March 2023 arXiv papers — page 3

Tim Waters, Daniel Proga

The literature on thermal instability (TI) reveals that even for a simple homogeneous plasma, the nonlinear outcome ranges from a gentle reconfiguration of the initial state to an explosive one, depending on whether the condensations that form evolve in an isobaric or nonisobaric manner. After summarizing recent developments on the linear and nonlinear theor

Seongyoon Kim, Hangsoon Jung, Minho Lee, Yun Young Choi

Early degradation prediction of lithium-ion batteries is crucial for ensuring safety and preventing unexpected failure in manufacturing and diagnostic processes. Long-term capacity trajectory predictions can fail due to cumulative errors and noise. To address this issue, this study proposes a data-centric method that uses early single-cycle data to predict t

Sanjit Bhowmick, Javier de la Cruz, Edgar Martínez-Moro, Anuradha Sharma

We provide a simple proof for a complementary pair of group codes over a finite non-commutative Frobenius ring of the fact that one of them is equivalent to the other one. We also explore this fact for checkeable codes over the same type of alphabet.

Mallikarjun B R, Xingang Pan, Mohamed Elgharib, Christian Theobalt

Advances in 3D-aware generative models have pushed the boundary of image synthesis with explicit camera control. To achieve high-resolution image synthesis, several attempts have been made to design efficient generators, such as hybrid architectures with both 3D and 2D components. However, such a design compromises multiview consistency, and the design of a

Markus Tempelmayr

We give a novel characterization of the centered model in regularity structures which persists for rough drivers even as a mollification fades away. We present our result for a class of quasilinear equations driven by noise, however we believe that the method is robust and applies to a much broader class of subcritical equations. Furthermore, we prove that a

Leon Derczynski, Hannah Rose Kirk, Vidhisha Balachandran, Sachin Kumar

This paper introduces RiskCards, a framework for structured assessment and documentation of risks associated with an application of language models. As with all language, text generated by language models can be harmful, or used to bring about harm. Automating language generation adds both an element of scale and also more subtle or emergent undesirable tend

H. W. Braden, Linden Disney-Hogg

We classify all possible charge-3 monopole spectral curves with non-trivial automorphism group and within these identify those with elliptic quotients. By focussing on elliptic quotients the transcendental constraints for a monopole spectral curve become ones regarding periods of elliptic functions. We construct the Nahm data and new monopole spectral curves

Venkata Manikantha Sai Ganesh Tanuku, Peter Vogel, Thomas Palberg, Ivo Buttinoni

Individual self-propelled colloidal particles, like active Brownian particles (ABP) or run-and-tumble swimmers (RT), exhibit characteristic and well-known motion patterns. However, their interaction with obstacles remains an open and important problem. We here investigate the two-dimensional motion of silica-gold Janus particles (JP) actuated by AC electric

Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for cont

Zsófia Juhász, Máté Bartalos, Péter Magyar, Gábor Farkas

A new generalisation of Goldbach's conjecture (GGC) - also generalising that of Lemoine - is tested, introduced by the first author. It states that for every pair of positive integers $m_1, m_2$, every sufficiently large integer $n$ satisfying certain simple criteria can be expressed as $n=m_1p+m_2q$ for some primes $p$ and $q$. GGC is checked up to $10^{12}

Jiahao Huang, Pedro F. Ferreira, Lichao Wang, Yinzhe Wu

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the micro-structure of myocardial tissue in the living heart, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice is ch

Yifan Liu, Tao Hu, Xiaoqing Guan, Yixu Wang

Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework

P. K. Mishra, V. M. Tripathi

In this work we study the following nonlocal problem \begin{equation*} \left\{ \begin{aligned} M(\|u\|^2_X)(-\Delta)^s u&= \lambda {f(x)}|u|^{\gamma-2}u+{g(x)}|u|^{p-2}u &&\mbox{in}\ \ \Omega, u&=0 &&\mbox{on}\ \ \mathbb R^N\setminus \Omega, \end{aligned} \right. \end{equation*} where $\Omega\subset \mathbb R^N$ is open and bounded with smooth boundary, $N>2

