May 2023 arXiv papers — page 35

ATLAS Collaboration

A measurement of novel event shapes quantifying the isotropy of collider events is performed in 140 fb$^{-1}$ of proton-proton collisions with $\sqrt s=13$ TeV centre-of-mass energy recorded with the ATLAS detector at CERN's Large Hadron Collider. These event shapes are defined as the Wasserstein distance between collider events and isotropic reference geome

Nausheen Basha, Thomas Savage, Jonathan McDonough, Ehecatl Antonio Del-Rio Chanona

Processes involving the manufacture of fine/bulk chemicals, pharmaceuticals, biofuels, and waste treatment require plug flow characteristics to minimise their energy consumption and costs, and maximise product quality. One such versatile flow chemistry platform is the coiled tube reactor subjected to oscillatory motion, producing excellent plug flow qualitie

Sanyam Jain

Marine animals and deep underwater objects are difficult to recognize and monitor for safety of aquatic life. There is an increasing challenge when the water is saline with granular particles and impurities. In such natural adversarial environment, traditional approaches like CNN start to fail and are expensive to compute. This project involves implementing

CMS Collaboration

The second-order ($v_2$) and third-order ($v_3$) Fourier coefficients describing the azimuthal anisotropy of prompt and nonprompt (from b-hadron decays) J/$\psi$, as well as prompt $\psi$(2S) mesons are measured in lead-lead collisions at a center-of-mass energy per nucleon pair of $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. The analysis uses a data set corresponding

Wanxin Li, Collin Meese, Mark Nejad, Hao Guo

Consensus algorithms play a critical role in blockchains and directly impact their performance. During consensus processing, nodes need to validate and order the pending transactions into a new block, which requires verifying the application-specific data encapsulated within a transaction. This exposes the underlying data to the consensus nodes, presenting p

Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García

In the recently proposed Lace framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: all occurrences of those entity references (possibly across multiple database tuples) are deemed equal and can be merged.

Junting Chen, Guohao Li, Suryansh Kumar, Bernard Ghanem

Object goal navigation is an important problem in Embodied AI that involves guiding the agent to navigate to an instance of the object category in an unknown environment -- typically an indoor scene. Unfortunately, current state-of-the-art methods for this problem rely heavily on data-driven approaches, \eg, end-to-end reinforcement learning, imitation learn

Tom Bewley, Jonathan Lawry, Arthur Richards

We propose a method to capture the handling abilities of fast jet pilots in a software model via reinforcement learning (RL) from human preference feedback. We use pairwise preferences over simulated flight trajectories to learn an interpretable rule-based model called a reward tree, which enables the automated scoring of trajectories alongside an explanator

Shanwen Tan, Zhengui Li, Wangxu Li

In this study, we propose a computational method for solving the turbulence problem of incompressible viscous Newtonian fluids based on the extended Navier-Stokes (N-S) equations. With some phenomenological observations and H. J. Kreuer's interpretation of the origin of viscosity, we make a hypothesis in the fluid flow that the shear process is accompanied b

Nataliia Molchanova, Bénédicte Maréchal, Jean-Philippe Thiran, Tobias Kober

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing fac

Marina A. Ferreira, Eugenia Franco, Jani Lukkarinen, Alessia Nota

We study the long-time behaviour of the solutions to Smoluchowski coagulation equations with a source term of small clusters. The source drives the system out-of-equilibrium, leading to a rich range of different possible long-time behaviours, including anomalous self-similarity. The coagulation kernel is non-gelling, homogeneous, with homogeneity $\gamma \le

Ondřej F. K. Kalenda, Jakub Rondoš, Jiří Spurný

We develop a theory of abstract intermediate function spaces on a compact convex set $X$ and study the behaviour of multipliers and centers of these spaces. In particular, we provide some criteria for coincidence of the center with the space of multipliers and a general theorem on boundary integral representation of multipliers. We apply the general theory i

