April 2023 arXiv papers — page 15

Laurits Fromberg, Sneha Das, Line Katrine Harder Clemmensen

Blood-volume-pulse (BVP) is a biosignal commonly used in applications for non-invasive affect recognition and wearable technology. However, its predisposition to noise constitutes limitations for its application in real-life settings. This paper revisits BVP processing and proposes standard practices for feature extraction from empirical observations of BVP.

Sebastian Hartwig, Christian van Onzenoodt, Dominik Engel, Pedro Hermosilla

Cluster separation is a task typically tackled by widely used clustering techniques, such as k-means or DBSCAN. However, these algorithms are based on non-perceptual metrics, and our experiments demonstrate that their output does not reflect human cluster perception. To bridge the gap between human cluster perception and machine-computed clusters, we propose

Davide Bilò, Keerti Choudhary, Sarel Cohen, Tobias Friedrich

An $f$-edge fault-tolerant distance sensitive oracle ($f$-DSO) with stretch $\sigma \geq 1$ is a data structure that preprocesses an input graph $G$. When queried with the triple $(s,t,F)$, where $s, t \in V$ and $F \subseteq E$ contains at most $f$ edges of $G$, the oracle returns an estimate $\widehat{d}_{G-F}(s,t)$ of the distance $d_{G-F}(s,t)$ between $

M. Oulghelou, A. Ammar, R. Ayoub

Generally, reduced order models of fluid flows are obtained by projecting the Navier-Stokes equations onto a reduced subspace spanned by vector functions that carry the meaningful information of the dynamics. A common method to generate such subspace is the Proper Orthogonal Decomposition. This projection strategy is intrusive since it assumes that the appro

Marc Comadran, Cristina Manuel, Stefano Carignano

We evaluate the collisional energy loss of a energetic fermion with mass $M$ propagating through a hot QED plasma with temperature $T$, including mass corrections, that is, keeping the mass $m$ of the fermion constituents of the plasma, assuming $m \ll T \ll M$. We use the bare theory to compute the contribution of hard momentum transfer collisions, and the

Chun-Ju Lai, Daniel K. Nakano, Ziqing Xiang

In this paper the authors introduce a new notion called the quantum wreath product, which is the algebra $B \wr_Q \mathcal{H}(d)$ produced from a given algebra $B$, a positive integer $d$, and a choice $Q=(R,S,\rho,\sigma)$ of parameters. Important examples {that arise from our construction} include many variants of the Hecke algebras, such as the Ariki-Koik

Yuanwei Liu, Jiaqi Xu, Zhaolin Wang, Xidong Mu

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360{\deg} coverage and enhanced degrees of freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models,

Neele Falk, Annerose Eichel, Prisca Piccirilli

Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augment

Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye

Large language models (LLMs) have demonstrated impressive zero-shot abilities on a variety of open-ended tasks, while recent research has also explored the use of LLMs for multi-modal generation. In this study, we introduce mPLUG-Owl, a novel training paradigm that equips LLMs with multi-modal abilities through modularized learning of foundation LLM, a visua

Roberto Martínez-Cruz, Alvaro J. López-López, José Portela

Transformer-based language models, including ChatGPT, have demonstrated exceptional performance in various natural language generation tasks. However, there has been limited research evaluating ChatGPT's keyphrase generation ability, which involves identifying informative phrases that accurately reflect a document's content. This study seeks to address this

Satsuki Nishimura, Coh Miyao, Hajime Otsuka

We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with $U(1)$ flavor symmetry. By training neural networks on the $U(1)$ charges of quarks and leptons, the agent finds 21 models to be consistent with experimentally measured masses and

Asad Aftab, Chrysostomos Chrysostomou, Hassaan Khaliq Qureshi, Semeen Rehman

The Internet-of-Things (IoT) is an imminent and corporal technology that enables the connectivity of smart physical devices with virtual objects contriving in distinct platforms with the help of the internet. The IoT is under massive experimentation to operate in a distributed manner, making it favourable to be utilized in the healthcare ecosystem. However,

