May 2022 arXiv papers — page 49

Weijun Tan, Jingfeng Liu

Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type

Tim De Ryck, Siddhartha Mishra

We propose a very general framework for deriving rigorous bounds on the approximation error for physics-informed neural networks (PINNs) and operator learning architectures such as DeepONets and FNOs as well as for physics-informed operator learning. These bounds guarantee that PINNs and (physics-informed) DeepONets or FNOs will efficiently approximate the u

Hongliang Luo, Feifei Gao

The beam squint phenomenon in massive multi-input and multi-output wideband communications has been widely concerned recently, which generally deteriorates the beamforming performance. In this paper, we find that with the aid of the time-delay lines (TDs), the range and trajectory of the beam squint of a near-field communications system can be freely control

Gregory J. Galloway, Eric Ling

In this paper we review and extend some results in the literature pertaining to spacetime topology while naturally utilizing properties of the codimension 2 null cut locus. Our results fall into two classes, depending on whether or not one assumes the presence of horizons. Included among the spacetimes we consider are those that apply to the asymptotically (

Adil Han Orta, Martin Roelfs, Koen Van Den Abeele

A new solution strategy for quadratic eigenvalue problems, and the derivatives of the eigenvalues, is proposed, by combining the generalized reduction method with dual numbers. To demonstrate the method, we use the quadratic eigenvalue problem encountered in the semi-analytical finite element method (SAFE) as a guiding example. The SAFE method is designed to

Md Jubayer Shawon, Vishal Saxena

Automatic reconfiguration of optical filters is the key to novel flexible RF photonic receivers and Software Defined Radios (SDRs). Although silicon photonics (SiP) is a promising technology platform to realize such receivers, process variations and lack of in-situ tuning capability limits the adoption of SiP filters in widely-tunable RF photonic receivers.

Muhammed O. Sayin, Kaiqing Zhang, Asuman Ozdaglar

Certain but important classes of strategic-form games, including zero-sum and identical-interest games, have the fictitious-play-property (FPP), i.e., beliefs formed in fictitious play dynamics always converge to a Nash equilibrium (NE) in the repeated play of these games. Such convergence results are seen as a (behavioral) justification for the game-theoret

Adam Liška, Tomáš Kočiský, Elena Gribovskaya, Tayfun Terzi

Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia. However, our world is dynamic, evolves over time, and our models' knowledge becomes outdated. To study how semi-parametric QA models and their underlying parametric language models (LMs) adapt to

Yuji Takubo, Masahiro Kanazaki

An integrated optimization method based on the constrained multi-objective evolutionary algorithm (MOEA) and non-intrusive polynomial chaos expansion (PCE) is proposed, which solves robust multi-objective optimization problems under time-series dynamics. The constraints in such problems are difficult to handle, not only because the number of the dynamic cons

Gabriela Aguilar-Argüello, Octavio Valenzuela, Arturo Trelles

Recent studies have reported the detection of the galactic stellar halo wake and dipole triggered by the Large Magellanic Cloud (LMC), mirroring the corresponding response from dark matter (DM). These studies open up the possibility of adding constraints on the global mass distribution of the Milky Way (MW), and even on the nature of DM itself, with current

Anna Beliakova, Marco De Renzi

In this paper we refine our recently constructed invariants of $4$-dimensional $2$-handlebodies up to $2$-deformations. More precisely, we define invariants of pairs of the form $(W,\omega)$, where $W$ is a $4$-dimensional $2$-handlebody, $\omega$ is a relative cohomology class in $H^2(W,\partial W;G)$, and $G$ is an abelian group. The algebraic input requir

Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold, Abhinav Valada

Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that

Cosme G. Ayani, Michele Pisarra, Iván M. Ibarburu, Manuela Garnica

Strongly correlated materials exhibit exotic electronic states arising from the strong correlation between electrons. Dimensionality provides a tuning knob because thinning down to atomic thickness reduces screening effects and enhances electron correlations. In this work, a 2D Kondo lattice has been created by stacking a layer of 1T-TaS2 on a 2H-TaS2 crysta

