April 2024 arXiv papers — page 26

Yingqing Chen, Christos G. Cassandras

We develop adaptive data-driven traffic light controllers for a grid-like traffic network considering straight, left-turn, and right-turn traffic flows. The analysis incorporates transit delays and blocking effects on vehicle movements between neighboring intersections. Using a stochastic hybrid system model with parametric traffic light controllers, we use

Ruba Islayem, Fatima Alhosani, Raghad Hashem, Afra Alzaabi

Autonomous Vehicles (AVs) redefine transportation with sophisticated technology, integrating sensors, cameras, and intricate algorithms. Implementing machine learning in AV perception demands robust hardware accelerators to achieve real-time performance at reasonable power consumption and footprint. Lot of research and development efforts using different tec

Susanna Kirchhoff, Frank K. Wilhelm, Felix Motzoi

The M{\o}lmer-S{\o}rensen gate is a widely used entangling gate for ion platforms with inherent robustness to trap heating. The gate performance is limited by coherent errors, arising from the Lamb-Dicke (LD) approximation and sideband errors. Here, we provide explicit analytical formulas for errors up to fourth order in the LD parameter, by using the Magnus

Paul Kinsler

Decision making can be difficult when there are many actors (or agents) who may be coordinating or competing to achieve their various ideas of the optimum outcome. Here I present a simple decision making model with an explicitly hierarchical binary-tree structure, and evaluate how this might cooperate to take actions that match its various evaluations of the

V. Guzey, M. Strikman

We determine the nuclear suppression factor $S_{Pb}(x)$, where $x=M_{J/\psi}^2/W_{\gamma p}^2$ with $M_{J/\psi}$ the $J/\psi$ mass and $W_{\gamma p}$ the photon-nucleon energy, for the cross section of coherent $J/\psi$ photoproduction in heavy-ion ultraperipheral collisions (UPCs) at the Large Hadron Collider (LHC) and Relativistic Heavy Ion Collider (RHIC)

Van Bach Nguyen, Jörg Schlötterer, Christin Seifert

Counterfactual text generation aims to minimally change a text, such that it is classified differently. Judging advancements in method development for counterfactual text generation is hindered by a non-uniform usage of data sets and metrics in related work. We propose CEval, a benchmark for comparing counterfactual text generation methods. CEval unifies cou

Gabriel Mantegna, Wilson Ricks, Aneesha Manocha, Neha Patankar

Long duration energy storage (LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. LDES is difficult to model in existing energy sys

Simone Scardapane

Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what

Samuel Olivier, Ben S. Southworth, James S. Warsa, HyeongKae Park

Second Moment Methods (SMMs) are developed that are consistent with the Discontinuous Galerkin (DG) spatial discretization of the discrete ordinates (or \Sn) transport equations. The low-order (LO) diffusion system of equations is discretized with fully consistent \Pone, Local Discontinuous Galerkin (LDG), and Interior Penalty (IP) methods. A discrete residu

Biljana Bojovic, Sandra Lagen

MIMO technology has been studied in textbooks for several decades, and it has been adopted in 4G and 5G systems. Due to the recent evolution in 5G and beyond networks, designed to cover a wide range of use cases with every time more complex applications, it is essential to have network simulation tools (such as ns-3) to evaluate MIMO performance from the net

Wei Xie, Yalchin Efendiev, Yunqing Huang, Wing Tat Leung

In this paper, we develop a general framework for multicontinuum homogenization in perforated domains. The simulations of problems in perforated domains are expensive and, in many applications, coarse-grid macroscopic models are developed. Many previous approaches include homogenization, multiscale finite element methods, and so on. In our paper, we design m

Steven Greenwood, Thomas Leistner

For a Lorentzian homogeneous space, we study how algebraic conditions on the isotropy group affect the geometry and curvature of the homogeneous space. More specifically, we prove that a Lorentzian locally homogeneous space is locally isometric to a plane wave if it admits an Ambrose--Singer connection with indecomposable, non-irreducible holonomy. This gene

