April 2024 arXiv papers — page 114

Michael Ruzhansky, Anjali Shriwastawa, Daulti Verma

In this paper, we discuss the Hardy inequality with bilinear operators on general metric measure spaces. We give the characterization of weights for the bilinear Hardy inequality to hold on general metric measure spaces having polar decompositions. We also provide several examples of the results, finding conditions on the weights for integral Hardy inequalit

Yanhao Zheng, Kai Liu

Open-vocabulary object detection (OVOD) aims at localizing and recognizing visual objects from novel classes unseen at the training time. Whereas, empirical studies reveal that advanced detectors generally assign lower scores to those novel instances, which are inadvertently suppressed during inference by commonly adopted greedy strategies like Non-Maximum S

Lorenzo Bardone, Sebastian Goldt

Neural networks extract features from data using stochastic gradient descent (SGD). In particular, higher-order input cumulants (HOCs) are crucial for their performance. However, extracting information from the $p$th cumulant of $d$-dimensional inputs is computationally hard: the number of samples required to recover a single direction from an order-$p$ tens

Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins, Sudip Mittal

Data augmentation is an important facilitator of deep learning applications in the time series domain. A gap is identified in the literature, demonstrating sparse exploration of the transformer, the dominant sequence model, for data augmentation in time series. A architecture hybridizing several successful priors is put forth and tested using a powerful time

Lyle Poley, Tobias Galla, Joseph W. Baron

A central concern of community ecology is the interdependence between interaction strengths and the underlying structure of the network upon which species interact. In this work we present a solvable example of such a feedback mechanism in a generalised Lotka-Volterra dynamical system. Beginning with a community of species interacting on a network with arbit

Cristina Bazgan, André Nichterlein, Sofia Vazquez Alferez

We analyze the computational complexity of the following computational problems called Bounded-Density Edge Deletion and Bounded-Density Vertex Deletion: Given a graph $G$, a budget $k$ and a target density $\tau_\rho$, are there $k$ edges ($k$ vertices) whose removal from $G$ results in a graph where the densest subgraph has density at most $\tau_\rho$? Her

Javier Mazzitelli, Vasily Sotnikov, Marius Wiesemann

We consider the production of a Z boson decaying to leptons in association with a bottom-quark pair in hadronic collisions. For the first time, we compute predictions at next-to-next-to-leading order (NNLO) in QCD, and we combine them with the all-orders radiative corrections from a parton-shower simulation (NNLO+PS). Our method represents the first approach

Leonardo Bonino, Thomas Gehrmann, Markus Löchner, Kay Schönwald

Semi-inclusive hadron production in longitudinally polarized deep-inelastic lepton-nucleon scattering is a powerful tool for resolving the quark flavor decomposition of the proton's spin structure. We present the full next-to-next-to-leading order (NNLO) QCD corrections to the coefficient functions of polarized semi-inclusive deep-inelastic scattering (SIDIS

F. E. Burstall

We construct harmonic Riemannian submersions that are retractions from symmetric spaces of noncompact type onto their rank-one totally geodesic subspaces. Among the consequences, we prove the existence of a non-constant, globally defined complex-valued harmonic morphism from the Riemannian symmetric space associated to a split real semisimple Lie group. This

Rafael Gonzalez-Hernandez, Bernardo Uribe

In this work, we propose the average spin Chern number (ASCN) as an indicator of the topological significance of the spin degree of freedom within insulating materials. Whenever this number is a non-zero even integer, it distinguishes the material as a spin Chern insulator, and the number is a topological invariant whenever there is a symmetry that commutes

T. C. Hinse, O. Baştürk, J. Southworth, G. A. Feiden

The binary star NY Hya is a bright, detached, double-lined eclipsing system with an orbital period of just under five days with two components each nearly identical to the Sun and located in the solar neighbourhood. The objective of this study is to test and confront various stellar evolution models for solar-type stars based on accurate measurements of stel

Peter beim Graben

According to Kant's (1724 -- 1804) philosophical aesthetics, laid down in his Critique of the Power of Judgement (1790), beauty is "subjective purposefulness", reflected by the "harmony of the cognitive faculties", which are "understanding" and "imagination". On the one hand, understanding refers to the mental capability to find regularities in sensory manif

