March 2024 arXiv papers — page 138

Alexandre de Oliveira Bezerra, Rodrigo Goncalves Mateus, Vanessa Ap. de Moraes Weber, Fabricio de Lima Weber

Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding. This paper presents the results of a correlation analysis between scores produced by humans for Nelore cattle and a variety of measurements that can be derived from images or other instruments. It also presents a study using t

David Cheikhi, Daniel Russo

Identifying the trade-offs between model-based and model-free methods is a central question in reinforcement learning. Value-based methods offer substantial computational advantages and are sometimes just as statistically efficient as model-based methods. However, focusing on the core problem of policy evaluation, we show information about the transition dyn

Simon Bohlen, Olena Kononenko, Jan-Patrick Schwinkendorf, Florian Grüner

The charge contained in an electron bunch is one of the most important parameters in accelerator physics. Several techniques to measure the electron bunch charge exist. However, many conventional charge diagnostics face serious drawbacks when applied to plasma accelerators. For example, integrating current transformers (ICTs or toroids) have been shown to be

Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi

Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these models to their low-bit counterparts without compromising the original accuracy remains a key challenge. In this paper, we propose an innovative PTQ algorithm termed COMQ, whi

Julien Marche

We give a closed formula for the volume of a two-bridge knot, more precisely for its Bloch invariant. We obtain this formula without triangulating the complement: instead, we derive it from the Hopf formula for the second homology of the fundamental group of the complement and a systematic use of Fox derivatives.

Gabriel Toshio Hirokawa Higa, Joyce Katiuccia Medeiros Ramos Carvalho, Paolo Brito Pascoalini Zanoni, Gisele Braziliano de Andrade

Brachycephaly, a conformation trait in some dog breeds, causes BOAS, a respiratory disorder that affects the health and welfare of the dogs with various symptoms. In this paper, a new annotated dataset composed of 190 images of bulldogs' nostrils is presented. Three degrees of stenosis are approximately equally represented in the dataset: mild, moderate and

Steve Paul, Nathan Maurer, Souma Chowdhury

Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods. These methods often assume a form that lends better explainability compared to an end-to-end (learnt) neural net

Jonas Pinheiro da Silva, Hermano Velten

We study scalar cosmological perturbations in $f(R, T)$ modified gravity theories being $T$ the trace of the energy-momentum tensor. We provide detailed equations for the matter energy density contrast. We solve then numerically to promote a comparison with available large scale structure (LSS) formation observational data on $f \sigma_8$ and also addressing

Raphael Trumpp, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada

The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. To address this, we introduce RaceMOP, a novel method for mapless online p

Keith Rush, Zachary Charles, Zachary Garrett, Sean Augenstein

We present DrJAX, a JAX-based library designed to support large-scale distributed and parallel machine learning algorithms that use MapReduce-style operations. DrJAX leverages JAX's sharding mechanisms to enable native targeting of TPUs and state-of-the-art JAX runtimes, including Pathways. DrJAX embeds building blocks for MapReduce computations as primitive

S. Laporta, U. D. Jentschura

We present a complete reevaluation of the irreducible two-loop vacuum-polarization correction to the photon propagator in quantum electrodynamics, i.e. with an electron-positron pair in the fermion propagators. The integration is carried out by reducing the integrations to a limited set of master integrals, which are calculated using integration-by-parts ide

Alec Reinhardt, Newsha Nikzad, Raven J. Hollis, Galia Jacobson

Diagnostic imaging has gained prominence as potential biomarkers for early detection and diagnosis in a diverse array of disorders including cancer. However, existing methods routinely face challenges arising from various factors such as image heterogeneity. We develop a novel imaging-based distributional data analysis (DDA) approach that incorporates the pr

Achira Boonrath, Feng Liu, Elenora M. Botta, Souma Chowdhury

Maneuverable tether-net systems launched from an unmanned spacecraft offer a promising solution for the active removal of large space debris. Guaranteeing the successful capture of such space debris is dependent on the ability to reliably maneuver the tether-net system -- a flexible, many-DoF (thus complex) system -- for a wide range of launch scenarios. Her