Yolanda Lozano, Niall T. Macpherson, Nicolò Petri, Anayeli Ramírez

We study the class of $\text{AdS}_3\times \mathbb{CP}^3$ solutions to massive Type IIA supergravity with $\mathfrak{osp}(6|2)$ superconformal algebra recently constructed in arXiv:2304.12207 [hep-th]. These solutions are foliations over an interval preserving $\mathcal{N}=(0,6)$ supersymmetry in two dimensions, that in the massless limit can be mapped to the

Imen Ayadi, Florent Bouchard, Frédéric Pascal

This paper deals with the Elliptical Wishart and Inverse Elliptical Wishart distributions, which play a major role when handling covariance matrices. Similarly to multivariate elliptical distributions, these form a large family of covariance distributions, encompassing, e.g., the Wishart or \textit{t}-Wishart ones. Our first major contribution is to derive a

David Conlon, Joonkyung Lee, Leo Versteegen

A graph $H$ is said to be positive if the homomorphism density $t_H(G)$ is non-negative for all weighted graphs $G$. The positive graph conjecture proposes a characterisation of such graphs, saying that a graph is positive if and only if it is symmetric, in the sense that it is formed by gluing two copies of some subgraph along an independent set. We prove s

Zhe Bai, Xishuo Wei, William Tang, Leonid Oliker

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity for building efficient models to identify plasma instabilities for real-time control. Our

Simona Bernardi, Tommaso Zoppi

The goal of the Fast Abstracts track is to bring together researchers and practitioners working on dependable computing to discuss work in progress or opinion pieces. Contributions are welcome from academia and industry. Fast Abstracts aim to serve as a rapid and flexible mechanism to: (i) Report on current work that may or may not be complete; (ii) Introduc

Hassan Pazira, Emanuele Massa, Jetty AM Weijers, Anthony CC Coolen

In cancer research, overall survival and progression free survival are often analyzed with the Cox model. To estimate accurately the parameters in the model, sufficient data and, more importantly, sufficient events need to be observed. In practice, this is often a problem. Merging data sets from different medical centers may help, but this is not always poss

Francesco Prinzi, Carmelo Militello, Calogero Zarcaro, Tommaso Vincenzo Bartolotta

In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that expl

Jian-Dong Zhang, Yiwen Fu, Lili Hou, Shuai Wang

Resolving the separation between two incoherent optical sources with high precision is of great significance for fluorescence imaging and astronomical observations. In this paper, we focus on a more general scenario where two sources have unequal brightnesses. We give the ultimate precision limit with respect to separation by using the quantum Fisher informa

Abhishek Kumar Singh, Ioannis Patras

The rapid evolution of the fashion industry increasingly intersects with technological advancements, particularly through the integration of generative AI. This study introduces a novel generative pipeline designed to transform the fashion design process by employing latent diffusion models. Utilizing ControlNet and LoRA fine-tuning, our approach generates h

Zhiqing Wei, Jinzhu Jia, Yangyang Niu, Lin Wang

Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communic

Rustem Takhanov

A neural architecture with randomly initialized weights, in the infinite width limit, is equivalent to a Gaussian Random Field whose covariance function is the so-called Neural Network Gaussian Process kernel (NNGP). We prove that a reproducing kernel Hilbert space (RKHS) defined by the NNGP contains only functions that can be approximated by the architectur

Robin Schmucker, Meng Xia, Amos Azaria, Tom Mitchell

Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Nonetheless, the cost associated with authoring CTS content is a major obstacle to widespread adoption and to research on effec

Maria Lucia Sambataro, Vincenzo Greco, Gabriele Parisi, Salvatore Plumari

In the last decade a Quasi-Particle Model ($QPM$) has supplied the basis for the study of HQ production in ultra-relativistic AA collisions, allowing for a phenomenological estimate of the HQ diffusion coefficient $D_s(T)$. Taking advantage of the new lattice QCD results for the Equation of State (EoS) with 2+1+1 dynamical flavors, we extend our $QPM$ approa

Wai Yeung Lam

We consider circle patterns on surfaces with complex projective structures. We investigate two symplectic forms pulled back to the deformation space of circle patterns. The first one is Goldman's symplectic form on the space of complex projective structures on closed surfaces. The other is the Weil-Petersson symplectic form on the Teichm\"uller space of punc