Xiang Zhang, Khatoon Khedri, Reza Rawassizadeh

Large Language Models (LLMs) can automate or substitute different types of tasks in the software engineering process. This study evaluates the resource utilization and accuracy of LLM in interpreting and executing natural language queries against traditional SQL within relational database management systems. We empirically examine the resource utilization an

Alvaro Pampano, Miraj Samarakkody, Hung Tran

For every $p\in\mathbb{R}$, we study $p$-elastic curves in the hyperbolic plane $\mathbb{H}^2$ and in the de Sitter $2$-space $\mathbb{H}_1^2$. We analyze the existence of closed $p$-elastic curves with nonconstant curvature showing that in the hyperbolic plane $\mathbb{H}^2$ these curves exist provided that $p>1$, while in the de Sitter $2$-space $\mathbb{H

Shomik Jain, Kathleen Creel, Ashia Wilson

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods

Siyuan Li, T. G. Steele, J. Ho, R. Raza

QCD bounds on the leading-order (LO) hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon ($a_\mu^{\mathrm{HVP,LO}}$, $a_\mu=\left(g-2\right)_\mu/2$) are determined by imposing H\"older inequalities and related inequality constraints on systems of Finite-Energy QCD sum-rules. This novel methodology is complementary to

Hai Nguyen-Truong, E-Ro Nguyen, Tuan-Anh Vu, Minh-Triet Tran

Referring image segmentation is a challenging task that involves generating pixel-wise segmentation masks based on natural language descriptions. The complexity of this task increases with the intricacy of the sentences provided. Existing methods have relied mostly on visual features to generate the segmentation masks while treating text features as supporti

Övgü Özdemir, Erdem Akagündüz

Visual question answering (VQA) is known as an AI-complete task as it requires understanding, reasoning, and inferring about the vision and the language content. Over the past few years, numerous neural architectures have been suggested for the VQA problem. However, achieving success in zero-shot VQA remains a challenge due to its requirement for advanced ge

Lars Niedermeier, Jeffrey L. Krichmar

Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their ad

Marco Fabris, Riccardo Ceccato, Andrea Zanella

The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet of Things (IoT)-oriented wireless sensor network (WSN). Meanwhile, 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to

Mateo Dutra, Martin Monteiro, Arturo C. Marti

We present an experiment on forced convection where a previously heated object is cooled under the effect of a controlled stream of air. We consider a square copper plate in which temperature variations can be considered negligible and we measure the cooling rate as a function of the average velocity of the air stream. We use a thermal camera to measure the

Timothy Clifton, Neil Hyatt

The Hubble tension has proven to be stubbornly persistent, despite widespread efforts to relax it. As a possible resolution of this problem we propose a radical alternative to the way in which cosmological models are viewed. Specifically, we consider building cosmological models from spaces that exhibit intrinsic symmetries, rather than as space-times with e

Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas, Núbia R. da Silva

In recent years, we have seen many advancements in wood species identification. Methods like DNA analysis, Near Infrared (NIR) spectroscopy, and Direct Analysis in Real Time (DART) mass spectrometry complement the long-established wood anatomical assessment of cell and tissue morphology. However, most of these methods have some limitations such as high costs

Abu Bakor Hayat Arnob, Xiangxue Wang, Yiping Jiao, Xiao Gan

Medical image processing usually requires a model trained with carefully crafted datasets due to unique image characteristics and domain-specific challenges, especially in pathology. Primitive detection and segmentation in digitized tissue samples are essential for objective and automated diagnosis and prognosis of cancer. SAM (Segment Anything Model) has re

Jian-Zhou Zhu

Irregular compactons and peakons from some nonlinear dispersions can be regularized by another type of nonlinear dispersion, defined by a pseudo-differential operator in physical space for the Galerkin truncation preserving finite Fourier modes of wavenumbers no larger than $K$. This resembles yet differs from the Korteweg-de Vries (KdV) regularization of th

