November 2024 arXiv papers — page 179

Ilijas Farah, Gábor Szabó

Let $\mathcal D$ be a strongly self-absorbing $\mathrm{C}^*$-algebra. Given any separable $\mathrm{C}^*$-algebra $A$, our two main results assert the following. If $A$ is $\mathcal D$-stable, then the corona algebra of $A$ is $\mathcal D$-saturated, i.e., $\mathcal D$ embeds unitally into the relative commutant of every separable $\mathrm{C}^*$-subalgebra. C

Abhishek Baruah, Prabwal Phukon

We study the Restricted Phase Space Thermodynamics (RPST) for the AdS dyonic black hole carrying the central charge $C$ and the chemical potential $\mu$, neglecting the pressure and conjugate volume along with comparison of different entropy models namely the Bekenstein-Hawking and the R\'enyi entropy model. Inclusion of the magnetic charge $\tilde{Q}_m$ giv

Wen-Ding Li, Keya Hu, Carter Larsen, Yuqing Wu

When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC by training neural models for induction (inferring latent functions) and transduction (directly predicting the

Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb, Ran Gilad-Bachrach

Graph Neural Networks have inherent representational limitations due to their message-passing structure. Recent work has suggested that these limitations can be overcome by using unique node identifiers (UIDs). Here we argue that despite the advantages of UIDs, one of their disadvantages is that they lose the desirable property of permutation-equivariance. W

Ioanna Psaradaki, Missagh Mehdipour, Daniele Rogantini, Elisa Costantini

Cosmic dust plays a crucial role in the evolution of galaxies, significantly influencing star formation and the interstellar medium. However, in active galactic nuclei (AGN), the role and origin of dust remain poorly understood. High-resolution X-ray spectroscopy is a powerful tool for probing the properties of dust in AGN. NGC 6860, an X-ray bright type-1 q

Mithilesh Kumar

Quantum states can be written in infinitely many ways depending on the choices of basis. Schmidt decomposition of a quantum state has a lot of properties useful in the study of entanglement. All bipartite states admit Schmidt decomposition, but this does not extend to multipartite systems. We obtain necessary and sufficient conditions for the existence of Sc

Karuppasamy Pandian Soundarapandian, Sebastián Castilla, Stefan M. Koepfli, Simone Marconi

In recent years, the telecommunications field has experienced an unparalleled proliferation of wireless data traffic. Innovative solutions are imperative to circumvent the inherent limitations of the current technology, in particular in terms of capacity. Carrier frequencies in the sub-terahertz (sub-THz) range (~0.2-0.3 THz) can deliver increased capacity a

Subhajit Sahu

Community detection in graphs identifies groups of nodes with denser connections within the groups than between them, and while existing studies often focus on optimizing detection performance, memory constraints become critical when processing large graphs on shared-memory systems. We recently proposed efficient implementations of the Louvain, Leiden, and L

Anat Bremler Barr, Ofek Lavi, Yaniv Naor, Sanjeev Rampal

Service Mesh has become essential for modern cloud-native applications by abstracting communication between microservices and providing zero-trust security, observability, and advanced traffic control without requiring code changes. This allows developers to leverage new network capabilities and focus on application logic without managing network complexitie

Thorgal Hinault, Tony Yue Yu, Chi Zhang, Shaowu Zhang

F-bundle is a formal/non-archimedean version of variation of nc-Hodge structures which plays a crucial role in the theory of atoms as birational invariants from Gromov-Witten theory. In this paper, we establish the spectral decomposition theorem for F-bundles according to the generalized eigenspaces of the Euler vector field action. The proof relies on solvi

Xingwu Sun, Yanfeng Chen, Yiqing Huang, Ruobing Xie

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including langu

Macarena Lagos, Tomás Andrade, Jordi Rafecas-Ventosa, Lam Hui

The future detection of quasi-normal modes (QNMs) from black hole ringdown will allow for consistency and independent tests of general relativity (GR) in the strong-field regime. In this paper, we perform a ringdown Fisher forecast when including the dominant quadratic QNM (QQNM) expected in nearly equal-mass quasi-circular binary black holes (BBHs) observed

Advait Sarkar

Let's transform our robot secretaries into Socratic gadflies.