Hancong Pan, Xiaojing Zhu, Cantay Caliskan, Dino P. Christenson

In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation

Mümün Can, Levent Kargın, Mehmet Cenkci, Ayhan Dil

This study deals with certain harmonic zeta functions, one of them occurs in the study of the multiplication property of the harmonic Hurwitz zeta function. The values at the negative even integers are found and Laurent expansions at poles are described. Closed-form expressions are derived for the Stieltjes constants that occur in Laurent expansions in a nei

David Mal, Nina Döllinger, Erik Wolf, Stephan Wenninger

Virtual humans play a pivotal role in social virtual environments, shaping users' VR experiences. The diversity in available options and users' preferences can result in a heterogeneous mix of appearances among a group of virtual humans. The resulting variety in higher-order anthropomorphic and realistic cues introduces multiple (in)congruencies, eventually

Enrique Ruiz Arriola, Pablo Sanchez-Puertas

We employ a dispersion relation that allows to recover the phase of the electromagnetic form factor of the pion from its absolute value above threshold. Compared to alternative approaches building on the phase, this approach builds on experimental input directly accessible at colliders. Employing the precise datasets from the $e^+e^-\to\pi^+\pi^-$ reaction,

Jared Coleman, Bhaskar Krishnamachari

Scheduling a task graph representing an application over a heterogeneous network of computers is a fundamental problem in distributed computing. It is known to be not only NP-hard but also not polynomial-time approximable within a constant factor. As a result, many heuristic algorithms have been proposed over the past few decades. Yet it remains largely uncl

Yuming Chen, Vitali Vougalter

The article is devoted to the existence of solutions of a certain system of quadratic integral equations in H^1(R, R^N). We show the existence of a perturbed solution by using a fixed point technique in the Sobolev space on the real line.

Atharva Phatak, Vijay K. Mago, Ameeta Agrawal, Aravind Inbasekaran

The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability of large pretrained language models to generate text from causal graphs, where salient concepts are represented as nodes and causality is represented via directed, typed edges. The

Yutaro Nagae, Andreas P. Schnyder, Satoshi Ikegaya

We show theoretically that specular Andreev reflection occurs stably at altermagnet--superconductor interfaces, which is a phenomenon that has previously been predicted only in a limited range of materials, such as Dirac/Weyl materials with fine-tuned chemical potentials. Furthermore, the characteristic spin-split bands of the altermagnet lead to a distincti

Bastian Wittmann, Lukas Glandorf, Johannes C. Paetzold, Tamaz Amiranashvili

Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzheimer's, on the vascular network. However, to accurately segment blood vessels with state-of-the-art deep learning methods, a vast amount of voxel-level annotations is required. Sin

Jiasheng Liu, Rene Meyer, Zhuo-Yu Xian

We investigate the growth of operator size in the Lindbladian Sachdev-Ye-Kitaev model with $q$-body interaction terms and linear jump terms at finite dissipation strength. We compute the operator size as well as its distribution numerically at finite $q$ and analytically at large $q$. With dissipative (productive) jump terms, the size converges to a value sm

Lindsay P. Walter, Mathieu Francoeur

A generalized fluctuational electrodynamics-based many-body approach for calculating near-field radiative heat transfer (NFRHT) between nonspherical dipoles is proposed. The geometric parameters of nonspherical dipoles are implemented in the definition of the self-term of the free-space Green's function. Dipole polarizability is defined a posteriori from the

Nieves Crasto

Object detection, a pivotal task in computer vision, is frequently hindered by dataset imbalances, particularly the under-explored issue of foreground-foreground class imbalance. This lack of attention to foreground-foreground class imbalance becomes even more pronounced in the context of single-stage detectors. This study introduces a benchmarking framework

Jared Coleman, Ravi Vivek Agrawal, Ebrahim Hirani, Bhaskar Krishnamachari

Scheduling distributed applications modeled as directed, acyclic task graphs to run on heterogeneous compute networks is a fundamental (NP-Hard) problem in distributed computing for which many heuristic algorithms have been proposed over the past decades. Many of these algorithms fall under the list-scheduling paradigm, whereby the algorithm first computes p

Piotr Sierant, Maciej Lewenstein, Antonello Scardicchio, Lev Vidmar

Statistical mechanics provides a framework for describing the physics of large, complex many-body systems using only a few macroscopic parameters to determine the state of the system. For isolated quantum many-body systems, such a description is achieved via the eigenstate thermalization hypothesis (ETH), which links thermalization, ergodicity and quantum ch