Riza Velioglu, Robin Chan, Barbara Hammer

In the realm of fashion object detection and segmentation for online shopping images, existing state-of-the-art fashion parsing models encounter limitations, particularly when exposed to non-model-worn apparel and close-up shots. To address these failures, we introduce FashionFail; a new fashion dataset with e-commerce images for object detection and segment

Q. Y. Luo, X. Zhang, L. H. Chen, J. M. Yao

We present a benchmark study of generator coordinate method (GCM) combined with eigenvector continuation (EC) in two different schemes for the low-lying states of Lipkin-Meshkov-Glick (LMG) model, where the interaction strength is treated as a controlling parameter, simulating quantum many-body systems with the phase transition from non-collective to collect

Federico L. Bottesi, Guillermo R. Zemba

We consider the known effective field theory of the Calogero-Sutherland model in the thermodynamic limit of large number of particles, obtained from the standard procedure in conformal field theory: the Hilbert space is constructed a priori in terms of irreducible representations of the symmetry algebra, and not by diagonalization of the hamiltonian, which i

Lucas Relic, Roberto Azevedo, Markus Gross, Christopher Schroers

Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates. Previous methods focus on using diffusion models as expressive decoders robust to quantization errors in the conditioning signals, yet achieving competitive results in this manner requires c

William Gantt, Aaron Steven White

Effective ontology transfer has been a major goal of recent work on event argument extraction (EAE). Two methods in particular -- question answering (QA) and template infilling (TI) -- have emerged as promising approaches to this problem. However, detailed explorations of these techniques' ability to actually enable this transfer are lacking. In this work, w

Ferenc Bencs, Guus Regts

Answering a question of Gamarnik and Smedira, we give a polynomial time algorithm that approximately computes the volume of a truncation of a relaxation of the independent set polytope, improving on their quasi-polynomial time algorithm. Our algorithm is obtained by viewing the volume as an evaluation of a graph polynomial and we approximate this evaluation

Quirijn Meijer, Sarah Caudill

Gravitational-wave analyses depend heavily on waveforms that model the evolution of compact binary coalescences as seen by observing detectors. In many cases these waveforms are given by waveform approximants, models that approximate the amplitude and phase of the waveform at a set of frequencies. Because of their omnipresence, improving the speed at which a

Christine Chang

Arguin, Dubach & Hartung recently conjectured that an intermediate regime exists between IID and log-correlated statistics for extreme values of a random model of the Riemann zeta function. For the same model, we prove a matching upper and lower tail for the distribution of its maximum. This tail interpolates between that of the two aforementioned regimes. W

Deepali Agarwal, Joseph D. Romano

Gravitational waves (GWs) induce correlated perturbations to the arrival times of pulses from an array of galactic millisecond pulsars. The expected correlations, obtained by averaging over many pairs of pulsars having the same angular separation (pulsar averaging) and over an ensemble of model universes (ensemble averaging), are described by the Hellings an

Wolfram Ratzinger, Sebastian Schenk, Pedro Schwaller

In an external electric or magnetic field, a gravitational wave (GW) may be converted into electromagnetic radiation. We present a coordinate-invariant framework to describe the GW signal in a detector that is based on this effect, such as cavities for axion searches. In this framework, we pay special attention to the definition of manifestly coordinate-inde

Sung Kei Li, Jose M. Diego, Patrick L. Kelly, Jeremy Lim

We report the discovery of transients among star clusters in a distant galaxy that is gravitationally lensed by a foreground galaxy cluster, and explore whether these transients correspond to: (i) intrinsic variations associated with stellar outbursts; or (ii) extrinsic variations imposed through microlensing by intraclusters stars along, perhaps, with primo

Hanlin Tian, Kethan Reddy, Yuxiang Feng, Mohammed Quddus

This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle (AV) training and testing. CRITICAL stands out for its ability to generate diverse scenarios, focusing on critical driving situations that target specific learning and performance gaps identified in the Reinforcement Learning (RL) agent. The framework achieves this by integr

Cedric J. Gommes, Purushottam S. Dubey, Andreas M. Stadler, Baohu Wu

A mathematical model is developed, to jointly analyze elastic and inelastic scattering data of fluctuating membranes within a single theoretical framework. The model builds on a non-homogeneously clipped time-dependent Gaussian random field. This specific approach provides one with general analytical expressions for the intermediate scattering function, for