William Giarè

The standard $\Lambda$CDM model of cosmology is largely successful in describing many observations, including precise measurements of the Cosmic Microwave Background (CMB) radiation. However, some intriguing anomalies remain currently unexplained within this theoretical framework. Such discrepancies can be broadly categorized into two groups: those involving

A. P. Balachandran

After a brief recount of small and large gauge transformations and the nature of observables, we discuss superselection sectors in gauge theories. There are an infinity of them, classified by large gauge transformations. Gauge theory sectors are labelled by the eigenvalues of a complete commuting set (CCS) of these transformations. In QED, the standard chemi

Suet-Ying Lam, Qingcheng Zeng, Kexun Zhang, Chenyu You

While a large body of literature suggests that large language models (LLMs) acquire rich linguistic representations, little is known about whether they adapt to linguistic biases in a human-like way. The present study probes this question by asking whether LLMs display human-like referential biases using stimuli and procedures from real psycholinguistic expe

Yongwei Guo, Wenliang Li

In the free $\Box^k$ scalar conformal field theory, there exist conserved and partially-conserved higher-spin currents. We study their anomalous dimensions associated with $\phi^{2n}$ interaction in the $\epsilon$ expansion. We derive general formulae for the leading corrections from the conformal multiplet recombination, and verify their consistency with cr

Victor Le Coz, Iacopo Mastromatteo, Damien Challet, Michael Benzaquen

Trading pressure from one asset can move the price of another, a phenomenon referred to as cross impact. Using tick-by-tick data spanning 5 years for 500 assets listed in the United States, we identify the features that make cross-impact relevant to explain the variance of price returns. We show that price formation occurs endogenously within highly liquid a

Fusang Wang, Arnaud Louys, Nathan Piasco, Moussab Bennehar

Neural Radiance Fields (NeRF) enable 3D scene reconstruction from 2D images and camera poses for Novel View Synthesis (NVS). Although NeRF can produce photorealistic results, it often suffers from overfitting to training views, leading to poor geometry reconstruction, especially in low-texture areas. This limitation restricts many important applications whic

Kartik Chandra, Tzu-Mao Li, Josh Tenenbaum, Jonathan Ragan-Kelley

Great storytellers know how to take us on a journey. They direct characters to act -- not necessarily in the most rational way -- but rather in a way that leads to interesting situations, and ultimately creates an impactful experience for audience members looking on. If audience experience is what matters most, then can we help artists and animators *directl

Vandad Imani, Carlos Sevilla-Salcedo, Elaheh Moradi, Vittorio Fortino

Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase in high-dimensional data, which poses significant challenges for the prediction models that can lead to poor generalization. Therefore, relevant feature selection from multi-view d

Wei Tang, Weijia Zhang, Min-Ling Zhang

In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approac

Hayeon Lee, Rui Hou, Jongpil Kim, Davis Liang

Distillation from Weak Teacher (DWT) is a method of transferring knowledge from a smaller, weaker teacher model to a larger student model to improve its performance. Previous studies have shown that DWT can be effective in the vision domain and natural language processing (NLP) pre-training stage. Specifically, DWT shows promise in practical scenarios, such

Haoran Liu, Peng Li, Ming-Zhe Liu, Kai-Ming Wang

This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU accelerati

Sina Akbari, Luca Ganassali, Negar Kiyavash

We study the problem of causal structure learning from data using optimal transport (OT). Specifically, we first provide a constraint-based method which builds upon lower-triangular monotone parametric transport maps to design conditional independence tests which are agnostic to the noise distribution. We provide an algorithm for causal discovery up to Marko

Athanasios Lekosiotis, Federico Belli, Christian Brahms, Mohammed Sabbah

We report the flexible on-target delivery of 800 nm wavelength, 5 GW peak power, 40 fs duration laser pulses through an evacuated and tightly coiled 10 m long hollow-core nested anti-resonant fiber by positively chirping the input pulses to compensate for the anomalous dispersion of the fiber. Near-transform-limited output pulses with high beam quality and a

