April 2024 arXiv papers — page 144

Yue-Hua Han, Tai-Ming Huang, Kai-Lung Hua, Jun-Cheng Chen

Generative models have enabled the creation of highly realistic facial-synthetic images, raising significant concerns due to their potential for misuse. Despite rapid advancements in the field of deepfake detection, developing efficient approaches to leverage foundation models for improved generalizability to unseen forgery samples remains challenging. To ad

Yunhai Han, Zhenyang Chen, Kyle A Williams, Harish Ravichandar

When human acquire physical skills (e.g., tennis) from experts, we tend to first learn from merely observing the expert. But this is often insufficient. We then engage in practice, where we try to emulate the expert and ensure that our actions produce similar effects on our environment. Inspired by this observation, we introduce Combining IMitation and Emula

Souravik Dutta, Yiyu Cai

For autonomous crane lifting, optimal trajectories of the crane are required as reference inputs to the crane controller to facilitate feedforward control. Reducing the unactuated payload motion is a crucial issue for under-actuated tower cranes with spherical pendulum dynamics. The planned trajectory should be optimal in terms of both operating time and ene

Minheng Ni, Yeli Shen, Lei Zhang, Wangmeng Zuo

With recent advancements in visual synthesis, there is a growing risk of encountering images with detrimental effects, such as hate, discrimination, or privacy violations. The research on transforming harmful images into responsible ones remains unexplored. In this paper, we formulate a new task, responsible visual editing, which entails modifying specific c

Artem Vysogorets, Kartik Ahuja, Julia Kempe

In the era of exceptionally data-hungry models, careful selection of the training data is essential to mitigate the extensive costs of deep learning. Data pruning offers a solution by removing redundant or uninformative samples from the dataset, which yields faster convergence and improved neural scaling laws. However, little is known about its impact on cla

Mahsa Ehsanpour, Ian Reid, Hamid Rezatofighi

For a complete comprehension of multi-person scenes, it is essential to go beyond basic tasks like detection and tracking. Higher-level tasks, such as understanding the interactions and social activities among individuals, are also crucial. Progress towards models that can fully understand scenes involving multiple people is hindered by a lack of sufficient

Said Rafa, Abbes Benaissa

We study polynomial stability to the one-dimensional system in the linear isothermal theory of swelling porous elastic soils with an internal fractional damping. We establish an optimal decay result by frequency domain method

Shuai Guo, Jielei Chu, Lin Ma, Zhaoyu Li

Generative Flow Networks (GFlowNets or GFNs) are probabilistic models predicated on Markov flows, and they employ specific amortization algorithms to learn stochastic policies that generate compositional substances including biomolecules, chemical materials, etc. With a strong ability to generate high-performance biochemical molecules, GFNs accelerate the di

Hao Tian, Wenjun Xie, Maohai Xie, Chuanhui Zhu

Based on first-principles calculations, we propose a potential access to the yet unrealized freestanding blue phosphorus (blueP) through transformation of black phosphorus (blackP) induced by surface bromine (Br) adsorption. Formation of the Br-P bonds disrupts the original sp3 configurations in blackP, generates unpaired pz electrons and induces a structura

Yiwen Ding, Krishna Manoorkar, Mattia Panettiere, Ruoding Wang

In this paper, we introduce the simulations and bisimulations on polarity-based semantics for non-distributive modal logic, which are natural generalizations of those notions on Kripke semantics for modal logic. We also generalize other important model-theoretic notions about Kripke semantics such as image-finite models, modally-saturated models, ultrafilter

J. Francis Baer

We compute the $\mathbb{C}$-motivic Adams spectral sequence for $\mathit{mmf}/\tau$. Up to reindexing, this spectral sequence is isomorphic to the algebraic Novikov spectral sequence for topological modular forms. We give a full analysis of the inclusion and projection maps which occur in the long exact sequence induced by multiplication by $\tau$ on Adams $

Franziska Huth, Maurice Koch, Miriam Awad, Daniel Weiskopf

The interplay between text and visualization is gaining importance for media where traditional text is enriched by visual elements to improve readability and emphasize facts. In two controlled eye-tracking experiments ($N=12$), we approach answers to the question: How do visualization techniques influence reading behavior? We compare plain text to that marke