A. Ali, F. Barbosa, J. Bessuille, E. Chudakov

The GlueX experiment at Jefferson Laboratory aims to perform quantitative tests of non-perturbative QCD by studying the spectrum of light-quark mesons and baryons. A Detector of Internally Reflected Cherenkov light (DIRC) was installed to enhance the particle identification (PID) capability of the GlueX experiment by providing clean $\pi$/K separation up to

Zack Lasner, Annika Lunstad, Chaoqun Zhang, Lan Cheng

The vibrational branching ratios of SrOH for radiative decay to the ground electronic state, $X^{2}\Sigma^{+}$, from the first two electronically excited states, $A^{2}\Pi$ and $B^{2}\Sigma^{+}$, are determined experimentally at the $\sim10^{-5}$ level. The observed small branching ratios enable the design of a full, practical laser-cooling scheme, including

Giovanni Puccetti, Anna Rogers, Aleksandr Drozd, Felice Dell'Orletta

While Transformer-based language models are generally very robust to pruning, there is the recently discovered outlier phenomenon: disabling only 48 out of 110M parameters in BERT-base drops its performance by nearly 30% on MNLI. We replicate the original evidence for the outlier phenomenon and we link it to the geometry of the embedding space. We find that

Min Cai, George Em Karniadakis, Changpin Li

We study the dynamic evolution of COVID-19 cased by the Omicron variant via a fractional susceptible-exposedinfected-removed (SEIR) model. Preliminary data suggest that the symptoms of Omicron infection are not prominent and the transmission is therefore more concealed, which causes a relatively slow increase in the detected cases of the new infected at the

Robert Wolfe, Aylin Caliskan

We evaluate the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for biases related to the marking of age, gender, and race or ethnicity. Given the option to label an image as "a photo of a person" or to select a label denoting race or ethnicity, CLIP chooses the "person" label 47.9% of the time for White in

Peter Hammond, Ian Hawke, Nils Andersson

Nuclear reactions may affect gravitational-wave signals from neutron-star mergers, but the impact is uncertain. In order to quantify the effect, we compare two numerical simulations representing intuitive extremes. In one case reactions happen instantaneously. In the other case, they occur on timescales much slower than the evolutionary timescale. We show th

Mohannad Abu-romoh, Nelson Costa, Antonio Napoli, Bernhard Spinnler

Digital back-propagation (DBP) and learned DBP (LDBP) are proposed for nonlinearity mitigation in WDM dual-polarization dispersion-managed systems. LDBP achieves Q-factor improvement of 1.8 dB and 1.2 dB, respectively, over linear equalization and a variant of DBP adapted to DM systems.

Andrew Flynn, Oliver Heilmann, Daniel Köglmayr, Vassilios A. Tsachouridis

Multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we explore the performance of a continuous-time, leaky-integrator, and next-generation `reservoir computer' (RC), when trained on tasks which test the limits of multifunctionality. In the first task we train each RC to reconstr

Conrad Borchers, Dalia Sara Gala, Benjamin Gilburt, Eduard Oravkin

The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generat

Roberto Pereira, Anay Ajit Deshpande, Cristian J. Vaca-Rubio, Xavier Mestre

Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among them. Hence, one of the major challenges for such HRS schemes is the necessity to know the optimal clustering of th

Joy Christian

In previous publications I have proposed a geometrical framework underpinning the local, realistic, and deterministic origins of the strong quantum correlations observed in Nature, without resorting to superdeterminism, retrocausality, or other conspiracy loopholes usually employed to circumvent Bell's argument against such a possibility. The geometrical fra

Benjamin Voß, Christoph Weise, Michael Ruderman, Johann Reger

The key idea of this contribution is the partial compensation of non-minimum phase zeros or unstable poles. Therefore the integer-order zero/pole is split into a product of fractional-order pseudo zeros/poles. The amplitude and phase response of these fractional-order terms is derived to include these compensators into the loop-shaping design. Such compensat

Edward Gow-Smith, Mark McConville, William Gillies, Jade Scott

The Book of the Dean of Lismore (BDL) is a 16th-century Scottish Gaelic manuscript written in a non-standard orthography. In this work, we outline the problem of transliterating the text of the BDL into a standardised orthography, and perform exploratory experiments using Transformer-based models for this task. In particular, we focus on the task of word-lev