Kai Huang, Zhengzi Xu, Su Yang, Hongyu Sun

With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. Software defect has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automat

David K. Ferry

In semi-classical transport, it has become common practice over the past few decades to use ensemble Monte Carlo (EMC) methods for the simulation of transport in semiconductor devices. This method utilizes particles while still addressing the full physics within the device, leaving the computational difficulties to the computer. More recently, the study of q

Micha Berkooz, Yiyang Jia, Navot Silberstein

We consider a model of Parisi where a single particle hops on an infinite-dimensional hypercube, under the influence of a uniform but disordered magnetic flux. We reinterpret the hypercube as the Fock-space graph of a many-body Hamiltonian and the flux as a frustration of the return amplitudes in Fock space. We will identify the set of observables that have

Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su

Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient. Motivated by the recent progress in natural language processing, we investigate parameter-efficient tuning in large diffusion models by inserting small learnable modules (te

Jens Lang, Bernhard A. Schmitt

It is common practice to apply gradient-based optimization algorithms to numerically solve large-scale ODE constrained optimal control problems. Gradients of the objective function are most efficiently computed by approximate adjoint variables. High accuracy with moderate computing time can be achieved by such time integration methods that satisfy a sufficie

Niclas S. Mueller, Rakesh Arul, Ashley P. Saunders, Amalya C. Johnson

Anti-Stokes photoluminescence (PL) is light emission at a higher photon energy than the excitation, with applications in optical cooling, bioimaging, lasing, and quantum optics. Here, we show how plasmonic nano-cavities activate anti-Stokes PL in WSe$_2$ monolayers through resonant excitation of a dark exciton. The tightly confined plasmonic fields excite th

Xiaoyu Zhu, Po-Yao Huang, Junwei Liang, Celso M. de Melo

We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with int

Hatice Gunes, Nikhil Churamani

Social Robotics and Human-Robot Interaction (HRI) research relies on different Affective Computing (AC) solutions for sensing, perceiving and understanding human affective behaviour during interactions. This may include utilising off-the-shelf affect perception models that are pre-trained on popular affect recognition benchmarks and directly applied to situa

Simon Wundling

If you want to fill $n \in \mathbb{N}$ seats in succession with $n$ people and the rule that each person chooses one of the seats with the maximum distance to an occupied seat, then you can ask yourself how many possibilities there are for this. In this paper, based on initially mentioned ideas, a formula for the number of these possibilities will be found.

Yue Zhu, Joachim Maier

Here we show the description of the full incorporation thermodynamics in nanocrystalline LixFePO4 where the full range from x=0 to 1 is experimentally accessible. The following points are most important: the treatment in terms of the neutral lattice-gas model is incorrect rather ions and electrons need to be considered separately. One needs to invoke point d

Yongshuo Zong, Oisin Mac Aodha, Timothy Hospedales

Multimodal learning, which aims to understand and analyze information from multiple modalities, has achieved substantial progress in the supervised regime in recent years. However, the heavy dependence on data paired with expensive human annotations impedes scaling up models. Meanwhile, given the availability of large-scale unannotated data in the wild, self

Siwei Luo

The three-body problem is essentially to solve three curves that satisfy Newton's equations. Given initial conditions found in numerical simulation, this paper introduces the Antikythera algorithm that solves three-body problem Fourier series solution via the Runge-Kutta method and Fourier transform. The Lagrange, BHH, figure-8, and IA100 solutions are repor

Thomas Thiemann, Kristina Giesel

This chapter focuses on the status of the implementation of the dynamics in the canonical version of Loop Quantum Gravity (LQG). Concretely this means to provide a mathematical meaning of the quantum Einstein equations, sometimes called Wheeler-DeWitt equations, to give a physical interpretation and Hilbert space structure to its solutions and to construct a

