February 2024 arXiv papers — page 31

Domonkos Csuzdi, Olivér Törő, Tamás Bécsi

Particle filters are a frequent choice for inference tasks in nonlinear and non-Gaussian state-space models. They can either be used for state inference by approximating the filtering distribution or for parameter inference by approximating the marginal data (observation) likelihood. A good proposal distribution and a good resampling scheme are crucial to ob

Zhixin Cyrillus Tan, Aaron S. Meyer

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the con

Eusebio Gardella, Julian Kranz, Andrea Vaccaro

We exhibit examples of actions of countable discrete groups on both simple and non-simple nuclear stably finite C*-algebras that are tracially amenable but not amenable. We furthermore obtain that, under the additional assumption of strict comparison, amenability is equivalent to tracial amenability plus the equivariant analogue of Matui--Sato's property (SI

Michael Greenblatt

In a well-known paper by Bruna, Nagel and Wainger [BNW], Fourier transform decay estimates were proved for smooth hypersurfaces of finite line type bounding a convex domain. In this paper, we generalize their results in the following ways. First, for a surface that is locally the graph of a convex real analytic function, we show that a natural analogue holds

Ming Wang, Yuanzhong Liu, Xiaoyu Liang, Songlian Li

LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to instruct LLMs proficiently poses a challenge for non-AI experts. Existing research in prompt engineering suggests somewhat scattered optimization principles and designs empirically dependent prompt optimizers. Unfortunately, these endeavor

P. A. Palicio, F. Matteucci, M. Della Valle, E. Spitoni

Type Ia supernovae play a key role in the evolution of galaxies by polluting the interstellar medium with a fraction of iron peak elements larger than that released in the core collapse supernova events. Their light-curve, moreover, is widely used in cosmological studies as it constitutes a reliable distance indicator at extra-galactic scales. Among the mech

William Kelley, Nathan Ngo, Adrian V. Dalca, Bruce Fischl

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested

Rita Ferreira, José Matias, Elvira Zappale

We consider a thin multi-domain of $\mathbb R^N$, with $N\geq 2$, consisting of a vertical rod upon a horizontal disk. In this thin multi-domain, we introduce a bulk energy density of the kind $W(D^2U)$, where $W$ is a continuous function with linear growth at $\infty$ and $D^2U$ denotes the Hessian tensor of a vector-valued function $U$ that represents a de

Alessandro Maisto

In this work, we propose a Distributional Semantic resource enriched with linguistic and lexical information extracted from electronic dictionaries, designed to address the challenge of bridging the gap between the continuous semantic values represented by distributional vectors and the discrete descriptions offered by general semantics theory. Recently, man

Hang Zou, Qiyang Zhao, Samson Lasaulce, Lina Bariah

Generative Artificial Intelligence (GenAI) and communication networks are expected to have groundbreaking synergies for 6G. Connecting GenAI agents via a wireless network can potentially unleash the power of Collective Intelligence (CI) and pave the way for Artificial General Intelligence (AGI). However, current wireless networks are designed as a "data pipe

B. Evrard, H. S. Adlong, A. A. Ghita, T. Uto

We use nonlinear pump-probe spectroscopy to study optical excitations in a charge-tunable MoSe$_2$\WS$_2$ moir\'e heterostructure. An intense red-detuned laser pulse creates a photonic dressing of the material by introducing a large virtual population of excitons or exciton polarons in a deep moir\'e potential. By measuring the resulting ac Stark effect with

Sepideh Javadi, Sajad Faramarzi, Farshad Zeinali, Hosein Zarini

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) s

Christoph Weilenmann, Alexandros Ziogas, Till Zellweger, Kevin Portner

Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that

Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu

Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion models primarily focus on incorporating text-visual relationships exclusively into the reverse process, often disregarding their relevance in the forward process. This inconsistency

Federico Schianchi, Maximiliano Ujevic, Anna Neuweiler, Henrique Gieg

Accurate modeling of the multi-messenger signatures connected to binary neutron star mergers requires proper knowledge on the final remnant's fate and the conditions under which black holes (BHs) can form in such mergers. In this article, we use a suite of 84 numerical-relativity simulations in 28 different physical setups to explore the impact of the indivi

Roger Van Peski

Recently, Sawin and Wood (arXiv:math/2210.06279) proved a formula for the distribution of a random abelian group $G$ in terms of its $H$-moments $\mathbb{E} [\#\operatorname{Sur}(G,H)]$. We show that properties of Macdonald polynomials yield an alternate proof.