Felix Hennings, Kai Hoppmann-Baum, Janina Zittel

Optimizing the transient control of gas networks is a highly challenging task. The corresponding model incorporates the combinatorial complexity of determining the settings for the many active elements as well as the non-linear and non-convex nature of the physical and technical principles of gas transport. In this paper, we present the latest improvements o

Tom Smeding, Matthijs Vákár

Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tangent derivative, dual-numbers /reverse-mode/ AD attempts to achieve reverse AD using a similarly simple idea: by pairing each scalar value with a backpropagator function. Its correctness and efficiency on higher-order input languages have been analysed by Brune

Md Sazzad Hossain, Pritom Saha, Townim Faisal Chowdhury, Shafin Rahman

It is common to have continuous streams of new data that need to be introduced in the system in real-world applications. The model needs to learn newly added capabilities (future tasks) while retaining the old knowledge (past tasks). Incremental learning has recently become increasingly appealing for this problem. Task-incremental learning is a kind of incre

Hyun Keun Lee, Chulan Kwon, Yong Woon Kim

Statistical inference from data is a foundational task in science. Recently, it has received growing attention for its central role in inference systems of primary interest in data sciences and machine learning. However, the understanding of statistical inference is not that solid while remains as a matter of subjective belief or as the routine procedures on

Kiran Tomlinson, Austin R. Benson

Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, socia

Beniamin Bogosel, Pedro R. S. Antunes

This work deals with theoretical and numerical aspects related to the behavior of the Steklov-Lam\'e eigenvalues on variable domains. After establishing the eigenstructure for the disk, we prove that for a certain class of Lam\'e parameters, the disk maximizes the first non-zero eigenvalue under area or perimeter constraints in dimension two. Upper bounds fo

Bhaskar Ray Chaudhury, Jugal Garg, Peter McGlaughlin, Ruta Mehta

We study the computational complexity of finding a competitive equilibrium (CE) with chores when agents have linear preferences. CE is one of the most preferred mechanisms for allocating a set of items among agents. CE with equal incomes (CEEI), Fisher, and Arrow-Debreu (exchange) are the fundamental economic models to study allocation problems, where CEEI i

Mounia Laassiri, Marie Clementine Nibamureke, Bertrand Tchanche Fankam, Sam Ramaila

The second African Conference of Fundamental and Applied Physics (ACP2021) took place in the week of March 7-11, 2022. During this conference, all the African Strategy for Fundamental and Applied Physics (ASFAP) working groups had been reserved specials sessions to discuss their scope, activities (past & current) and topics of common interests. The aim of th

Soon Hoe Lim, Yijun Wan, Umut Şimşekli

Recent studies have shown that gradient descent (GD) can achieve improved generalization when its dynamics exhibits a chaotic behavior. However, to obtain the desired effect, the step-size should be chosen sufficiently large, a task which is problem dependent and can be difficult in practice. In this study, we incorporate a chaotic component to GD in a contr

Andrew Adams, Richard F. Obrecht, Miller Wilt, Andrew Adams

In this paper, we explore the use of multiple deep learning techniques to detect weak interference in WiFi networks. Given the low interference signal levels involved, this scenario tends to be difficult to detect. However, even signal-to-interference ratios exceeding 20 dB can cause significant throughput degradation and latency. Furthermore, the resultant

Pulkit Gopalani, Sayar Karmakar, Dibyakanti Kumar, Anirbit Mukherjee

In recent times machine learning methods have made significant advances in becoming a useful tool for analyzing physical systems. A particularly active area in this theme has been "physics-informed machine learning" which focuses on using neural nets for numerically solving differential equations. In this work, we aim to advance the theory of measuring out-o

A. E. Schwertner, F. N. C. Sobral

Complexity analysis has become an important tool in the convergence analysis of optimization algorithms. For derivative-free optimization algorithms, it is not different. Interestingly, several constants that appear when developing complexity results hide the dimensions of the problem. This work organizes several results in literature about bounds that appea

Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn

The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved. Although agents initialized with such a prior policy can achieve a significantly higher reward with fewer samples when finetuned on the downstream task, it is still an op