Abdul Kalam, Ashok Kumar, Prasanta K. Panigrahi

The accurate prediction and understanding of molecular energy and chemical reactivity are fundamental pursuits in the field of molecular quantum chemistry. With the limitations of the current noisy intermediate scale quantum computer (NISQ) era, the Variational Quantum Eigensolver (VQE) algorithm offers a promising approach to efficiently estimate the stable

Chris van der Ploeg, Jacco van de Sluis, Sebastian Gerres, Szabolcs Novaczki

Infrastructure-to-Vehicle (I2V) and Vehicle-to-Infrastructure (V2I) communication is likely to be a key-enabling technology for automated driving in the future. Using externally placed sensors, the digital infrastructure can support the vehicle in perceiving surroundings that would otherwise be difficult to perceive due to, for example, high traffic density

Leonardo Di Gaetano, Giorgio Carugno, Federico Battiston, Francesco Coghi

Although higher-order interactions are known to affect the typical state of dynamical processes giving rise to new collective behavior, how they drive the emergence of rare events and fluctuations is still an open problem. We investigate how fluctuations of a dynamical quantity of a random walk exploring a higher-order network arise over time. \newtext{In th

Alexander Christie, Yuanyuan Feng, Gautam Iyer, Alexei Novikov

We study an overdamped Langevin equation on the $d$-dimensional torus with stationary distribution proportional to~$p = e^{-U / \kappa}$. When~$U$ has multiple wells the mixing time of the associated process is exponentially large (of size~$e^{O(1/\kappa)}$). We add a drift to the Langevin dynamics (without changing the stationary distribution) and obtain qu

Lionel Voirol, Davide A. Cucci, Mucyo Karemera, Wenfei Chu

The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements therefore need to be extremely precise to obtain correct system diagnostics and accurate predictions, consequently requiring a rigorous calibration procedure which models their errors before be

Danny Fainozzi, Matteo Ippoliti, Fulvio Billè, Dario De Angelis

The possibility to obtain a three-dimensional representation of a single object with sub-$\mu$m resolution is crucial in many fields, from material science to clinical diagnostics. This is typically achieved through tomography, which combines multiple two-dimensional images of the same object captured at different orientations. However, this serial imaging m

Niels Lodder, Chris van der Ploeg, Laura Ferranti, Emilia Silvas

With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system health and maintain safe operations in faulty scenarios is increasing. A variety of faults or failures could occur, and there exists a high variety of ways to respond to such events. On

Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte

Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth esti

Matteo Barigozzi, Yong He, Lingxiao Li, Lorenzo Trapani

We consider (robust) inference in the context of a factor model for tensor-valued sequences. We study the consistency of the estimated common factors and loadings space when using estimators based on minimising quadratic loss functions. Building on the observation that such loss functions are adequate only if sufficiently many moments exist, we extend our re

Chuangguan Ye, Hongyuan Zhu, Yongbin Liao, Yanggang Zhang

Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods require extensive data collection and retraining when dealing with novel classes never seen before. On the other hand, we humans can quickly recognize new classes by looking at a fe

Son T. Luu, Khoi Trong Hoang, Tuong Quang Pham, Kiet Van Nguyen

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehen

Nicole Bäuerle, Tamara Göll

We consider the strategic interaction of $n$ investors who are able to influence a stock price process and at the same time measure their utilities relative to the other investors. Our main aim is to find Nash equilibrium investment strategies in this setting in a financial market driven by a Brownian motion and investigate the influence the price impact has

David Gundana, Hadas Kress-Gazit

In this paper we present a grammar and control synthesis framework for online modification of Event-based Signal Temporal Logic (STL) specifications, during execution. These modifications allow a user to change the robots' task in response to potential future violations, changes to the environment, or user-defined task design changes. In cases where a modifi

T. T. Sergeev, A. A. Zyablovsky, E. S. Andrianov, Yu. E. Lozovik

An ultra-strong coupling regime takes place in a compound system when a coupling strength between the subsystems exceeds one tenth of the system eigenfrequency. It transforms into a deep-strong coupling regime when the coupling strength exceeds the system eigenfrequency. In these regimes, there are difficulties with description of relaxation processes withou