Muhammad Saeed Saleem, Michel Versluis, Guillaume Lajoinie

The study of vapor bubble growth following droplet vaporization in a superheated liquid involves research areas such as hydrodynamics, heat transfer, mass transfer, and thermodynamics. The interplay between these multiscale aspects is strongly dependent on the geometry, the thermodynamic response, and the local physical properties of the system. To understan

Chufan Gao, Tianfan Fu, Jimeng Sun

Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint. This process predominantly involves the development of machine learning models that utilize a variety of data sources such as descriptions of the clinical trials, characteristics of the drug molecules, and specific disease c

Zhipeng Huang, Jianhao Ding, Zhiyu Pan, Haoran Li

Spiking neural networks (SNNs) have garnered interest due to their energy efficiency and superior effectiveness on neuromorphic chips compared with traditional artificial neural networks (ANNs). One of the mainstream approaches to implementing deep SNNs is the ANN-SNN conversion, which integrates the efficient training strategy of ANNs with the energy-saving

Martín Hernández, Martin Lazar, Sebastián Zamorano

Our main contribution in this article is the achievement of the turnpike property in its integral and exponential forms for parameter-dependent systems with averaged observations in the cost functional. Namely, under suitable assumptions with respect to the matrices that defined the dynamics and the cost functional, we prove that the optimal control and stat

Kaichen Xu, Yueyang Ding, Suyang Hou, Weiqiang Zhan

Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportunities for this task. However, current anomaly detection methods struggle to handle domain shifts prevalent in multi-sample and multi-domain single-cell sequencing data, leading to

M. Giacomin, D. Dickinson, W. Dorland, N. R. Mandell

A quasi-linear reduced transport model is developed from a database of high-$\beta$ electromagnetic nonlinear gyrokinetic simulations performed with Spherical Tokamak for Energy Production (STEP) relevant parameters. The quasi-linear model is fully electromagnetic and accounts for the effect of equilibrium flow shear using a novel approach. Its flux predicti

Richard Michael, Simon Bartels, Miguel González-Duque, Yevgen Zainchkovskyy

To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can be computationally tractable. We consider in particular the optimization domain where very few observations and strict bud

Zhong-Wen Feng, Shi-Yu Li, Xia Zhou, Haximjan Abdusattar

In this paper, we explore the the phase transition, critical behavior and microstructure of the FRW in the framework of a new higher order generalized uncertainty principle. Our initial step involves deriving the equation of state by defining the work density $W$ from GUP-corrected Friedmann equations as the thermodynamic pressure $P$. Based on the modified

Slawek Smyl, Boris N. Oreshkin, Paweł Pełka, Grzegorz Dudek

Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Distributional forecasting is used to control and mitigate risks associated with this uncertainty. Recent progress in deep learning has helped to significantly improve the accuracy of point forecasts, while accurate distributional f

Arthur Vangeffelen, Geert Buckinx, Carlo De Servi, Maria Rosaria Vetrano

In the present work, we examine to what degree the heat transfer can be described as developed on a macro-scale level in typical micro- and mini-channels with offset strip fin arrays subject to a uniform heat flux, considering flow entrance and side-wall effects. Full-scale numerical heat transfer simulations are conducted to determine the extent of the deve

Andjelka B. Kovacevic

We present a concise report on the '2DHybrid' method, an innovative extension of two-dimensional correlation spectroscopy (2D COS), tailored for quasar light curve analysis. Addressing the challenge of discerning periodic variations against the background of intrinsic "red" noise fluctuations, this method employs cross-correlation of wavelet transform matric

Jordan François, Lucrezia Ravera

We propose our account of the meaning of local symmetries. We argue that the general covariance principle and gauge principle both are principles of democratic epistemic access to the law of physics, leading to ontological insights about the objective nature of spacetime. We further argue that relationality is a core notion of general-relativistic gauge fiel

Beatriz B. Siffert, Raquel G. Gonçalves Farias, Matias Garcia, Luiz Felipe Melo de Menezes

Small rocky planets, as well as larger planets that suffered extensive volatile loss, tend to be drier and have thinner atmospheres as compared to Earth. Such planets probably outnumber worlds better endowed with volatiles, being the most common habitable planets. For the subgroup of fast rotators following eccentric orbits, atmospheres suffer radiative forc