Bijan Bagchi, Rahul Ghosh, Sauvik Sen

In the light of a general scenario of a two-level non-Hermitian $\mathcal{PT}$-symmetric Hamiltonian we apply the tetrad-based method to analyze the possibility of analogue Hawking radiation. It is done by making use of the conventional null-geodesic approach wherein the associated Hawking radiation is described as a quantum tunneling process across a classi

Matteo Baggioli, Yanyan Bu, Vaios Ziogas

We study the quasi-hydrodynamics of a system with a softly broken $U(1)$ global symmetry using effective field theory (EFT) and holographic methods. In the gravity side, we consider a holographic Proca model in the limit of small bulk mass, which is responsible for a controllable explicit breaking of the $U(1)$ global symmetry in the boundary field theory. W

Jūlija Ovčiņņikiva, Agris Šostaks, Kārlis Čerāns

Knowledge graphs (KG) have become an important data organization paradigm. The available textual query languages for information retrieval from KGs, as SPARQL for RDF-structured data, do not provide means for involving non-technical experts in the data access process. Visual query formalisms, alongside form-based and natural language-based ones, offer means

Yuping Gao, Songling Shan, Gexin Yu

For any graph (hypergraph) $G$ with vertex set $V$ and edge set $E$, we define its incidence bipartite graph $\mathcal{I}(G)$ as the bipartite graph with bipartition $(E, V)$, where an edge $e \in E$ is adjacent to a vertex $v \in V$ in $\mathcal{I}(G)$ if and only if $e$ is incident to $v$ in $G$. This representation allows all concepts and properties of $G

Suman Pramanick, Rajesh Mondal, Somnath Bharadwaj

The Epoch of Reionization (EoR) neutral Hydrogen (HI) 21-cm signal evolves significantly along the line-of-sight (LoS) due to the light-cone (LC) effect. It is important to accurately incorporate this in simulations in order to correctly interpret the signal. The 21-cm LC simulations are typically produced by stitching together slices from a finite number $(

Xin Cao, Jingzhong Yang, Tom Fandrich, Yiteng Zhang

Large-scale quantum networks require the implementation of long-lived quantum memories as stationary nodes interacting with qubits of light. Epitaxially grown quantum dots hold great potential for the on-demand generation of single and entangled photons with high purity and indistinguishability. Coupling these emitters to memories with long coherence times e

David Krieg

It is well-known that the problem of sampling recovery in the $L_2$-norm on unweighted Korobov spaces (Sobolev spaces with mixed smoothness) as well as classical smoothness classes such as H\"older classes suffers from the curse of dimensionality. We show that the problem is tractable for those classes if they are intersected with the Wiener algebra of funct

Santiago Cepeda, Sergio Garcia-Garcia, Ignacio Arrese, Francisco Herrero

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as e

Rodolfo G. Cionco, Sergey M. Kudryavtsev, Willie Soon

The hypothesis that tidal forces on the Sun are related to the modulations of the solar-activity cycle has gained increasing attention. The works proposing physical mechanisms of planetary action via tidal forcing have in common that quasi-alignments between Venus, Earth, and Jupiter (V-E-J configurations) would provide a basic periodicity of $\approx 11.0$

Aaron Berger, Nitya Mani

We study the following natural arithmetic question regarding intersecting families: how large can a family of subsets of integers from $\{1, \ldots n\}$ be such that, for every pair of subsets in the family, the intersection contains a sum $x + y = z$? We conjecture that any such sum-intersecting family must have size at most $\frac14 \cdot 2^{n}$ (which wou

Eeshan Modak, Neha Sangwan, Mayank Bakshi, Bikash Kumar Dey

We study the Chernoff-Stein exponent of the following binary hypothesis testing problem: Associated with each hypothesis is a set of channels. A transmitter, without knowledge of the hypothesis, chooses the vector of inputs to the channel. Given the hypothesis, from the set associated with the hypothesis, an adversary chooses channels, one for each element o

Edward Pearce-Crump

The learnable, linear neural network layers between tensor power spaces of $\mathbb{R}^{n}$ that are equivariant to the orthogonal group, $O(n)$, the special orthogonal group, $SO(n)$, and the symplectic group, $Sp(n)$, were characterised in arXiv:2212.08630. We present an algorithm for multiplying a vector by any weight matrix for each of these groups, usin

