May 2023 arXiv papers — page 82

Hao Fei, Qian Liu, Meishan Zhang, Min Zhang

In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs. First, we represent the input images and texts with the visual and language scene graphs (SG), where such fine-grained vision-

Chandra Shekhar Lohani, Suraj Kumar Nayak, Kannabiran Seshasayanan

We study the effect of confinement on the three-dimensional linear instability of fastly rotating two-dimensional turbulent flows. Using the large scale friction to model the effect of top and bottom boundaries, we study the onset of three-dimensional perturbations on a rapidly rotating flow. The friction term is taken to affect both the evolution of the two

Jia Cheng Hu, Roberto Cavicchioli, Alessandro Capotondi

The Image Captioning research field is currently compromised by the lack of transparency and awareness over the End-of-Sequence token (<Eos>) in the Self-Critical Sequence Training. If the <Eos> token is omitted, a model can boost its performance up to +4.1 CIDEr-D using trivial sentence fragments. While this phenomenon poses an obstacle to a fair evaluation

Fernando Albiac, Jose L. Ansorena, Miguel Berasategui

The main results in this paper contribute to bring to the fore novel underlying connections between the contemporary concepts and methods springing from greedy approximation theory with the well established techniques of classical Banach spaces. We do that by showing that bounded-oscillation unconditional bases, introduced by Dilworth et al. in 2009 in the s

Jie Yang, Bingliang Li, Fengyu Yang, Ailing Zeng

This paper investigates the problem of the current HOI detection methods and introduces DiffHOI, a novel HOI detection scheme grounded on a pre-trained text-image diffusion model, which enhances the detector's performance via improved data diversity and HOI representation. We demonstrate that the internal representation space of a frozen text-to-image diffus

Majid Rahro Zargar, Mohsen Gheibi

Let $(R,\fm)$ be a local ring, and let $C$ be a semidualizing complex. We establish the equality $r_R(Z) = \nu(\Ext^{g-\inf C}_R(Z,C))\mu^{\depth C}_R(\mathfrak{m}, C)$ for a homologically finite and bounded complex $Z$ with finite $\GC$-dimension $g$. Additionally, we prove that if $\Ext^i(M,N)=0$ for sufficiently large $i$, while $\id_R\Ext^i(M,N)$ remains

Javier Tirado-Garín, Frederik Warburg, Javier Civera

Current deep visual local feature detectors do not model the spatial uncertainty of detected features, producing suboptimal results in downstream applications. In this work, we propose two post-hoc covariance estimates that can be plugged into any pretrained deep feature detector: a simple, isotropic covariance estimate that uses the predicted score at a giv

Dylan Cope

This paper presents a real-time simulation involving ''protozoan-like'' cells that evolve by natural selection in a physical 2D ecosystem. Selection pressure is exerted via the requirements to collect mass and energy from the surroundings in order to reproduce by cell-division. Cells do not have fixed morphologies from birth; they can use their resources in

Jerry Tang, Meng Du, Vy A. Vo, Vasudev Lal

Encoding models have been used to assess how the human brain represents concepts in language and vision. While language and vision rely on similar concept representations, current encoding models are typically trained and tested on brain responses to each modality in isolation. Recent advances in multimodal pretraining have produced transformers that can ext

Thomas Messmer, Michael Hölling, Joachim Peinke

An experimental study in a wind tunnel is presented to explore the wake of a floating wind turbine subjected to harmonic side-to-side and fore-aft motions under laminar inflow conditions. The wake recovery is analysed as a function of the frequency of motion, measured by the Strouhal number, St. Our findings indicate that both directions of motion accelerate

Benjamin N Katz, Vincent Crespi

A crystal structure with a point defect typically returns to its ideal local structure upon moving a few bond lengths away from the defect; topological defects such as dislocations or disclinations also heal rapidly in this regard. Here we describe a simple point defect -- a two-fold atom incorporated at the growth edge of a 2D hexagonal honeycomb material -