Sabrina F. Tigik, Andris Vaivads, David M. Malaspina, Stuart D. Bale

Wave emissions at frequencies near electron gyrofrequency harmonics are observed at small heliocentric distances below about 40 solar radii and are known to occur in regions with quiescent magnetic fields. We show the close connection of these waves with the large-scale properties of the magnetic field. Near electron gyrofrequency harmonics emissions occur o

Hao-Ran Liu, Kai Leong Chong, Rui Yang, Roberto Verzicco

We numerically investigate turbulent Rayleigh-B\'enard convection with gas bubbles attached to the hot plate, mimicking a core feature in electrolysis, catalysis, or boiling. The existence of bubbles on the plate reduces the global heat transfer due to the much lower thermal conductivity of gases as compared to liquids and changes the structure of the bounda

Jian Liu, Xin He, Baihua Wu

Nonadiabatic dynamical processes are one of the most important quantum mechanical phenomena in chemical, materials, biological, and environmental molecular systems, where the coupling between different electronic states is either inherent in the molecular structure or induced by the (intense) external field. The curse of dimensionality indicates the intracta

Yu-Min Chung, Michael Hull, Austin Lawson, Neil Pritchard

Topological data analysis (TDA) is a rising field in the intersection of mathematics, statistics, and computer science/data science. The cornerstone of TDA is persistent homology, which produces a summary of topological information called a persistence diagram. To utilize machine and deep learning methods on persistence diagrams, These diagrams are further s

Xavier Cabre

This article concerns the results obtained in [Cabr\'e, Figalli, Ros-Oton, and Serra, Acta Math. 224 (2020)], which established the H\"older regularity of stable solutions to semilinear elliptic equations in the optimal range of dimensions $n\leq 9$. For expository purposes, we provide self-contained proofs of all results. They involve only basic Analysis to

Soumyarup Banerjee, Atul Dixit, Shivajee Gupta

An explicit transformation for the series $\sum\limits_{n=1}^{\infty}\displaystyle\frac{\log(n)}{e^{ny}-1},$ Re$(y)>0$, which takes $y$ to $1/y$, is obtained for the first time. This series transforms into a series containing $\psi_1(z)$, the derivative of Deninger's function $R(z)$. In the course of obtaining the transformation, new important properties of

Hongyu Liu, Chenchen Mou, Shen Zhang

The theory of mean field games studies the limiting behaviors of large systems where the agents interact with each other in a certain symmetric way. The running and terminal costs are critical for the agents to decide the strategies. However, in practice they are often partially known or totally unknown for the agents, while the total cost is known at the en

Florian Matz, Thomas-Christian Jagau

Auger decay is a relaxation process of core-vacant states in atoms and molecules, in which one valence electron fills the core vacancy while a second one is emitted. These states pose a challenge to electronic-structure theory, because they are embedded in the ionization continuum. Recently, we showed that molecular Auger decay can be described using complex

Yuanbin Jin, Jiangwei Yan, Shah Jee Rahman, Xudong Yu

We experimentally study the interference of dipole scattered light from two optically levitated nanoparticles in vacuum, which present an environment free of particle-substrate interactions. We illuminate the two trapped nanoparticles with a linearly polarized probe beam orthogonal to the propagation of the trapping laser beams. The scattered light from the

Cornelius Rampf, Sonja Ornella Schobesberger, Oliver Hahn

The cosmological fluid equations describe the early gravitational dynamics of cold dark matter (CDM), exposed to a uniform component of dark energy, the cosmological constant $\Lambda$. Perturbative predictions for the fluid equations typically assume that the impact of $\Lambda$ on CDM can be encapsulated by a refined growing mode $D$ of linear density fluc

Xin Wang, Sheng Wang, Honglin Xiong, Kai Xuan

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing wor

Kanka Ghosh, Andrzej Kusiak, Jean-Luc Battaglia

Phonon hydrodynamics is an exotic phonon transport phenomenon that challenges the conventional understanding of diffusive phonon scattering in crystalline solids. It features a peculiar collective motion of phonons with various unconventional properties resembling fluid hydrodynamics, facilitating non Fourier heat transport. Hence, it opens up several new av