Lukas Hauer, Abhinav Naga, Rodrique G. M. Badr, Jonathan T. Pham

Silicone is frequently used as a model system to investigate and tune wetting on soft materials. Silicone is biocompatible and shows excellent thermal, chemical, and UV stability. Moreover, the mechanical properties of the surface can be easily varied by several orders of magnitude in a controlled manner. Polydimethylsiloxane (PDMS) is a popular choice for c

Mathias B. M. Svendsen, Marcel Cech, Max Schemmer, Beatriz Olmos

We establish the concept of topological pumping in one-dimensional systems with long-range couplings and apply it to the transport of a photon in quantum optical systems. In our theoretical investigation, we introduce an extended version of the Rice-Mele model with all-to-all couplings. By analyzing its properties, we identify the general conditions for topo

Adarsh Prasad Behera, Roberto Morabito, Joerg Widmer, Jaya Prakash Champati

The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective method for balancing inference accuracy, data processing, transmission throughput, and offloading cost. This appro

Jiayuan Dong, Christian Jacobsen, Mehdi Khalloufi, Maryam Akram

Bayesian optimal experimental design (OED) seeks experiments that maximize the expected information gain (EIG) in model parameters. Directly estimating the EIG using nested Monte Carlo is computationally expensive and requires an explicit likelihood. Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by app

Shen Gao, Hao Li, Chengrui Huang, Quan Tu

Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance

Daniel K. Brattan, Masataka Matsumoto, Matteo Baggioli, Andrea Amoretti

The capability of hydrodynamics to accurately describe slow and long-wavelength fluctuations around non-equilibrium steady states (NESS), characterized by a stationary flow of energy or matter in the presence of a driving force, remains an open question. In this study, we explicitly construct a hydrodynamic description of electrically driven non-equilibrium

Bowen Pan, Yikang Shen, Haokun Liu, Mayank Mishra

Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios. However, MoE models generally require 2-4$\times$ times more parameters to achieve comparable performance to a dense model, which incurs larger GPU memory

Thais Pacheco Menezes, Thomas Brendan Murphy, Michael Fop

Matching households and individuals across different databases poses challenges due to the lack of unique identifiers, typographical errors, and changes in attributes over time. Record linkage tools play a crucial role in overcoming these difficulties. This paper presents a multi-step record linkage procedure that incorporates household information to enhanc

Steven Clontz

Recent advances in computing have changed not only the nature of mathematical computation, but mathematical proof and inquiry itself. While artificial intelligence and formalized mathematics have been the major topics of this conversation, this paper explores another class of tools for advancing mathematics research: databases of mathematical objects that en

Konstantin M. Dyakonov

Given an $L^2$ function $f$ on the unit circle $\mathbb T$, we put $$\Phi_f(z):=\mathcal P(|f|^2)(z)-|\mathcal Pf(z)|^2,\qquad z\in\mathbb D,$$ where $\mathbb D$ is the open unit disk and $\mathcal P$ is the Poisson integral operator. The Garsia norm $\|f\|_G$ is then defined as $\sup_{z\in\mathbb D}\Phi_f(z)^{1/2}$, and the space ${\rm BMO}$ is formed by th

A. Fox, F. De Pellegrini, F. Faticanti, E. Altman

With the uptake of intelligent data-driven applications, edge computing infrastructures necessitate a new generation of admission control algorithms to maximize system performance under limited and highly heterogeneous resources. In this paper, we study how to optimally select information flows which belong to different classes and dispatch them to multiple

Aaruni Kaushik

As part of Mathematical Research Data Initiative (MaRDI), we have developed a way to preserve a software package into an easy to deploy and use sandbox environment we call a "runtime", via a program we developed called MaPS : MaRDI Packaging System. The program relies on Linux user namespaces to isolate a library environment from the host system, making the

Riccardo Rurali, Carlos Escorihuela-Sayalero, Josep Lluís Tamarit, Jorge Íñiguez-González

Solid-state cooling presents an energy-efficient and environmentally friendly alternative to traditional refrigeration technologies that rely on thermodynamic cycles involving greenhouse gases. However, conventional caloric effects face several challenges that impede their practical application in refrigeration devices. Firstly, operational temperature condi