Aaron Hendrickson, David P. Haefner

Of all sensor performance parameters, the conversion gain is arguably the most fundamental as it describes the conversion of photoelectrons at the sensor input into digital numbers at the output. Due in part to the emergence of deep sub-electron read noise image sensors in recent years, the literature has seen a resurgence of papers detailing methods for est

Qing Huang, Zishuai Li, Zhenchang Xing, Zhengkang Zuo

Developers' API needs should be more pragmatic, such as seeking suggestive, explainable, and extensible APIs rather than the so-called best result. Existing API search research cannot meet these pragmatic needs because they are solely concerned with query-API relevance. This necessitates a focus on enhancing the entire query process, from query definition to

Min Woong Ahn

The digits of Pierce expansion obey the law of large numbers, the central limit theorem, and the law of the iterated logarithm in the Lebesgue measure sense. We calculate the Hausdorff dimensions of the exceptional sets of each of the three laws. We further determine the Hausdorff dimensions of certain sets arising in Pierce expansions.

Tony Feng, Michael Harris, Barry Mazur

We sketch the construction of a derived enhancement of the reciprocity isomorphism of class field theory. Details will appear in a forthcoming joint paper of the authors with A. Raksit.

Ze-Yi Tu, Tao Zhu, Anzhong Wang

This article provides a detailed investigation into the motion of the surrounding particles around a polymer black hole in loop quantum gravity (LQG). Using effective potential, the critical bound orbits and innermost stable circular orbits (ISCO) are analyzed. The study finds that the radii and angular momentum of the critical bound orbits decrease with an

Axel Kleidon

The transition of our energy system to renewable energies is necessary in order not to heat up the climate any further and to achieve climate neutrality. The use of wind energy plays an important role in this transition in Germany. But how much wind energy can be used and what are the possible consequences for the atmosphere if more and more wind energy is u

Marius Buliga

We explain a dissipative version of hamiltonian mechanics, based on the information content of the deviation from hamiltonian dynamics. From this formulation we deduce minimal dissipation principles, dynamical inclusions, or constrained evolution with hamiltonian drift reformulations. Among applications we recover a dynamics generalization of Mielke et al qu

M. Gasperini, G. Veneziano

We reformulate in Hamiltonian language the recent proposal by Hohm and Zwiebach of an action yielding the most general $O(d,d)$-symmetric string cosmology equations, at tree-level in the string-loop expansion, but to all orders in the $\alpha'$ expansion. This allows us to give a simple characterization of a large class of non-singular, non-perturbative, pre

Alex Andersson, Chris Lintott, Rob Fender, Joe Bright

The newest generation of radio telescopes are able to survey large areas with high sensitivity and cadence, producing data volumes that require new methods to better understand the transient sky. Here we describe the results from the first citizen science project dedicated to commensal radio transients, using data from the MeerKAT telescope with weekly caden

Virginia Carnevali, Alessandro Sala, Pietro Biasin, Mirco Panighel

The electronic properties of graphene can be modified by the local interaction with a selected metal substrate. To probe this effect, Scanning Tunneling Microscopy is widely employed, particularly by means of local measurement via lock-in amplifier of the differential conductance and of the field emission resonance. In this article we propose an alternative,

D. Calchetti, M. Stangalini, S. Jafarzadeh, G. Valori

In November 2021, Solar Orbiter started its nominal mission phase. The remote-sensing instruments on board the spacecraft acquired scientific data during three observing windows surrounding the perihelion of the first orbit of this phase. The aim of the analysis is the detection of magnetohydrodynamic (MHD) wave modes in an active region by exploiting the ca

Rongxiao Fu, Ornela Dardha, Michel Steuwer

Strategy languages enable programmers to compose rewrite rules into strategies and control their application. This is useful in programming languages, e.g., for describing program transformations compositionally, but also in automated theorem proving, where related ideas have been studies with tactics languages. Clearly, not all compositions of rewrites are

Zhi-Gang Wang

In this work, we tentatively assign the $Y(4500)$ as the $[uc]_{\tilde{A}}[\overline{uc}]_{V}+[uc]_{V}[\overline{uc}]_{\tilde{A}}+[dc]_{\tilde{A}}[\overline{dc}]_{V} +[dc]_{V}[\overline{dc}]_{\tilde{A}}$ tetraquark state with the quantum numbers $J^{PC}=1^{--}$, and study the three-body strong decay $Y(4500)\to D^{*-}D^{*0}\pi^+$ with the light-cone QCD sum

Alexander von Bank, Eike-Manuel Edelmann, Laurent Schmalen

A spiking neural network (SNN) equalizer with a decision feedback structure is applied to an IM/DD link with various parameters. The SNN outperforms linear and artificial neural network (ANN) based equalizers.