Seamus D. O'Hara, Joseph B. Costello, Qile Wu, Ken West

We report that the polarizations of sidebands emitted from bulk gallium arsenide (GaAs) driven by a strong terahertz (THz) laser while probed with a weak near-infrared laser can be viewed as interferograms from a Michelson-like interferometer for Bloch waves. A simple analytical model is introduced to calculate the difference in quantum mechanical phases acc

Nina Rizun, Aleksandra Revina, Noella Edelmann

The public sector faces several challenges, such as a number of external and internal demands for change, citizens' dissatisfaction and frustration with public sector organizations, that need to be addressed. An alternative to the traditional top-down development of public services is co-creation of public services. Co-creation promotes collaboration between

Rodrigo Almeida, Rui Dilão

Physarum polycephalum is an acellular slime mould that grows as a highly adaptive network of veins filled with protoplasm. As it forages, Physarum dynamically rearranges its network structure as a response to local stimuli information, optimising the connection between food sources. This high-level behaviour was already exploited to solve numerous optimisati

Kanon Toda, Kenta Otsubo, Kohei Noda, Heeyoung Lee

We present a new approach for measuring fiber tip temperature using low-coherence Brillouin optical correlation-domain reflectometry, which eliminates the need for an independent reference path and does not entail specific processing of the fiber tip.

Andrei A. Shiryaev, Yurii Chesnokov, Alexander L. Vasiliev, Thomas Hainschwang

Diamond single crystals showing Infra-red features of pressurized CO2-I phase were studied using Transmission Electron Microscopy (TEM) and tomography. Numerous O-containing precipitates with sizes up to 45 nm are observed. The absolute majority of these precipitates decorate dislocation loops or are located inside them; individual scattered precipitates are

Qimao Yang, Huili Chen, Qiwei Dong

This report presents a comprehensive study on deep learning models for brand logo classification in real-world scenarios. The dataset contains 3,717 labeled images of logos from ten prominent brands. Two types of models, Convolutional Neural Networks (CNN) and Vision Transformer (ViT), were evaluated for their performance. The ViT model, DaViT small, achieve

Taekyung Kim, Jungwi Mun, Junwon Seo, Beomsu Kim

In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computational capabilities, this approach is becoming increasingly important for solving challenging control problems in robotics by learning unkno

Mana Sakamoto, Tomoya Miyamae, Kohei Noda, Kentaro Nakamura

We extend the measurement range of optical correlation-domain reflectometry (OCDR) by modulating the laser output frequency at two frequencies, while preserving spatial resolution. We demonstrate distributed reflectivity sensing with a ten-fold extended measurement range.

Naman Saxena, Subhojyoti Khastigir, Shishir Kolathaya, Shalabh Bhatnagar

The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average reward actor-critic algorithms, but average reward off-policy actor-critic is relatively less explored. In this work, we present both on-pol

Nandi Schoots, Dylan Cope

We study the relationship between the entropy of intermediate representations and a model's robustness to distributional shift. We train models consisting of two feed-forward networks end-to-end separated by a discrete $n$-bit channel on an unsupervised contrastive learning task. Different masking strategies are applied after training that remove a proportio

Pietro Bonfà, Ifeanyi John Onuorah, Franz Lang, Iurii Timrov

Magnetostriction drives a rhombohedral distortion in the cubic rock salt antiferromagnet MnO at the N\'eel temperature $T_{N}=118$ K. As an unexpected consequence we show that this distortion acts to localize the site of an implanted muon due to the accompanying redistribution of electron density. This lifts the degeneracy between equivalent sites, resulting

Zhu Liu, Jinyuan Liu, Guanyao Wu, Zihang Chen

In recent years, learning-based methods have achieved significant advancements in multi-exposure image fusion. However, two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. Reliance on aligned image pairs in existing methods causes susceptibility to artifacts due to device motion. Additionally, existing t

Dylan Cope, Peter McBurney

In this paper, we propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem. We then present a probabilistic model for inferring a speaker's intentions and a listener's semantics from observing communications between a team of language-users. This model builds on the assumptions that speakers ar