Liu Yang, Qiang Li, Xiaoyang Ren, Yi Fang

Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works co

Pauline J. Ollitrault, Cristian L. Cortes, Jerome F. Gonthier, Robert M. Parrish

The quantum phase estimation algorithm stands as the primary method for determining the ground state energy of a molecular electronic Hamiltonian on a quantum computer. In this context, the ability to initialize a classically tractable state that has a strong overlap with the desired ground state is critical as it directly affects the runtime of the algorith

Hexiang Wei, Jianhao Jiao, Xiangcheng Hu, Jingwen Yu

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present Fu

Chirag Parikh, Rohit Saluja, C. V. Jawahar, Ravi Kiran Sarvadevabhatla

Intelligent vehicle systems require a deep understanding of the interplay between road conditions, surrounding entities, and the ego vehicle's driving behavior for safe and efficient navigation. This is particularly critical in developing countries where traffic situations are often dense and unstructured with heterogeneous road occupants. Existing datasets,

Tianwen Tang, Tong Zhu, Haodong Liu, Yin Bai

Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets. Previous zero-shot DST models mainly suffer from domain transferring and partial prediction problems. To address these challenges, we propose Mixture of Prefix Experts (MoPE) to establish connections between similar slots in different

Janine Hoelscher, Inbar Fried, Spiros Tsalikis, Jason Akulian

Steerable needles are minimally invasive devices that enable novel medical procedures by following curved paths to avoid critical anatomical obstacles. Planning algorithms can be used to find a steerable needle motion plan to a target. Deployment typically consists of a physician manually inserting the steerable needle into tissue at the motion plan's start

Josie Harrison, Alexander Hollberg, Yinan Yu

Computer vision models trained on Google Street View images can create material cadastres. However, current approaches need manually annotated datasets that are difficult to obtain and often have class imbalance. To address these challenges, this paper fine-tuned a Swin Transformer model on a synthetic dataset generated with DALL-E and compared the performan

Ginestra Bianconi

We propose a theory for coupling matter fields with discrete geometry on higher-order networks, i.e. cell complexes. The key idea of the approach is to associate to a higher-order network the quantum entropy of its metric. Specifically we propose an action having two contributions. The first contribution is proportional to the logarithm of the volume associa

Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit

State-of-the-art large language models (LLMs) have become indispensable tools for various tasks. However, training LLMs to serve as effective assistants for humans requires careful consideration. A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences a

Radosław Adamczak, Michał Kotowski

Continuous-time Mallows processes are processes of random permutations of the set $\{1, \ldots, n\}$ whose marginal at time $t$ is the Mallows distribution with parameter $t$. Recently Corsini showed that there exists a unique Markov Mallows process whose left inversions are independent counting processes. We prove that this process admits a global and a loc

Shawn X. Cui, César Galindo, Diego Romero

In this paper, we present a detailed mathematical description of the error correction process for Kitaev's model for finite Abelian groups. The number of errors Kitaev's model can correct depends on the lattice and its topology. Although there is a theoretical maximum number of errors that can be corrected, we prove that correcting this number of errors, in

Marco Abbadini, Francesca Guffanti

This paper aims to incorporate the notion of quantifier-free formulas modulo a first-order theory and the stratification of formulas by quantifier alternation depth modulo a first-order theory into the algebraic treatment of classical first-order logic. The set of quantifier-free formulas modulo a theory is axiomatized by what we call a quantifier-free fragm

Mikhail Chernyavsky, Andrei Lebedev, Yurii Trubnikov

The well-known mathematical instrument for detection common roots for pairs of polynomials and multiple roots of polynomials are resultants and discriminants. For a pair of polynomials $f$ and $g$ their resultant $R(f,g)$ is a function of their coefficients. Zeros of resultant $R(f,g)$ correspond to the families of coefficients of $f$ and $g$ such that $f$ a

Boyuan Peng, Jiaju Chen, P. Bilha Githinji, Ijaz Gul

Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope optical aberrations remains a critical cha