Waylon Jepsen

This study investigates the capabilities of Cyclic Redundancy Checks(CRCs) to detect burst and random errors. Researchers have favored these error detection codes throughout the evolution of computing and have implemented them in communication protocols worldwide. CRCs are integrated into almost every device, in software and hardware. CRCs play a critical ro

Minjae Park

The recommendation system provides users with an appropriate limit of recent online large amounts of information. Session-based recommendation, a sub-area of recommender systems, attempts to recommend items by interpreting sessions that consist of sequences of items. Recently, research to include user information in these sessions is progress. However, it is

Zhi Hong, Aswathy Ajith, Gregory Pauloski, Eamon Duede

Transformer-based masked language models such as BERT, trained on general corpora, have shown impressive performance on downstream tasks. It has also been demonstrated that the downstream task performance of such models can be improved by pretraining larger models for longer on more data. In this work, we empirically evaluate the extent to which these result

Eyisto J. Aguilar Trejo, Daniel A. Martin, Dulara De Zoysa, Zac Bowen

In this article, a correlation metric $\kappa_C$ is proposed for the inference of the dynamical state of neuronal networks. $\kappa_C$ is computed from the scaling of the correlation length with the size of the observation region, which shows qualitatively different behavior near and away from the critical point of a continuous phase transition. The implemen

Weiwei Jin, Amit Datye, Udo D. Schwarz, Mark D. Shattuck

Experimental data on the compressive strength $\sigma_{\rm max}$ versus strain rate ${\dot \varepsilon}_{\rm eng}$ for metallic glasses undergoing uniaxial compression shows significantly different behavior for different alloys. For some metallic glasses, $\sigma_{\rm max}$ decreases with increasing ${\dot \varepsilon}_{\rm eng}$, for others, $\sigma_{\rm ma

Yuya Tanizaki, Mithat Ünsal

We study quantum chromodynamics including the two-index symmetric or anti-symmetric quark (QCD(Sym/ASym)) on small $\mathbb{R}^2\times T^2$ with a suitable magnetic flux. We first discuss the 't Hooft anomaly of these theories and claim that discrete chiral symmetry should be spontaneously broken completely to satisfy the anomaly matching condition. The $T^2

Audun Myers, David Muñoz, Firas Khasawneh, Elizabeth Munch

This work presents a framework for studying temporal networks using zigzag persistence, a tool from the field of Topological Data Analysis (TDA). The resulting approach is general and applicable to a wide variety of time-varying graphs. For example, these graphs may correspond to a system modeled as a network with edges whose weights are functions of time, o

E. Wilawer, D. Oszkiewicz, A. Kryszczyńska, A. Marciniak

The amount of sparse asteroid photometry being gathered by both space- and ground-based surveys is growing exponentially. This large volume of data poses a computational challenge owing to both the large amount of information to be processed and the new methods needed to combine data from different sources (e.g. obtained by different techniques, in different

Denis V. Novitsky, Adia Canos Valero, Alexander Krotov, Toms Salgals

Non-Hermitian photonic systems with loss and gain attract much attention due to their exceptional abilities in molding the flow of light. Introducing asymmetry to the $\mathcal{PT}$-symmetric system with perfectly balanced loss and gain, we reveal the mechanism of transition from the quasibound state in the continuum (quasi-BIC) to the simultaneous coherent

Gonzalo J. Anaya-Lopez, Jose P. Gonzalez-Coma, F. Javier Lopez-Martinez

We investigate the achievable secrecy sum-rate in a multi-user XL-MIMO system, on which user distances to the base station become comparable to the antenna array dimensions. We show that the consideration of spherical-wavefront propagation inherent to these set-ups is beneficial for physical-layer security, as it provides immunity against eavesdroppers locat

Helio N. Cunha Neto, Ivana Dusparic, Diogo M. F. Mattos, Natalia C. Fernandes

Fast identification of new network attack patterns is crucial for improving network security. Nevertheless, identifying an ongoing attack in a heterogeneous network is a non-trivial task. Federated learning emerges as a solution to collaborative training for an Intrusion Detection System (IDS). The federated learning-based IDS trains a global model using loc

Xinyi Yuan

The goal of this paper is to explicitly compute the Kodaira-Spencer map for a quaternionic Shimura curve over Q and its effect on the metrics of the Hodge bundle. The results are known to experts.

Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camo

Liang Zeng, Lanqing Li, Ziqi Gao, Peilin Zhao

Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, the underlying class distribution of unlabeled nodes for the given graph is usually imbalanced. This highly imbalanced class distribution inevitably deteriorates the quality of learn

Lei Xie, Shenghui Song, Khaled B. Letaief

Sensing will be an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to add sensing capability to current cellular networks. Different from traditional radar, the cellular structure of PMNs offers multiple perspectives to sense

Paul L. Schechter

A simple, novice-friendly scheme for classifying the image configurations of quadruply lensed quasars is proposed. With only six classes, it is intentionally coarse-grained. It focuses on the kitelikeness and circularity of these configurations, or the absence thereof. Other features are deliberately ignored, their importance to professional astronomers notw

Gioacchino Alex Anastasi, Mario Buscemi, Marco Aglietta, Rossella Caruso

In the framework of the upgrade of the Pierre Auger Observatory, a new high voltage module is being employed for the power supply of the 1-inch photomultiplier added to each water-Cherenkov detector of the surface array with the aim of increasing the dynamic range of the measurements. This module is located in a dedicated box near the electronics and compris

Suprovat Ghoshal, Anand Louis

Constraint satisfaction problems (CSPs) are ubiquitous in theoretical computer science. We study the problem of StrongCSPs, i.e. instances where a large induced sub-instance has a satisfying assignment. More formally, given a CSP instance $\Psi(V, E, [k], \{\Pi_{ij}\}_{(i,j) \in E})$ consisting of a set of vertices $V$, a set of edges $E$, alphabet $[k]$, a

Matti Karppa, Rasmus Pagh

We present HyperLogLogLog, a practical compression of the HyperLogLog sketch that compresses the sketch from $O(m\log\log n)$ bits down to $m \log_2\log_2\log_2 m + O(m+\log\log n)$ bits for estimating the number of distinct elements~$n$ using $m$~registers. The algorithm works as a drop-in replacement that preserves all estimation properties of the HyperLog

Maria Giulia Campitiello, Stefano Ettori, Lorenzo Lovisari, Iacopo Bartalucci

In this work, we performed an analysis of the X-ray morphology of the 118 CHEX-MATE (Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation) galaxy clusters, with the aim to provide a classification of their dynamical state. To investigate the link between the X-ray appearance and the dynamical stat

Thibault Dardinier, Gaurav Parthasarathy, Noé Weeks, Alexanders J. Summers

The magic wand $\mathbin{-\!\!*}$ (also called separating implication) is a separation logic connective commonly used to specify properties of partial data structures, for instance during iterative traversals. A footprint of a magic wand formula $A \mathbin{-\!\!*} B$ is a state that, combined with any state in which $A$ holds, yields a state in which $B$ ho

Ritwik Kulkarni, Enrico Di Minin

Unsustainable trade in wildlife is one of the major threats affecting the global biodiversity crisis. An important part of the trade now occurs on the internet, especially on digital marketplaces and social media. Automated methods to identify trade posts are needed as resources for conservation are limited. Here, we developed machine vision models based on

Abigail J. Lee, Laurie Rousseau-Nepton, Wendy L. Freedman, Barry F. Madore

The J-region asymptotic giant branch (JAGB) method is a new standard candle that is based on the stable intrinsic J-band magnitude of color-selected carbon stars, and has a precision comparable to other primary distance indicators such as Cepheids and the TRGB. We further test the accuracy of the JAGB method in the Local Group Galaxy M33. M33's moderate incl

Jincheng Huang, Ping Li, Rui Huang, Chen Na

Graph representation learning aim at integrating node contents with graph structure to learn nodes/graph representations. Nevertheless, it is found that many existing graph learning methods do not work well on data with high heterophily level that accounts for a large proportion of edges between different class labels. Recent efforts to this problem focus on