Mobina Zibandehpoor, Mehdi Delrobaei

Spatial navigation ability is essential for daily functioning, and the Wayfinding Questionnaire (WQ) is a validated self-report tool assessing this ability through 22 items across three subscales: Navigation and Orientation (11 items), Distance Estimation (3 items), and Spatial Anxiety (8 items). This study introduces the Persian translation of the WQ, adapt

Lia Kley, Nicolas Heimann, Aslam Parvej, Lukas Broers

Quantum computing in atom tweezers requires high-fidelity implementations of quantum operations. Here, we demonstrate the optimal implementation of the transition $|0\rangle \rightarrow |1\rangle$ of two levels, serving as a qubit, of an atom in a tweezer potential, driven by a single-photon Rabi pulse. The Rabi pulse generates a photon recoil of the atom, d

Mario Ballardini, Nicola Barbieri

Primordial oscillatory features in the power spectrum of curvature perturbations are sensitive probes of the dynamics of the early Universe and can provide insights beyond the standard inflationary scenario. While these features have been the focus of extensive studies using cosmic microwave background anisotropy data, large-scale structure surveys now provi

Martin Rakovsky

We consider a family of critical points of the Ambrosio-Tortorelli energy with an obstacle condition on the phase field variable. This problem can be interpreted as a time discretization of a quasistatic evolution problem where the obstacle at step $n$ is defined as the solution at step $n-1$. The obstacle condition now reads as an irreversibility condition

Marcello Bullo, Seifallah Jardak, Pietro Carnelli, Deniz Gündüz

The growing demand for intelligent applications beyond the network edge, coupled with the need for sustainable operation, are driving the seamless integration of deep learning (DL) algorithms into energy-limited, and even energy-harvesting end-devices. However, the stochastic nature of ambient energy sources often results in insufficient harvesting rates, fa

Paraskevas Pegios, Aasa Feragen, Andreas Abildtrup Hansen, Georgios Arvanitidis

The adoption of increasingly complex deep models has fueled an urgent need for insight into how these models make predictions. Counterfactual explanations form a powerful tool for providing actionable explanations to practitioners. Previously, counterfactual explanation methods have been designed by traversing the latent space of generative models. Yet, thes

Fedor K. Popov

We analyze the real-time dynamics of the large $N$ vector model, focusing on heavy states with energies of the order $N$. In this regime, we demonstrate that interactions become sufficiently strong to produce non-zero condensate of the Hubbard-Stratonovich field $\sigma$, which, in turn, induces particle production. This process leads to a significant transf

William Freitas, B. Abreu, S. A. Vitiello

Simulating strongly correlated fermionic systems remains a fundamental challenge in quantum physics, largely due to the sign problem in quantum Monte Carlo (QMC) methods. We present a neural network-based variational Monte Carlo (NN-VMC) approach, leveraging a flexible neural network ansatz to represent the many-body wavefunction. Focusing on quantum dots wi

Alexandros Haliassos, Rodrigo Mira, Honglie Chen, Zoe Landgraf

Research in auditory, visual, and audiovisual speech recognition (ASR, VSR, and AVSR, respectively) has traditionally been conducted independently. Even recent self-supervised studies addressing two or all three tasks simultaneously tend to yield separate models, leading to disjoint inference pipelines with increased memory requirements and redundancies. Thi

Jun Cui

This study investigates the impact of integrating DevSecOps and Generative Artificial Intelligence (GAI) on software delivery performance within technology firms. Utilizing a qualitative research methodology, the research involved semi-structured interviews with industry practitioners and analysis of case studies from organizations that have successfully imp

Daniel Gibney, Jan-Niklas Boyn, David A. Mazziotti

A critical challenge for density functional theory (DFT) in practice is its limited ability to treat static electron correlation, leading to errors in its prediction of charges, multiradicals, and reaction barriers. Recently, we combined one- and two-electron reduced density matrix theories with DFT to obtain a universal $O(N^3)$ generalization of DFT for st

Oleksii Molodchyk, Johannes Teutsch, Timm Faulwasser

Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, the performance of BO crucially relies on tight probabilistic error bounds related to the uncertainty surrounding the surrogate model. For the case of Gaussian Process surrogates an

Asad Hussain, Maximiliano Isi, Aaron Zimmerman

The complex astrophysical processes leading to the formation of binary black holes and their eventual merger are imprinted on the spins of the individual black holes. We revisit the astrophysical distribution of those spins based on gravitational waves from the third gravitational wave transient catalog GWTC-3, (Abbott et al. 2023a), looking for structure in