Soroosh Shafiee, Fatma Kılınç-Karzan

Optimization problems involving minimization of a rank-one convex function over constraints modeling restrictions on the support of the decision variables emerge in various machine learning applications. These problems are often modeled with indicator variables for identifying the support of the continuous variables. In this paper we investigate compact exte

Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi

Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based solutions for Traffic Engineering (TE), which is a fundamental problem in ISP networks. Nowadays, state-of-the-art TE optimizers rely on traditional optimization techniques, such as L

Arnab Auddy, Ming Yuan

In this paper, we investigate the optimal statistical performance and the impact of computational constraints for independent component analysis (ICA). Our goal is twofold. On the one hand, we characterize the precise role of dimensionality on sample complexity and statistical accuracy, and how computational consideration may affect them. In particular, we s

Chang-Xu Yan, Chao-Yang Tan, Hong Guo, Hao-Ran Chang

Within linear response theory, the absorptive part of highly anisotropic optical conductivities are analytically calculated for distinct tilts in two-dimensional (2D) tilted semi-Dirac bands (SDBs). The transverse optical conductivities always vanish. The interband longitudinal optical conductivities (LOCs) in 2D tilted SDBs differ qualitatively in the power

Daniel Campos, Alessandro Magnani, ChengXiang Zhai

In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymmetry between the context and query encoders. First, we investigate the impact of pre and post-training compression on the MSMARCO, Natural Questions, TriviaQA, SQUAD, and SCIFACT,

Manas Mejari, Ankit Gupta

This paper presents a direct data-driven approach for computing robust control invariant (RCI) sets and their associated state-feedback control laws for linear time-invariant systems affected by bounded disturbances. The proposed method utilizes a single state-input trajectory generated from the system, to compute a polytopic RCI set with a desired complexit

Adriana Simancas, Justus Braach, Eric Buschmann, Ankur Chauhan

Monolithic CMOS sensors in a 65 nm imaging technology are being investigated by the CERN EP Strategic R&D Programme on Technologies for Future Experiments for an application in particle physics. The appeal of monolithic detectors lies in the fact that both sensor volume and readout electronics are integrated in the same silicon wafer, providing a reduction i

Raj Kumar Nayak

In this article, we proved upper bounds for numerical radius of bounded linear operator and product of operators which generalize and improve existing inequalities. We also obtain a numerical radius inequality of invertible operator using Kantorovich's ratio.

Md Tahmid Rahman Laskar, Mizanur Rahman, Israt Jahan, Enamul Hoque

Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years. However, it has been recently found that this dataset is limited by noise and even most queries in this dataset do not have any releva

P. Klausner, F. Barranco, P. M. Pizzochero, X. Roca-Maza

The structure of a vortex in the inner crust of a pulsar is calculated microscopically in the Wigner-Seitz cell approximation, simulating the conditions of the inner crust of a cold, non-accreting neutron star, in which a lattice of nuclei coexists with a sea of superfluid neutrons. The calculation is based on the axially deformed Hartree-Fock-Bogolyubov fra

Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for

M. Z. Naser, Brandon Ross, Jennier Ogle, Venkatesh Kodur

The engineering community has recently witnessed the emergence of chatbot technology with the release of OpenAI ChatGPT-4 and Google Bard. While these chatbots have been reported to perform well and even pass various standardized tests, including medical and law exams, this forum paper explores whether these chatbots can also pass the Fundamentals of Enginee

Felix L. Schwenninger, Alexander A. Wierzba, Hans Zwart

In this paper we consider BIBO stability of systems described by infinite-dimensional linear state-space representations, filling the so far unattended gap of a formal definition and characterization of BIBO stability in this general case. Furthermore, we provide several sufficient conditions guaranteeing BIBO stability of a particular system and discuss to