Brivael Laloux, Antonis Georgakakis, David M. Alexander, Johannes Buchner

We adopt a Bayesian X-ray spectral approach to investigate the accretion properties of unobscured ($20<\log(N_{\rm H}/{\rm cm}^{-2}<22$) and obscured ($22< \log(N_{\rm H}/{\rm cm}^{-2}<24$) active galactic nuclei (AGN) to shed light on the orientation vs evolution scenarios for the origin of the obscuring material. For a sample of 3882 X-ray-selected AGN fro

Martin Beneke, Tobias Binder, Lorenzo De Ros, Mathias Garny

We scrutinize the Sommerfeld enhancement in dark matter pair annihilation for $p$-wave and higher-$\ell$ partial waves. For the Yukawa potential these feature a super-resonant Breit-Wigner peak in their velocity-dependence close to Sommerfeld resonances as well as a universal scaling with velocity for all $\ell\geq 1$ that differs from the $s$-wave case. We

Fabio Bacchini, Wenzhi Ruan, Rony Keppens

We present a study of energetic-electron trapping and acceleration in the Kelvin-Helmholtz-induced magnetohydrodynamic (MHD) turbulence of post-flare loops in the solar corona. Using the particle-tracing capabilities of MPI-AMRVAC 3.0, we evolve ensembles of test electrons (i.e. without feedback to the underlying MHD) inside the turbulent looptop, using the

Alessandro Candeloro, Zahra Pazhotan, Matteo G. A. Paris

We study the role of probe dimension in determining the bounds of precision and the level of incompatibility in multi-parameter quantum estimation problems. In particular, we focus on the paradigmatic case of unitary encoding generated by $\mathfrak{su}(2)$ and compare precision and incompatibility in the estimation of the same parameters across representati

Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O

Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians attention to the important slices. In this work, we train a ResNet-18 network to classify axial slices of lymph

Olivier Flasseur, Eric Thiébaut, Loïc Denis, Maud Langlois

We tackle covariance estimation in low-sample scenarios, employing a structured covariance matrix with shrinkage methods. These involve convexly combining a low-bias/high-variance empirical estimate with a biased regularization estimator, striking a bias-variance trade-off. Literature provides optimal settings of the regularization amount through risk minimi

Guido Roberts-Borsani, Tommaso Treu, Alice Shapley, Adriano Fontana

The exceptional spectra of the most luminous $z>10$ sources observed so far have challenged our understanding of early galaxy evolution, requiring a new observational benchmark for meaningful interpretation. As such, we construct spectroscopic templates representative of high-redshift, star-forming populations, using 482 confirmed sources at $z=5.0-12.9$ wit

Sudhir R. Ghorpade, Rakhi Pratihar, Tovohery H. Randrianarisoa, Hugues Verdure

The theory of shellable simplicial complexes brings together combinatorics, algebra, and topology in a remarkable way. Initially introduced by Alder for $q$-simplicial complexes, recent work of Ghorpade, Pratihar, and Randrianarisoa extends the study of shellability to $q$-matroid complexes and determines singular homology groups for a subclass of these $q$-

Jonathan Frey, Armin Nurkanovic, Moritz Diehl

The Real-Time Iteration (RTI) is an online nonlinear model predictive control algorithm that performs a single Sequential Quadratic Programming (SQP) per sampling time. The algorithm is split into a preparation and a feedback phase, where the latter one performs as little computations as possible solving a single prepared quadratic program. To further improv

Giulio Crognaletti, Giovanni Di Bartolomeo, Michele Vischi, Luciano Loris Viteritti

Level spectroscopy stands as a powerful method for identifying the transition point that delineates distinct quantum phases. Since each quantum phase exhibits a characteristic sequence of excited states, the crossing of energy levels between low-lying excited states offers a reliable mean to estimate the phase transition point. While approaches like the Vari

Kim Calabrese, David Doty

We study the model of continuous chemical reaction networks (CRNs), consisting of reactions such as $A+B \to C+D$ that can transform some continuous, nonnegative real-valued quantity (called a *concentration*) of chemical species $A$ and $B$ into equal concentrations of $C$ and $D$. Such a reaction can occur from any state in which both reactants $A$ and $B$