Kushagra Garg, Zeeshan Ahmed, Andreas Thomasen

We study the deep multi-scale entanglement renormalization ansatz (DMERA) on quantum hardware and the causal cone of a subset of the qubits which make up the ansatz. This causal cone spans $O(M+\log{N})$ physical qubits on a quantum device, where $M$ and $N$ are the subset size and the total number qubits in the ansatz respectively. This allows for the deter

Maxime Lucas, Luca Gallo, Arsham Ghavasieh, Federico Battiston

Empirical complex systems can be characterized not only by pairwise interactions, but also by higher-order (group) interactions influencing collective phenomena, from metabolic reactions to epidemics. Nevertheless, higher-order networks' apparent superior descriptive power -- compared to classical pairwise networks -- comes with a much increased model comple

Run-Qiang Jian, Zhu-Hong Zhang

We establish three circles theorems for subharmonic functions on Riemannian manifolds with nonnegative Ricci curvature, as well as on gradient shrinking Ricci solitons with scalar curvature bounded from below by $\frac{n-2}{2}$. We also establish a three circiles theorem for holomorphic functions on gradient shrinking K\"{a}hler-Ricci solitons with some curv

Yi Wang, Jingye Yang

For most aspherical Seifert-fibered 3-manifolds $M$, the space of Seifert fiberings $SF(M)$ is known to have contractible components. It is also known that the space of Hopf fiberings of the three-sphere is noncontractible. We provide the second example of a non-aspherical 3-manifold $M$ such that $SF(M)$ has noncontractible components. In particular, we sho

Li Zhang, Shihe Wang, Xianqing Jia, Zhihan Zheng

The emergent large language/multimodal models facilitate the evolution of mobile agents, especially in mobile UI task automation. However, existing evaluation approaches, which rely on human validation or established datasets to compare agent-predicted actions with predefined action sequences, are unscalable and unfaithful. To overcome these limitations, thi

Girmaw Abebe Tadesse, Caleb Robinson, Gilles Quentin Hacheme, Akram Zaytar

This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia using sub-meter gray-scale aerial imagery from 1943 and 1972. In this work, we propose a workflow for analyzing

Jean-Marie Chauvet

Memory traces--changes in the memory system that result from the perception and encoding of an event--were measured in pioneering studies by Endel Tulving and Michael J. Watkins in 1975. These and further experiments informed the maturation of Tulving's memory model, from the GAPS (General Abstract Processing System} to the SPI (Serial-Parallel Independent)

Alessia Valzelli, Alice Boschetti, Francesco Mattiotti, Armin Kargol

Large scale simulations of light-matter interaction in natural photosynthetic antenna complexes containing more than one hundred thousands chlorophyll molecules, comparable with natural size, have been performed. Photosynthetic antenna complexes present in Green sulfur bacteria and Purple bacteria have been analyzed using a radiative non-Hermitian Hamiltonia

Jacob Bernstein, Letian Chen, Lu Wang

Given a smooth asymptotically conical self-expander that is strictly unstable we construct a (singular) Morse flow line of the expander functional that connects it to a stable self-expander. This flow is monotone in a suitable sense and has small singular set.

Agneet Chatterjee, Tejas Gokhale, Chitta Baral, Yezhou Yang

Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results, the impact of the language prior, particularly in terms of generalization and robustness, remains unexplored. In this paper, we address this gap by quantifying the impact of this prior and introduce meth

Scott T. Kay, Joey Braspenning, Jens Chluba, John C. Helly

The relativistic Sunyaev-Zel'dovich (SZ) effect can be used to measure intracluster gas temperatures independently of X-ray spectroscopy. Here, we use the large-volume FLAMINGO simulation suite to determine whether SZ $y$-weighted temperatures lead to more accurate hydrostatic mass estimates in massive ($M_{\rm 500c} > 7.5\times 10^{14}\,{\rm M}_{\odot}$) cl

Jonathan Rusert

Text classification systems have continuously improved in performance over the years. However, nearly all current SOTA classifiers have a similar shortcoming, they process text in a horizontal manner. Vertically written words will not be recognized by a classifier. In contrast, humans are easily able to recognize and read words written both horizontally and