Igor Minevich, Gabe Cunningham, Aditya Karan, Joshua V. Gyllinsky

The Parks Puzzle is a paper-and-pencil puzzle game that is classically played on a square grid with different colored regions (the parks). The player needs to place a certain number of "trees" in each row, column, and park such that none are adjacent, even diagonally. We define a doubly-infinite family of such puzzles, the $(c, r)$-tree Parks puzzles, where

David Rodríguez Rueda, Carlos Cotta, Antonio J. Fernández-Leiva

The balanced incomplete block design (BIBD) problem is a difficult combinatorial problem with a large number of symmetries, which add complexity to its resolution. In this paper, we propose a dual (integer) problem representation that serves as an alternative to the classical binary formulation of the problem. We attack this problem incrementally: firstly, w

Tsung-Ching Yang, Tetsuya Hashimoto, Tzu-Yin Hsu, Tomotsugu Goto

Measuring the Hubble constant (H$_0$) is one of the most important missions in astronomy. Nevertheless, recent studies exhibit differences between the employed methods. Fast radio bursts (FRBs) are coherent radio transients with large dispersion measures (DM) with a duration of milliseconds. DM$_{\rm IGM}$, DM in the intergalactic medium (IGM), could open a

Tianzhixi Yin, Syed Ahsan Raza Naqvi, Sai Pushpak Nandanoori, Soumya Kundu

This paper explores the detection and localization of cyber-attacks on time-series measurements data in power systems, focusing on comparing conventional machine learning (ML) like k-means, deep learning method like autoencoder, and graph neural network (GNN)-based techniques. We assess the detection accuracy of these approaches and their potential to pinpoi

A. Bayo, V. Mesa, G. Damke, M. Cerda

The worlds of Data Science (including big and/or federated data, machine learning, etc) and Astrophysics started merging almost two decades ago. For instance, around 2005, international initiatives such as the Virtual Observatory framework rose to standardize the way we publish and transfer data, enabling new tools such as VOSA (SED Virtual Observatory Analy

Felix Selz, Johanna Kölbel, Felix Paries, Georg von Freymann

Measuring terahertz waveforms in terahertz spectroscopy often relies on electrooptic sampling employing a ZnTe crystal. Although the nonlinearities in such zincblende semiconductors induced by intense terahertz pulses have been studied at optical frequencies, a quantitative study of nonlinearities in the terahertz regime has not been reported. In this work,

Neil R. Sheeley

This paper describes a new way of determining the high-latitude solar rotation rate statistically from simultaneous observations of many polar faculae. In this experiment, I extracted frames from a movie made previously from flat-fielded images obtained in the 6767 A continuum during February 1997-1998 and used those frames to construct space-time maps from

Zhaoyang Chen, Lvzhou Li, Jingquan Luo

Junta testing for Boolean functions has sparked a long line of work over recent decades in theoretical computer science, and recently has also been studied for unitary operators in quantum computing. Tolerant junta testing is more general and challenging than the standard version. While optimal tolerant junta testers have been obtained for Boolean functions,

Federico Marchesin, Matěj Hejda, Tzamn Melendez Carmona, Stefano Di Carlo

Matrix-vector multiplications (MVMs) are essential for a wide range of applications, particularly in modern machine learning and quantum computing. In photonics, there is growing interest in developing architectures capable of performing linear operations with high speed, low latency, and minimal loss. Traditional interferometric photonic architectures, such

Sadaf Syed, Donia Ben Amor, Michael Joham, Wolfgang Utschick

In this work, we propose a low-cost rate splitting (RS) technique for a multi-user multiple-input single-output (MISO) system operating in frequency division duplex (FDD) mode. The proposed iterative optimisation algorithm only depends on the second-order statistical channel knowledge and the pilot training matrix. Additionally, it offers a closed-form solut

Piotr Kapuscinski, Artur O. Slobodeniuk, Alex Delhomme, Clément Faugeras

Semiconducting transition metal dichalcogenides of group VI are well-known for their prominent excitonic effects and the transition from an indirect to a direct band gap when reduced to monolayers. While considerable efforts have elucidated the Rydberg series of excitons in monolayers, understanding their properties in multilayers remains incomplete. In thes

Soumyajyoti Biswas, Parongama Sen, Bhargav Thota, Hemanth Kodali

The strongly polarizing political discourse in the U. S. implies that a small minority of the population, determining the outcome of the presidential elections in a few so called battleground-states, also determines the outcome of the overall election. Given the almost equal distributions of the electoral college members in the so-called blue and red states,