Aldo Conca

In 1965 Buchberger defined Gr\"obner bases and an algorithm to compute them. Despite a slow start, already in the eighties Gr\"obner bases had become the main device for symbolic computations involving polynomials as well as a theoretical tool for the investigation of ideals and varieties via the so-called Gr\"obner deformation techniques. Rings and algebrai

E. Tiberi, M. Borkowski, B. Iritani, R. Moszynski

Precision measurements with ultracold atoms and molecules are primed to probe beyond-the-Standard Model physics. Isotopologues of homonuclear molecules are a natural testbed for new Yukawa-type mass-dependent forces at nanometer scales, complementing existing mesoscopic-body and neutron scattering experiments. Here we propose using isotopic shift measurement

Karol Lesnik, Tomas Roskovec, Filip Soudsky

We prove a new type of pointwise estimate of the Kalamajska-Mazya-Shaposhnikova type, where sparse averaging operators replace the maximal operator. It allows us to extend the Gagliardo-Nirenberg interpolation inequality to all rearrangement invariant Banach function spaces without any assumptions on their upper Boyd index, i.e. omitting problems caused by u

Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader

Training neural networks with high certified accuracy against adversarial examples remains an open challenge despite significant efforts. While certification methods can effectively leverage tight convex relaxations for bound computation, in training, these methods, perhaps surprisingly, can perform worse than looser relaxations. Prior work hypothesized that

Xiang Meng, Wenyu Chen, Riade Benbaki, Rahul Mazumder

The increasing computational demands of modern neural networks present deployment challenges on resource-constrained devices. Network pruning offers a solution to reduce model size and computational cost while maintaining performance. However, most current pruning methods focus primarily on improving sparsity by reducing the number of nonzero parameters, oft

Dimitrios Ntounis, Emilio Alessandro Nanni, Caterina Vernieri

A high-energy electron-positron collider has been widely recognized by the particle physics community to be the next crucial step for detailed studies of the Higgs boson and other fundamental particles and processes. Several proposals for such colliders, either linear or circular, are currently under evaluation. Any such collider will be required to reach hi

Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy

Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent. In this work, we d

Jaume Albardaner, Alberto San Miguel, Néstor García, Magí Dalmau-Moreno

This paper explores policy-learning approaches in the context of sim-to-real transfer for robotic manipulation using a TIAGo mobile manipulator, focusing on two state-of-art simulators, Isaac Gym and Isaac Sim, both developed by Nvidia. Control architectures are discussed, with a particular emphasis on achieving collision-less movement in both simulation and

Andy Skumanich, Han Kyul Kim

Social media platforms hold valuable insights, yet extracting essential information can be challenging. Traditional top-down approaches often struggle to capture critical signals in rapidly changing events. As global events evolve swiftly, social media narratives, including instances of disinformation, become significant sources of insights. To address the n

Sasanka GRS, Ayushi Agrawal, Santosh Nannuru, Kavita Vemuri

Functional MRI (fMRI) research, employing naturalistic stimuli like movies, explores brain network interactions in complex cognitive processes such as empathy. The empathy network encompasses multiple brain areas, including the Insula, PFC, ACC, and parietal regions. Our novel processing pipeline applies graph learning methods to whole-brain timeseries signa

Yanming Liu, Xinyue Peng, Ningjing Sang, Yafeng Yan

Large language models(LLMs) have shown its outperforming ability on various tasks and question answering. However, LLMs require substantial memory storage on low-resource devices. More critically, the computational speed on these devices is also severely limited. In this paper, we propose SPA(Side Plugin Adaption), a lightweight architecture for fast on-devi

Mustafa Abbas Hussein Hussein, Serkan Savaş

This paper presents an exploration of Long Short-Term Memory (LSTM) networks in the realm of text generation, focusing on the utilization of historical datasets for Shakespeare and Nietzsche. LSTMs, known for their effectiveness in handling sequential data, are applied here to model complex language patterns and structures inherent in historical texts. The s

Keigo Fukumura, Missagh Mehdipour, Ehud Behar, Chris Shrader

X-ray obscuration of active galactic nuclei (AGNs) is considered in the context of ionized winds of stratified structure launched from accretion disks. We argue that a Compton-thick layer of a large-scale disk wind can obscure continuum X-rays and also lead to broad UV absorption such as in the blue wing of Civ; the former originates from the inner wind whil

Tomasz Kochanek, Marek Miarka

For Banach spaces with a shrinking FDD, we provide estimates for the radii of the enveloping balls of the $\varepsilon$-Szlenk derivations of the dual unit ball.