Edgar Assing, Valentin Blomer, Paul D. Nelson

We prove Sarnak's spherical density conjecture for the principal congruence subgroup of SL(n, Z) of arbitrary level. Applications include a complete version of Sarnak's optimal lifting conjecture for principal congruence subgroups of SL(n, Z), as well as a transfer of the density theorem to certain co-compact situations. The main ingredients are new lower bo

Longhui Zhang, Dingkun Long, Meishan Zhang, Yanzhao Zhang

Chinese sequence labeling tasks are heavily reliant on accurate word boundary demarcation. Although current pre-trained language models (PLMs) have achieved substantial gains on these tasks, they rarely explicitly incorporate boundary information into the modeling process. An exception to this is BABERT, which incorporates unsupervised statistical boundary i

Jacob Chalk, Jaesung Huh, Evangelos Kazakos, Andrew Zisserman

Diverse actions give rise to rich audio-visual signals in long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of events and distinct labels. We address the interplay between the two modalities in long videos by explicitly modelling the temporal extents of audio and visual events. We propose the Tim

Wenshuai Cheng, Ying D. Liu, Hao Ran, Yiming Jiao

We identify and examine the solar wind intervals near the sonic critical point (i.e., $M_S \sim 1$) observed by the Parker Solar Probe (PSP). The near subsonic wind intervals show similar properties: a low density, an extremely low velocity, a low proton temperature, and essentially no magnetic field deflections compared with the surrounding solar wind. The

Judith Angel, Jörn Behrens, Sebastian Götschel, Marten Hollm

Knowledge of the bottom topography, also called bathymetry, of rivers, seas or the ocean is important for many areas of maritime science and civil engineering. While direct measurements are possible, they are time consuming and expensive. Therefore, many approaches have been proposed how to infer the bathymetry from measurements of surface waves. Mathematica

Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee

One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the conceptualization of the plasticity-stability dilemma. However, the convergence of continual learning for each sequential task is less studied so far. In this paper, we provide a converg

Mohamed Ben Alaya, Martin Friesen, Jonas Kremer

We study statistical inference of the drift parameters for the Volterra Ornstein-Uhlenbeck process on R in the ergodic regime. For continuous-time observations, we derive the corresponding maximum likelihood estimators and show that they are strongly consistent and asymptotically normal locally uniformly in the parameters. For the case of discrete high-frequ

Jonathan Xu, Bruno Aristimunha, Max Emanuel Feucht, Emma Qian

We present Alljoined1, a dataset built specifically for EEG-to-Image decoding. Recognizing that an extensive and unbiased sampling of neural responses to visual stimuli is crucial for image reconstruction efforts, we collected data from 8 participants looking at 10,000 natural images each. We have currently gathered 46,080 epochs of brain responses recorded

Stephen J. Gardiner, Tomas Sjödin

Kow, Larson, Salo and Shahgholian recently initiated the study of quadrature domains for the Helmholtz equation and developed an associated theory of partial balayage of measures. The present paper offers an alternative approach to partial balayage in this context that yields stronger results. Applications are given to quadrature domains and to a domain evol

Laura S. Herzog, Friedrich Wagner, Christian Ufrecht, Lilly Palackal

Quantum computing is a promising technology to address combinatorial optimization problems, for example via the quantum approximate optimization algorithm (QAOA). Its potential, however, hinges on scaling toy problems to sizes relevant for industry. In this study, we address this challenge by an elaborate combination of two decomposition methods, namely grap

Marco Barbieri, Luca Sabatini

Fix $\varepsilon > 0$. We say that a finite group $G$ is $\varepsilon$-quasirandom if every nontrivial irreducible complex representation of $G$ has degree at least $|G|^\varepsilon$. In this paper, we give a structure theorem for large $\varepsilon$-quasirandom groups, and we completely classify the $\frac{1}{5}$-quasirandom groups.