A. G. Nouri, Y. Liu, P. Givi, H. Babaee

A novel methodology is developed to extract accurate skeletal reaction models for nuclear combustion. Local sensitivities of isotope mass fractions with respect to reaction rates are modeled based on the forced optimally time-dependent (f-OTD) scheme. These sensitivities are then analyzed temporally to generate skeletal models. The methodology is demonstrate

M. Girguś, S. D. Głazek

Theory of the quantum quartic oscillator is developed with close attention to the energy cutoff one needs to impose on the system in order to approximate the smallest eigenvalues and corresponding eigenstates of its Hamiltonian by diagonalizing matrices of limited size. The matrices are obtained by evaluating matrix elements of the Hamiltonian between the as

Romain Duverger, Alexis Bonnin, Romain Granier, Quentin Marolleau

We demonstrate a new approach for the metrology of microwave fields based on the trap-loss-spectroscopy of cold Rydberg atoms in a magneto-optical trap. Compared to state-of-the-art sensors using room-temperature vapors, cold atoms allow longer interaction times, better isolation from the environment and a reduced Doppler effect. Our approach is particularly

Yu-Fei Wang, Michael Döring, Jackson Hergenrather, Maxim Mai

Hadronic resonances emerge from strong interactions encoding the dynamics of quarks and gluons. The structure of these resonances can be probed by virtual photons parameterized in transition form factors. In this study, twelve $N^*$ and $\Delta$ transition form factors at the pole are extracted from data with the center-of-mass energy from $\pi N$ threshold

Bruno Pereira Cipriano, Pedro Alves

Large Language Models (LLMs) like GPT and Bard are capable of producing code based on textual descriptions, with remarkable efficacy. Such technology will have profound implications for computing education, raising concerns about cheating, excessive dependence, and a decline in computational thinking skills, among others. There has been extensive research on

Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli

We propose data-dependent uniform generalization bounds by approaching the problem from a PAC-Bayesian perspective. We first apply the PAC-Bayesian framework on "random sets" in a rigorous way, where the training algorithm is assumed to output a data-dependent hypothesis set after observing the training data. This approach allows us to prove data-dependent b

Jonathan Ansari, Moritz Ritter

Positive dependencies have been compared in the literature under rather strong assumptions such as equality of conditional distributions, exchangeability, or stationarity. We establish supermodular ordering results for distributions that are Markov with respect to a tree structure. Our comparison results rely on simple stochastic monotonicity conditions and

L. P. S. Leal, S. Rosauro-Alcaraz

The first observation of $\mathcal{B}\left(B^+\rightarrow K^+\nu\nu\right)$ by the Belle II experiment lies almost $3\sigma$ away from the Standard Model expectation. In this letter we study this result in the SMEFT, extended by a light right-handed neutrino. We explore the correlations between the measured decay rate and other observables, such as $\mathcal

Abhinav Gupta, Radim Bartos

HTTP/3, the latest evolution of the Hypertext Transfer Protocol, utilizes QUIC, a new transport protocol leveraging UDP to overcome limitations such as connection time and head-of-line blocking prevalent in HTTP/2. This advancement is enhanced by the Extensible Prioritization Scheme (EPS), which introduces a flexible prioritization framework for improving we

Aya Ghoul, Jiazhen Pan, Andreas Lingg, Jens Kübler

Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We ex

Martina Stadler Kurtz, Samuel Prentice, Yasmin Veys, Long Quang

We would like to enable a collaborative multiagent team to navigate at long length scales and under uncertainty in real-world environments. In practice, planning complexity scales with the number of agents in the team, with the length scale of the environment, and with environmental uncertainty. Enabling tractable planning requires developing abstract models

Zheng Cong, Xintong Dong, Shaoping Lu, Shiqi Dong

Full waveform inversion (FWI) is used to reconstruct the physical properties of subsurface media which plays an important role in seismic exploration. However, the precision of FWI is seriously affected by the absence or inaccuracy of low-frequency information. Therefore, reconstructing the low-frequency signals accurately is highly significant in seismic da

Giada Franceschi, Andrea Conti, Luca Lezuo, Rainer Abart

The interaction between ammonia (NH$_3$) and (alumino)silicates is of fundamental and applied importance, yet the specifics of NH$_3$ adsorption on silicate surfaces remain largely unexplored, mainly because of experimental challenges related to their electrically insulating nature. An example of this knowledge gap is evident in the context of ice nucleation