Nathan Herring, Shuyang Cao, Daniel Boyanovsky

We critically examine the applicability of the effective potential within dynamical situations and find, in short, that the answer is negative. An important caveat of the use of an effective potential in dynamical equations of motion is an explicit violation of energy conservation. An \emph{adiabatic} effective potential is introduced in a consistent quasi-s

Giovanni M. Strampelli, Massimo Robberto, Laurent Pueyo, Mario Gennaro

We have performed a comprehensive study of the Orion Nebula Cluster (ONC) combining the photometric data obtained by the two \textit{HST} Treasury programs that targeted this region. To consistently analyze the rich dataset obtained in a wide variety of filters, we adopted a Bayesian approach to fit the Spectral Energy Distribution of the sources, deriving m

Han Kyul Kim, Shriniwas Nayak, Aleyeh Roknaldin, Xiaoci Zhang

The growing popularity of generative AI, particularly ChatGPT, has sparked both enthusiasm and caution among practitioners and researchers in education. To effectively harness the full potential of ChatGPT in educational contexts, it is crucial to analyze its impact and suitability for different educational purposes. This paper takes an initial step in explo

Vladimir U. Nazarov, Tchavdar N. Todorov, E. K. U. Gross

We study the motion (translational, vibrational, and rotational) of a diatomic impurity immersed in an electron liquid and exposed to electronic current. An approach based on the linear response time-dependent density functional theory combined with the Ehrenfest dynamics leads to a system of linear algebraic equations, which account for the competing and co

Anlong Chua

Let $G$ be a connected reductive group over $\mathbb{C}$ with Weyl group $W$. Following a suggestion of Bezrukavnikov, we define a map from two-sided cells to conjugacy classes in $W$ using the geometry of the affine flag variety. This is an affine analog of the classical story of two-sided cells of $W$, special nilpotent orbits and special representations o

Giulia Fardelli, A. Liam Fitzpatrick, Wei Li

We use holography to study the large spin $J$ limit of the spectrum of low energy states with charge $Q$ under a $U(1)$ conserved current in CFTs in $d>2$ dimensions, with a focus on $d=3$ and $d=4$. For $Q=2$, the spectrum of such states is known to be universal and properly captured by the long-distance limit of holographic theories, regardless of whether

Fuseinin Mumuni, Alhassan Mumuni

We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-short learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their

Shrobona Banerje, Luca Mattarozzi, Nicolo Maccaferri, Sandro Cattarin

There is a high current interest for novel plasmonic platforms and materials able to extend their applicability into the ultraviolet (UV) region of the electromagnetic spectrum. In the UV it is possible to explore spectral properties of biomolecules with small cross section in the visible spectral range. However, most used metals in plasmonics have their res

Roberto Bigazzi, Lorenzo Baraldi, Shreyas Kousik, Rita Cucchiara

Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive focus on object labeling and exhaustive scene graph generation; less effort has been focused on the task of purely identifying and mapping large semantic regions. The present work p

Maryvonne Gerin, Harvey Liszt, Jerome Pety, Alexandre Faure

To provide constraints on the chemical processes responsible for the observed columns of organic species, we used NOEMA to observe the sight line toward NRAO150 in the 2mm spectral window. We targeted the low excitation lines of o-H2CO 2(1,1)-1(1,0) and p-H2CO 2(0,2)-1(0,1) as well as the nearby transitions of CS(3-2) and c-C3H2. We combined these data with

B. -H. Yue, P. N. Best, K. J. Duncan, G. Calistro-Rivera

Studies show that both radio jets from the active galactic nuclei (AGN) and the star formation (SF) activity in quasar host galaxies contribute to the quasar radio emission; yet their relative contributions across the population remain unclear. Here, we present an improved parametric model that allows us to statistically separate the SF and AGN components in

Elijah K. Blankenship, Conor K. Trygstad, Francisco M. F. R. Gonçalves, Néstor O. Pérez-Arancibia