Kimiko Yamada

For complex projective smooth surface $X$, let $M$ be the coarse moduli scheme of rank-two stable sheaves with fixed Chern classes. Grasping the birational structure of $M$, for example its Kodaira dimension, is a fundamental problem. However, in the case where $\kappa(X)>0$, the study of this problem has not necessarily been active in recent years. In this

E. Pancino, A. Zocchi, M. Rainer, M. Monaci

For decades, it has been theorized that a tenuous but detectable intracluster medium should be present in globular clusters, which is continuously replenished by the gas and dust ejected by bright giants and periodically cleared by interactions with the Galactic disk. However, dedicated searches, especially in infrared and radio wavelengths, have returned mo

Claudia Castillo-Moreno, Kazi Rafsanjani Amin, Ingrid Strandberg, Mikael Kervinen

Atom-photon bound states arise from the coupling of quantum emitters to the band edge of dispersion-engineered waveguides. Thanks to their tunable-range interactions, they are promising building blocks for quantum simulators. Here, we study the dynamics of an atom-photon bound state emerging from coupling a frequency-tunable quantum emitter - a transmon-type

Jihwan Do, Lining Han, Xiaoxi Li

This paper studies a stylized model of a monopoly data seller when information-sharing network exists among data buyers. We show that, if the buyers' prior information is sufficiently noisy, the optimal selling strategy is characterized by a maximum independent set, which is the largest set of buyers who do not have information-sharing link at all. In additi

Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin

Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consider using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these eval

Hongwei Wang, Jun Fang, Huiping Duan, Hongbin Li

Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency within the mmWave/THz bands, leads to a large Rayleigh distance. As a r

Shubham Purwar, Susmita Changdar, Susanta Ghosh, Tushar Kanti Bhowmik

We report the magnetic, electrical, and magnetotransport (Hall effect) properties of the hexagonal itinerant ferromagnet Cr$_{0.83}$Te. Further, a comprehensive study of the magneto-entropy scaling behavior has been done around the Curie temperature of $T_C \approx$ 338 K. A maximum entropy change (-$\Delta S_{m}^{max}$) of 2.77 $J/kg-K$ and relative cooling

Vincenzo Mancuso, Paolo Castagno, Leonardo Badia, Matteo Sereno

The allocation of computing tasks for networked distributed services poses a question to service providers on whether centralized allocation management be worth its cost. Existing analytical models were conceived for users accessing computing resources with practically indistinguishable (hence irrelevant for the allocation decision) delays, which is typical

Michael Anastos, Simona Boyadzhiyska, Silas Rathke, Juanjo Rué

For a given graph $G=(V,E)$, we define its \emph{$n$th subdivision} as the graph obtained from $G$ by replacing every edge by a path of length $n$. We also define the \emph{$m$th power} of $G$ as the graph on vertex set $V$ where we connect every pair of vertices at distance at most $m$ in $G$. In this paper, we study the chromatic number of powers of subdiv

Selina Dirnböck, Seung-Sup B. Lee, Fabian B. Kugler, Sebastian Huber

Two-particle response functions are a centerpiece of both experimental and theoretical quantum many-body physics. Yet, due to their size and discontinuity structure, they are challenging to handle numerically. Recently, two advances were made to tackle this problem: first, the overcomplete intermediate representation (OIR), which provides a highly efficient

Mehran Safayani, Amir Sartipi, Amir Hossein Ahmadi, Parniyan Jalali

The proliferation of hate speech and offensive comments on social media has become increasingly prevalent due to user activities. Such comments can have detrimental effects on individuals' psychological well-being and social behavior. While numerous datasets in the English language exist in this domain, few equivalent resources are available for Persian lang

David Gérard-Varet, Amina Mecherbet

We consider a sedimenting suspension in a Stokes flow, in the presence of a vertical wall. We study the effect of a particle-depleted fluid layer near the wall on the bulk dynamics of the suspension. We show that this effect can be captured by an appropriate wall law of Navier type. We provide in this way a rigorous justification of the apparent slip observe

Matteo Zecchin, Kai Yu, Osvaldo Simeone

Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new task based on limited contextual information, typically in the form of a few training examples for the given task. Previous work has explored the use of ICL for channel equalizati

Tingxiang Ji, Jianqing Liu, Zheshen Zhang

Quantum graph state is a special class of nonlocal state among multiple quantum particles, underpinning several nonclassical and promising applications such as quantum computing and quantum secret sharing. Recently, establishing quantum graph states among physically distant nodes has gained increasing popularity owing to its potential in expanding current qu