Ulysse Gazin, Ruth Heller, Etienne Roquain, Aldo Solari

In a split conformal framework with $K$ classes, a calibration sample of $n$ labeled examples is observed for inference on the label of a new unlabeled example. We explore the setting where a `batch' of $m$ independent such unlabeled examples is given, and the goal is to construct a batch prediction set with 1-$\alpha$ coverage. Unlike individual prediction

A. W. Shaw, D. L. Kaplan, P. Gandhi, T. J. Maccarone

JWST, despite not being designed to observe astrophysical phenomena that vary on rapid time scales, can be an unparalleled tool for such studies. If timing systematics can be controlled, JWST will be able to open up the sub-second infrared timescale regime. Rapid time-domain studies, such as lag measurements in accreting compact objects and Solar System stel

Kacper Cybiński, James Enouen, Antoine Georges, Anna Dawid

Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating their properties. Novel physics benefits most from insights about phases, traditionally extracted in spin systems using spin correlators. Here, we combine two approaches and design

Artem Sokolov, Swapnil Bhosale, Xiatian Zhu

Recognizing the sounding objects in scenes is a longstanding objective in embodied AI, with diverse applications in robotics and AR/VR/MR. To that end, Audio-Visual Segmentation (AVS), taking as condition an audio signal to identify the masks of the target sounding objects in an input image with synchronous camera and microphone sensors, has been recently ad

Zhengfang Fan, Yumeng Liu, Yizuo Wang, Shuwen Guo

Self-assembled molecular monolayer doping remains as a research focus for its nature of being conformal, nondestructive, and self-limiting. Herein, we demonstrate a sulfur monolayer doping in GaAs, facilitated by (NH4)2Sx solution. The Van der Pauw technique, secondary-ion mass spectroscopy, and low-temperature Hall effect measurements show that the sulfur d

Alexander Esterov, Arina Voorhaar

Many (if not most) of convex polytopes, important for combinatorial and algebraic geometry, are closely related to secondary polytopes of point configurations, or base polytopes of submodular functions, or their numerous variations and generalizations. The aim of this text is to introduce the class of basecondary polytopes. This class includes (and allows to

Jae Goode, Franz Herzog, Sam Teale

We present OPITeR, a FORM program for the reduction of multi-loop tensor Feynman integrals. The program can handle tensors, including spinor indices, with rank of up to 20 and can deal with up to 8 independent external momenta. The reduction occurs in $D$ dimensions compatible with conventional dimensional regularization. The program is able to manifest symm

Yan Luo, Sid Maibach

We study a generalization of the Schramm-Loewner evolution loop measure to pairs of non-intersecting Jordan curves on the Riemann sphere. We also introduce four equivalent definitions for a two-loop Loewner potential: respectively expressing it in terms of normalized Brownian loop measure, zeta-regularized determinants of the Laplacian, an integral formula g

Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich, Sandrine Katsahian

In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we introduce novel bounds and derive confidence intervals for the Average Potential Outcome (APO) - a standard metric for e

Aiman Munir, Ayan Dutta, Ramviyas Parasuraman

We propose a distributed control law for a heterogeneous multi-robot coverage problem, where the robots could have different energy characteristics, such as capacity and depletion rates, due to their varying sizes, speeds, capabilities, and payloads. Existing energy-aware coverage control laws consider capacity differences but assume the battery depletion ra

Ruihong Yin, Vladimir Yugay, Yue Li, Sezer Karaoglu

The field of novel view synthesis from images has seen rapid advancements with the introduction of Neural Radiance Fields (NeRF) and more recently with 3D Gaussian Splatting. Gaussian Splatting became widely adopted due to its efficiency and ability to render novel views accurately. While Gaussian Splatting performs well when a sufficient amount of training

Jing Yang, Ahmed Abdelkawy, Mira Todorova, Jörg Neugebauer

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical phase evaluation approach (SPEA). The SPEA model assumes a Boltzmann distribution of finite size phase fractions and calculates their statistical average. To benchmark the performance of the model, we apply it to construct binary surface ph

Jiancheng Tu, Wu Zhibin, Yueyuan Li, Chuankai Xiang

Deriving a priority vector from a pairwise comparison matrix (PCM) is a crucial step in the Analytical Hierarchy Process (AHP). Although there exists a priority vector that satisfies the conditions of order preservation (COP), the priority vectors obtained through existing prioritization methods frequently violate these conditions, resulting in numerous COP