Michele Pizzardo, Margaret J. Geller, Scott J. Kenyon, Ivana Damjanov

The TNG300-1 run of the IllustrisTNG simulations includes 1697 clusters of galaxies with $M_{200c}>10^{14}$M$_\odot$ covering the redshift range $0.01-1.04$. We build mock spectroscopic redshift catalogues of simulated galaxies within these clusters and apply the caustic technique to estimate the cumulative cluster mass profiles. We compute the total true cu

Aditya Dhakal, Sameer G. Kulkarni, K. K. Ramakrishnan

Hardware accelerators such as GPUs are required for real-time, low-latency inference with Deep Neural Networks (DNN). However, due to the inherent limits to the parallelism they can exploit, DNNs often under-utilize the capacity of today's high-end accelerators. Although spatial multiplexing of the GPU, leads to higher GPU utilization and higher inference th

Lucas Fresse, Ivan Penkov

We study the homogeneous ind-spaces $\mathrm{GL}(\mathbf{s})/\mathbf{P}$ where $\mathrm{GL}(\mathbf{s})$ is a strict diagonal ind-group defined by a supernatural number $\mathbf{s}$ and $\mathbf{P}$ is a parabolic ind-subgroup of $\mathrm{GL}(\mathbf{s})$. We construct an explicit exhaustion of $\mathrm{GL}(\mathbf{s})/\mathbf{P}$ by finite-dimensional parti

Simran Chourasia, Lina Johnsen Kamra, Irina V. Bobkova, Akashdeep Kamra

Spinful triplet Cooper pairs can be generated from their singlet counterparts available in a conventional superconductor (S) using two or more noncollinear magnetic moments, typically contributed by different magnets in a multilayered heterostructure. Here, we theoretically demonstrate that an S interfaced with a canted antiferromagnet (AF) harbors spinful t

Zeren Chen, Gengshi Huang, Wei Li, Jianing Teng

Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-

Mohammed Khalil, Alessandra Buonanno, Héctor Estellés, Deyan P. Mihaylov

Waveform models are essential for gravitational-wave (GW) detection and parameter estimation of coalescing compact-object binaries. More accurate models are required for the increasing sensitivity of current and future GW detectors. The effective-one-body (EOB) formalism combines the post-Newtonian (PN) and small mass-ratio approximations with numerical-rela

Marta Lisowska, Yannis Angelis, Stephan Aune, Jonathan Bortfeldt

The PICOSEC Micromegas (MM) detector is a precise timing gaseous detector consisting of a Cherenkov radiator combined with a photocathode and a MM amplifying structure. A 100-channel non-resistive PICOSEC MM prototype with 10x10 cm^2 active area equipped with a Cesium Iodide (CsI) photocathode demonstrated a time resolution below 18 ps. The objective of this

Patrick Morton

A proof of several identities of Ramanujan involving theta functions of level $7$ is given which uses a specific modular function for $\Gamma_1(7)$ and Klein's projective representation of $PSL(2,7)$ into $PSL(3, \mathbb{C})$. Four identities of Berndt and Zhang are derived as algebraic corollaries of the main proof.

Thomas Tanay, Aleš Leonardis, Matteo Maggioni

While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations. In this work, we introduce the first 3D-based multi-frame denoising method that significantly outperforms its 2D-

N. S. Dhami, V. Balédent, O. Bednarchuk, D. Kaczorowski

We report on the pressure evolution of the electronic and crystal structures of the noncentrosymmetric antiferromagnet EuCoGe3. Using a diamond anvil cell, we performed high pressure fluorescence detected near-edge x-ray absorption spectroscopy at the Eu L3, Co K, and Ge K edges and synchrotron powder x-ray diffraction. In the Eu L3 spectrum, both divalent a

Agnieszka Janiuk

The code HARM\_COOL, a conservative scheme for relativistic magnetohydrodynamics, is being developed in our group and works with a tabulated equation of state of dense matter. This EOS can be chosen and used during dynamical simulation, instead of the simple ideal gas one. In this case, the inversion scheme between the conserved and primitive variables is no