Rado Lapuh, Jan Kucera, Jakub Kovac, Bostjan Voljc

This paper reports on an evaluation of the Fluke 8588A digital multimeter (DMM) sampling performance and comparison to Keysight 3458A DMM sampling performance. The design of 8588A type DMM shows both similarities and striking differences in design compared to 3458A type DMM. Apart from their specified sampling capabilities, measurements were performed to eva

Ofer Yehuda, Avihu Dekel, Guy Hacohen, Daphna Weinshall

Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry. Until recently, deep active learning methods were ineffectual in the low-budget regime, where only a small number of examples are annotated. The situation has been alleviated by recent advances in representation and self-supervised learn

Valerio Marsocci, Simone Scardapane

In the field of Earth Observation (EO), Continual Learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally. The majority of these algorithms assume that data is (a) coming from a single source, and (b) fully labeled. Real-world EO datasets are instead characterized by a

J. A. Toalá, D. Bowman, T. Van Reeth, H. Todt

We present the analysis of the optical variability of the early, nitrogen-rich Wolf-Rayet (WR) star WR7. The analysis of multi-sector Transiting Exoplanet Survey Satellite (TESS) light curves and high-resolution spectroscopic observations confirm multi-periodic variability that is modulated on time-scales of years. We detect a dominant period of $2.6433 \pm

Xinfeng Xu, Alaina Henry, Timothy Heckman, John Chisholm

Star-forming galaxies are considered the likeliest source of the H I ionizing Lyman Continuum (LyC) photons that reionized the intergalactic medium at high redshifts. However, above z >~ 6, the neutral intergalactic medium prevents direct observations of LyC. Therefore, recent years have seen the development of indirect indicators for LyC that can be calibra

Jonathan Lozano, Ulf-G. Meißner, Fernando Romero-López, Akaki Rusetsky

A novel method for the extraction of form factors of unstable particles on the lattice is proposed. The approach is based on the study of two-particle scattering in a static, spatially periodic external field by using a generalization of the L\"uscher method in the presence of such a field. It is shown that the resonance form factor is given by the derivativ

Younghoon Jeong, Juhyun Oh, Jaimeen Ahn, Jongwon Lee

Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction. These improvements are focused on English and do not transfer well to other languages because of cultural and linguistic differences. In this paper, we pre

Sunny Vagnozzi, Luca Visinelli

Several works over the past years have discussed the possibility of testing fundamental physics using Very Long Baseline Interferometry horizon-scale black hole (BH) images, such as the Event Horizon Telescope (EHT) images of M87$^*$ and Sagittarius A$^*$ (Sgr A$^*$), using the size $r_{\rm sh}$ and deviation from circularity $\Delta \mathcal{C}$ of the BH s

Chenyang Xu, Pinyan Lu

Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of well-studied mechanism design settings, we make use of imperfect predictions to design mechanisms that perform much better than traditional mechanisms if the predictions are accurate

Dario Spirito

We prove a necessary and sufficient criterion for the ring of integer-valued polynomials to behave well under localization. Then, we study how the Picard group of $\mathrm{Int}(D)$ and the quotient group $\mathcal{P}(D):=\mathrm{Pic}(\mathrm{Int}(D))/\mathrm{Pic}(D)$ behave in relation to Jaffard, weak Jaffard and pre-Jaffard families; in particular, we show

Maria Violaris, Chiara Marletto

Erasure is fundamental for information processing. It is also key in connecting information theory and thermodynamics, as it is a logically irreversible task. We provide a new angle on this connection, noting that there may be an additional cost to erasure, that is not captured by standard results such as Landauer's principle. To make this point we use a mod

Anders S. Kortegaard

Consider a $k$-linear Frobenius category $\mathscr{E}$ with a projective generator such that the corresponding stable category $\mathscr{C}$ is 2-Calabi--Yau, Hom-finite with split idempotents. Let $l,m\in\mathscr{C}$ be maximal rigid objects with self-injective endomorphism algebras. We will show that their endomorphism algebras $\mathscr{C}(l,l)$ and $\mat

Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Q. Zhu

Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated according to a knowledge graph of the 38