Alex Bergman

Let $f$ be an entire function of finite exponential type less than or equal to $\sigma$ which is bounded by $1$ on the real axis and satisfies $f(0) = 1$. Under these assumptions H\"ormander showed that $f$ cannot decay faster than $\cos(\sigma x)$ on the interval $(-\pi/\sigma,\pi/\sigma)$. We extend this result to the setting of de Branges spaces with cosi

Haitham Kanj, Seonho Kim, Kiryung Lee

This paper presents Sparse Gradient Descent as a solution for variable selection in convex piecewise linear regression, where the model is given as the maximum of $k$-affine functions $ x \mapsto \max_{j \in [k]} \langle a_j^\star, x \rangle + b_j^\star$ for $j = 1,\dots,k$. Here, $\{ a_j^\star\}_{j=1}^k$ and $\{b_j^\star\}_{j=1}^k$ denote the ground-truth w

Christoforos Brozos, Jan G. Rittig, Elie Akanny, Sandip Bhattacharya

Surfactants are key ingredients in foaming and cleansing products across various industries such as personal and home care, industrial cleaning, and more, with the critical micelle concentration (CMC) being of major interest. Predictive models for CMC of pure surfactants have been developed based on recent ML methods, however, in practice surfactant mixtures

Philip Lippmann, Matthijs T. J. Spaan, Jie Yang

Intelligent agents designed for interactive environments face significant challenges in text-based games, a domain that demands complex reasoning and adaptability. While agents based on large language models (LLMs) using self-reflection have shown promise, they struggle when initially successful and exhibit reduced effectiveness when using smaller LLMs. We i

Peter Huxford, Nick Salter

An equicritical stratum is the locus of univariate monic squarefree complex polynomials where the critical points have prescribed multiplicities. Tracking the positions of both roots and critical points, there is a natural ``monodromy map'' taking the fundamental group into a braid group. We show here that when there are exactly two critical points, this mon

Xiaohan Wang, Yunzhe Zhou, Giles Hooker

Variable importance is one of the most widely used measures for interpreting machine learning with significant interest from both statistics and machine learning communities. Recently, increasing attention has been directed toward uncertainty quantification in these metrics. Current approaches largely rely on one-step procedures, which, while asymptotically

Ryoma Yataka, Pu Perry Wang, Petros Boufounos, Ryuhei Takahashi

Conventional radar feature extraction faces limitations due to low spatial resolution, noise, multipath reflection, the presence of ghost targets, and motion blur. Such limitations can be exacerbated by nonlinear object motion, particularly from an ego-centric viewpoint. It becomes evident that to address these challenges, the key lies in exploiting temporal

Gareth A. Jones

We obtain formulae for the numbers of isomorphism and conjugacy classes of non-identity proper subgroups of the groups $G={\rm PSL}_2(p)$, $p$ prime, and for the numbers of those conjugacy classes which do or do not consist of self-normalising subgroups. The formulae are used to prove lower bounds $17$, $18$, $6$ and $12$ respectively satisfied by these inva

Angel S. Miramontes, K. Raya, A. Bashir, P. Roig

We investigate the properties of the radially excited charged pion, with a specific focus on its electromagnetic form factor (EFF) and its box contribution to the hadronic light-by-light (HLbL) component of the muon's anomalous magnetic moment, $a_{\mu}$. Utilizing a coupled non-perturbative framework combining Schwinger-Dyson and Bethe-Salpeter equations, w

Alessandro Mastrototaro, Mathias Müller, Jimmy Olsson

General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. SSMs, comprising latent Markovian states, can be subjected to variational inference (VI), but standard VI methods like the importance-weighted autoencoder (IWAE) lack functionality for streaming

C. R. García, Diego F. Torres, Jia-Ming Zhu-Ge, Bing Zhang

Fast radio bursts (FRBs) represent one of the most intriguing phenomena in modern astrophysics. However, their classification into repeaters and non-repeaters is challenging. Here, we present the application of the graph theory Minimum Spanning Tree (MST) methodology as an unsupervised classifier of repeaters and non-repeaters FRBs. By constructing MSTs base

