Boris Blok, Chang Wu

We show that scaling is a good approximation for the energy loss of the heavy quark propagating through linear expanding quark-gluon plasma (QGP), the effective quenching parameter does not depend on quark mass.

Hugo Schnoering, Michalis Vazirgiannis

Bitcoin, launched in 2008 by Satoshi Nakamoto, established a new digital economy where value can be stored and transferred in a fully decentralized manner - alleviating the need for a central authority. This paper introduces a large scale dataset in the form of a transactions graph representing transactions between Bitcoin users along with a set of tasks and

Théophile Dolmaire, Eleni Hübner-Rosenau

We consider a one-dimensional system of four inelastic hard spheres, colliding with a fixed restitution coefficient $r$, and we study the inelastic collapse phenomenon for such a particle system. We study a periodic, asymmetric collision pattern, proving that it can be realized, despite its instability. We prove that we can associate to the four-particle dyn

Joseph Liu, Joshua Geddes, Ziyu Guo, Haomiao Jiang

Diffusion Transformers (DiT) have emerged as powerful generative models for various tasks, including image, video, and speech synthesis. However, their inference process remains computationally expensive due to the repeated evaluation of resource-intensive attention and feed-forward modules. To address this, we introduce SmoothCache, a model-agnostic inferen

Siyuan Hu, Mingyu Ouyang, Difei Gao, Mike Zheng Shou

The recently released model, Claude 3.5 Computer Use, stands out as the first frontier AI model to offer computer use in public beta as a graphical user interface (GUI) agent. As an early beta, its capability in the real-world complex environment remains unknown. In this case study to explore Claude 3.5 Computer Use, we curate and organize a collection of ca

SangHyuk Kim, Edward Gaibor, Brian Matejek, Daniel Haehn

Early detection of melanoma is crucial for improving survival rates. Current detection tools often utilize data-driven machine learning methods but often overlook the full integration of multiple datasets. We combine publicly available datasets to enhance data diversity, allowing numerous experiments to train and evaluate various classifiers. We then calibra

Guodong Sun, Qixiang Ma, Liqiang Zhang, Hongwei Wang

Atmospheric turbulence introduces severe spatial and geometric distortions, challenging traditional image restoration methods. We propose the Probabilistic Prior Turbulence Removal Network (PPTRN), which combines probabilistic diffusion-based prior modeling with Transformer-driven feature extraction to address this issue. PPTRN employs a two-stage approach:

Zheng Zheng, Pierre Beck, Tian Yang, Omid Ashtari

Close to a saddle-node bifurcation, when two invariant solutions collide and disappear, the behavior of a dynamical system can closely resemble that of a solution which is no longer present at the chosen parameter value. For bifurcating equilibria in low-dimensional ODEs, the influence of such 'ghosts' on the temporal behavior of the system, namely delayed t

Giuseppe Di Battista, Fabrizio Grosso, Giulia Maragno, Maurizio Patrignani

Let $\mathcal{G}$ be the set of all the planar embeddings of a (not necessarily connected) $n$-vertex graph $G$. We present a bijection $\Phi$ from $\mathcal{G}$ to the natural numbers in the interval $[0 \dots |\mathcal{G}| - 1]$. Given a planar embedding $\mathcal{E}$ of $G$, we show that $\Phi(\mathcal{E})$ can be decomposed into a sequence of $O(n)$ natu

Kyprianos-Iason Prodromidis, Allan Sly

In this note, we prove that on any graph of maximal degree $d$ the mixing time of the Glauber Dynamics for the Ising Model at $\beta_c=\tanh^{-1}(\frac1{d-1})$, the uniqueness threshold on the infinite $d$-regular tree, is at most polynomial in $n$. The proof follows by a simple combination of new log-Sobolev bounds of Bauerschmidt and Dagallier, together wi

Colette De Coster, Simone Dovetta, Damien Galant, Enrico Serra

We develop a new approach to the investigation of normalized solutions for nonlinear Schr\"odinger equations based on the analysis of the masses of ground states of the corresponding action functional. Our first result is a complete characterization of the masses of action ground states, obtained via a Darboux-type property for the derivative of the action g

Fabian Immel, Richard Fehler, Frank Bieder, Jan-Hendrik Pauls

Autonomous vehicles rely on HD maps for their operation, but offline HD maps eventually become outdated. For this reason, online HD map construction methods use live sensor data to infer map information instead. Research on real map changes shows that oftentimes entire parts of an HD map remain unchanged and can be used as a prior. We therefore introduce M3T

He-Bin Zheng, Meng-Qi Wu, Guo-Ping Li, Qing-Quan Jiang

In this paper, we study the shadow and images of the accretion disk of Kerr-Newman (KN) black hole (BH) in modified gravity (MOG) theory by using backward ray-tracing method. And, the influence of spin parameter ($a$), charge ($Q$), and MOG parameter ($\alpha$) on the observed features of BHs are carefully addressed. Interestingly, as $\alpha$ increases, the