Andjelka B. Kovacevic, Yu-Yang Songsheng, Jian-Min Wang, Luka C. Popovic

We test the potential of Bayesian synthesis of upcoming multi-instrument data to extract orbital parameters and individual light curves of close binary supermassive black holes (CB-SMBH) with subparsec separations. Next generation (ng) interferometers, will make possible the observation of astrometric wobbles in CB-SMBH. Combining them with periodic variable

Tianyi Zhang, Guoying Zu, Taimoor Ul Islam, Evan Gossling

The study of wireless channel behavior has been an active research topic for many years. However, there exists a noticeable scarcity of studies focusing on wireless channel characteristics in rural areas. With the advancement of smart agriculture practices in rural regions, there has been an increasing demand for affordable, high-capacity, and low-latency wi

Bingchen Li, Xin Li, Yiting Lu, Ruoyu Feng

Blind Compressed Image Restoration (CIR) has garnered significant attention due to its practical applications. It aims to mitigate compression artifacts caused by unknown quality factors, particularly with JPEG codecs. Existing works on blind CIR often seek assistance from a quality factor prediction network to facilitate their network to restore compressed

Celia Kerr, Nicholas W. Mayers, Nicholas Russoniello

In this paper, we are concerned with identifying among the family of posets associated with Kohnert polynomials, those whose order complex has a certain combinatorial property. In particular, for numerous families of Kohnert polynomials, including key polynomials, we determine when the associated Kohnert posets are (EL-)shellable. Interestingly, under certai

Henning Kirchberg, Abraham Nitzan

In this study, we investigate the crucial role of measurement time ($t_m$), information gain and energy consumption in information engines (IEs) utilizing a von-Neumann measurement model. These important measurement parameters allow us to analyze the efficiency and power output of these devices. As the measurement time increases, the information gain and sub

Jiahong Wang, Yinwei Du, Stelian Coros, Bernhard Thomaszewski

We propose a self-supervised approach for learning physics-based subspaces for real-time simulation. Existing learning-based methods construct subspaces by approximating pre-defined simulation data in a purely geometric way. However, this approach tends to produce high-energy configurations, leads to entangled latent space dimensions, and generalizes poorly

Giuliano Angelone, Paolo Facchi, Marilena Ligabò

We consider a non-relativistic particle in a one-dimensional box with all possible quantum boundary conditions that make the kinetic-energy operator selfadjoint. We determine the Wigner functions of the corresponding eigenfunctions and analyze in detail their classical limit in the high-energy regime. We show that the quantum boundary conditions split into t

Youness Boutaib

A natural hypothesis for the success of reservoir computing in generic tasks is the ability of the untrained reservoir to map distinct input time series to separable reservoir states, a property we term separation capacity. In this work, we develop a rigorous mathematical framework for analysing the separation capacity of random linear reservoirs. We show th

Manuel Dubinsky, Kun-Mao Chao, César Massri, Gabriel Taubin

Minimum spanning trees are important tools in the analysis and design of networks. Many practical applications require their computation, ranging from biology and linguistics to economy and telecommunications. The set of cycles of a network has a vector space structure. Given a spanning tree, the set of non-tree edges defines cycles that determine a basis. T

Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth, Nadja Klein

Object detectors in real-world applications often fail to detect objects due to varying factors such as weather conditions and noisy input. Therefore, a process that mitigates false detections is crucial for both safety and accuracy. While uncertainty-based thresholding shows promise, previous works demonstrate an imperfect correlation between uncertainty an

Deborah Pereg

Image restoration, or inverse problems in image processing, has long been an extensively studied topic. In recent years supervised learning approaches have become a popular strategy attempting to tackle this task. Unfortunately, most supervised learning-based methods are highly demanding in terms of computational resources and training data (sample complexit

Charlotte Van Petegem, Kasper Demeyere, Rien Maertens, Niko Strijbol

In programming education, providing manual feedback is essential but labour-intensive, posing challenges in consistency and timeliness. We introduce ECHO, a machine learning method to automate the reuse of feedback in educational code reviews by analysing patterns in abstract syntax trees. This study investigates two primary questions: whether ECHO can predi