Marwan Zeggari, Aydin Abadi, Renaud Lambiotte, Mohamad Kassab

Sneakers were designated as the most counterfeited fashion item online, with three times more risk in a trade than any other fashion purchase. As the market expands, the current sneaker scene displays several vulnerabilities and trust flaws, mostly related to the legitimacy of assets or actors. In this paper, we investigate various blockchain-based mechanism

Philip T. Metzger, James G. Mantovani

This manuscript analyzes lunar lander soil erosion models and trajectory models to calculate how much damage will occur to spacecraft orbiting in the vicinity of the Moon. The soil erosion models have considerable uncertainty due to gaps in our understanding of the basic physics. The results for ~40 t landers show that the Lunar Orbital Gateway will be impac

Dylan Cope, Peter McBurney

In most conversations about explanation and AI, the recipient of the explanation (the explainee) is suspiciously absent, despite the problem being ultimately communicative in nature. We pose the problem `explaining AI systems' in terms of a two-player cooperative game in which each agent seeks to maximise our proposed measure of explanatory effectiveness. Th

Omri Haim, Jeremy Boger-Lombard, Ori Katz

Optical imaging through scattering media is an important challenge in a variety of fields ranging from microscopy to autonomous vehicles. While advanced wavefront shaping techniques have offered significant breakthroughs in the past decade, current techniques still require a known guide-star and a high-resolution spatial-light-modulator (SLM), or a very larg

Chen Wenting, Liu Jie, Yuan Yixuan

Medical reports with substantial information can be naturally complementary to medical images for computer vision tasks, and the modality gap between vision and language can be solved by vision-language matching (VLM). However, current vision-language models distort the intra-model relation and mainly include class information in prompt learning that is insu

Nikolay Moshchevitin

We prove an easy statement about inhomogeneous approximation in metric theory of Diophantine Approximation.

Firas Dhaouadi, Sergey Gavrilyuk

In this paper, we present a new model for heat transfer in compressible fluid flows. The model is derived from Hamilton's principle of stationary action in Eulerian coordinates, in a setting where the entropy conservation is recovered as an Euler--Lagrange equation. The governing system is shown to be hyperbolic. It is asymptotically consistent with the Eule

Yiming Chen, Simin Chen, Zexin Li, Wei Yang

Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference. Multi-exit is a mainstream approach to address this issue by making a trade-off between efficiency and accuracy, where the saving of computation comes from an early exit. However, whether such saving from early-

Jian Tan

The main purpose of this paper is to develop the theory of product Hardy spaces built on Banach lattices on $\mathbb R^n\times\mathbb R^m$. First we introduce new product Hardy spaces ${H}_X(\mathbb R^n\times\mathbb R^m)$ associated with ball quasi-Banach function spaces $X(\mathbb R^n\times\mathbb R^m)$ via applying the Littlewood-Paley-Stein theory. Then w

Taylor Dupuy, Ehud Hrushovski

Let A be the integral closure of the ring of polynomials CC[t], within the field of algebraic functions in one variable. We show that A interprets the ring of integers. This contrasts with the analogue for finite fields, proved to have a decidable theory (see Prestel-Schmid and van den Dries-A. Macintyre).

Jaemin Choi

Chase-Lev deque is a concurrent data structure designed for efficient load balancing in multiprocessor scheduling. It employs a work-stealing strategy, where each thread possesses its own work-stealing deque to store tasks, and idle threads steal tasks from other threads. However, given the inherent risk of bugs in software, particularly in a multiprocessor

Jason Baretz, Nicholas Carrara, Jacob Hollingsworth, Daniel Whiteson

We describe a set of novel methods for efficiently sampling high-dimensional parameter spaces of physical theories defined at high energies, but constrained by experimental measurements made at lower energies. Often, theoretical models such as supersymmetry are defined by many parameters, $\mathcal{O}(10-100)$, expressed at high energies, while relevant expe

Jieyu Zhang, Bohan Wang, Zhengyu Hu, Pang Wei Koh

Pre-training datasets are critical for building state-of-the-art machine learning models, motivating rigorous study on their impact on downstream tasks. In this work, we study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training datas