This paper presents the VLEIBot^* (Very Little Eel-Inspired roBot), a 45-mg/23-mm^3 microrobotic swimmer that is propelled by a bioinspired anguilliform propulsor. The propulsor is excited by a single 6-mg high-work-density (HWD) microactuator and undulates periodically due to wave propagation phenomena generated by fluid-structure interaction (FSI) during s

Kurt Butler, Guanchao Feng, Petar M. Djuric

The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods of explanation have focused on the concept of feature attribution, a decomposition of the model's prediction into individual contributions corresponding to each input feature. In

Stefan Baur, Frank Moosmann, Andreas Geiger

3D object detection is one of the most important components in any Self-Driving stack, but current state-of-the-art (SOTA) lidar object detectors require costly & slow manual annotation of 3D bounding boxes to perform well. Recently, several methods emerged to generate pseudo ground truth without human supervision, however, all of these methods have various

Geoffrey Goodell, Hazem Danny Al-Nakib, Tomaso Aste

Nations around the world are conducting research into the design of central bank digital currency (CBDC), a new, digital form of money that would be issued by central banks alongside cash and central bank reserves. Retail CBDC would be used by individuals and businesses as form of money suitable for routine commerce. An important motivating factor in the dev

Alexandre Landry, Fayçal Hammad, Reza Saadati

The quantum Hall effect under the influence of gravity and inertia is studied in a unified way. We make use of an algebraic approach, as opposed to an analytic approach. We examine how both the integer and the fractional quantum Hall effects behave under a combined influence of gravity and inertia using a unified Hamiltonian. For that purpose, we first re-de

Otto Veltheim, Esko Keski-Vakkuri

Quantum tomography approaches typically consider a set of observables which we wish to measure, design a measurement scheme which measures each of the observables and then repeats the measurements as many times as necessary. We show that instead of considering only the simple set of observables, one should consider a multiset of the observables taking into a

Ameer Musa Imran Alhseeni, Hossein Bevrani

The Bell regression model (BRM) is a statistical model that is often used in the analysis of count data that exhibits overdispersion. In this study, we propose a Bayesian analysis of the BRM and offer a new perspective on its application. Specifically, we introduce a G-prior distribution for Bayesian inference in BRM, in addition to a flat-normal prior distr

Philip Harris, Michael Kagan, Jeffrey Krupa, Benedikt Maier

Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose RS3L ("Re-simulation-based self-supervised re

David Andriot, Fabian Ruehle

Finding string backgrounds with de Sitter spacetime, where all approximations and corrections are controlled, is an open problem. We revisit the search for de Sitter solutions in the classical regime for specific type IIB supergravity compactifications on group manifolds, an under-explored corner of the landscape that offers an interesting testing ground for

Pinaki Banerjee, Lorenz Eberhardt, Sebastian Mizera

We study the high-energy limit of $2 \to 2$ one-loop string amplitudes at fixed momentum transfer. For the closed string, the high-energy behaviour of the amplitudes can be determined from Regge theory just like in field theory, as was first discussed by Amati, Ciafaloni and Veneziano. However, field theory intuition partially breaks down for the open-string

Justin D. Finke, Parshad Patel

We compute in detail the absorption optical depth for astrophysical $\gamma$-ray photons interacting with solar photons to produce electron positron pairs. This effect is greatest for $\gamma$-ray sources at small angular distances from the Sun, reaching optical depths as high as $\tau_{\gamma\gamma}\sim 10^{-2}$. We also calculate this effect including modi

Rachel Gledhill, Victoria Strait, Guillaume Desprez, Gregor Rihtaršič

We report an updated mass and magnification model of galaxy cluster Abell 370 using new NIRCam and NIRISS data from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). Using Lenstool and a combination of archival HST and MUSE data with new JWST data as constraints, we derive an improved gravitational lensing model and extract magnifications of background g

Elizabeth R. Bennewitz, Brayden Ware, Alexander Schuckert, Alessio Lerose

Studying high-energy collisions of composite particles, such as hadrons and nuclei, is an outstanding goal for quantum simulators. However, preparation of hadronic wave packets has posed a significant challenge, due to the complexity of hadrons and the precise structure of wave packets. This has limited demonstrations of hadron scattering on quantum simulato