Vadim Lebovici, Jan-Paul Lerch, Steve Oudot

Local conditions for the direct summands of a persistence module to belong to a certain class of indecomposables have been proposed in the 2-parameter setting, notably for the class of indecomposables called block modules, which plays a prominent role in levelset persistence. Here we generalize the local condition for decomposability into block modules to th

Jonathan Lindbloom, Jan Glaubitz, Anne Gelb

Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian prior model for the unknown which is augmented by a generalized gamma hyper-prior model for variance hyper-parameters. This investigation generalizes these models and their efficient

Esmée Theewis, Mark Veraar

Using the weak convergence approach, we prove the large deviation principle (LDP) for solutions to quasilinear stochastic evolution equations with small Gaussian noise in the critical variational setting, a recently developed general variational framework. No additional assumptions are made apart from those required for well-posedness. In particular, no mono

Matthias Kellner, Michele Ceriotti

Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or theoretical setup. Uncertainty estimation is essential to quantify this error, and make application of data

Nadia Skoglund Taki

In this paper we establish existence, uniqueness, and boundedness results for an elliptic variational inequality coupled with a nonlinear ordinary differential equation. Under the general framework, we present a new application modelling the antiplane shear deformation of a static frictional adhesive contact problem. The adhesion process has been extensively

Simin Keykhosravi, Ebrahim Bedeer

This paper investigates doubly-selective (i.e., time- and frequency-selective) channel estimation in faster-than-Nyquist (FTN) signaling HF communications. In particular, we propose a novel IM-based channel estimation algorithm for FTN signaling HF communications including pilot sequence placement (PSP) and pilot sequence location identification (PSLI) algor

Howard Yen, Tianyu Gao, Danqi Chen

Extending large language models (LLMs) to process longer inputs is crucial for a wide range of applications. However, the substantial computational cost of transformers and limited generalization of positional encoding restrict the size of their context window. We introduce Context Expansion with Parallel Encoding (CEPE), a framework that can be applied to a

Tareq Jaouni, Francesco Di Colandrea, Lorenzo Amato, Filippo Cardano

The characterization of a unitary gate is experimentally accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to reconstruct the underlying operator. The process matrix is typically extracted from maximum-likelihood estimation. Recently, optimization strategies based on evolutionary and machine-learnin

Julia Santana-Andreo, Holger-Dietrich Saßnick, Caterina Cocchi

Modern advances in generating ultrabright electron beams have unlocked unprecedented experimental advances based on synchrotron radiation. Current challenges lie in improving the quality of electron sources with novel photocathode materials such as alkali-based semiconductors. To unleash their potential, a detailed characterization and prediction of their fu

Jae Yong Lee, Steffen Schotthöfer, Tianbai Xiao, Sebastian Krumscheid

This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e

Zhenwei Lyu, Lijing Shao

Coalescing neutron star--white dwarf (NS-WD) binaries are among the primary targets for upcoming space-borne gravitational wave (GW) detectors such as LISA, TaiJi, TianQin, etc. During close interaction, these binaries undergo mass transfer, emitting simultaneous X-rays and GWs. This offers a unique opportunity to measure mass transfer rates and study compac

Valentina Valori, Sunming Qin, Victor Petrov, Annalisa Manera

Accurate models of turbulent buoyant flows are essential for the design of nuclear reactors thermal hydraulics and passive safety systems. However, available models fail to fully capture the physics of turbulent mixing when buoyancy becomes predominant with respect to momentum. Therefore, high-fidelity experiments of well-controlled fundamental flows are nee

Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier

Conversational systems have made significant progress in generating natural language responses. However, their potential as conversational search systems is currently limited due to their passive role in the information-seeking process. One major limitation is the scarcity of datasets that provide labelled ambiguous questions along with a supporting corpus o

Xinqi Liu, Chenming Wu, Jialun Liu, Xing Liu

In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA). Our innovation lies in addressing the intricate challenges of delivering high-fidelity human body reconstructions and aligning 3D Gaussians with human skin surfaces accurately. The key contributions of this paper are twofold.

Luigi C. Berselli, Alex Kaltenbach

In the present paper, we establish the well-posedness, stability, and (weak) convergence of a fully-discrete approximation of the unsteady $p(\cdot,\cdot)$-Navier-Stokes equations employing an implicit Euler step in time and a discretely inf-sup-stable finite element approximation in space. Moreover, numerical experiments are carried out that supplement the

Valerio Bocci

Silicon photomultipliers are photon sensors based on an array of pixels, each consisting of a single photon avalanche photodiode (SPAD). Reading of the array is not done on a single-pixel basis, but all pixels are read simultaneously. Because of thermal agitation and other phenomena, each pixel can fire even in the dark with a small but not zero probability

Ravindra K. Bisht, Evgeniy Petrov

In this paper, we introduce a new category of mappings within metric spaces, specifically focusing on three-point analogs of the well-established Chatterjea type mappings. We demonstrate that Chatterjea type mappings and their three-point analogs are different classes of mappings. A fixed point theorem for generalized Chatterjea type mappings is established.

Iva Březinová, Markus Stimpfle, Stefan Donsa, Angel Rubio

We study dynamical (quasi)-condensation in the Fermi-Hubbard model starting from a completely uncorrelated initial state of adjacent doubly occupied sites. We show that upon expansion of the system in one dimension, dynamical (quasi)-condensation occurs not only for large interactions via the condensation of doublons, but also for small interactions. The beh

Maria Carolina Volpato, Gabriel da Silva Sampaio, Pierre-Louis de Assis

As quantum photonic hardware scales toward computationally relevant sizes, energy consumption has emerged as a key constraint. Programmable photonic integrated circuits, composed of interferometer meshes with tunable phase modulators, provide a flexible platform for quantum information processing using both qubits and qudits. In this work, we analyze the ene

Yuyang Ding, Juntao Li, Pinzheng Wang, Zecheng Tang

Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema. In this work, we explore the potential enhancement o

Yorick Leonard Adrian Schmerwitz, Núria Urgell Ollé, Gianluca Levi, Hannes Jónsson

Excited electronic states of molecules and solids play a fundamental role in fields such as catalysis and electronics. In electronic structure calculations, excited states typically correspond to saddle points on the surface described by the variation of the energy as a function of the electronic degrees of freedom. A direct optimization algorithm based on g

Erhard Reschenhofer

The results of a recent simulation with a complex global climate model suggest that the overturning component of the freshwater transport at the southern boundary of the Atlantic could be used as an early-warning indicator of an AMOC collapse. However, there are two shortcomings. Firstly, the simulation is based on some implausible assumptions. It is therefo

Yifei Li, Xiaohong Liu, Yicong Peng, Guangtao Zhai

Video conferencing has caught much more attention recently. High fidelity and low bandwidth are two major objectives of video compression for video conferencing applications. Most pioneering methods rely on classic video compression codec without high-level feature embedding and thus can not reach the extremely low bandwidth. Recent works instead employ mode

Seong Hun Lee, Javier Civera, Patrick Vandewalle

We propose a robust method for point cloud registration that can handle both unknown scales and extreme outlier ratios. Our method, dubbed PCR-99, uses a deterministic 3-point sampling approach with two novel mechanisms that significantly boost the speed: (1) an improved ordering of the samples based on pairwise scale consistency, prioritizing the point corr