Sabeesh Ethiraj, Bharath Kumar Bolla

Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. Many techniques have evolved over the past decade that made models lighter, faster, and robust with better generalization. However, many deep learning practitioners persist with pre-trained models and architectur

Patricia Gonçalves, Ricardo Misturini, Alessandra Occelli

In this article, we consider the ABC model in contact with slow/fast reservoirs. In this model, there is at most one particle per site, which can be of type $\alpha\in\{A,B,C\}$ and particles exchange positions in the discrete set of points $\{1,\cdots, N-1\}$ with a weakly asymmetric rate that depends on the type of particles involved in the exchange mechan

Dylan Phelps, Xuan-Rui Fan, Edward Gow-Smith, Harish Tayyar Madabushi

Deep neural models, in particular Transformer-based pre-trained language models, require a significant amount of data to train. This need for data tends to lead to problems when dealing with idiomatic multiword expressions (MWEs), which are inherently less frequent in natural text. As such, this work explores sample efficient methods of idiomaticity detectio

Deepak Kumar Sahu, Sanjoy Datta

In the one-dimensional quasiperiodic Aubry-Andr\'{e}-Harper Hamiltonian with nearest-neighbor hopping, all single-particle eigenstates undergo a phase transition from ergodic to localized states at a critical disorder strength $W_c/t = 2.0$. There is no mobility edge in this system. However, in the presence of power-law hopping having the form $1/r^a$, beyon

Peter Sovietov

Massive training of developers following the growing demands of the information technology industry requires teachers to automate their repetitive tasks. For training courses on programming, it is promising to use automatic generation and automatic grading of exercises that require a student to write a program. This article discusses the general scheme for c

Istvan David, Eugene Syriani

The need for real-time collaborative solutions in model-driven engineering has been increasing over the past years. Conflict-free replicated data types (CRDT) provide scalable and robust replication mechanisms that align well with the requirements of real-time collaborative environments. In this paper, we propose a real-time collaborative multi-level modelin

Christopher Blier-Wong, Hélène Cossette, Etienne Marceau

Copulas are a powerful tool to model dependence between the components of a random vector. One well-known class of copulas when working in two dimensions is the Farlie-GumbelMorgenstern (FGM) copula since their simple analytic shape enables closed-form solutions to many problems in applied probability. However, the classical definition of high-dimensional FG

Monojit Bhattacharjee, B. Krishna Das, Ramlal Debnath, Samir Panja

We revisit the study of $\omega$-hypercontractions corresponding to a single weight sequence $\omega=\{\omega_k\}_{k\geq0}$ introduced by Olofsson in \cite{O} and find an analogue of Nagy-Foias characteristic function in this setting. Explicit construction of characteristic functions is obtained and it is shown to be a complete unitary invariant. By consider

Thomas Beelen, Ella Velner, Roeland Ordelman, Khiet P. Truong

Our research project (CHATTERS) is about designing a conversational robot for children's digital information search. We want to design a robot with a suitable conversation, that fosters a responsible trust relationship between child and robot. In this paper we give: 1) a preliminary view on an empirical study around children's trust in robots that provide in

Luca Ferranti, Kalle Åström, Magnus Oskarsson, Jani Boutellier

Positioning using wave signal measurements is used in several applications, such as GPS systems, structure from sound and Wifi based positioning. Mathematically, such problems require the computation of the positions of receivers and/or transmitters as well as time offsets if the devices are unsynchronized. In this paper, we expand the previous state-of-the-

Yuanyuan Chen, Lixiang Chen

Wiener-Khinchin theorem, the fact that the autocorrelation function of a time process has a spectral decomposition given by its power spectrum intensity, can be used in many disciplines. However, the applications based on a quantum counterpart of Wiener-Khinchin theorem that provides a translation between time-energy degrees of freedom of biphoton wavefuncti

Andrea Loi, Roberto Mossa

We prove two rigidity theorems on holomorphic isometries into homogeneous bounded domains. The first shows that a K\"ahler-Ricci soliton induced by the homogeneous metric of a homogeneous bounded domain is trivial, i.e. K\"ahler-Einstein. In the second one we prove that a homogeneous bounded domain and the flat (definite or indefinite) complex Euclidean spac