Konstantinos Bountrogiannis, Anthony Ephremides, Panagiotis Tsakalides, George Tzagkarakis

The Age of Incorrect Information (AoII) is a recently proposed metric for real-time remote monitoring systems. In particular, AoII measures the time the information at the monitor is incorrect, weighted by the magnitude of this incorrectness, thereby combining the notions of freshness and distortion. This paper addresses the definition of an AoII-optimal tra

Delphine Quereilhac, Lola Pinsard, Elouan Guillou, Marina Fazzini

Flax fibres are valuable reinforcements for tomorrow's composites. However, defects called kink-bands, which mainly appear on fibres during the extraction and transformation phases, might affect their mechanical properties. Defects induced pores, within the kink-band are investigated in this work. They were morphologically explored using synchrotron phase-co

Sylvio R. Bistafa

The history of the elastica is examined through the works of various contributors, including those of Jacob and Daniel Bernoulli, since its first appearance in a 1690 contest on finding the profile of a hanging flexible cord. Emphasis will be given to Leonhard Euler's variational approach to the elastica, laid out in his landmark 1744 book on variational tec

Delin Qu, Yizhen Lao, Zhigang Wang, Dong Wang

This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion. Existing methods suffer from two main drawbacks. Firstly, they face challenges in estimating the accurate correction field due to the uniform velocity assumption, leading to significant image correction errors under complex motion. S

Matteo Springolo, Miquel Royo, Massimiliano Stengel

We predict a large in-plane polarization response to bending in a broad class of trigonal two-dimensional crystals. We define and compute the relevant flexoelectric coefficients from first principles as linear-response properties of the undistorted layer, by using the primitive crystal cell. The ensuing response (evaluated for SnS$_{2}$, silicene, phosphoren

Maria Gabriela Navarro, Roberto Capuzzo-Dolcetta, Manuel Arca-Sedda, Dante Minniti

The infall and merger scenario of massive clusters in the Milky Way's potential well, as one of the Milky Way formation mechanisms, is reexamined to understand how the stars of the merging clusters are redistributed during and after the merger process using, for the first time, simulations with a high resolution concentrated in the 300 pc around the Galactic

Alexandros Karam, Maciej Kierkla, Bogumiła Świeżewska

We have conducted a revised analysis of the first-order phase transition that is associated with symmetry breaking in a classically scale-invariant model that has been extended with a new $SU(2)$ gauge group. By incorporating recent developments in the understanding of supercooled phase transitions, we were able to calculate all of its features and significa

Matteo Muffo, Enrico Bertino

The recent introduction of Transformers language representation models allowed great improvements in many natural language processing (NLP) tasks. However, if on one hand the performances achieved by this kind of architectures are surprising, on the other their usability is limited by the high number of parameters which constitute their network, resulting in

Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang

The continuous development of Question Answering (QA) datasets has drawn the research community's attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. However, with the proliferation of QA models in online

Luca Fortini, Mattia Leonori, Juan M. Gandarias, Elena de Momi

Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless

Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon

Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over all the samples. A simple method to solve this task is to threshold the softmax output of a model trained with the cross-entropy loss. This approach is theoretically proven to be asy

Giulia Bernardini, Leo van Iersel, Esther Julien, Leen Stougie

Combining a set of phylogenetic trees into a single phylogenetic network that explains all of them is a fundamental challenge in evolutionary studies. Existing methods are computationally expensive and can either handle only small numbers of phylogenetic trees or are limited to severely restricted classes of networks. In this paper, we apply the recently-int

Alim Ruzi, Chen Zhou, Xiaohu Sun, Dayong Wang

We propose a new method to detect sub-GeV dark matter, through their scatterings from free leptons and the resulting kinematic shifts. Specially, such an experiment can detect dark matter interacting solely with muons. The experiment proposed here is to directly probe muon-philic dark matter, in a model-independent way. Its complementarity with the muon on t