Minhua Ding, Italo Atzeni, Antti Tölli, A. Lee Swindlehurst

This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE channel estimator has not been fully investigated in this context due to its general non-linearity and computatio

Joris Verhagen, Lars Lindemann, Jana Tumova

This work addresses maximally robust control synthesis under unknown disturbances. We consider a general nonlinear system, subject to a Signal Temporal Logic (STL) specification, and wish to jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many work

Alina Wernick

This chapter explores the role of patent protection in algorithmic surveillance and whether ordre public exceptions from patentability should apply to such patents, due to their potential to enable human rights violations. It concludes that in most cases, it is undesirable to exclude algorithmic surveillance patents from patentability, as the patent system i

Hongyun Zhang, Qian Li, Youngju Park, Yujin Jia

Twisted bilayer graphene (tBLG) provides a fascinating platform for engineering flat bands and inducing correlated phenomena. By designing the stacking architecture of graphene layers, twisted multilayer graphene can exhibit different symmetries with rich tunability. For example, in twisted monolayer-bilayer graphene (tMBG) which breaks the C2z symmetry, tra

Parvez Rasul, Ronnie Sebastian

Let $C$ be a smooth projective curve over $\mathbb C$ of genus $g\geqslant 1$. Let $E$ be a vector bundle on $C$ of rank $r$ and degree $e$. Given integers $k_1,k_2,d_1,d_2$ such that $r>k_1>k_2>0$, let $\mathcal Q^{k_1,k_2}_{d_1,d_2}(E)$ denote the nested Quot scheme which parametrizes pair of quotients $[E \twoheadrightarrow F_1 \twoheadrightarrow F_2]$ su

Chaoyi Cai, Jianxian Qiu, Kailiang Wu

A long-standing and formidable challenge faced by all conservative schemes for relativistic magnetohydrodynamics (RMHD) is the recovery of primitive variables from conservative ones. This process involves solving highly nonlinear equations subject to physical constraints. An ideal solver should be "robust, accurate, and fast -- it is at the heart of all cons

Tim Baumgärtner, Yang Gao, Dana Alon, Donald Metzler

Reinforcement Learning from Human Feedback (RLHF) is a popular method for aligning Language Models (LM) with human values and preferences. RLHF requires a large number of preference pairs as training data, which are often used in both the Supervised Fine-Tuning and Reward Model training and therefore publicly available datasets are commonly used. In this wor

H. M. Nagesh

The first Zagreb index $M_{1}(G)$ is equal to the sum of squares of the degrees of the vertices, and the second Zagreb index $M_{2}(G)$ is equal to the sum of the products of the degrees of pairs of adjacent vertices of the underlying molecular graph $G$. This paper aims to investigate the Zagreb indices and coindices of the line graph and line cut-vertex gr

Min Wang, Zhengyi Hou, Chenyi Wang, Zhengjie Yan

We demonstrate approximate storage based on NAND-like spin-orbit torque (SOT) MRAM, through "device-modeling-architecture" explorations. We experimentally achieve down to 1E-5 level selectivity. Selectivity and low-power solutions are established by numerical calculation workflow. System-level power consumption is evaluated in the 512 KB last-level cache acc

Houssam Abdul-Rahman, Robert Sims, Günter Stolz

We provide an analytic method for estimating the entanglement of the non-gaussian energy eigenstates of disordered harmonic oscillator systems. We invoke the explicit formulas of the eigenstates of the oscillator systems to establish bounds for their $\epsilon$-R\'enyi entanglement entropy $\epsilon\in(0,1)$. Our methods result in a logarithmically corrected

Kris Tucker, Amit Kiran Rege, Conor Smith, Claire Monteleoni

We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning where no

M. A. Cordiner, A. E. Thelen, I. -L. Lai, W. -L. Tseng

The subsurface ocean of Europa is a high priority target in the search for extraterrestrial life, but direct investigations are hindered by the presence of a thick, exterior ice shell. Here we present spectral line and continuum maps of Europa obtained over four epochs in May-June 2021 using the Atacama Large Millimeter/submillimeter Array (ALMA), to search