Paul Geuchen, Thomas Jahn, Hannes Matt

We study the universality of complex-valued neural networks with bounded widths and arbitrary depths. Under mild assumptions, we give a full description of those activation functions $\varrho:\mathbb{C}\to \mathbb{C}$ that have the property that their associated networks are universal, i.e., are capable of approximating continuous functions to arbitrary accu

Daniel Dörfler, Andreas Löhne

This article is concerned with the problem of approximating a not necessarily bounded spectrahedral shadow, a certain convex set, by polyhedra. By identifying the set with its homogenization the problem is reduced to the approximation of a closed convex cone. We introduce the notion of homogeneous {\delta}-approximation of a convex set and show that it defin

Hongyi Chen, Maurits Kaptein

In order to achieve unbiased and efficient estimators of causal effects from observational data, covariate selection for confounding adjustment becomes an important task in causal inference. Despite recent advancements in graphical criterion for constructing valid and efficient adjustment sets, these methods often rely on assumptions that may not hold in pra

Nils Müller, Kaibin Bao, Jörg Matthes, Kai Heussen

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged

Carolyn Abbott, Jason Behrstock, Jacob Russell

We study the boundaries of relatively hyperbolic HHGs. Using the simplicial structure on the hierarchically hyperbolic boundary, we characterize both relative hyperbolicity and being thick of order 1 among HHGs. In the case of relatively hyperbolic HHGs, we show that the Bowditch boundary of the group is the quotient of the HHS boundary obtained by collapsin

Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch

Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks. However, they lack inherent mechanisms that provide calibrated uncertainties and enable selection of relevant features and interactions. Approaching NAMs from a Bayesian perspective, we augment them in three primary way

Hongyi Chen, Maurits Kaptein

We focus on the extension of bivariate causal learning methods into multivariate problem settings in a systematic manner via a novel framework. It is purposive to augment the scale to which bivariate causal discovery approaches can be applied since contrast to traditional causal discovery methods, bivariate methods render estimation in the form of a causal D

Loay Mualem, Ethan R. Elenberg, Moran Feldman, Amin Karbasi

Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings. In this paper, we fill this gap by providing a characterization of submodular minimax optimization, the problem of finding a set (for either the min or the max player) that is effective

Gábor Hofer-Szabó

It will be shown that the Peres-Mermin square admits value-definite noncontextual hidden-variable models if the observables associated with the operators can be measured only sequentially but not simultaneously. Namely, sequential measurements allow for noncontextual models in which hidden states update between consecutive measurements. Two recent experiment

Sesa Singha Roy, Reid Tingley, Alan Dorin

The effects of climate change, urbanisation and agriculture are changing the way insects occupy habitats. Some species may utilise anthropogenic microhabitat features for their existence, either because they prefer them to natural features, or because of no choice. Other species are dependent on natural microhabitats. Identifying and analysing these insects'

Benedikt Brantner

One of the primary reasons behind the success of neural networks has been the emergence of an array of new, highly-successful optimizers, perhaps most importantly the Adam optimizer. It is widely used for training neural networks, yet notoriously hard to interpret. Lacking a clear physical intuition, Adam is difficult to generalize to manifolds. Some attempt

YeolLin ChoeJo, Yechan Kim, Hye-Sung Lee

Some properties of a neutrino may differ significantly depending on whether it is Dirac or Majorana type. The type is determined by the relative size of Dirac and Majorana masses, which may vary if they arise from an oscillating scalar dark matter. We show that the change can be significant enough to convert the neutrino type between Dirac and Majorana perio

Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer

Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator or not, for instance, to prove intellectual property theft. However, previous works are either limited to the closed-world setting or require undesirable changes to the generative