Guangzhi Wang, Yixiao Ge, Xiaohan Ding, Mohan Kankanhalli

We empirically investigate proper pre-training methods to build good visual tokenizers, making Large Language Models (LLMs) powerful Multimodal Large Language Models (MLLMs). In our benchmark, which is curated to evaluate MLLMs visual semantic understanding and fine-grained perception capabilities, we discussed different visual tokenizers pre-trained with do

Muhammad U Nasir, Julian Togelius

Large Language Models (LLMs) have proven to be useful tools in various domains outside of the field of their inception, which was natural language processing. In this study, we provide practical directions on how to use LLMs to generate 2D-game rooms for an under-development game, named Metavoidal. Our technique can harness the power of GPT-3 by Human-in-the

Xiaowen Zhang, Patrick Lachance, Yueying Ni, Yin Li

In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time consistent evolving representations of the particle phase-space distribution. We employ a style-based constrained generative adversarial network (Style-GAN) where the changing cosmic time is an input style parameter to the network. Th

Mădălina-Andreea Mitran, Anna V. Kononova, Fabio Caraffini, Daniela Zaharie

This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated. The empirical analysis is conducted on the SBOX-COST benchmarking t

Zheyi Fan, Szu Hui Ng, Qingpei Hu

We present an effective framework for improving the breakdown point of robust regression algorithms. Robust regression has attracted widespread attention due to the ubiquity of outliers, which significantly affect the estimation results. However, many existing robust least-squares regression algorithms suffer from a low breakdown point, as they become stuck

Fereshte Khani, Marco Tulio Ribeiro

Despite substantial advancements, Natural Language Processing (NLP) models often require post-training adjustments to enforce business rules, rectify undesired behavior, and align with user values. These adjustments involve operationalizing "concepts"--dictating desired model responses to certain inputs. However, it's difficult for a single entity to enumera

Peng Jin, Hao Li, Zesen Cheng, Jinfa Huang

Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with natural language descriptions. Current methods either fail to leverage the local details or are computationally expensive. What's worse, they fail to leverage the heterogeneous concepts in data. In this paper, we propose the Disentangled Conceptualization and Set

Mozhi Zhang, Hang Yan, Yaqian Zhou, Xipeng Qiu

Few-shot Named Entity Recognition (NER) is a task aiming to identify named entities via limited annotated samples. Recently, prototypical networks have shown promising performance in few-shot NER. Most of prototypical networks will utilize the entities from the support set to construct label prototypes and use the query set to compute span-level similarities

Mohammad Taha Toghani, Sebastian Perez-Salazar, César A. Uribe

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can quickly adapt to new environments and tasks. In this work, we study the MRL problem under the policy gradient formulation, where we propose a novel algorithm that uses Moreau envelope surrogate regularizers to jointly learn a meta-policy that is adjustable to the environm

Pietropaolo Frisoni

These notes are a transcript of Carlo Rovelli's lectures on Loop Quantum Gravity, given in Marseille in 2018, which (at present) can be entirely found on YouTube. I transcribed them in LaTeX in early 2020 as an exercise to get ready for my Ph.D. in LQG at Western University. This transcript is meant to be a (hopefully helpful) integration for the video versi

Andi Liu, Fangyuan Song, Zhaohu Li, Malik Ashtar

Rare-earth (RE) based honeycomb-lattice materials with strong spin-orbit coupled Jeff=1/2 moments have attracted great interest as a platform to realize Kitaev quantum spin liquid (QSL) state. Herein, we report the discovery of a new family of RE based honeycomb-lattice magnets Ba9RE2(SiO4)6(RE=Ho-Yb), which crystallize into the rhombohedral structure with s

Sahil Tyagi, Prateek Sharma

Current techniques and systems for distributed model training mostly assume that clusters are comprised of homogeneous servers with a constant resource availability. However, cluster heterogeneity is pervasive in computing infrastructure, and is a fundamental characteristic of low-cost transient resources (such as EC2 spot instances). In this paper, we devel

Jinyuan Li, Han Li, Zhuo Pan, Di Sun

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing studies mainly focus on maximizing the utilization of pertinent image information or incorporating external knowledge from explicit knowledge bases. However, these methods either neglect the necessity of providing