Xusheng Du, Hui Yang

In this paper, we study asymptotic expansions of positive solutions of the conformal scalar curvature equation $$ - \Delta u = K(x) u^\frac{n + 2}{n - 2} ~~~~~~ \textmd{in} ~ B_1 \setminus \{ 0 \} $$ with an isolated singularity at the origin. Under certain flatness conditions on $K$, we establish a higher-order expansion of solutions near the origin. In par

Marko Pranjić, Kaja Dobrovoljc, Senja Pollak, Matej Martinc

In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provides insight into the evolution of language caused by changes in society and culture. We present the first Slovene dataset for evaluating semantic change detection systems, which cont

Fei Sun, Jingdong Shao, Rui Wen, Kun Xu

By using the extrapolation method, a polarized Polykov-loop potential at finite real angular velocity is constructed from the lattice results at finite imaginary angular velocity. The chiral and deconfinement phase transitions under rotation have been simultaneously investigated in the Polarized-Polyakov-loop Nambu-Jona-Lasinio (PPNJL) model. It is observed

Qixuan Zheng, Ming Zhang, Hong Yan

To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods, despite the sparsity of their compatibility tensor, still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introdu

Yuzhen Qi, Jin Yan, Jia Zhou

A digraph is strongly connected if it has a directed path from $x$ to $y$ for every ordered pair of distinct vertices $x, y$ and it is strongly $k$-connected if it has at least $k+1$ vertices and remains strongly connected when we delete any set of at most $k-1$ vertices. For a digraph $D$, we use $\delta(D)$ to denote $\mathop{\text{min}}\limits_{v\in V (D)

Mateusz Dubiel, Matthew Aylett, Anuschka Schmitt, Zilin Ma

The aim of this workshop is two-fold. First, it aims to establish a research community focused on design and evaluation of synthetic speech (TTS) interfaces that are tailored not only to goal oriented tasks (e.g., food ordering, online shopping) but also personal growth and resilience promoting applications (e.g., coaching, mindful reflection, and tutoring).

Julia Beuster, Carsten Jan Smeenk, Saw James Myint, Reza Faramarzahanagri

With the upcoming multitude of commercial and public applications envisioned in the mobile 6G radio landscape using unmanned aerial vehicles (UAVs), integrated sensing and communication (ISAC) plays a key role to enable the detection and localization of passive objects with radar sensing, while optimizing the utilization of scarce resources. To explore the p

Andrea Gabrielli

In this paper we analyse d-dimensional Langevin equations in Ito representation characterised by anisotropic multiplicative noise, composed by the superposition of an isotropic tensorial component and a radial one, and a radial power law drift term. This class of model is relevant in many contexts ranging from vortex stochastic dynamics, passive scalar trans

Thomas Apel, Philipp Zilk

The Laplace eigenvalue problem on circular sectors has eigenfunctions with corner singularities. Standard methods may produce suboptimal approximation results. To address this issue, a novel numerical algorithm that enhances standard isogeometric analysis is proposed in this paper by using a single-patch graded mesh refinement scheme. Numerical tests demonst

Borka Jadrijević, Kristina Miletić

In this paper, we give characterization of quadratic $\varepsilon-$canonical number system ($\varepsilon-$CNS) polynomials for all values $\varepsilon \in\lbrack0,1)$. Our characterization provides a unified view of the well-known characterizations of the classical quadratic CNS polynomials ($\varepsilon=0$) and quadratic SCNS polynomials ($\varepsilon=1/2$)

Ahmad Abubakar, Yahya Zweiri, AbdelGafoor Haddad, Mubarak Yakubu

Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-induced longitudinal slippage. However, latency arising from transmission delays over a network presents a challenge in maintaining high-fidelity closed-loop integration, potentially h

Kefu Guo, Fengfan Zhou, Hefei Ling, Ping Li

JPEG compression can significantly impair the performance of adversarial face examples, which previous adversarial attacks on face recognition (FR) have not adequately addressed. Considering this challenge, we propose a novel adversarial attack on FR that aims to improve the resistance of adversarial examples against JPEG compression. Specifically, during th