Martin Braß, Jan M. Tomczak, Karsten Held

Experimental studies have found unusual transport properties in Ce$_3$Bi$_4$Pd$_3$ which are potentially a consequence of the interplay between band-structure topology and electronic correlations. Based on these measurements, the existence of Weyl points in strongly renormalized, flat quasiparticle bands has been postulated. However, so far, there has been n

Patrick Letendre

Let $\mathcal{D}_{n} \subset \mathbb{N}$ be the set of the $\tau(n)$ divisors of $n$. We generalize a method developed by Erd\H os, Tenenbaum and de la Bret\`eche for the study of the set $\mathcal{D}_{n}$. In particular, using these ideas, we establish that $$ |\{(d_{1},d_{2},d_{3}) \in \mathcal{D}_{n}^3 : d_{1}+d_{2}=d_{3}\}| \le \tau(n)^{2-\delta} $$ with

Thibaut L. François, Christian M. Boily, Jonathan Freundlich, Simon Rozier

It is well established that black holes reside in the central regions of virtually all types of known galaxies. Recent observational and numerical studies however challenge this picture, suggesting that intermediate-mass black holes in dwarf galaxies may be found on orbits far from the center. In particular, constant-density cores minimize orbital energy los

Mercè Claverol, Andrea de las Heras-Parrilla, Clemens Huemer, Dolores Lara

Let $S$ be a set of $n$ points in general position in $\mathbb{R}^d$. The order-$k$ Voronoi diagram of $S$, $V_k(S)$, is a subdivision of $\mathbb{R}^d$ into cells whose points have the same $k$ nearest points of $S$. Sibson, in his seminal paper from 1980 (A vector identity for the Dirichlet tessellation), gives a formula to express a point $Q$ of $S$ as a

Massimo Benerecetti, Laura Bozzelli, Fabio Mogavero, Adriano Peron

Characterisations theorems serve as important tools in model theory and can be used to assess and compare the expressive power of temporal languages used for the specification and verification of properties in formal methods. While complete connections have been established for the linear-time case between temporal logics, predicate logics, algebraic models,

Walter O. Krawec, Bing Wang, Ryan Brown

Simplified trusted nodes (STNs) are a form of trusted node for quantum key distribution (QKD) networks which do not require running a full QKD stack every instance (i.e., they do not need to run error correction and privacy amplification each session). Such systems hold the advantage that they may be implemented with weaker computational abilities, than regu

Seungwook Kim, Yichun Shi, Kejie Li, Minsu Cho

Using image as prompts for 3D generation demonstrate particularly strong performances compared to using text prompts alone, for images provide a more intuitive guidance for the 3D generation process. In this work, we delve into the potential of using multiple image prompts, instead of a single image prompt, for 3D generation. Specifically, we build on ImageD

Kimberly K. Boddy, Bhaskar Dutta, Addy J. Evans, Wei-Chih Huang

We consider the nuclear absorption of dark matter as an alternative to the typical indirect detection search channels of dark matter decay or annihilation. In this scenario, an atomic nucleus transitions to an excited state by absorbing a pseudoscalar dark matter particle and promptly emits a photon as it transitions back to its ground state. The nuclear exc

Eduardo Guerra, Everaldo Gomes, Jeferson Ferreira, Igor Wiese

Context: Code annotations have gained widespread popularity in programming languages, offering developers the ability to attach metadata to code elements to define custom behaviors. Many modern frameworks and APIs use annotations to keep integration less verbose and located nearer to the corresponding code element. Despite these advantages, practitioners' an

Lukas Rieland, Julian Wagner, Robin Bernhardt, Tianyi Wang

Using heterodyne transient grating spectroscopy, we observe a significant enhancement of exciton diffusion within a monolayer WSe$_2$ stacked on top of graphene. We further demonstrate that the diffusion dynamics can be optically tuned on the ultrafast time scale (i.e. a few picoseconds) by altering the photoexcited charge carrier density in graphene. The re