Jinhee Paeng, Jisun Park, Ernest K. Ryu

Coordinate update/descent algorithms are widely used in large-scale optimization due to their low per-iteration cost and scalability, but their behavior on infeasible or misspecified problems has not been much studied compared to the algorithms that use full updates. For coordinate-update methods to be as widely adopted to the extent so that they can be used

Peyman Alipour

This article describes a numerical method based on the dual reciprocity boundary elements method (DRBEM) for solving some well-known nonlinear parabolic partial differential equations (PDEs). The equations include the classic and generalized Fisher's equations, Allen-Cahn equation, Newell-Whithead equation, Fitz-HughNagumo equation and generalized Fitz-HughN

Yunshui Li, Junhao Liu, Chengming Li, Min Yang

In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples. Specifically, we first propose a dynamic pruning technique to obtain a small pruned model from a large source model, where the pruning mask of t

Wenxiang Yan, Yuan Gao, Xian Long, Zheng Yuan

Free space optical (FSO) communication can exploit mode-division multiplexing using orthogonal spatial modes of Laguerre Gaussian beams, such as orbital angular momentum (OAM) modes, wherein OAM multiplexing offers potentially infinite information capacity due to the arbitrary quantization of OAM. Combined with polarization-division multiplexing and waveleng

Roberta Coletti, Mónica L. Mendonça, Susana Vinga, Marta B. Lopes

Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official WHO classification CNS. These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this c

Yi Yang, Biao Yang, Guancong Ma, Jensen Li

There has been a recent surge of interest in using light and sound as platforms for studying non-Abelian physics. Through a kaleidoscope of physical effects, light and sound provide diverse ways to manipulate their degrees of freedom to constitute the Hilbert space for demonstrating non-Abelian phenomena. The review aims to provide a timely and comprehensive

Yongqiang Cai

In recent years, deep learning-based sequence modelings, such as language models, have received much attention and success, which pushes researchers to explore the possibility of transforming non-sequential problems into a sequential form. Following this thought, deep neural networks can be represented as composite functions of a sequence of mappings, linear

Tim Hageman, Carmen Andrade, Emilio Martínez-Pañeda

Laboratory and numerical corrosion experiments impose an electric potential on the metal surface, differing from natural corrosion conditions, where corrosion typically occurs in the absence of external current sources. In this work, we present a new computational model that enables predicting corrosion under charge-conservation conditions. The metal potenti

Georg Borges, Ken Satoh, Erich Schweighofer

This volume contains the papers presented at LN2FR 2022: The International Workshop on Methodologies for Translating Legal Norms into Formal Representations, held on December 14, 2022 in association with 35th International Conference on Legal Knowledge and Information Systems (JURIX 2022). Using symbolic logic or similar methods of knowledge representation t

Jose Pinto, Fernando Henríquez, Carlos Jerez-Hanckes

We establish shape holomorphy results for general weakly- and hyper-singular boundary integral operators arising from second-order partial differential equations in unbounded two-dimensional domains with multiple finite-length open arcs. After recasting the corresponding boundary value problems as boundary integral equations, we prove that their solutions de

Sahil Tyagi, Martin Swany

Distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at each iteration incurs considerable overhead, exacerbated by the increasing size and complexity of state-of-the-art neural networks. Although ma

Yuyue Wang, Huan Xiao, Yihan Wu, Ruihua Song

Text to Speech (TTS) models can generate natural and high-quality speech, but it is not expressive enough when synthesizing speech with dramatic expressiveness, such as stand-up comedies. Considering comedians have diverse personal speech styles, including personal prosody, rhythm, and fillers, it requires real-world datasets and strong speech style modeling

Xuan-Quy Dao, Ngoc-Bich Le, The-Duy Vo, Xuan-Dung Phan

The VNHSGE (VietNamese High School Graduation Examination) dataset, developed exclusively for evaluating large language models (LLMs), is introduced in this article. The dataset, which covers nine subjects, was generated from the Vietnamese National High School Graduation Examination and comparable tests. 300 literary essays have been included, and there are