Wasim Ahmad, Maha Shadaydeh, Joachim Denzler

Time series data from real-world systems often display non-stationary behavior, indicating varying statistical characteristics over time. This inherent variability poses significant challenges in deciphering the underlying structural relationships within the data, particularly in correlation and causality analyses, model stability, etc. Recognizing distinct

Gang Liu

We consider complete K\"ahler manifolds with nonnegative Ricci curvature. The main results are: 1. When the manifold has nonnegative bisectional curvature, we show that $\lim\limits_{r\to\infty}\frac{r^{2}}{vol(B(p, r))}\int_{B(p, r)}S$ exists. In other words, it depends only on the manifold. This solves a question of Ni. Also, we establish estimates among v

Leo Schäfer, Federico Vigolo

We introduce a coarse algebraic invariant for coarse groups and use it to differentiate various coarsifications of the group of integers. This lets us answer two questions posed by Leitner and the second author. The invariant is obtained by considering the set of exponents n such that taking n-th powers defines a coarse equivalence of the coarse group.

Tianyu Zhu, Myong Chol Jung, Jesse Clark

Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular training frameworks typically learn from binary (positive/negative) relevance, making them ineffective at incorporating desired rankings. As a result, the poor ranking performance of these models forces systems to em

Stephen Jun Villejo, Sara Martino, Finn Lindgren, Janine Illian

This work aims to combine two primary meteorological data sources in the Philippines: data from a sparse network of weather stations and outcomes of a numerical weather prediction model. To this end, we propose a data fusion model which is primarily motivated by the problem of sparsity in the observational data and the use of a numerical prediction model as

M. Frosini, T. Duguet, P. Tamagno

Whether for fundamental studies or nuclear data evaluations, first-principle calculations of atomic nuclei constitute the path forward. Today, performing \textit{ab initio} calculations (a) of heavy nuclei, (b) of doubly open-shell nuclei or (c) with a sub-percent accuracy is at the forefront of nuclear structure theory. While combining any two of these feat

Zhiwei Yang, Jing Liu, Peng Wu

Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-grained pseudo-labels based on weak-label and then self-training a classifier is currently a promising solution. However, since the existing methods use only RGB visual modality and the utilization of category text information is neglected, thus limiting the generation o

Francesco Vercesi, Susie Poirier, Anna Minguzzi, Léonie Canet

We study the phase turbulence of the one-dimensional complex Ginzburg-Landau equation, in which the defect-free chaotic dynamics of the order parameter maps to a phase equation well approximated by the Kuramoto-Sivashinsky model. In this regime, the behaviour of the large wavelength modes is captured by the Kardar-Parisi-Zhang equation, determining universal

Matthieu Cadiot, Dominic Blanco

In this article, we present a comprehensive framework for constructing smooth, localized solutions in systems of semi-linear partial differential equations, with a particular emphasis to the Gray-Scott model. Specifically, we construct a natural Hilbert space $\mathcal{H}$ for the study of systems of autonomous semi-linear PDEs, on which products and differe

Yen-Hsun Lin, Meng-Ru Wu

It has been recently proposed that the boosted dark matter (BDM) by supernova neutrinos (SN$\nu$) from SN1987a or from the next Galactic supernova (SN) can serve as a novel component to probe nonvanishing interaction between dark matter (DM) and the standard model leptons [Y.-H. Lin et al., Phys. Rev. Lett 130, 111002 (2023) and Y.-H. Lin et al., Phys. Rev.

Paweł Szumniak, Daniel Loss, Jelena Klinovaja

Non-equilibrium transport in hybrid semiconductor-superconductor nanowires is crucial for many quantum phenomena such as generating entangled states via cross Andreev reflection (CAR) processes, detecting topological superconductivity, reading out Andreev spin qubits, coupling spin qubits over long distances and so on. Here, we investigate numerically transp

Robin Weiler, Matthias Brucklacher, Cyriel M. A. Pennartz, Sander M. Bohté

To make sense of their surroundings, intelligent systems must transform complex sensory inputs to structured codes that are reduced to task-relevant information such as object category. Biological agents achieve this in a largely autonomous manner, presumably via self-supervised learning. Whereas previous attempts to model the underlying mechanisms were larg