Jan Górecki

Without writing a single line of code by a human, an example Monte Carlo simulation based application for stochastic dependence modeling with copulas is developed using a state-of-the-art large language model (LLM) fine-tuned for conversations. This includes interaction with ChatGPT in natural language and using mathematical formalism, which, under careful s

Mohammad Akil, Mouhammad Ghader, Zayd Hajjej, Mohamad Ali sammoury

In this paper, we investigate the stability of the transmission problem for Rayleigh beam model with heat conduction. First, we reformulate our system into an evolution equation and prove our problem's well-posedness. Next, we demonstrate the resolvent of the operator is compact in the energy space, then by using the general criteria of Arendt-Batty, we prov

Thales Silva, Jorge Vieira

Plasma-based positron acceleration is still an open question, as the most efficient regimes for electron acceleration (quasi-linear and blowout) are not directly applicable to positrons. Nevertheless, positron acceleration is a stepping stone on the path toward a plasma-based lepton collider. In this work, we propose a scheme for positron acceleration based

Peter Crooks, Jonathan Weitsman

The bundle map $T^*\hspace{-2pt}\operatorname{U}(n)\longrightarrow\operatorname{U}(n)$ provides a real polarization of the cotangent bundle $T^*\hspace{-2pt}\operatorname{U}(n)$, and yields the geometric quantization $Q_1(T^*\hspace{-2pt}\operatorname{U}(n)) = L^2(\operatorname{U}(n))$. We use the Gelfand-Cetlin systems of Guillemin and Sternberg to show tha

Paweł Duch, Massimiliano Gubinelli, Paolo Rinaldi

We present a construction of the fractional $\Phi^4$ Euclidean quantum field theory on $\mathbb{R}^3$ in the full subcritical regime via parabolic stochastic quantisation. Our approach is based on the use of a truncated flow equation for the effective description of the model at sufficiently small scales and on coercive estimates for the non-linear stochasti

Kristina Giesel, Hongguang Liu

We consider an extended phase space formulation for cosmological and spherically symmetric models in which the choice of a given $\overline{\mu}$-scheme can be implemented dynamically. These models are constructed in the context of the relational formalism by using a canonical transformation on the extended phase space which provides a Kucha\v{r} decompositi

Ramon Sanabria, Nikolay Bogoychev, Nina Markl, Andrea Carmantini

English is the most widely spoken language in the world, used daily by millions of people as a first or second language in many different contexts. As a result, there are many varieties of English. Although the great many advances in English automatic speech recognition (ASR) over the past decades, results are usually reported based on test datasets which fa

A. de Miguel-Arribas, J. Morón-Vidal, L. M. Floría, C. Gracia-Lázaro

Within the framework of Game Theory, contests study decision-making in those situations or conflicts when rewards depend on the relative rank between contenders rather than their absolute performance. By relying on the formalism of Tullock success functions, we propose a model where two contenders fight in a conflict on two fronts with different technology l

Ivo Schulthess, Anastasio Fratangelo, Patrick Hautle, Philipp Heil

We present an apparatus that applies Ramsey's method of separated oscillatory fields to proton spins in water molecules. The setup consists of a water circuit, a spin polarizer, a magnetically shielded interaction region with various radio frequency elements, and a nuclear magnetic resonance system to measure the spin polarization. We show that this apparatu

Henna Farheen, Andreas Strauch, J. Christoph Scheytt, Viktor Myroshnychenko

Silicon photonics in conjunction with complementary metal-oxide-semiconductor (CMOS) fabrication has greatly enhanced the development of integrated optical phased arrays. This facilitates a dynamic control of light in a compact form factor that enables the synthesis of arbitrary complex wavefronts in the infrared spectrum. We numerically demonstrate a large-

Ziheng Wang, Conor Perreault, Xi Liu, Anthony Jarc

Endoscopic video recordings are widely used in minimally invasive robot-assisted surgery, but when the endoscope is outside the patient's body, it can capture irrelevant segments that may contain sensitive information. To address this, we propose a framework that accurately detects out-of-body frames in surgical videos by leveraging self-supervision with min