Radostina Tasheva, Ivan Stefanov

The investigation of the data for quasi-periodic pulsations observed in the X-ray spectra of the accreting millisecond pulsar XTEJ 1807-294 allows some conclusions to be made about its main parameters - mass and angular momentum. Seven different geodesic models - namely RP, RP1, RP2, TP, TP1, WD and TD are applied in attempt to assess their ability to descri

Daniel Thuerck, Boro Sofranac, Marc E. Pfetsch, Sebastian Pokutta

Cutting-planes are one of the most important building blocks for solving large-scale integer programming (IP) problems to (near) optimality. The majority of cutting plane approaches rely on explicit rules to derive valid inequalities that can separate the target point from the feasible set. Local cuts, on the other hand, seek to directly derive the facets of

Gregor vom Scheidt

Can large language models be used to complete mathematical tasks that are traditionally performed either manually or with the aid of theorem provers? To answer this question, a state-of-the-art system, GPT-4, was provided with a concise natural language specification for a previously unpublished formal system and asked to complete a number of tasks, from sta

Haiyan Lu, Li Huang

The intricate interplay between itinerant-localized 5f states and strongly correlated electronic states have been systematically investigated in isostructural actinide compounds AnSn3 (An=U, Np, Pu) by using a combination of the density functional theory and the embedded dynamical mean-field approach. The obvious narrow flat 5f electronic band with remarkabl

M. M. Ettefaghi, Z. Askaripour Ravari

Although neutrino-antineutrino states originating from neutral-current interactions are blind concerning the flavor state, an oscillation pattern is predicted provided that both neutrino and antineutrino are detected. This issue arises from both the coherence and entanglement of the neutrino-antineutrino states. Based on quantum resource theory, we use the l

Sagar Hazra

We report the measurements of various hadronic $B$ decays at the Belle II experiment using a $362 fb^{-1}$ sample of electron-positron collisions collected at the $\Upsilon(4S)$ resonance. All results agree with the previous determination, and some of them are already competitive with the world's best measurement. In addition, we present a newly developed al

Ming Xu, Jing Zhang

The identification of important nodes with strong propagation capabilities in road networks is a vital topic in urban planning. Existing methods for evaluating the importance of nodes in traffic networks only consider topological information and traffic volumes, the diversity of the traffic characteristics in road networks, such as the number of lanes and av

Giampiero M. Gallo, Demetrio Lacava, Edoardo Otranto

Central Banks interventions are frequent in response to exogenous events with direct implications on financial market volatility. In this paper, we introduce the Asymmetric Jump Multiplicative Error Model (AJM), which accounts for a specific jump component of volatility within an intradaily framework. Taking the Federal Reserve (Fed) as a reference, we propo

Yatin Nandwani, Vineet Kumar, Dinesh Raghu, Sachindra Joshi

A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not \textit{faithful} to the underlying document. Existing automated metrics used for evaluating the faithfulness of response with respect to the grounding document measure the degree of similarity between the generat

Zoran Medić, Jan Šnajder

Citation recommendation (CR) models may help authors find relevant articles at various stages of the paper writing process. Most research has dealt with either global CR, which produces general recommendations suitable for the initial writing stage, or local CR, which produces specific recommendations more fitting for the final writing stages. We propose the

Pengfei Zhang, Yuliang Xin

Motivated by the recent discovery of a low surface brightness diffuse emission, a supernova remnant (SNR) candidate, surrounding the young pulsar PSR~J0837--2454, we carry out a likelihood analysis of the $\gamma$-ray data obtained by the \emph{Fermi} Gamma-ray Space Telescope from August 2008 to November 2022. Using a 2D Gaussian spatial template, we detect

Narayan Subramanian, Atharva Joshi, Daksh Bagga

The food supply chain has a number of challenges, including a lack of transparency and disengagement among stakeholders. By providing a transparent and traceable digital ledger of transactions and movements for all supply chain actors, blockchain technology can provide a resolution to these problems. We propose a blockchain-based system for tracking a produc