Anne Etien, Nicolas Anquetil

Relational databases play a central role in many information systems. Their schema contains structural (e.g. tables and columns) and behavioral (e.g. stored procedures or views) entity descriptions. Then, just like for ``normal'' software, changes in legislation, offered functionalities, or functional contexts, impose to evolve databases and their schemas. B

Yutaka Kinoshita, Nariya Uchida

Coupling between flow and orientation is a central issue in understanding the collective dynamics of active biofilaments and cells. Active stresses generated by motor activity destroy (quasi-)long-range orientational order and induce chaotic flows with many vortices. In cellular and subcellular environment, alignment is also hindered by heterogeneous filamen

Lucas Murray, Tatiana Castillo, Jaime Carrasco, Andrés Weintraub

Over the past decades, the increase in both frequency and intensity of large-scale wildfires due to climate change has emerged as a significant natural threat. The pressing need to design resilient landscapes capable of withstanding such disasters has become paramount, requiring the development of advanced decision-support tools. Existing methodologies, incl

Xiaoze Xu, Xiuyu Sun, Wei Han, Xiaohui Zhong

Data assimilation (DA), as an indispensable component within contemporary Numerical Weather Prediction (NWP) systems, plays a crucial role in generating the analysis that significantly impacts forecast performance. Nevertheless, the development of an efficient DA system poses significant challenges, particularly in establishing intricate relationships betwee

Qi Li, Weili Zhen, Ning Wang, Meng Shi

Recent experimental and theoretical work has focused on two-dimensional van der Waals (2D vdW) magnets due to their potential applications in sensing and spintronics devises. In measurements of these emerging materials, conventional magnetometry often encounters challenges in characterizing the magnetic properties of small-sized vdW materials, especially for

M. Mori, K. Abe, Y. Hayato, K. Hiraide

Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10\,s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that is critical for understanding the dynamics of the supernova explosion me

Tatsuya Gima, Tesshu Hanaka, Kohei Noro, Hirotaka Ono

We show that for every $n$-vertex graph with at least one edge, its treewidth is greater than or equal to $n \lambda_{2} / (\Delta + \lambda_{2}) - 1$, where $\Delta$ and $\lambda_{2}$ are the maximum degree and the second smallest Laplacian eigenvalue of the graph, respectively. This lower bound improves the one by Chandran and Subramanian [Inf. Process. Le

Frederik Zuiderveen Borgesius, Nina Baranowska, Philipp Hacker, Alessandro Fabris

This brief paper provides an introduction to non-discrimination law in Europe. It answers the questions: What are the key characteristics of non-discrimination law in Europe, and how do the different statutes relate to one another? Our main target group is computer scientists and users of artificial intelligence (AI) interested in an introduction to non-disc

Xuan Xie, Jiayang Song, Zhehua Zhou, Yuheng Huang

While Large Language Models (LLMs) have seen widespread applications across numerous fields, their limited interpretability poses concerns regarding their safe operations from multiple aspects, e.g., truthfulness, robustness, and fairness. Recent research has started developing quality assurance methods for LLMs, introducing techniques such as offline detect

Xinze Lyu, Sundar Aditya, Bruno Clerckx

Non-orthogonal unicast multicast (NOUM) is a variant of multi-antenna multi-user communications where the users desire a shared message (multicast) in addition to their respective unique messages (unicast). The multicast rate is capped in many emerging NOUM applications, such as live-event broadcasting, location-based services and vehicular communications. G

Konlavach Mengsuwan, Juan Camilo Rivera Palacio, Masahiro Ryo

Object counting is a popular task in deep learning applications in various domains, including agriculture. A conventional deep learning approach requires a large amount of training data, often a logistic problem in a real-world application. To address this issue, we examined how well ChatGPT (GPT4V) and a general-purpose AI (foundation model for object count

Rongjian Xu, Zhilu Zhang, Renlong Wu, Wangmeng Zuo

Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments. Leveraging near-infrared (NIR) images to assist visible RGB image denoising shows the potential to address this issue, becoming a promising technology. Nonetheless, existing works sti

Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley

Adversarial imitation learning (AIL) has stood out as a dominant framework across various imitation learning (IL) applications, with Discriminator Actor Critic (DAC) (Kostrikov et al.,, 2019) demonstrating the effectiveness of off-policy learning algorithms in improving sample efficiency and scalability to higher-dimensional observations. Despite DAC's empir

Elliz Akindji, Julia Slipantschuk, Oscar F. Bandtlow, Wolfram Just

Extended dynamic mode decomposition (EDMD) is a data-driven algorithm for approximating spectral data of the Koopman operator associated to a dynamical system, combining a Galerkin method of order N and collocation method of order M. Spectral convergence of this method subtly depends on appropriate choice of the space of observables. For chaotic analytic ful

Shiva Aryal, Tuyen Do, Bisesh Heyojoo, Sandeep Chataut

In the rapidly evolving field of artificial intelligence, the ability to harness and integrate knowledge across various domains stands as a paramount challenge and opportunity. This study introduces a novel approach to cross-domain knowledge discovery through the deployment of multi-AI agents, each specialized in distinct knowledge domains. These AI agents,

Sayan Goswami

Recently, using machinery's from Ergodic theory, Z. Lian, and R. Xiao proved if $P$ is any polynomial with no constant term, then for every finite coloring of $\mathbb{N}$, there exists two infinite subsets $B,C$ of $\mathbb{N}$ such that the set $\{P(b)+P(c):b\in B, c\in C\}$ is monochromatic. In this article we improve their result by proving that instead

Weijun Li, Marco Roda, Julia Tena-Vidal, Costas Andreopoulos

We present the first combined tuning, using GENIE, of four transverse kinematic imbalance measurements of neutrino-hydrocarbon scattering, both with and without pion final states, from the T2K and MINERvA experiments. As a proof of concept, we have simultaneously tuned the initial state and final-state interaction models (SF-CFG and hA, respectively), produc

Haoran Qiu, Weichao Mao, Archit Patke, Shengkun Cui

Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains. However, efficiently serving LLM inference requests is challenging due to their unpredictable execution times originating from the autoregressive nature of generative models. Existing LLM serving systems exploit first-come-first-serve (FCFS) sche

Maria Ubiali

The unprecedented precision of experimental measurements at the Large Hadron Collider (LHC) and the increased statistics that will be reached in the High-Luminosity phase of the LHC (HL-LHC) are pushing the phenomenology community to a new precision frontier, in which new challenges present themselves and new questions arise. A key ingredients of theoretical

Patrick M. Shober, Jeremie Vaubaillon

Context. The determination of meteor shower or parent body associations is inherently a statistical problem. Traditional methods, primarily the similarity discriminants, have limitations, particularly in handling the increasing volume and complexity of meteoroid orbit data. Aims. We aim to introduce a new, more statistically robust and generalizable method f

Cong Wei, Haoxian Tan, Yujie Zhong, Yujiu Yang

Recent advancements have empowered Large Language Models for Vision (vLLMs) to generate detailed perceptual outcomes, including bounding boxes and masks. Nonetheless, there are two constraints that restrict the further application of these vLLMs: the incapability of handling multiple targets per query and the failure to identify the absence of query objects

Gaofeng Huang

The real Calogero--Moser space $\mathcal{C}_n^\mathbb{R}$ is a noncompact, totally real submanifold of the complex Calogero--Moser space $\mathcal{C}_n$. We prove that every symplectic diffeomorphism of $\mathcal{C}_n^\mathbb{R}$ smoothly isotopic to the identity can be approximated in the fine Whitney topology -- the strongest in this context -- by holomorp

Kai Kohyama, Shintaro Shiba, Yoshimitsu Aoki

Capturing a 3D human body is one of the important tasks in computer vision with a wide range of applications such as virtual reality and sports analysis. However, conventional frame cameras are limited by their temporal resolution and dynamic range, which imposes constraints in real-world application setups. Event cameras have the advantages of high temporal

Yushan Bai, Jiawei Chen, Kaiping Liu

In this paper, in order to find critical points of vector-valued functions with respect to the partial order induced by a closed, convex, and pointed cone with nonempty interior, we propose a nonlinear modified Polak-Ribiere-Polyak type conjugate gradient method with a nonnegative conjugate parameter. We show that the search direction in our method satisfies