Souradeep Bhattacharya, Magda Arnaboldi, Francois Hammer, Yanbin Yang

M31 has experienced a recent tumultuous merger history as evidenced from the many substructures that are still present in its inner halo, particularly the G1-Clump, NE- and W- shelves, and the Giant Stream (GS). We present planetary nebulae (PNe) line-of-sight velocity (LOSV) measurements covering the entire spatial extent of these four substructures. We fur

Giuseppe Filippone

The Discrete Logarithm Problem (DLP) for elliptic curves has been extensively studied since, for instance, it is the core of the security of cryptosystems like Elliptic Curve Cryptography (ECC). In this paper, we present an attack to the DLP for elliptic curves based on its connection to the problem of lifting, by using the exponential map for elliptic curve

Michał Cieśla, Piotr Kubala, Aref Abbasi Moud

We study two-dimensional random sequential adsorption (RSA) of flat polygons and rounded squares aligned in parallel to find a transition in the asymptotic behavior of the kinetics of packing growth. Differences in the kinetics for RSA of disks and parallel squares were confirmed in previous analytical and numerical reports. Here, by analyzing the two classe

Haibao Lu

Can the laws of physics be unified. One of the most puzzling challenges is to reconcile physics and chemistry, where molecular physics meets condensed-matter physics, resulting from the scaling effect and dynamic fluctuation of glassy matter at the glass transition temperature. Pioneer of condensed-matter physics, the Nobel Prize-winning physicist Philip War

Nikhil Balaji, Lorenzo Clemente, Klara Nosan, Mahsa Shirmohammadi

In this paper we obtain complexity bounds for computational problems on algebraic power series over several commuting variables. The power series are specified by systems of polynomial equations: a formalism closely related to weighted context-free grammars. We focus on three problems -- decide whether a given algebraic series is identically zero, determine

Edward Pearce-Crump

We present a novel application of category theory for deep learning. We show how category theory can be used to understand and work with the linear layer functions of group equivariant neural networks whose layers are some tensor power space of $\mathbb{R}^{n}$ for the groups $S_n$, $O(n)$, $Sp(n)$, and $SO(n)$. By using category theoretic constructions, we

Minoru Eto, Yu Hamada, Muneto Nitta

We study various composites of global solitons consisting of domain walls, strings, and monopoles in linear $O(N)$ models with $N=2$ and $3$. Spontaneous symmetry breaking (SSB) of the $O(N)$ symmetry down to $O(N-1)$ results in the vacuum manifold $S^{N-1}$, together with a perturbed scalar potential in the presence of a small explicit symmetry breaking (ES

Bing-Kai Sheng, Yong-Liang Ma

Inspired by the fact that both the dilaton potential encoding the trace anomalies of QCD and the Polyakov loop potential measuring the deconfinement phase transition can be expressed in the logarithmic forms, as well as the fact that the scale symmetry is expected to be restoring and colors are deconfined in extreme conditions such as high temperatures and/o

Pham Truong Xuan, Nguyen Thi Van

In this paper, we study the existence, uniqueness and asymptotic behaviour of almost periodic and asymptotically almost periodic mild solutions to the incompressible Navier-Stokes equations on $d$-dimensional non-compact manifold $(\mathcal{M},g)$ which satisfies some bounded conditions on curvature tensors. First, we use the $L^p-L^q$-dipsersive and smoothi

Jose Daniel Castro-Cisneros, Renu Malhotra, Aaron J. Rosengren

Near-Earth asteroid, Kamo'oalewa (469219), is one of a small number of known quasi-satellites of Earth; it transitions between quasi-satellite and horseshoe orbital states on centennial timescales, maintaining this dynamics over megayears. The similarity of its reflectance spectrum to lunar silicates and its Earth-like orbit both suggest that it originated f