Kaspar Schmerling, Hajrudin Bešić, Andreas Kugi, Silvan Schmid

External disturbances exciting a mechanical resonator can be exploited to gain information on the environment. Many of these interactions manifest as momentum kicks, such as the recoil of residual gas, radioactive decay, or even hypothetical interactions with dark matter. These disturbances are often rare enough that they can be resolved as singular events r

Younghyo Park, Jagdeep Singh Bhatia, Lars Ankile, Pulkit Agrawal

The quest to build a generalist robotic system is impeded by the scarcity of diverse and high-quality data. While real-world data collection effort exist, requirements for robot hardware, physical environment setups, and frequent resets significantly impede the scalability needed for modern learning frameworks. We introduce DART, a teleoperation platform des

Hugo C. Botós, Felipe A. Franco

In $\mathrm{PU}(2,1)$, the group of holomorphic isometries of the complex hyperbolic plane, we study the space of involutions $R_1, R_2, R_3, R_4, R_5$ satisfying $R_5R_4R_3R_2R_1=1$, where $R_1$ is a reflection in a complex geodesic and the other $R_i$'s are reflections in points of the complex hyperbolic plane. We show that this space modulo $\mathrm{PU}(2

G. Baccolo, A. Barresi, D. Chiesa, M. Nastasi

In the framework of physics experiments searching for rare events, the selection of extremely radiopure materials is a challenging task, as the signal of interest is often hidden by instrumental background. Neutron activation is a powerful technique to measure trace contaminants with high sensitivity but, to be properly applied, it requires a good characteri

Eric Pilling, Martin Bähr, Ralf Wunderlich

The optimal control of sustainable energy supply systems, including renewable energies and energy storage, takes a central role in the decarbonization of industrial systems. However, the use of fluctuating renewable energies leads to fluctuations in energy generation and requires a suitable control strategy for the complex systems in order to ensure energy s

Deepayan Das, Davide Talon, Massimiliano Mancini, Yiming Wang

Vision-Language Models (VLMs) have shown significant promise in Visual Question Answering (VQA) tasks by leveraging web-scale multimodal datasets. However, these models often struggle with continual learning due to catastrophic forgetting when adapting to new tasks. As an effective remedy to mitigate catastrophic forgetting, rehearsal strategy uses the data

Jun Cui

This study examines the impact of DevOps practices on enterprise software delivery success, focusing on enhancing R&D efficiency and source code management (SCM). Using a qualitative methodology, data were collected from case studies of large-scale enterprises implementing DevOps to explore how these practices streamline software development processes. Findi

Grigoriy Blekherman, Rainer Sinn, Mauricio Velasco, Shixuan Zhang

We study spurious second-order stationary points and local minima in a nonconvex low-rank formulation of sum-of-squares optimization on a real variety $X$. We reformulate the problem of finding a spurious local minimum in terms of syzygies of the underlying linear series, and also bring in topological tools to study this problem. When the variety $X$ is of m

Jyothish Pari, Samy Jelassi, Pulkit Agrawal

In this work, we explore the limitations of combining models by averaging intermediate features, referred to as model merging, and propose a new direction for achieving collective model intelligence through what we call compatible specialization. Current methods for model merging, such as parameter and feature averaging, struggle to effectively combine speci

Gabriele Cimador

After several software and hardware upgrades during LS2, ALICE records 50 KHz of minimum bias Pb--Pb collisions in continuous readout mode. To cope with the high data rate of 3.5 TB/s from the detectors, multiple stages of compression are employed during data taking, the last one requiring full TPC tracking. This compression chain is part of the synchronous

Yumeng Liu, Yizhuo Wang, Zhengfang Fan, Jianyong Wei

Atomically thin MoS2 is a promising material for field-effect transistors (FETs) and electronic devices. However, traditional photolithographic processes introduce surface contamination to 2D materials, leading to poor electrical contacts when metals are deposited. In this work, we present a novel fabrication method using gold as a mask for patterning and et

Yumeng Liu, Yanhao Deng, Yizhuo Wang, Li Wang

Defect engineering in two-dimensional (2D) materials is essential for advancing applications such as gas sensing, single-atom catalysis, and guided nanoparticle self-assembly, enabling the creation of materials with tailored functionalities. This study investigates ion implantation effects on highly ordered pyrolytic graphite (HOPG) surfaces, using scanning

Anjali C. Dhabu, Felix Bernauer, Chun-Man Liao, Ernst Niederleithinger

Earthquakes cause catastrophic damage to buildings and loss of human life. Civil engineers across the globe design earthquake-resistant buildings to minimize this damage. Conventionally, the structures are designed to resist the translational motions caused by an earthquake. However, with the increasing evidence of rotational ground motions in addition to th