Theodore Anton, Timothy Clifton

We consider the form of Hubble diagrams that would be constructed by observers in universes that are homogeneous but anisotropic, when averaged over suitably large length-scales. This is achieved by ray-tracing in different directions on the sky in families of exact inhomogeneous cosmological solutions of Einstein's equations, in order to determine the redsh

Alexander Nolte

We observe Thurston's asymmetric metric on Teichm\"uller space may be expressed in terms of the H\"older regularity of boundary maps. We then associate $2$-dimensional stratified loci in $\mathbb{RP}^{n-1}$ to $\text{PSL}_n(\mathbb{R})$ Hitchin representations with $n > 3$. We prove that measuring the relative H\"older distortion of these loci gives asymmetr

Prativa Biswas, Mousumi Mandal, Kamalesh Saha

Recently, Ficarra and Sgroi initiated the study of v-numbers of powers of graded ideals. They proved that for a graded ideal $I$ in a polynomial ring $S$, $\mathrm{v}(I^k)$ is a linear function in $k$ for $k>>0$. Later, Ficarra conjectured that if $I$ is a monomial ideal with linear powers, then $\mathrm{v}(I^k)=\alpha(I)k-1$ for all $k\geq 1$, where $\alpha

Mario Fuest

The chemotaxis-fluid system \begin{align}\tag{$\star$}\label{prob:star} \begin{cases} n_t + u \cdot \nabla n = \Delta n - \nabla \cdot (n \nabla c), \\ c_t + u \cdot \nabla c = \Delta c - nc, \\ u_t + (u \cdot \nabla) u = \Delta u + \nabla P + n \nabla \Phi, \quad \nabla \cdot u = 0, \end{cases} \end{align} models aerobic bacteria interacting with a fluid vi

Haixiao Gao, Mengying Sun, Xiaodong Xu, Shujun Han

In this paper, we propose an adaptive panoramic video semantic transmission (APVST) framework enabled by rate splitting multiple access (RSMA). The APVST framework consists of a semantic transmitter and receiver, utilizing a deep joint source-channel coding structure to adaptively extract and encode semantic features from panoramic frames. To achieve higher

Thilo Reinschlüssel, Martin C. Arnold

We propose a novel approach to elicit the weight of a potentially non-stationary regressor in the consistent and oracle-efficient estimation of autoregressive models using the adaptive Lasso. The enhanced weight builds on a statistic that exploits distinct orders in probability of the OLS estimator in time series regressions when the degree of integration di

Indaco Biazzo, Dian Wu, Giuseppe Carleo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this domain. However, these neural networks are often treated as black boxes, with architectures primarily i

Massieh Kordi Boroujeny, Ya Jiang, Kai Zeng, Brian Mark

Methods for watermarking large language models have been proposed that distinguish AI-generated text from human-generated text by slightly altering the model output distribution, but they also distort the quality of the text, exposing the watermark to adversarial detection. More recently, distortion-free watermarking methods were proposed that require a secr

Yibo Wu, Ulf Gustavsson, Mikko Valkama, Alexandre Graell i Amat

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of PAs

ATLAS Collaboration

A search for high-mass resonances decaying into a $\tau$-lepton and a neutrino using proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV is presented. The full Run 2 data sample corresponding to an integrated luminosity of 139 fb$^{-1}$ recorded by the ATLAS experiment in the years 2015-2018 is analyzed. The $\tau$-lepton is reconstructe

Sascha Eichmann

We are looking for an optimal convex domain on which the boundary value problem $$\left\{\begin{array}{cc}(-\Delta)^2 u_\gamma-\gamma\Delta u_\gamma = f,& \mbox{ in }\Omega\\ u_\gamma=\partial_\nu u_\gamma=0,& \mbox{ on }\partial\Omega\end{array}\right.$$ admits a nonnegative solution for the most $\gamma$, if $f$ is a given nonnegative function.