Francesco Ancilotto, Manuel Barranco, Marti Pi

We have investigated how the Rayleigh-Plateau instability of a filament made of a 41K-87Rb self-bound mixture may lead to an array of identical quantum droplets, with typical breaking times which are shorter than the lifetime of the mixture. If the filament is laterally confined -- as it happens in a toroidal trap -- and atoms of one species are in excess wi

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos, Panagiotis C. Petrantonakis

Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and prevent its spread. In recent years, the challenge of multimodal misinformation detection (MMD) has garnered significant attention by researchers and has mainly involved the creat

Pengfei Song, Luoyu Mei, Han Cheng

This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. Compared with cameras and lidars, millimeter-wave radars have the advantage of not revealing privacy, having a strong anti-interference ability, and having long detection distance. The sparsity and capturing temporal-topological features of m

Shengchao Chen, Ting Shu, Huan Zhao, Guo Zhong

Meteorological radar reflectivity data (i.e. radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex Numerical Weather Prediction (NWP) models. In comparison to conventional models, Deep Learning (DL)-based radar echo extrapolation algori

Ioannis Papantonis, Vaishak Belle

AI and ML models have already found many applications in critical domains, such as healthcare and criminal justice. However, fully automating such high-stakes applications can raise ethical or fairness concerns. Instead, in such cases, humans should be assisted by automated systems so that the two parties reach a joint decision, stemming out of their interac

Shaun David Brocus Fell, Lavinia Heisenberg, Doğa Veske

The advent of gravitational wave astronomy has seen a huge influx of new predictions for potential discoveries of beyond the Standard Model fields. The coupling of all fundamental fields to gravity, together with its dominance on large scales, makes gravitational physics a rich laboratory to study fundamental physics. This holds especially true for the searc

Min Liu, Shengwei Han, Isar Stubbe

We study ideals in, and continuity of, quantaloid-enriched categories (Q-categories for short) as a 'many-valued and many-typed' generalization of domain theory. Abstractly, for any (saturated) class Phi of presheaves, we define and study the Phi-continuity of Q-categories. Concretely, we compute three examples of such saturated classes of presheaves - the c

Lucas Perotin, Hongyang Sun

We consider the online scheduling problem of moldable task graphs on multiprocessor systems for minimizing the overall completion time (or makespan). Moldable job scheduling has been widely studied in the literature, in particular when tasks have dependencies (i.e., task graphs) or when tasks are released on-the-fly (i.e., online). However, few studies have

Junlin Lu

It is challenging to quantify numerical preferences for different objectives in a multi-objective decision-making problem. However, the demonstrations of a user are often accessible. We propose an algorithm to infer linear preference weights from either optimal or near-optimal demonstrations. The algorithm is evaluated in three environments with two baseline

Sami Arja, Alexandre Marcireau, Richard L. Balthazor, Matthew G. McHarg

Contrast maximization (CMax) techniques are widely used in event-based vision systems to estimate the motion parameters of the camera and generate high-contrast images. However, these techniques are noise-intolerance and suffer from the multiple extrema problem which arises when the scene contains more noisy events than structure, causing the contrast to be

Zihao Li, Pan Gao, Hui Yuan, Ran Wei

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to le

Jannis Priesnitz, Axel Weißenfeld, Laurenz Ruzicka, Christian Rathgeb

We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the pred

Xuehu Liu, Chenyang Yu, Pingping Zhang, Huchuan Lu

Advanced deep Convolutional Neural Networks (CNNs) have shown great success in video-based person Re-Identification (Re-ID). However, they usually focus on the most obvious regions of persons with a limited global representation ability. Recently, it witnesses that Transformers explore the inter-patch relations with global observations for performance improv

Takumi Noda, Yuu Jinnai, Naoki Tomii, Takashi Azuma

Breast cancer is the most prevalent cancer with a high mortality rate in women over the age of 40. Many studies have shown that the detection of cancer at earlier stages significantly reduces patients' mortality and morbidity rages. Ultrasound computer tomography (USCT) is considered as a promising screening tool for diagnosing early-stage breast cancer as i