T. T. Hong, L. T. T. Phuong, T. Phong Nguyen, N. H. T. Nha

The lepton flavor violating (LFV) decays $h, Z\to e_b e_a $, and $e_b\to e_a \gamma$ are discussed in a class of general 3-3-1 models adding heavy neutral leptons and singly charged Higgs bosons to accommodate experimental data of neutrino oscillation and $(g-2)_{e_a}$ anomalies of charged leptons through the inverse seesaw mechanism. We show that the models

Shang Gao

Emergent molecular-like excitations have been discovered in a series of pyrochlore antiferromagnets, yet their origins and relationships with the coexisting magnon excitations remain a puzzle. Here, by incorporating the dynamic spin-lattice coupling through the site-phonon model, we accomplish a unified description of the molecular and magnon excitations, wh

Qingyuan Zhou, Weidong Yang, Ben Fei, Jingyi Xu

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on small-scale synthetic or real-world datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quant

A. Greco, Q. Pichard, E. Strambini, F. Giazotto

The development of superconducting electronics requires careful characterization of the components that make up electronic circuits. Superconducting weak links are the building blocks of most superconducting electronics components and are characterized by highly nonlinear current-to-phase relations (CPR), which are often not perfectly known. Recent research

Nikhil Parasaram, Huijie Yan, Boyu Yang, Zineb Flahy

Recent research has shown that incorporating bug-related facts, such as stack traces and GitHub issues, into prompts enhances the bug-fixing capabilities of large language models (LLMs). Considering the ever-increasing context window of these models, a critical question arises: what and how many facts should be included in prompts to maximise the chance of c

Taiyi Wang, Eiko Yoneki

This study introduces the Instance-Aware Index Advisor (IA2), a novel deep reinforcement learning (DRL)-based approach for optimizing index selection in databases facing large action spaces of potential candidates. IA2 introduces the Twin Delayed Deep Deterministic Policy Gradient - Temporal Difference State-Wise Action Refinery (TD3-TD-SWAR) model, enabling

Saman Motamed, Wouter Van Gansbeke, Luc Van Gool

With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusion models has shown great promise in controlling the shape and location of objects in the scene. Transferring i

Jiapeng Wu, Yichen Liu

Accurately distinguishing each object is a fundamental goal of Multi-object tracking (MOT) algorithms. However, achieving this goal still remains challenging, primarily due to: (i) For crowded scenes with occluded objects, the high overlap of object bounding boxes leads to confusion among closely located objects. Nevertheless, humans naturally perceive the d

Ling-Bing He, Jin-Cheng Jiang, Hung-Wen Kuo, Meng-Hao Liang

We prove the Hardy-Littlewood-Sobolev type $L^p$ estimates for the gain term of the Boltzmann collision operator including Maxwellian molecule, hard potential and hard sphere models. Combining with the results of Alonso et al. [2] for the soft potential and Maxwellian molecule models, we provide an unified form of $L^p$ estimates for all cutoff models which

Antón Makarov, Carlos Pérez-Herradón, Giacomo Franceschetto, Márcio M. Taddei

Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solv

Panagiota Fatourou, Nikolaos D. Kallimanis, Eleni Kanellou, Odysseas Makridakis

We study general techniques for implementing distributed data structures on top of future many-core architectures with non cache-coherent or partially cache-coherent memory. With the goal of contributing towards what might become, in the future, the concurrency utilities package in Java collections for such architectures, we end up with a comprehensive colle

Seoyoung Kim, Chi Hoi Yip, Semin Yoo

A Diophantine $m$-tuple over a finite field $\mathbb{F}_q$ is a set $\{a_1,\ldots, a_m\}$ of $m$ distinct elements in $\mathbb{F}_{q}^{*}$ such that $a_{i}a_{j}+1$ is a square in $\mathbb{F}_q$ whenever $i\neq j$. In this paper, we study $M(q)$, the maximum size of a Diophantine tuple over $\mathbb{F}_q$, assuming the characteristic of $\mathbb{F}_q$ is fixe

Ignacio Vergara

We present an introduction to weak amenability for locally compact groups, and a survey of some of the most important results regarding this property.