Sihao Wu, Chen Qian, Yang-Guang Yang, Qun Wang

The rapid developments of Quantum Information Science (QIS) have opened up new avenues for exploring fundamental physics. Quantum nonlocality, a key aspect for distinguishing quantum information from classical one, has undergone extensive examinations in particles' decays through the violation of Bell-type inequalities. Despite these advancements, a comprehe

Abdelilah Karara, Mohamed Rossafi

In this paper, we will introduce the concept of a continuous K-biframe for Hilbert spaces and we present various examples of continuous K-biframes. Furthermore, we investigate their characteristics from the perspective of operator theory by establishing various properties.

Hao Wang, Zeyu Gao, Chao Zhang, Zihan Sha

Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and zero-shot scenarios where few or no training samples are available for the tasks. To address this problem, we present CLAP (Contrastive Language-Assembly Pre-training), which empl

Stefan Hougardy, Bart Zondervan

The bottom-left algorithm is a simple heuristic for the Strip Packing Problem. It places the rectangles in the given order at the lowest free position in the strip, using the left most position in case of ties. Despite its simplicity, the exact approximation ratio of the bottom-left algorithm remains unknown. We will improve the more-than-40-year-old value f

Lucas Dahinden, Liang Jin

Morse spacetime is a model of singular Lorentzian manifold, built upon a Morse function which serves as a global time function outside its critical points. The Borde-Sorkin conjecture states that a Morse spacetime is causally continuous if and only if the index and coindex of critical points of the corresponding Morse function are both different from 1. The

Wen-Jia Tang, Xiao Liu, Peng Gao, Fei Wang

Manually-designed network architectures for thermal infrared pedestrian tracking (TIR-PT) require substantial effort from human experts. AlexNet and ResNet are widely used as backbone networks in TIR-PT applications. However, these architectures were originally designed for image classification and object detection tasks, which are less complex than the chal

Michael Kirchhof, Mark Collier, Seong Joon Oh, Enkelejda Kasneci

Accurate uncertainty estimation is vital to trustworthy machine learning, yet uncertainties typically have to be learned for each task anew. This work introduces the first pretrained uncertainty modules for vision models. Similar to standard pretraining this enables the zero-shot transfer of uncertainties learned on a large pretraining dataset to specialized

Yifu Gao, Linbo Qiao, Zhigang Kan, Zhihua Wen

Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over structured data, their application to the TKGQA task is a re

Yuanyuan Liang, Keren Tan, Tingyu Xie, Wenbiao Tao

Graph Databases (Graph DB) find extensive application across diverse domains such as finance, social networks, and medicine. Yet, the translation of Natural Language (NL) into the Graph Query Language (GQL), referred to as NL2GQL, poses significant challenges owing to its intricate and specialized nature. Some approaches have sought to utilize Large Language

Daniela Lupu, Joseph L. Garrett, Tor Arne Johansen, Milica Orlandic

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-yea

Julian Rodemann, Hannah Blocher

We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions. Based on a recently introduced union-free generic depth function for partial orders/rankings, it fully exploits the ordinal information and allows for incomparability. Our method describes the distribution of all partial orders/rankings, avoiding

Ahmad Lalti, Yuri V. Khotyaintsev, Daniel B. Graham

Adiabatic and non-adiabatic electron dynamics have been proposed to explain electron heating across collisionless shocks. We analyze the evolution of the suprathermal electrons across 310 quasi-perpendicular shocks with $1.7<M_A<48$ using in-situ measurements. We show that the electron heating mechanism shifts from predominantly adiabatic to non-adiabatic fo

Alea Schröder, Steffen Gracla, Maik Röper, Dirk Wübben

Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boosting spectral efficiency. While optimal SDMA precoding solutions have been proposed for ideal channel knowledge in various scenarios, addressin

Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid

In reinforcement learning algorithms, the hyperparameters tuning method refers to choosing the optimal parameters that may increase the overall performance. Manual or random hyperparameter tuning methods can lead to different results in the reinforcement learning algorithms. In this paper, we propose a new method called QF-tuner for automatic hyperparameter