Efraín Moreles, Benjamín Martínez-López, Susana Higuera-Parra, Erick R. Olvera-Prado

This work uses twin 22-year free-running simulations of the Gulf of Mexico hydrodynamics performed with the HYCOM, one considering only ocean dynamics and the other incorporating atmospheric forcing, to study the behavior of the Yucatan Channel transport (YCT), the Loop Current (LC), the Loop Current Eddies (LCEs), their relationships, and the atmospheric fo

Manuel Rissel

We show the global approximate controllability of the Boussinesq system with viscosity and diffusion in a planar periodic channel by using only a temperature control supported in a thin strip. At the walls, a slip boundary condition is chosen for the fluid and the normal derivative of the temperature is assumed to vanish. This contributes a first global cont

Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda

Transformer-based large language models (LLMs) have displayed remarkable creative prowess and emergence capabilities. Existing empirical studies have revealed a strong connection between these LLMs' impressive emergence abilities and their in-context learning (ICL) capacity, allowing them to solve new tasks using only task-specific prompts without further fi

Jannatul Chhoa, Michael Ivanitskiy, Fushuai Jiang, Shiying Li

The Gromov-Wasserstein (GW) distances define a family of metrics, based on ideas from optimal transport, which enable comparisons between probability measures defined on distinct metric spaces. They are particularly useful in areas such as network analysis and geometry processing, as computation of a GW distance involves solving for registration between the

Kristóf Bérczi, Boglárka Gehér, András Imolay, László Lovász

The study of matroid products traces back to the 1970s, when Lov\'asz and Mason studied the existence of various types of matroid products with different strengths. Among these, the tensor product is arguably the most important, which can be considered as an extension of the tensor product from linear algebra. However, Las Vergnas showed that the tensor prod

Daniel Stremmer, Malgorzata Worek

We present a complete description of top quark pair production in association with a hard photon in the di-lepton decay channel. The calculation is performed at NLO QCD and includes all resonant and non-resonant Feynman diagrams, interferences, and finite-width effects of the top quarks and $W^\pm/Z$ gauge bosons. We provide the results for the $pp\to e^+\nu

Samuel J. Harris, Ryan A. Palmer, N. R. McDonald

Relationships between plants and insects vitally underpin the health of global ecosystems and food production. Through co-evolution, insects have acquired a variety of senses in response to the emergence of floral cues such as scent, colour and shape. Therefore, the recent discovery of electroreception among terrestrial arthropods motivates the investigation

Nick Heller, Phil Ilten, Tony Menzo, Stephen Mrenna

We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precis

Sviatoslav Chalnev, Matthew Siu, Arthur Conmy

To control the behavior of language models, steering methods attempt to ensure that outputs of the model satisfy specific pre-defined properties. Adding steering vectors to the model is a promising method of model control that is easier than finetuning, and may be more robust than prompting. However, it can be difficult to anticipate the effects of steering

Sabrina F. Tigik, Daniel B. Graham, Yuri V. Khotyaintsev

We use Magnetospheric Multiscale (MMS) mission data to investigate electron-scale energy transfer due to lower hybrid drift waves during magnetopause reconnection. We analyze waves observed in an electron-scale plasma mixing layer at the edge of the magnetospheric outflow. Using high-resolution 7.5 ms electron moments, we obtain an electron current density w

Mikihiro Fujii, Keiichi Watanabe

We consider the three-dimensional compressible Navier--Stokes system with the Coriolis force and prove the long-time existence of a unique strong solution. More precisely, we show that for any $0<T<\infty$ and arbitrary large initial data in the scaling critical Besov spaces, the solution uniquely exists on $[0,T]$ provided that the speed of rotation is high

V. Gelfreich, A. Vieiro

This paper contains a proof of the Nekhoroshev theorem for quasi-integrable symplectic maps. In contrast to the classical methods, our proof is based on the discrete averaging method and does not rely on transformations to normal forms. At the centre of our arguments lies the theorem on embedding of a near-the-identity symplectic map into an autonomous Hamil

Joshua Bagajo, Clemens Schwarke, Victor Klemm, Ignat Georgiev

Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from images. In this work, we demonstrate that locomotion policies trained with analytic gradients from a differentiable simulator can be successfully transferred to the real world. Typ