Ali Eghbali, Tayebe Parvizi, Adel Rezaei-Aghdam

By calculating inequivalent classical r-matrices for the $gl(2,\mathbb{R})$ Lie algebra as solutions of (modified) classical Yang-Baxter equation ((m)CYBE)), we classify the YB deformations of Wess-Zumino-Witten (WZW) model on the $GL(2,\mathbb{R})$ Lie group in twelve inequivalent families. Most importantly, it is shown that each of these models can be obta

Alessia andò, Dimitri Breda

We extend the piecewise orthogonal collocation method to computing periodic solutions of coupled renewal and delay differential equations. Through a rigorous error analysis, we prove convergence of the relevant finite-element method and provide a theoretical estimate of the error. We conclude with some numerical experiments to further support the theoretical

V. Gutlyanski\uı, O. Nesmelova, V. Ryazanov, E. Yakubov

The present paper is devoted to the study of the Dirichlet problem ${\rm{Re}}\,\omega(z)\to\varphi(\zeta)$ as $z\to\zeta,$ $z\in D,\zeta\in \partial D,$ with continuous boundary data $\varphi :\partial D\to\mathbb R$ for Beltrami equations $\omega_{\bar{z}}=\mu(z) \omega_z+\sigma (z)$, $|\mu(z)|<1$ a.e., with sources $\sigma :D\to\mathbb C$ in the case of lo

Bing Liu, Wei Luo, Gang Li, Jing Huang

As deep learning gains popularity in modelling dynamical systems, we expose an underappreciated misunderstanding relevant to modelling dynamics on networks. Strongly influenced by graph neural networks, latent vertex embeddings are naturally adopted in many neural dynamical network models. However, we show that embeddings tend to induce a model that fits obs

Lucy D'Agostino McGowan, Shirlee Wohl, Justin Lessler

The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer wit

Biagio Ricceri

In this paper, in particular, we prove the following result: Let $E$ be a reflexive real Banach space and let $C\subset E$ be a closed convex set, with non-empty interior, whose boundary is sequentially weakly closed and non-convex. Then, for every function $\varphi:\partial C\to {\bf R}$ and for every convex set $S\subseteq E^*$ dense in $E^*$, there exists

Takamitsu Koyano, Jake S. Bobowski

We present an analytical calculation of the transient response of ideal (i.e. lossless) transmission lines. The calculation presented considers a length of transmission line connected to a signal generator with output impedance $Z_\mathrm{g}$ and terminated with a load impedance $Z_\mathrm{L}$. The approach taken is to analyze a circuit model of the system i

Ayyoob Imani, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini

The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a c

Yasushi Yoneta

We study the dynamics of quantum statistical ensembles at first-order phase transition points of finite macroscopic systems. First, we show that at the first-order phase transition point of systems with an order parameter that does not commute with the Hamiltonian, any quantum state with a non-zero value of the order parameter always evolves towards a macros

Biagio Ricceri

Let $\Omega\subset {\bf R}^n$ be a smooth bounded domain. In this paper, we prove a result of which the following is a by-product: Let $q\in ]0,1[$, $\alpha\in L^{\infty}(\Omega)$, with $\alpha>0$, and $k\in {\bf N}$. Then, the problem $$\cases {-\tan\left(\int_{\Omega}|\nabla u(x)|^2dx\right)\Delta u= \alpha(x)u^q & in $\Omega$\cr & \cr u>0 & in $\Omega$\cr

Marzio Di Vece, Frank P. Pijpers, Diego Garlaschelli

Triadic motifs are the smallest building blocks of higher-order interactions in complex networks and can be detected as over-occurrences with respect to null models with only pair-wise interactions. Recently, the motif structure of production networks has attracted attention in light of its possible role in the propagation of economic shocks. However, its ch

Jindi Zhang, Luning Wang, Dan Su, Yongxiang Huang

Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e., representation) through disentangled representation learning and then discard the latent code dimensions correlated with sensitive attributes (e.g., gender). Nevertheless, these

Clive Gomes, Lalitha Natraj, Shijun Liu, Anushka Datta

In this survey paper, we deep dive into the field of Explainable Artificial Intelligence (XAI). After introducing the scope of this paper, we start by discussing what an "explanation" really is. We then move on to discuss some of the existing approaches to XAI and build a taxonomy of the most popular methods. Next, we also look at a few applications of these