Iván Muñoz Rodríguez, Antonis Georgakakis, Francesco Shankar, Ángel Ruiz

Environmental effects are believed to play an important yet poorly understood role in triggering accretion events onto the supermassive black holes (SMBHs) of galaxies (Active Galactic Nuclei; AGN). Massive clusters, which represent the densest structures in the Universe, provide an excellent laboratory to isolate environmental effects and study their impact

Joseph Bowles, Shahnawaz Ahmed, Maria Schuld

Benchmarking models via classical simulations is one of the main ways to judge ideas in quantum machine learning before noise-free hardware is available. However, the huge impact of the experimental design on the results, the small scales within reach today, as well as narratives influenced by the commercialisation of quantum technologies make it difficult t

Kaho Morii, Patricio Sanhueza, Qizhou Zhang, Fumitaka Nakamura

Fragmentation during the early stages of high-mass star formation is crucial for understanding the formation of high-mass clusters. We investigated fragmentation within thirty-nine high-mass star-forming clumps as part of the Atacama Large Millimeter/submillimeter Array (ALMA) Survey of 70 $\mu$m Dark High-mass Clumps in Early Stages (ASHES). Considering pro

Paola Pinilla, Myriam Benisty, Rens Waters, Jaehan Bae

The K7 T Tauri star PDS 70 remains the best laboratory for investigating the influence of giant planet formation on the structure of the parental disk. One of the most intriguing discoveries is the detection of a resolved inner disk from ALMA observations that extends up to the orbit of PDS 70b. It is challenging to explain this inner disk because most of th

ChunJun Cao, Gong Cheng, Alioscia Hamma, Lorenzo Leone

We study the interplay between magic and entanglement in quantum many-body systems. We show that non-local magic, which is supported by the quantum correlations is lower bounded by the non-flatness of entanglement spectrum and upper bounded by the amount of entanglement in the system. We then argue that a smoothed version of non-local magic bounds the hardne

Rhonald Burgos Atencia, Amit Agarwal, Dimitrie Culcer

The investigation of orbital angular momentum (OAM) of delocalised Bloch electrons has advanced our understanding of magnetic, transport, and optical phenomena in crystals, drawing widespread interest across various materials science domains, from metals and semiconductors to topological and magnetic materials. Here, we review OAM dynamics in depth, focusing

Chao-Ming Jian, Meng Cheng, Cenke Xu

We propose a "minimal" fractional topological insulator (mFTI), motivated by the recent experimental report on the signatures of FTI at total filling factor $\nu_{\rm tot} = 3$ in a transition metal dichalcogenide moir\'{e} system. The observed FTI at $\nu_{\rm tot} = 3$ is likely given by a topological state living in a pair of half-filled conjugate Chern b

Riccardo Catena, Einar Urdshals

We train a deep neural network (DNN) to output rates of dark matter (DM) induced electron excitations in silicon and germanium detectors. Our DNN provides a massive speedup of around $5$ orders of magnitude relative to existing methods (i.e. QEdark-EFT), allowing for extensive parameter scans in the event of an observed DM signal. The network is also lighter

Nishchhal Verma, Raquel Queiroz

We present the time-dependent Quantum Geometric Tensor (tQGT) as a comprehensive tool for capturing the geometric character of insulators observable within linear response. We show that tQGT describes the zero-point motion of bound electrons and acts as a generating function for generalized sum rules of electronic conductivity. It therefore enables a systema

Lorenz Eberhardt, Sebastian Mizera

We engineer compact contours on the moduli spaces of genus-zero Riemann surfaces that achieve analytic continuation from Euclidean to Lorentzian worldsheets. These generalized Pochhammer contours are based on the combinatorics of associahedra and make the analytic properties of tree-level amplitudes entirely manifest for any number and type of external strin

Bingchu Fan, Zhong-Zhi Xianyu

The correlators of large-scale fluctuations belong to the most important observables in modern cosmology. Recently, there have been considerable efforts in analytically understanding the cosmological correlators and the related wavefunction coefficients, which we collectively call cosmological amplitudes. In this work, we provide a set of simple rules to dir

Andreas Blommaert, Chang-Han Chen, Yasunori Nomura

We consider a version of the typical state firewall setup recently reintroduced by Stanford and Yang, who found that wormholes may create firewalls. We examine a late-time double scaling limit in JT gravity in which one can resum the expansion in the number of wormholes, and we use this to study the exact distribution of interior slices at times exponential