I. del Moral-Castro, J. M. Vílchez, J. Iglesias-Páramo, A. Arroyo-Polonio

We apply a methodology to build a sample of extreme emission line galaxies (EELGs) using integral field spectroscopy data. In this work we follow the spectroscopic criteria corresponding for EELG selection and use the MUSE Hubble Ultra-Deep field survey, which includes the deepest spectroscopic survey ever performed. Objects in the primary (extended) sample

Xue-Jiao Wang

Each real number $x\in[0,1]$ admits a unique power-2-decaying Gauss-like expansion (P2GLE for short) as $x=\sum_{i\in\mathbb{N}} 2^{-(d_1(x)+d_2(x)+\cdots+d_i(x))}$, where $d_i(x)\in\mathbb{N}$. For any $x\in(0,1]$, the Khintchine exponent $\gamma(x)$ is defined by $\gamma(x):=\lim_{n\to\infty}\frac{1}{n}\sum_{j=1}^nd_j(x)$ if the limit exists. We investigat

Tianbao Zhou, Zhixin Liu, Yingying Xu

Based on the quarterly data from 26 advanced economies (AEs) and 18 emerging market economies (EMs) over the past two decades, this paper estimates the short- and medium-term impacts of financial cycles on the duration and amplitude of public debt cycles. The results indicate that public debt expansions are larger than their contractions in duration and ampl

Rafaela Schroeder, Jiguang He, Hamza Djelouat, Markku Juntti

We investigate the channel estimation (CE) problem for hybrid RIS assisted systems and focus on the near-field (NF) regime. Different from their far-field counterparts, NF channels possess a block-sparsity property, which is leveraged in the two developed CE algorithms: (i) boundary estimation and sub-vector recovery (BESVR) and (ii) linear total variation r

Ella M. King, Mia C. Morrell, Jacqueline B. Sustiel, Matthew Gronert

Active matter taps into external energy sources to power its own processes. Systems of passive particles ordinarily lack this capacity, but can become active if the constituent particles interact with each other nonreciprocally. By reformulating the theory of classical wave-matter interactions, we demonstrate that interactions mediated by scattered waves gen

Callum Jones, Antonio Vidiella-Barranco, Jolly Xavier, Frank Vollmer

We present a theoretical investigation of a whispering gallery mode (WGM) resonator coupled to a Mach-Zehnder interferometer (MZI) and show a bimodal coincidence transmission spectrum when the input state is an indistinguishable photon pair. This is due to the doubled WGM phase shift experienced by the path-entangled state in the interferometer. Further, we

Xuanchen Zhao

In this paper, we study the Dirac cohomology of minimal representations for all real reductive groups G. The Dirac indices of these representations are also studied when G is of equal rank, giving some counterexamples of a conjecture of Huang proposed in 2015.

Stefano Nardulli, Reinaldo Resende

Given an area-minimizing integral $m$-current in $\Sigma$, we prove that the Hausdorff dimension of the interior singular set of $T$ cannot exceed $m-2$, provided that $\Sigma$ is an embedded $(m+\bar{n})$-submanifold of $\mathbb{R}^{m+n}$ of class $C^{2,\alpha}$, where $\alpha>0$. This result establishes the complete counterpart, in the arbitrary codimensio

Émile Deléage, Muhammed Ali Mehmood

We prove the non-linear stability of a class of travelling-wave solutions to the extended Aw-Rascle system with a singular offset function, which is formally equivalent to the compressible pressureless Navier-Stokes system with a singular viscosity. These solutions encode the effect of congestion by connecting a congested left state to an uncongested right s

Zhaolei Cui, Yuebao Wang

In this paper, we give a Breiman's theorem for conditional dependent random vector, where one component has a regularly-varying-tailed distribution with the index $\alpha\ge0$ and its slowly varying function satisfies a relaxed condition, while the other component is non-negative and its tail distribution is lighter than the former. This result substantially

Alexandros Tsakpinis, Alexander Pretschner

Industrial applications heavily rely on open-source software (OSS) libraries, which provide various benefits. But, they can also present a substantial risk if a vulnerability or attack arises and the community fails to promptly address the issue and release a fix due to inactivity. To be able to monitor the activities of such communities, a comprehensive lis