Chad Nester, Niels Voorneveld

We extend the free cornering of a symmetric monoidal category, a double categorical model of concurrent interaction, to support branching communication protocols and iterated communication protocols. We validate our constructions by showing that they inherit significant categorical structure from the free cornering, including that they form monoidal double c

Mangilal Choudhary

It is possible to excite various linear and non-linear low-frequency modes in dusty plasma which is an admixture of electrons, ions, gas atoms, and negatively charged solid particles. The experimental as well as theoretical study of these low-frequency dynamical modes in dusty plasma is very complex because of the involvement of dynamics of electrons, ions,

Yuta Nishikawa, Satoshi Nakamura

In end-to-end speech translation, speech and text pre-trained models improve translation quality. Recently proposed models simply connect the pre-trained models of speech and text as encoder and decoder. Therefore, only the information from the final layer of encoders is input to the decoder. Since it is clear that the speech pre-trained model outputs differ

Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, Sadao Kurohashi

Large language models (LLMs) have achieved impressive performance on various reasoning tasks. To further improve the performance, we propose MultiTool-CoT, a novel framework that leverages chain-of-thought (CoT) prompting to incorporate multiple external tools, such as a calculator and a knowledge retriever, during the reasoning process. We apply MultiTool-C

Fabio A. González, Raúl Ramos-Pollán, Joseph A. Gallego-Mejia

This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. In quantum mechanics, a density matrix is the most general way to describe the state of a quantum system. This work e

Shen Gao, Zhitao Yao, Chongyang Tao, Xiuying Chen

Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) reference-based: evaluating with human-labeled reference summary; (2) reference-free: evaluating the summary consistency of the document. Recent studies mainly focus on one of these scenarios and explore trainin

Dominik Macháček, Peter Polák, Ondřej Bojar, Raj Dabre

Automatic speech translation is sensitive to speech recognition errors, but in a multilingual scenario, the same content may be available in various languages via simultaneous interpreting, dubbing or subtitling. In this paper, we hypothesize that leveraging multiple sources will improve translation quality if the sources complement one another in terms of c

Ivan Homoliak, Martin Perešíni, Patrik Holop, Jakub Handzuš

The adoption of decentralized, tamper-proof ledger systems is paving the way for new applications and opportunities in different contexts. While most research aims to improve their scalability, privacy, and governance issues, interoperability has received less attention. Executing transactions across various blockchains is notably instrumental in unlocking t

Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi

Sparse linear regression is a central problem in high-dimensional statistics. We study the correlated random design setting, where the covariates are drawn from a multivariate Gaussian $N(0,\Sigma)$, and we seek an estimator with small excess risk. If the true signal is $t$-sparse, information-theoretically, it is possible to achieve strong recovery guarante

Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying

Recently, significant progress has been made in understanding the generalization of neural networks (NNs) trained by gradient descent (GD) using the algorithmic stability approach. However, most of the existing research has focused on one-hidden-layer NNs and has not addressed the impact of different network scaling parameters. In this paper, we greatly exte

Ragesh Jaiswal, Amit Kumar

Coresets for $k$-means and $k$-median problems yield a small summary of the data, which preserve the clustering cost with respect to any set of $k$ centers. Recently coresets have also been constructed for constrained $k$-means and $k$-median problems. However, the notion of coresets has the drawback that (i) they can only be applied in settings where the in

Zack Fitzsimmons, Edith Hemaspaandra

Determining the complexity of election attack problems is a major research direction in the computational study of voting problems. The paper "Towards completing the puzzle: complexity of control by replacing, adding, and deleting candidates or voters" by Erd\'elyi et al. (JAAMAS 2021) provides a comprehensive study of the complexity of control problems. The

M. Zubair, Muhammad Ali Raza, Furkat Sarikulov, Javlon Rayimbaev

We consider a static black hole immersed in the Power-Yang-Mills field in four dimensional Einstein-Gauss-Bonnet gravity and investigate the effect of various parameters on the radius of the photon sphere. The modified form of the Newman-Janis algorithm is used for obtaining a rotating black hole solution in this gravity. Further, we try to explore the influ