Ben Margalit, Eliot Quataert

Synchrotron emission is ubiquitous in explosive astrophysical events -- it is a natural byproduct of shocks formed when matter expelled by the explosion collides with ambient material. This emission is well-observed in various classes of transients, and is often interpreted within a canonical `equipartition' framework that allows physical properties of the s

Gilad Sadeh, Noya Linder, Eli Waxman

Binary neutron star mergers are expected to produce fast dynamical ejecta, with mildly relativistic velocities extending to $\beta=v/c>0.6$. In a preceding paper, we derived an analytic description of the time-dependent radio to X-ray synchrotron flux produced by collisionless shocks driven by such fast ejecta into the interstellar medium, for spherical ejec

Peter Espenshade, Jaiyul Yoo

The Lagrangian perturbation theory provides a simple yet powerful way of computing the nonlinear matter power spectrum, and it has been applied to biased tracers such as halos and galaxies. The number conservation of matter particles allows a simple relation between the fluctuations at the initial and the late times, which is essential in deriving the exact

Juan A. Valiente Kroon, Lidia J. Gomes Da Silva

We explicitly construct the analogue of the \v{d}Alembert solution to the 1+1 wave equation in an hyperboloidal setting. This hyperboloidal \v{d}Alembert solution is used, in turn, to gain intuition into the behaviour of solutions to the wave equation in a hyperboloidal foliation and to explain some apparently anomalous behaviour observed in numerically cons

Alec S. Hirschauer, Nicolas Crouzet, Nolan Habel, Laura Lenkić

We present a JWST imaging survey of I Zw 18, the archetypal extremely metal-poor, star-forming (SF), blue compact dwarf galaxy. With an oxygen abundance of only $\sim$3% $Z_{\odot}$, it is among the lowest-metallicity systems known in the local Universe, and is, therefore, an excellent accessible analog for the galactic building blocks which existed at early

Janosz W. Dewberry

Tidal torques can alter the spins of tidally interacting stars and planets, usually over shorter timescales than the tidal damping of orbital separations or eccentricities. Simple tidal models predict that, in eccentric binary or planetary systems, rotation periods will evolve toward a "pseudosynchronous" ratio with the orbital period. However, this predicti

Wele Gedara Chaminda Bandara, Vishal M. Patel

In this paper, we introduce Attention Prompt Tuning (APT) - a computationally efficient variant of prompt tuning for video-based applications such as action recognition. Prompt tuning approaches involve injecting a set of learnable prompts along with data tokens during fine-tuning while keeping the backbone frozen. This approach greatly reduces the number of

Kunchang Li, Xinhao Li, Yi Wang, Yinan He

Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMamba overcomes the limitations of existing 3D convolution neural networks and video transformers. Its linear-complexity operator enables efficient long-term modeling, which is cruci

Xuan Ju, Xian Liu, Xintao Wang, Yuxuan Bian

Image inpainting, the process of restoring corrupted images, has seen significant advancements with the advent of diffusion models (DMs). Despite these advancements, current DM adaptations for inpainting, which involve modifications to the sampling strategy or the development of inpainting-specific DMs, frequently suffer from semantic inconsistencies and red

Roger E. Behrend

For positive integers $m$ and $n$, the partial permutohedron $\mathcal{P}(m,n)$ is a certain integral polytope in $\mathbb{R}^m$, which can be defined as the convex hull of the vectors from $\{0,1,\ldots,n\}^m$ whose nonzero entries are distinct. For $n=m-1$, $\mathcal{P}(m,m-1)$ is (after translation by $(1,\ldots,1)$) the polytope $P_m$ of parking function

Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao

In this paper, we propose a new framework for online 3D scene perception. Conventional 3D scene perception methods are offline, i.e., take an already reconstructed 3D scene geometry as input, which is not applicable in robotic applications where the input data is streaming RGB-D videos rather than a complete 3D scene reconstructed from pre-collected RGB-D vi

Haiyang Xu, Yu Lei, Zeyuan Chen, Xiang Zhang

We present Bayesian Diffusion Models (BDM), a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We show the effectiveness of BDM on the 3D shape reconstruction task. Compared to prototypical deep learning data-driven ap