Kyrylo Bondarenko, Alexey Boyarsky, Anastasia Sokolenko, Ievgen Vovk

We use Faraday rotation measurements from the latest catalog LoTSS DR2 from LOFAR to probe intergalactic magnetic fields. To identify the extragalactic component of the observed rotation measure (RM) we use two different techniques: residual rotation measure (RRM) and close radio pairs. For the RRM approach, we conclude that, despite smaller measurement erro

Rémy Decoupes, Roberto Interdonato, Mathieu Roche, Maguelonne Teisseire

Language models now constitute essential tools for improving efficiency for many professional tasks such as writing, coding, or learning. For this reason, it is imperative to identify inherent biases. In the field of Natural Language Processing, five sources of bias are well-identified: data, annotation, representation, models, and research design. This stud

Zishu Yao, Guodong Fan, Jinfu Fan, Min Gan

Low-light remote sensing images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space. This continuity in scenes leads to extensive long-range correlations in spatial domains within remote sensing images. Convolutional Neural Networks, which rely on local correlations for long-distance modeling

Michael Aerni, Jie Zhang, Florian Tramèr

Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries. We identify severe pitfalls in existing empirical privacy evaluations (based on membership inference attacks) that result in misleading conclusions. In particular, we show that pr

Congyuan Duan, Jingyang Li, Dong Xia

Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that co

O. Petruk, T. Kuzyo

The momentum distribution of particles accelerated at strong non-relativistic shocks may be influenced by the spatial distribution of the flow speed around the shock. This phenomenon becomes evident in the cosmic-ray modified shock, where the particle spectrum itself determines the flow velocity profile upstream. However, what if the flow speed is not unifor

Chuhan Wang, Christopher M. Douglas, Yu Guan, Chunxiao Xu

We investigate self-excited axisymmetric oscillations of a lean premixed methane--air V-flame in a laminar annular jet. The flame is anchored near the rim of the centrebody, forming an inverted cone, while the strongest vorticity is concentrated along the outer shear layer of the annular jet. Consequently, the reaction and vorticity dynamics are largely sepa

W. J. Meijer, A. C. Kemmeren, J. M. van Bruggen, T. Haije

In this work, we support experts in the safety domain with safer dismantling of drug labs, by deploying robots for the initial inspection. Being able to act on the discovered environment is key to enabling this (semi-)autonomous inspection, e.g. to open doors or take a closer at suspicious items. Our approach addresses this with a novel environmental represe

Ruben Janssens, Eva Verhelst, Giulio Antonio Abbo, Qiaoqiao Ren

Automated Speech Recognition shows superhuman performance for adult English speech on a range of benchmarks, but disappoints when fed children's speech. This has long sat in the way of child-robot interaction. Recent evolutions in data-driven speech recognition, including the availability of Transformer architectures and unprecedented volumes of training dat

Guillem Cazassus

We provide constructions of equivariant Lagrangian Floer homology groups, by constructing and exploiting an $A_\infty$-module structure on the Floer complex.

Heng Li, Lorenzo Botto

Settling velocity statistics for dilute, non-Brownian suspensions of polydisperse spheres having a log-normal size distribution are analysed by Stokesian Dynamics, as a function of the total volume fraction and width of the size distribution. Several hundred instantaneous configurations are averaged to obtain reliable statistics. Average velocities for each

Htoo Wai Aung, Jiao Jiao Li, Yang An, Steven W. Su

Brain-Computer Interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph Neural Networks (GNNs) outperform Convolutional Neural Networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG_GLT-Net framework, fe

Lakmal Meegahapola, Hamza Hassoune, Daniel Gatica-Perez

Over the years, multimodal mobile sensing has been used extensively for inferences regarding health and well being, behavior, and context. However, a significant challenge hindering the widespread deployment of such models in real world scenarios is the issue of distribution shift. This is the phenomenon where the distribution of data in the training set dif

Thomas Marrinan, Madeleine Moeller, Alina Kanayinkal, Victor A. Mateevitsi

Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections

Ajit Jain, Andruid Kerne, Hannah Fowler, Jinsil Seo

We use the process and findings from a case study of design educators' practices of assessment and feedback to fuel theorizing about how to make AI useful in service of human experience. We build on Suchman's theory of situated actions. We perform a qualitative study of 11 educators in 5 fields, who teach design processes situated in project-based learning c