Jake Taylor, Michael Radica, Luis Welbanks, Ryan J. MacDonald

The newly operational JWST offers the potential to study the atmospheres of distant worlds with precision that has not been achieved before. One of the first exoplanets observed by JWST in the summer of 2022 was WASP-96 b, a hot-Saturn orbiting a G8 star. As part of the Early Release Observations program, one transit of WASP-96 b was observed with NIRISS/SOS

Elia Cunegatti, Matteo Farina, Doina Bucur, Giovanni Iacca

Pruning-at-Initialization (PaI) algorithms provide Sparse Neural Networks (SNNs) which are computationally more efficient than their dense counterparts, and try to avoid performance degradation. While much emphasis has been directed towards \emph{how} to prune, we still do not know \emph{what topological metrics} of the SNNs characterize \emph{good performan

Ke Ji, Yixin Lian, Jingsheng Gao, Baoyuan Wang

Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently, there is a growing trend of applying prompts on pre-trained language models (PLMs), which has exhibited effectiveness in the few-shot flat

Krzysztof Frączek, Minsung Kim

We study the cohomological equation $Xu=f$ for smooth locally Hamiltonian flows on compact surfaces. The main novelty of the proposed approach is that it is used to study the regularity of the solution $u$ when the flow has saddle loops, which has not been systematically studied before. Then we need to limit the flow to its minimum components. We show the ex

Dominic Deuber, Jan Gruber, Merlin Humml, Viktoria Ronge

Cryptocurrency forensics became standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations.

Ch. Dangalchev

The study of networks characteristics is an important subject in different fields, like math, chemistry, transportation, social network analysis etc. The residual closeness is one of the most sensitive measure of graphs vulnerability. In this article we calculate the link residual closeness of Harary graphs.

Patrik Zander, Valentin Zieglmeier

With increasingly digitalized workplaces, the potential for sophisticated analyses of employee data rises. This increases the relevance of people analytics (PA), which are tools for the behavioral analysis of employees. Despite this potential, the successful usage of PA is hindered by employee concerns. Especially in Europe, where the GDPR or equivalent laws

Daniel Turaev

This paper proves that a plactic monoid of any finite rank will have decidable first order theory. This resolves other open decidability problems about the finite rank plactic monoids, such as the Diophantine problem and identity checking. This is achieved by interpreting a plactic monoid of arbitrary rank in Presburger arithmetic, which is known to have dec

Zhen Zhao, Claudio Verdozzi, Ferdi Aryasetiawan

The exchange-correlation potential formalism previously introduced and applied to the one-dimensional Hubbard model has been extended to spin systems and applied to the case of the one-dimensional antiferromagnetic spin$-\frac{1}{2}$ Heisenberg model. Within the spin exchange-correlation potential formulation, a new sum rule for spin-systems is derived. The

Amir Abboud, Nick Fischer, Elazar Goldenberg, Karthik C. S.

We study the fundamental problem of finding the best string to represent a given set, in the form of the Closest String problem: Given a set $X \subseteq \Sigma^d$ of $n$ strings, find the string $x^*$ minimizing the radius of the smallest Hamming ball around $x^*$ that encloses all the strings in $X$. In this paper, we investigate whether the Closest String

Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth

Distributional reinforcement learning (RL) has proven useful in multiple benchmarks as it enables approximating the full distribution of returns and extracts rich feedback from environment samples. The commonly used quantile regression approach to distributional RL -- based on asymmetric $L_1$ losses -- provides a flexible and effective way of learning arbit

Alejandro Ortega

In this work we analyze a class of nonlinear fractional elliptic systems involving Hardy--type potentials and coupled by critical Hardy-Sobolev--type nonlinearities in $\mathbb{R}^N$. Due to the lack of compactness at the critical exponent the variational approach requires a careful analysis of the Palais-Smale sequences. In order to overcome this loss of co

André Sandmann, Florian Azendorf, Michael Eiselt

Superimposed temperature variations and dynamic strain applied through a 400 Hz acoustic signal on a 195 m single-mode fiber section are successfully measured using a coherent correlation optical time domain reflectometry as an interrogator.