Anjith George, Sebastien Marcel

The accuracy of face recognition systems has improved significantly in the past few years, thanks to the large amount of data collected and advancements in neural network architectures. However, these large-scale datasets are often collected without explicit consent, raising ethical and privacy concerns. To address this, there have been proposals to use synt

Francesco Grella, Alessandro Albini, Giorgio Cannata, Perla Maiolino

The use of data-driven techniques for tactile data processing and classification has recently increased. However, collecting tactile data is a time-expensive and sensor-specific procedure. Indeed, due to the lack of hardware standards in tactile sensing, data is required to be collected for each different sensor. This paper considers the problem of learning

Federico Califano, Daniel Logmans, Wesley Roozing

In the context of safety-critical control, we propose and analyse the use of Control Barrier Functions (CBFs) to limit the kinetic energy of torque-controlled robots. The proposed scheme is able to modify a nominal control action in a minimally invasive manner to achieve the desired kinetic energy limit. We show how this safety condition is achieved by appro

Artur Avkhadiev, Lena Funcke, Karl Jansen, Stefan Kühn

Lattice quantum field theory calculations may potentially combine the advantages of Hamiltonian formulations with the scalability and control of conventional Lagrangian frameworks. However, such hybrid approaches need to consider (1) the differences in renormalized coupling values between the two formulations, and (2) finite-volume and discretization effects

Mouïn Ben Ammar, David Brellmann, Arturo Mendoza, Antoine Manzanera

Out-of-distribution (OOD) detection is essential for ensuring the reliability and safety of machine learning systems. In recent years, it has received increasing attention, particularly through post-hoc detection and training-based methods. In this paper, we focus on post-hoc OOD detection, which enables identifying OOD samples without altering the model's t

Gangcheng Zhang, Yeshuo Shu, Keyi Liu, Yuxuan Wang

The widespread use of e-bikes has facilitated short-distance travel yet led to confusion and safety problems in road traffic. This study focuses on the dual characteristics of e-bikes in traffic conflicts: they resemble pedestrians when interacting with motor vehicles and behave like motor vehicles when in conflict with pedestrians, which raises the right of

Rodrigo Itzamná Becerra-Deana, Raphael Maltais-Tariant, Guillaume Ramadier, Martin Poinsinet de Sivry-Houle

The design space for photonic lanterns is large and complex, making it challenging to identify optimal parameters to achieve specific performances, such as coupling, bandwidth, and insertion loss. Effectively navigating this space requires modeling tools capable to extract the most characterizing parameters. This work contrasts theoretical modeling with expe

Junyu Hao, Jianheng Liu, Yongjia Zhao, Zuofan Chen

When presented with one or a few photos of a previously unseen object, humans can instantly recognize it in different scenes. Although the human brain mechanism behind this phenomenon is still not fully understood, this work introduces a novel technical realization of this task. It consists of two phases: (1) generating a Similarity Density Map (SDM) by conv

Sripan Mondal, A. K. Srivastava, David I. Pontin, Eric R. Priest

Fast-mode magnetohydrodynamic (MHD) waves in the solar corona are often known to be produced by solar flares and eruptive prominences. We here simulate the effect of the interaction of an external perturbation on a magnetic null in the solar corona which results in the formation of a current sheet (CS). Once the CS undergoes a sufficient extension in its len

Yiqin Zhao, Mallesham Dasari, Tian Guo

High-quality environment lighting is essential for creating immersive mobile augmented reality (AR) experiences. However, achieving visually coherent estimation for mobile AR is challenging due to several key limitations in AR device sensing capabilities, including low camera FoV and limited pixel dynamic ranges. Recent advancements in generative AI, which c

Giuseppe Colletta, Susan Johny, Jonathan A. Collins, Alessandro Casaburi

In this work, we present a numerical model specifically designed for 3D multilayer devices, with a focus on nanobridge junctions and coplanar waveguides. Unlike existing numerical models, ours does not approximate the physical layout or limit the number of constituent materials, providing a more accurate and flexible design tool. We calculate critical curren

Rodrigo Carmo Terin

Physics-informed neural networks (PINNs) are employed to solve the Dyson--Schwinger equations of quantum electrodynamics (QED) in Euclidean space, with a focus on the non-perturbative generation of the fermion's dynamical mass function in the Landau gauge. By inserting the integral equation directly into the loss function, our PINN framework enables a single