ATLAS Collaboration

A search for events with one top quark and missing transverse momentum in the final state is presented. The fully hadronic decay of the top quark is explored by selecting events with a reconstructed boosted top-quark topology produced in association with large missing transverse momentum. The analysis uses 139 fb$^{-1}$ of proton-proton collision data at a c

Hannah Blocher, Georg Schollmeyer

In this article we introduce a notion of depth functions for data types that are not given in standard statistical data formats. We focus on data that cannot be represented by one specific data structure, such as normed vector spaces. This covers a wide range of different data types, which we refer to as non-standard data. Depth functions have been studied i

Alexandru Chirvasitu

For any square-summable commuting family $(A_i)_{i\in I}$ of complex $n\times n$ matrices there is a normal commuting family $(B_i)_i$ no farther from it, in squared normalized $\ell^2$ distance, than the diameter of the numerical range of $\sum_i A_i^* A_i$. Specializing in one direction (limiting case of the inequality for finite $I$) this recovers a resul

Kajetan Enge, Elias Elmquist, Valentina Caiola, Niklas Rönnberg

The research communities studying visualization and sonification for data display and analysis share exceptionally similar goals, essentially making data of any kind interpretable to humans. One community does so by using visual representations of data, and the other community employs auditory (non-speech) representations of data. While the two communities h

Haoze He, Daniel Kressner

A family of symmetric matrices $A_1,\ldots, A_d$ is SDC (simultaneous diagonalization by congruence, also called non-orthogonal joint diagonalization) if there is an invertible matrix $X$ such that every $X^T A_k X$ is diagonal. In this work, a novel randomized SDC (RSDC) algorithm is proposed that reduces SDC to a generalized eigenvalue problem by consideri

Tarek Tohme, William Bialek

At the 2023 Les Houches Summer School on Theoretical Biological Physics, several students asked for some background on information theory, and so we added a tutorial to the scheduled lectures. This is largely a transcript of that tutorial, lightly edited. It covers basic definitions and context rather than detailed calculations. We hope to have maintained th

Alice Bernamonti, Federico Galli, Dongsheng Ge

Boundary effects play an interesting role in finite-size physical systems. In this work, we study the boundary-induced properties of 1+1-dimensional critical systems driven by inhomogeneous M\"obius-like quenches. We focus on the entanglement entropy in BCFTs with a large central charge and a sparse spectrum of low-dimensional operators. We find that the cho

Yiran Zhang, Siming Liu

Within the classical convection--diffusion approximation, we show that the angular distribution of cosmic rays (CRs) in a highly turbulent flow may exhibit significant small-scale anisotropies. The CR intensity angular power spectrum $ C_\ell $ is then a direct reflection of interstellar turbulence, from which one expects $ C_\ell\propto\ell^{-\gamma -1} $ f

Tomer Ezra, Stefano Leonardi, Matteo Russo

In the classical principal-agent hidden-action contract model, a principal delegates the execution of a costly task to an agent. In order to complete the task, the agent chooses an action from a set of actions, where each potential action is associated with a cost and a success probability to accomplish the task. To incentivize the agent to exert effort, the

Lorenz Diener, Solomiya Branets, Ando Saabas, Ross Cutler

Audio packet loss concealment is the hiding of gaps in VoIP audio streams caused by network packet loss. With the ICASSP 2024 Audio Deep Packet Loss Concealment Grand Challenge, we build on the success of the previous Audio PLC Challenge held at INTERSPEECH 2022. We evaluate models on an overall harder dataset, and use the new ITU-T P.804 evaluation procedur

Jan A. Fotakis, Jakob E. Lohr, Carsten Greiner

We present exact relations between the diffusion coefficients or conductivities, $\kappa_{qq'}/T = \sigma_{qq'}$, of strongly-interacting matter. We show that once the diagonal entries are known in two different charge representations, the off-diagonal coefficients are functions of the diagonal entries once isospin symmetry applies. As an important example,