K. J. Flöthner, F. Brunbauer, S. Ferry, F. Garcia

Removing ambiguities within a single stage becomes crucial when one can not use multiple detectors behind each other to resolve them which naturally is the case for neutral radiation. An example would be RICH detectors. Commonly pixilated readout is choosen for this purpose. However, this causes a remarkable increase in quantity of channels and does not scal

Mohammad Hatami, Markus Leinonen, Marian Codreanu

We study status updating under inexact knowledge about the battery levels of the energy harvesting sensors in an IoT network, where users make on-demand requests to a cache-enabled edge node to send updates about various random processes monitored by the sensors. To serve the request(s), the edge node either commands the corresponding sensor to send an updat

Sanxing Chen, Yongqiang Chen, Börje F. Karlsson

Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extraction, which severely limits the usability of identified mentions. In order for such entities to be useful in

Gheorghe Craciun, Jiaxin Jin, Miruna-Stefana Sorea

We consider complex-balanced mass-action systems, or toric dynamical systems. They are remarkably stable polynomial dynamical systems arising from reaction networks seen as Euclidean embedded graphs. We study the moduli spaces of toric dynamical systems, called the toric locus: given a reaction network, we are interested in the topological structure of the s

Julia Hindel, Nikhil Gosala, Kevin Bregler, Abhinav Valada

Perception datasets for agriculture are limited both in quantity and diversity which hinders effective training of supervised learning approaches. Self-supervised learning techniques alleviate this problem, however, existing methods are not optimized for dense prediction tasks in agriculture domains which results in degraded performance. In this work, we add

O. Tkach, S. Babenkov, Y. Lytvynenko, O. Fedchenko

Imaging energy filters in photoelectron microscopes and momentum microscopes use spherical fields with deflection angles of 90{\deg}, 180{\deg}, and even 2 x 180{\deg}. These instruments are optimized for high energy resolution, but exhibit image aberrations when operated in high transmission mode at medium energy resolution. Here we present a new approach f

Gilles Dowek

The basic notions of logic-predicate logic, Peano arithmetic, incompleteness theorems, etc.-have for long been an advanced topic. In the last decades, they became more widely taught, inphilosophy, mathematics, and computer science departments, to graduate and to undergraduate students. Many textbooks now present these notions, in particular the incompletenes

Jiahao Zhu, Yang Guo, Mingde Ding, Brigitte Schmieder

Coronal jets are eruptions identified by a collimated, sometimes twisted spire. They are small-scale energetic events compared with flares. Using multi-wavelength observations from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) and a magnetogram from Hinode/Spectro-Polarimeter (Hinode/SP), we study the formation and evolution of a jet

Artem Litvinenko, Akash Kumar, Mona Rajabali, Ahmad A. Awad

The reduction of phase noise in electronic systems is of utmost importance in modern communication and signal processing applications and requires an understanding of the underlying physical processes. Here, we systematically study the phase noise in mutually synchronized chains of nano-constriction spin Hall nano-oscillators (SHNOs). We find that longer cha

Jane Ivy Coons, Mark Curiel, Elizabeth Gross

The steady-state degree of a chemical reaction network is the number of complex steady-states for generic rate constants and initial conditions. One way to bound the steady-state degree is through the mixed volume of the steady-state system or an equivalent system. In this work, we show that for partionable binomial networks, whose resulting steady-state sys

Shu Kanno, Hajime Nakamura, Takao Kobayashi, Shigeki Gocho

Quantum computers have a potential for solving quantum chemistry problems with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a QMC with a trial state prepared in quantum circuit, which is employed to obtain the ground state with higher accuracy than QMC alone. We propose an algorithm combining QC-QMC with a hybri

Agapius Bou Ghosn, Marcus Nolte, Philip Polack, Arnaud de La Fortelle

Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learning-based observers are able to estimate a vehicle's velocity in normal driving conditions, the challenge remains to estimate the velocity in near-limits maneuvers while using only conventional in-car sensors. In this paper, we introduce a novel ne