Christophe Paul, Evangelos Protopapas, Dimitrios M. Thilikos

In a recent work, we introduced a parametric framework for obtaining obstruction characterizations of graph parameters with respect to a quasi-ordering $\leqslant$ on graphs. Towards this, we proposed the concepts of class obstruction, parametric obstruction, and universal obstruction as combinatorial objects that determine the approximate behaviour of a gra

Suchandrima Das, Andrea Sand, Felix Hofmann

The interaction of edge dislocation with helium-implantation-induced defects in tungsten is investigated using molecular dynamics. Following prior investigations, we consider defects with two helium ions in a vacancy with a self-interstitial bound to it (He2V-SIA). Our observations suggest 3-10 He2V-SIA cluster together, with their pinning strength on glide

Michael Beetz, Gayane Kazhoyan, David Vernon

This paper presents a hybrid robot cognitive architecture, CRAM, that enables robot agents to accomplish everyday manipulation tasks. It addresses five key challenges that arise when carrying out everyday activities. These include (i) the underdetermined nature of task specification, (ii) the generation of context-specific behavior, (iii) the ability to make

Makoto Takamoto, Francesco Alesiani, Mathias Niepert

Scientific Machine Learning (SciML) is concerned with the development of learned emulators of physical systems governed by partial differential equations (PDE). In application domains such as weather forecasting, molecular dynamics, and inverse design, ML-based surrogate models are increasingly used to augment or replace inefficient and often non-differentia

T. Obikhod

The character of the behavior of the RGE constant on a certain energy interval is studied and polynomial interpolation is carried out to obtain an analytic function describing the RGE constant in QCD. A graphical description of the nature of the dependence of the beta function and the interaction constant is carried out and it is shown that a good agreement

Antonio Lieto, Manuel Striani, Cristina Gena, Enrico Dolza

This article presents an affective based sensemaking system for grouping and suggesting stories created by the users about the items of a museum. By relying on the TCL commonsense reasoning framework1, the system exploits the spatial structure of the Plutchik's wheel of emotions to organize the stories according to their extracted emotions. The process of em

K. Gonzalez, D. Azevedo, T. Jordão

In this paper, we present sharp estimates for the covering numbers of the embedding of the reproducing kernel Hilbert space (RKHS) associated with the Weierstrass fractal kernel into the space of continuous functions. The method we apply is based on the characterization of the infinite-dimensional RKHS generated by the Weierstrass fractal kernel and it requi

Junlin Lu, Patrick Mannion, Karl Mason

Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often possible to observe the behavior of decision-makers. In multi-objective decision-making, preference inference is the process of inferring the preferences of a decisio

Qi Lai, ChiMan Vong

Weakly supervised object detection (WSOD) aims at learning precise object detectors with only image-level tags. In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection. In fact, most existing WSOD methods only consider the visual a

Sirinrat Sithajan, Sukanya Meethong

M dwarfs are the most abundant stars in the Solar Neighborhood and they are prime targets for searching for rocky planets in habitable zones. Consequently, a detailed characterization of these stars is in demand. The spectral sub-type is one of the parameters that is used for the characterization and it is traditionally derived from the observed spectra. How

Jean Van Schaftingen

These notes present Sobolev-Gagliardo-Nirenberg endpoint estimates for classes of homogeneous vector differential operators. Away of the endpoint cases, the classical Calder\'on-Zygmund estimates show that the ellipticity is necessary and sufficient to control all the derivatives of the vector field. In the endpoint case, Ornstein showed that there is no non

László Csató

Since there exist several completion methods to estimate the missing entries of pairwise comparison matrices, practitioners face a difficult task in choosing the best technique. Our paper contributes to this issue: we consider a special set of incomplete pairwise comparison matrices that can be represented by a weakly connected directed acyclic graph, and st

Pierfrancesco Alaimo Di Loro, Dankmar Boehning, Sujit Sahu

The COVID-19 pandemic provided many modeling challenges to investigate the evolution of an epidemic process over areal units. A suitable encompassing model must describe the spatio-temporal variations of the disease infection rate of multiple areal processes while adjusting for local and global inputs. We develop an extension to Poisson Auto-Regression that