Anna Zakharova, Vladimir V. Semenov

The widespread development and use of neural networks have significantly enriched a wide range of computer algorithms and promise higher speed at lower cost. However, the imitation of neural networks by means of modern computing substrates is highly inefficient, whereas physical realization of large scale networks remains challenging. Fortunately, delayed-fe

Hao-jie Xu, Jie Zhao, Fuqiang Wang

The hexadecapole deformation ($\beta_4$) of the $^{238}$U nucleus has not been determined because its effect is overwhelmed by those from the nucleus' large quadrupole deformation ($\beta_2$) in nuclear electric transition measurements. In this Letter, we identify the nonlinear response of the hexadecapole anisotropy to ellipticity in relativistic $^{238}$U+

Alexandra Bonat, S. Loepp

We demonstrate a class of local (Noetherian) unique factorization domains (UFDs) that are noncatenary at infinitely many places. In particular, if $A$ is in our class of UFDs, then the prime spectrum of $A$ contains infinitely many disjoint (except at the maximal ideal) noncatenary subsets. As a consequence of our result, there are infinitely many height one

Dewangga Alfarisy, Lavi Zuhal, Michael Ortiz, Fehmi Cirak

The solution approximation for partial differential equations (PDEs) can be substantially improved using smooth basis functions. The recently introduced mollified basis functions are constructed through mollification, or convolution, of cell-wise defined piecewise polynomials with a smooth mollifier of certain characteristics. The properties of the mollified

Martino Bernasconi, Matteo Castiglioni, Andrea Celli

We study the problem faced by a service provider that has to sell services to a user. In our model the service provider proposes various payment options (a menu) to the user which may be based, for example, on the quality of the service. Then, the user chooses one of these options and pays an amount to the service provider, contingent on the observed final o

Zheng Xu, Yulu Gong, Yanlin Zhou, Qiaozhi Bao

With the continuous expansion of the scale of cloud computing applications, artificial intelligence technologies such as Deep Learning and Reinforcement Learning have gradually become the key tools to solve the automated task scheduling of large-scale cloud computing systems. Aiming at the complexity and real-time requirement of task scheduling in large-scal

Han Wang, Sijia Yu, Chunyang Chen, Burak Turhan

Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness. However, it is unclear whether DL projects, as software systems, are tested thoroughly or functionally correct when there is a need to treat and test them like other software systems. Therefore, we empirically study the unit

E. D'Aniello, J. Fernández-Sánchez, M. Maiuriello, J. B. Seoane Sepúlveda

In this note we show that there exist a $2^\mathfrak{c}$-generated free algebra $\mathcal{S} \subset \mathbb{R}^\mathbb{R}$ of Riemann integrable functions and a free algebra $\mathcal{C} \subset \mathbb{R}^{[0,1]}$ of continuous functions, having $\mathfrak{c}$-generators, such that $r \circ c$ is not Riemann integrable for any $r \in \mathcal{S}$ and $c \i

Jing Li, Quanxue Gao, Qianqian Wang, Cheng Deng

Multi-view clustering method based on anchor graph has been widely concerned due to its high efficiency and effectiveness. In order to avoid post-processing, most of the existing anchor graph-based methods learn bipartite graphs with connected components. However, such methods have high requirements on parameters, and in some cases it may not be possible to

Andre Weiner, Janis Geise

In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based environments in reinforcement learning enables a priori end-to-end optimization of the control system, provides a virtual testbed for safety-critical control applications, and allows to gain a deep understanding of th

Benjamin Alt, Florian Stöckl, Silvan Müller, Christopher Braun

Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surfa

Kapil Goswami, Peter Schmelcher, Rick Mukherjee

Integer programming (IP), as the name suggests is an integer-variable-based approach commonly used to formulate real-world optimization problems with constraints. Currently, quantum algorithms reformulate the IP into an unconstrained form through the use of binary variables, which is an indirect and resource-consuming way of solving it. We develop an algorit