Raveerat Jaturapitpornchai, Giulio Poggi, Gregory Sech, Ziga Kokalj

Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeological objects present in the images. This paper investigates the impact of visualisations on deep learning performance through a comprehensive testing framework. The study involves t

Daniel Arnström, Daniel Axehill

We propose a combinatorial method for computing explicit solutions to multi-parametric quadratic programs, which can be used to compute explicit control laws for linear model predictive control. In contrast to classical methods, which are based on geometrical adjacency, the proposed method is based on combinatorial adjacency. After introducing the notion of

Debdip Ganguly, K. Jotsaroop, Prasun Roychowdhury

For Baouendi-Grushin vector fields, we prove Hardy, Hardy-Rellich, and Rellich identities and inequalities with sharp constants. Our explicit remainder terms significantly improve than those found in the literature. Our arguments are built on abstract Hardy-Rellich identities involving the Bessel pair along with the use of spherical harmonics developed by Ga

Konrad Mundinger, Sebastian Pokutta, Christoph Spiegel, Max Zimmer

We present two novel six-colorings of the Euclidean plane that avoid monochromatic pairs of points at unit distance in five colors and monochromatic pairs at another specified distance $d$ in the sixth color. Such colorings have previously been known to exist for $0.41 < \sqrt{2} - 1 \le d \le 1 / \sqrt{5} < 0.45$. Our results significantly expand that range

Nenad Petrovic, Fengjunjie Pan, Krzysztof Lebioda, Vahid Zolfaghari

We present a prototype of a tool leveraging the synergy of model driven engineering (MDE) and Large Language Models (LLM) for the purpose of software development process automation in the automotive industry. In this approach, the user-provided input is free form textual requirements, which are first translated to Ecore model instance representation using an

Tao Fang

Let $\mathcal{F}$ be a set of graphs. The planar Tur\'an number, $ex_{\mathcal{P}}(n,\mathcal{F})$, is the maximum number of edges in an $n$-vertex planar graph which does not contain any member of $\mathcal{F}$ as a subgraph. In this paper, we give upper bounds of $ex_{\mathcal{P}}(n,\{K_4,\Theta_5\})\leqslant25/11(n-2)$. We also give constructions which sh

Andreas Enge

The FastECPP algorithm is currently the fastest approach to prove theprimality of general numbers, and has the additional benefit of creatingcertificates that can be checked independently and with a lower complexity.This article shows how by parallelising over a linear number of cores,its quartic time complexity becomes a cubic wallclock time complexity;and

Hamed Haghighi, Amir Samadi, Mehrdad Dianati, Valentina Donzella

Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during sampling. However, DMs often fail to realistically model Lidar raydrop noise due to their inherent denoising process. To retain the strength of iterative sampling while enhancing

Ariles Remaki

Leibniz's mathematical texts are a perfect example of a type of historical document that is extremely difficult to deal with in the context of an editorial enterprise: the draft. The tables in Leibniz's mathematical manuscripts are a particularly good example of these difficulties, as they are equivocal sources containing many implicit operations. The public

Reimer Kuehn

We provide an explicit solution of the problem of level-set percolation for multivariate Gaussians defined in terms of weighted graph Laplacians on complex networks. The solution requires an analysis of the heterogeneous micro-structure of the percolation problem, i.e., a self-consistent determination of locally varying percolation probabilities. This is ach

Roman Kazakov, Kseniia Petukhova, Ekaterina Kochmar

In this paper, we present our submission to the SemEval-2023 Task~3 "The Competition of Multimodal Emotion Cause Analysis in Conversations", focusing on extracting emotion-cause pairs from dialogs. Specifically, our approach relies on combining fine-tuned GPT-3.5 for emotion classification and a BiLSTM-based neural network to detect causes. We score 2nd in t

Vivek Agarwal, Joshua Harvey, Dmitry Rinberg, Vasant Dhar

Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we ar

K. Uzawa, K. Hagino, G. F. Bertsch

Even though more than 80 years have passed since the discovery of fission, its microscopic understanding has still been unclear. To clarify the underlying mechanics of induced fission, we analyze the distribution of a fission width using a miscropic framework based on a configuration-interaction approach. The distribution is known to follow a chi-squared dis