Jarry Chen, Haytham M. Fayek

Causal datasets play a critical role in advancing the field of causality. However, existing datasets often lack the complexity of real-world issues such as selection bias, unfaithful data, and confounding. To address this gap, we propose a new synthetic causal dataset, the Structurally Complex with Additive paRent causalitY (SCARY) dataset, which includes th

Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase

Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the ML ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of

Jiahao Liu, Bing Liu, Ruizhi Yang

In this paper, we analyzed 12 years of Fermi LAT gamma-ray data towards three nearby giant molecular clouds (GMCs), i.e., R~CrA, Chamaeleon, and Lupus. We calibrated the gas column density of these regions by using the Planck dust opacity map as well as the Gaia extinction map. With both the gamma-ray observations and gas column density maps, we derived the

Shangqing Tu, Chunyang Li, Jifan Yu, Xiaozhi Wang

ChatGPT has achieved great success and can be considered to have acquired an infrastructural status. There are abundant works for evaluating ChatGPT on benchmarks. However, existing benchmarks encounter two challenges: (1) Disregard for periodical evaluation and (2) Lack of fine-grained features. In this paper, we construct ChatLog, an ever-updating dataset

Michele Cirafici

K3 surfaces play a prominent role in string theory and algebraic geometry. The properties of their enumerative invariants have important consequences in black hole physics and in number theory. To a K3 surface string theory associates an Elliptic genus, a certain partition function directly related to the theory of Jacobi modular forms. A multiplicative lift

Morris Alper, Hadar Averbuch-Elor

Interactions between humans are diverse and context-dependent, but previous works have treated them as categorical, disregarding the heavy tail of possible interactions. We propose a new paradigm of learning human-human interactions as free text from a single still image, allowing for flexibility in modeling the unlimited space of situations and relationship

T. Jordão, K. Gonzalez

We present accurate upper and lower bounds for the covering numbers, with explicit constants, of the unit ball for two general classes of Reproducing Kernel Hilbert Space (RKHS) on the unit sphere of $\mathbb{R}^{d+1}$. In both classes, the RKHS is generated by an isotropic continuous positive definite kernel. The upper and lower bounds we present carry prec

Aditya Kapoor, Sushant Swamy, Luis Manso, Pilar Bachiller

It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of

Kento Ogawa, Takayuki Okuda

Let $G$ be a locally-compact group and $(H,L)$ a pair of closed subgroups of $G$. For the cases where $G$ is a real linear reductive Lie group, T. Kobayashi [Math. Ann. '89, J. Lie Theory '96] established a criterion for properness of the $L$-action on the homogeneous space $G/H$ in terms of Cartan's KAK-decomposition of $G$. In this paper, we show that a si

Lalit Kumar, Sivaji Ganesh Sista, Konijeti Sreenadh

In this article, we study the semi discrete and fully discrete formulations for a Kirchhoff type quasilinear integro-differential equation involving time-fractional derivative of order $\alpha \in (0,1) $. For the semi discrete formulation of the equation under consideration, we discretize the space domain using a conforming FEM and keep the time variable co

Simone Baldassarri, Alexandre Gaudillière, Frank den Hollander, Francesca Romana Nardi

This is the second in a series of three papers in which we study a lattice gas subject to Kawasaki conservative dynamics at inverse temperature $\beta>0$ in a large finite box $\Lambda_\beta \subset\mathbb Z^2$ whose volume depends on $\beta$. Each pair of neighbouring particles has a negative binding energy $-U<0$, while each particle has a positive activat

Christian Bongiorno, Marco Berritta

In optimal covariance cleaning theory, minimizing the Frobenius norm between the true population covariance matrix and a rotational invariant estimator is a key step. This estimator can be obtained asymptotically for large covariance matrices, without knowledge of the true covariance matrix. In this study, we demonstrate that this minimization problem is equ

Ayano Nakai-Kasai, Tadashi Wadayama

The required signal processing rate in future wireless communication systems exceeds the performance of the latest electronics-based processors. Introduction of analog optical computation is one promising direction for energy-efficient processing. This paper considers a continuous-time minimum mean squared error detection for multiple-input multiple-output s

Rohit Sarma Sarkar, Bibhas Adhikari

This work presents an optimization-based scalable quantum neural network framework for approximating $n$-qubit unitaries through generic parametric representation of unitaries, which are obtained as product of exponential of basis elements of a new basis that we propose as an alternative to Pauli string basis. We call this basis as the Standard Recursive Blo