Naoki Hamamoto

We compute the best constant in functional integral inequality called the Hardy-Leray inequalities for solenoidal vector fields on $\mathbb{R}^N$. This gives a solenoidal improvement of the inequalities whose best constants are known for unconstrained fields, and develops of the former work by Costin-Maz'ya who found the best constant in the Hardy-Leray ineq

Welverton R. Silva, Fábio L. Usberti, Rafael C. S. Schouery

The electric vehicle sharing problem (EVSP) arises from the planning and operation of one-way electric car-sharing systems. It aims to maximize the total rental time of a fleet of electric vehicles while ensuring that all the demands of the customer are fulfilled. In this paper, we expand the knowledge on the complexity of the EVSP by showing that it is NP-h

Inès Hipolito

This paper proposes a framework for optimising the adaptation and attunement of a Complex Adaptive System (CAS) with its environments. The tendency towards stability can be explained by minimising free energy but high variability, noise, and over-specialized rigidity can lead to a "stuck state" in a CAS. Without perturbation (increasing free energy), the sys

M. V. Chushnyakova, I. I. Gontchar, E. V. Kulik

In the present work, we study the experimental data on the root mean square charge radii of the spherical atomic nuclei. The purpose of this analysis is to clarify what is the manifestation of the neutron shells in the considered observable and to find regularities in the behavior of the charge radii when a shell neutron is crossed. As the result, we have fo

Simon Jeanteur, Laura Kovács, Matteo Maffei, Michael Rawson

Cryptographic protocols are hard to design and prove correct, as witnessed by the ever-growing list of attacks even on protocol standards. Symbolic models of cryptography enable automated formal security proofs of such protocols against an idealized model, which abstracts away from the algebraic properties of cryptographic schemes and thus misses attacks. Co

Pierre Carbonnelle, Joost Vennekens, Bart Bogaerts, Marc Denecker

Many practical problems can be understood as the search for a state of affairs that extends a fixed partial state of affairs, the \emph{environment}, while satisfying certain conditions that are formally specified. Such problems are found in, e.g., engineering, law or economics. We study this class of problems in a context where some of the relevant informat

Lucas Cremer, Johannes Erdmann, Roni Harnik, Jan Lukas Späh

Flavour-changing-neutral currents (FCNCs) involving the top quark are highly suppressed within the Standard Model (SM). Hence, any signal in current or planned future collider experiments would constitute a clear manifestation of physics beyond the SM. We propose a novel, interference-based strategy to search for top-quark FCNCs involving the $Z$ boson that

Eley Ng, Ziang Liu, Monroe Kennedy

Modeling multimodal human behavior has been a key barrier to increasing the level of interaction between human and robot, particularly for collaborative tasks. Our key insight is that an effective, learned robot policy used for human-robot collaborative tasks must be able to express a high degree of multimodality, predict actions in a temporally consistent m

Mengze Xu, Jie Ma, Yuanyuan Zhu

Previous super-resolution reconstruction (SR) works are always designed on the assumption that the degradation operation is fixed, such as bicubic downsampling. However, as for remote sensing images, some unexpected factors can cause the blurred visual performance, like weather factors, orbit altitude, etc. Blind SR methods are proposed to deal with various

Lei Lin, Shuangtao Li, Yafang Zheng, Biao Fu

Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled, i.e., the syntactic a

A. A. Coley, R. J. van den Hoogen

Using a recently developed algorithm that chooses preferred coordinates and a preferred co-frame, we will determine the completely general Bianchi type I teleparallel geometry. In using this algorithm, any remaining gauge freedom is allocated to the choice of spin connection. We then solve the symmetry constraints placed on the spin connection to derive a ge

Hofit Wasserman Rozen, Niva Elkin-Koren, Ran Gilad-Bachrach

As artificial intelligence (AI) becomes more prevalent there is a growing demand from regulators to accompany decisions made by such systems with explanations. However, a persistent gap exists between the need to execute a meaningful right to explanation vs. the ability of Machine Learning systems to deliver on such a legal requirement. The regulatory appeal