Maria Petrillo, Daniel Valdenegro, Charles Rahal, Yanan Zhang

Informal carers provide the majority of care for people living with challenges related to older age, long-term illness, or disability. However, the care they provide often results in a significant income penalty for carers, a factor largely overlooked in the economics literature and policy discourse. Leveraging data from the UK Household Longitudinal Study,

S. R. Callos, M. M. Hedman, D. P. Hamilton

Spokes are localized clouds of fine particles that appear over the outer part of Saturn's B ring. Over the course of the Cassini Mission, the Imaging Science Subsystem (ISS) obtained over 20,000 images of the outer B ring, providing the most comprehensive data set for quantifying spoke properties currently available. Consistent with prior work, we find that

Yu Lu, Xinkai Zhou, Erjia Cui, Dustin Rogers

We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. The method scales up to very large functional data sets by estimating the principal components of the covariance matrix on the linear predictor scale conditional on the fixed effects

Torben Krüger, David Renfrew

The Novikov-Shubin invariant associated to a graph provides information about the accumulation of eigenvalues of the corresponding adjacency matrix close to the origin. For a directed graph these eigenvalues lie in the complex plane and having a finite value for the planar Novikov-Shubin invariant indicates a polynomial behaviour of the eigenvalue density as

Omar El Housni, Qing Feng, Huseyin Topaloglu

We consider an assortment optimization problem under the multinomial logit choice model with general covering constraints. In this problem, the seller offers an assortment that should contain a minimum number of products from multiple categories. We refer to these constraints as covering constraints. Such constraints are common in practice due to service lev

Ziqi Xie, Xiao Lai, Weidong Zhao, Siqi Jiang

Current image stitching methods often produce noticeable seams in challenging scenarios such as uneven hue and large parallax. To tackle this problem, we propose the Reference-Driven Inpainting Stitcher (RDIStitcher), which reformulates the image fusion and rectangling as a reference-based inpainting model, incorporating a larger modification fusion area and

Benjamin El-Zein, Dominik Eckert, Thomas Weber, Maximilian Rohleder

Collimator detection remains a challenging task in X-ray systems with unreliable or non-available information about the detectors position relative to the source. This paper presents a physically motivated image processing pipeline for simulating the characteristics of collimator shadows in X-ray images. By generating randomized labels for collimator shapes

Juan C. Rocha, Jean-Baptiste Jouffray, Frida Bengtsson, Bianca-Ioana Voicu

Climate change and other anthropogenic pressures are likely to induce tipping points in marine ecosystems, potentially leading to declines in primary productivity and fisheries. Despite increasing attention to nature-related financial risks and opportunities within the ocean economy, the extent to which these tipping points could affect investors has remaine

Elspeth K. H. Lee, Kazumasa Ohno

Understanding of cloud microphysics and the evolution of cloud structures in sub-stellar atmospheres remains a key challenge in the JWST era. The abundance of new JWST data necessitates models that are suitable for coupling with large-scale simulations, such as general circulation models (GCMs), in order to fully understand and assess the complex feedback ef

Yuichi Uesaka, Masato Yamanaka, Yoshitaka Kuno

We propose the $\mu^-\to e^-\gamma$ process in a muonic atom as a novel means to investigate charged lepton flavor violation (CLFV). We demonstrate its sensitivity in probing effective CLFV operators associated with both single and double photon fields. In comparison to $\mu^+\to e^+\gamma$ using free positive muon decays at rest, the emitted electron and ph

Arne Wulff, Swapan Madabhushi Venkata, Boyang Chen, Sebastian Feld

We, the QAIMS lab lab at the Aerospace Faculty of TU Delft, participated as finalists in the Airbus/BMW Quantum Computing Challenge 2024. Stacking sequence retrieval, a complex combinatorial task within a bi-level optimization framework, is crucial for designing laminated composites that meet aerospace requirements for weight, strength, and stiffness. This d

Károly Bezdek, Zsolt Lángi, Márton Naszódi

In this survey, we discuss volumetric and combinatorial results concerning (mostly finite) intersections or unions of balls (mostly of equal radii) in the $d$-dimensional real vector space, mostly equipped with the Euclidean norm. Our first topic is the Kneser--Poulsen Conjecture, according to which if a finite number of balls are rearranged so that the pair

Viorel Barbu

The mean-field game system is treated as an Euler Lagrange system corresponding to an optimal control problem governed by Fokker-Planck equation.

Mattia Ornaghi

In this paper we define the tensor product of two A$_{\infty}$-categories and two A$_{\infty}$-functors. This tensor product makes the category of A$_{\infty}$-categories symmetric monoidal (up to homotopy), and the category A$_{\infty}$Cat$^u$/$_{\approx}$ a closed symmetric monoidal category. Moreover, we define the derived tensor product making Ho(A$_{\in

Philippe Tranchida

For $q = p^n$ with $p$ an odd prime, the projective linear group $PGL(2,q)$ can be seen as the stabilizer of a conic $O$ in a projective plane $\pi = PG(2,q)$. In that setting, involutions of $PGL(2,q)$ correspond bijectively to points of $\pi$ not in $O$. Triples of involutions $\{ \alpha_P,\alpha_Q,\alpha_R \}$ of $PGL(2,q)$ can then be seen also as triple