Kenji Ishikawa, Daiki Takeuchi, Noboru Harada, Takehiro Moriya

This paper proposes a deep sound-field denoiser, a deep neural network (DNN) based denoising of optically measured sound-field images. Sound-field imaging using optical methods has gained considerable attention due to its ability to achieve high-spatial-resolution imaging of acoustic phenomena that conventional acoustic sensors cannot accomplish. However, th

Louise Axon, Dimitrios Panagiotakopoulos, Samuel Ayo, Carolina Sanchez-Hernandez

Air Traffic Management data systems today are inefficient and not scalable to enable future unmanned systems. Current data is fragmented, siloed, and not easily accessible. There is data conflict, misuse, and eroding levels of trust in provenance and accuracy. With increased autonomy in aviation, Artificially Intelligent (AI) enabled unmanned traffic managem

Pietro Barbiero, Stefano Fioravanti, Francesco Giannini, Alberto Tonda

Explainable AI (XAI) aims to address the human need for safe and reliable AI systems. However, numerous surveys emphasize the absence of a sound mathematical formalization of key XAI notions -- remarkably including the term "explanation" which still lacks a precise definition. To bridge this gap, this paper presents the first mathematically rigorous definiti

Sophie Marques, Damas Mgani

We present a novel approach to the concept of gluing in mathematics by introducing the notions of a gluing data category and a gluing data functor. Our work provides a formal categorical characterization of the notion of gluing in algebraic geometry. By using this characterization, we are able to describe gluing in a unified way that applies to a wide range

Shiyu Xing, Fengshui Jing, Min Tan

RegHEC is a registration-based hand-eye calibration technique with no need for accurate calibration rig but arbitrary available objects, applicable for both eye-in-hand and eye-to-hand cases. It tries to find the hand-eye relation which brings multi-view point clouds of arbitrary scene into simultaneous registration under a common reference frame. RegHEC fir

Andreas Strand, Patrick Gorton, Martin Asprusten, Karsten Brathen

A substantial part of fighter pilot training is simulation-based and involves computer-generated forces controlled by predefined behavior models. The behavior models are typically manually created by eliciting knowledge from experienced pilots, which is a time-consuming process. Despite the work put in, the behavior models are often unsatisfactory due to the

Ofer Aharony, Netanel Barel

We study the correlation functions of local operators in unitary $\textrm{T}\bar{\textrm{T}}$-deformed field theories, using their formulation in terms of Jackiw-Teitelboim gravity. The position of the operators is defined using the dynamical coordinates of this formalism. We focus on the two-point correlation function in momentum space, when the undeformed

L. Giacomazzi, N. S. Shcheblanov, M. E. Povarnitsyn, Y. Li

We present a combined study based on experimental measurements of infrared (IR) dielectric function and first-principles calculations of IR spectra and vibrational density of states (VDOS) of amorphous alumina (am-Al$_2$O$_3$). In particular, we show that the main features of the imaginary part of the dielectric function $\epsilon_2(\omega)$ at $\sim$380 and

Varsha Behrunani, Hanmin Cai, Philipp Heer, Roy S. Smith

The increasing penetration of renewable energy resources has transformed the energy system from traditional hierarchical energy delivery paradigm to a distributed structure. Such development is accompanied with continuous liberalization in the energy sector, giving rise to possible energy trading among networked local energy hub. Joint operation of such hubs

Alireza Afzal Aghaei, Kourosh Parand

This paper considers the hyperparameter optimization problem of mathematical techniques that arise in the numerical solution of differential and integral equations. The well-known approaches grid and random search, in a parallel algorithm manner, are developed to find the optimal set of hyperparameters. Employing rational Jacobi functions, we ran these algor

Linus Seelinger, Anne Reinarz, Jean Benezech, Mikkel Bue Lykkegaard

Treating uncertainties in models is essential in many fields of science and engineering. Uncertainty quantification (UQ) on complex and computationally costly numerical models necessitates a combination of efficient model solvers, advanced UQ methods and HPC-scale resources. The resulting technical complexities as well as lack of separation of concerns betwe