Jiaye Wang

We propose a method to guide Large Language Models (LLMs) in generating structured content adhering to specific conventions without fine-tuning. By utilizing coroutine-based content generation constraints through a pre-agreed context-free grammar (CFG), LLMs are directed during decoding to produce formal language compliant outputs. This enhances stability an

ATLAS Collaboration

The Higgs boson was discovered by the ATLAS and CMS Collaborations in 2012 using data from Run 1 of the Large Hadron Collider (2010$-$2012). In Run 2 (2015$-$2018), about 140 fb$^{-1}$ of proton$-$proton collisions at a centre-of-mass energy of 13 TeV were collected by the ATLAS experiment. This review presents the most important Run 2 results obtained by th

Loïc Foissy, Claudia Malvenuto

We compute an explicit formula for the antipode of the double bialgebra of graphs in terms of totally acyclic partial orientations, using some general results on double bialgebras. In analogy to what was already proven in Hopf-algebraic terms for the chromatic polynomial of a graph, we show that the Fortuin-Kasteleyn polynomial (a variant of the Tutte polyno

Elias Milios, Kim Peter Wabersich, Felix Berkel, Lukas Schwenkel

Predictive safety filters enable the integration of potentially unsafe learning-based control approaches and humans into safety-critical systems. In addition to simple constraint satisfaction, many control problems involve additional stability requirements that may vary depending on the specific use case or environmental context. In this work, we address thi

Mario Lezoche, Sanabria Freddy Muñoz, Collazos Cesar, Torres Diego

In a knowledge society, the term knowledge must be considered a core resource for organizations. So, beyond being a medium to progress and to innovate, knowledge is one of our most important resources: something necessary to decide.Organizations that are embracing knowledge retention activities are gaining a competitive advantage. Organizational rearrangemen

Borislav Polovnikov, Johannes Scherzer, Subhradeep Misra, Henning Schlömer

Moir\'e materials provide a unique platform for studies of correlated many-body physics of the Fermi-Hubbard model on triangular spin-charge lattices. Bilayer Hubbard models are of particular significance with regard to the physics of Mott insulating states and their relation to unconventional superconductivity, yet their experimental implementation in moir\

Juliette Plouin, Claude Marchand, Pierrick Hamel, Sergey Arsenyev

A new bunching method, named "kladistron" has been developed at CEA in order to provide high efficiency klystrons. A first "kladistron" prototype was designed and realized. It was adapted from the 4.9 GHz TH2166 from Thales, where the interaction line was transformed from 6 to 16 cavities. The design and fabrication phases of this prototype are developed in

The LTD Collaboration, Selomit Ramírez-Uribe, Andrés E. Rentería-Olivo, David F. Rentería-Estrada

We present the first proof-of-concept application to decay processes at higher perturbative orders of LTD causal unitary, a novel methodology that exploits the causal properties of vacuum amplitudes in the loop-tree duality (LTD) and is directly well-defined in the four physical dimensions of the space-time. The generation of loop- and tree-level contributio

Selomit Ramírez-Uribe, Prasanna K. Dhani, German F. R. Sborlini, Germán Rodrigo

We propose multiloop vacuum amplitudes as the optimal building blocks for efficiently assembling theoretical predictions at high-energy colliders. This hypothesis is strongly supported by the manifestly causal properties of the loop-tree duality (LTD) representation of a vacuum amplitude. The vacuum amplitude, acting as a kernel, encodes all the final states

Baiyi Li, Edmond S. L. Ho, Hubert P. H. Shum, He Wang

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skeletal motion is challenging. While direct motion capture is expensive and slow, motion editing/generation is also

Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude

The GitHub platform has fueled the creation of truly global software, enabling contributions from developers across various geographical regions of the world. As software becomes more entwined with global politics and social regulations, it becomes similarly subject to government sanctions. In 2019, GitHub restricted access to certain services for users in s

S. L. Casewell, J. Debes, T. J. Dupuy, P. Dufour

We present new results on PHL 5038AB, a widely separated binary system composed of a white dwarf and a brown dwarf, refining the white and brown dwarf parameters and determining the binary separation to be $66^{+12}_{-24}$~AU. New spectra of the white dwarf show calcium absorption lines suggesting the hydrogen-rich atmosphere is weakly polluted, inferring th