Denis Kleyko, Connor Bybee, Ping-Chen Huang, Christopher J. Kymn

We investigate the task of retrieving information from compositional distributed representations formed by Hyperdimensional Computing/Vector Symbolic Architectures and present novel techniques which achieve new information rate bounds. First, we provide an overview of the decoding techniques that can be used to approach the retrieval task. The techniques are

Joshua Gans, Abhishek Nagaraj

This paper explores the economics of Augmented Reality (AR) and Virtual Reality (VR) technologies within decision-making contexts. Two metrics are proposed: Context Entropy, the informational complexity of an environment, and Context Immersivity, the value from full immersion. The analysis suggests that AR technologies assist in understanding complex context

Youssef Aboudorra, Chiara Gabellieri, Ralph Brantjes, Quentin Sablé

This paper introduces for the first time the design, modelling, and control of a novel morphing multi-rotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-di

Zimin Liang, Miqing Li, Per Kristian Lehre

Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective evolutionary algorithms (MOEAs) in the late 1990s. In this paper, we take an opposite perspective to conduct the population update in MOEAs

Oskar A. Sultanov

An autonomous system of ordinary differential equations describing nonlinear oscillations on the plane is considered. The influence of time-dependent perturbations decaying at infinity in time is investigated. It is assumed that the perturbations satisfy the non-resonance condition and do not vanish at the equilibrium of the limiting system. Possible long-te

Wanxin Li, Collin Meese, Zijia Zhong, Hao Guo

Platooning technologies enable trucks to drive cooperatively and automatically, which bring benefits including less fuel consumption, more road capacity and safety. In order to establish trust during dynamic platoon formation, ensure vehicular data integrity, and guard platoons against potential attackers, it is pivotal to verify any given vehicle's identity

Elif Akata, Lion Schulz, Julian Coda-Forno, Seong Joon Oh

LLMs are increasingly used in applications where they interact with humans and other agents. We propose to use behavioural game theory to study LLM's cooperation and coordination behaviour. We let different LLMs play finitely repeated $2\times2$ games with each other, with human-like strategies, and actual human players. Our results show that LLMs perform pa

R . Anantharaman

Let $Q$ be the unit cube in $\mathbb{R}^n$ and $H$ a hyperplane thru the Origin. The intersection $H\cap Q$is called (central) Cube slice and was investigated by Henesley, Vaaler, Ball and others. A zonoid is the range of a measure into $R^n$. Our interest is : When is a cube slice a zonoid? We only give an example of a cube slice in $\mathbb{R}^4$ that is n

Jinsub Lee, Tae-Hyun Kim, Sang-Hwan Jeon, Sung-Hyun Park

Quality control in the manufacturing industry has improved with the use of artificial intelligence (AI). However, the manual inspection of trimming die designs, which is time-consuming and prone to errors, is still done by engineers. This study introduces an automatic design inspection system for automobile trimming dies by integrating AI modules and compute

Enrico Specogna, Eleonora Di Valentino, Jackson Levi Said, Nhat-Minh Nguyen

In this study we investigate the growth index $\gamma_L$, which characterizes the growth of linear matter perturbations, while analysing different cosmological datasets. We compare the approaches implemented by two different patches of the cosmological solver CAMB: MGCAMB and CAMB_GammaPrime_Growth. In our analysis we uncover a deviation of the growth index

Guido Sciavicco, Stan Ionel Eduard

Multivariate temporal, or time, series classification is, in a way, the temporal generalization of (numeric) classification, as every instance is described by multiple time series instead of multiple values. Symbolic classification is the machine learning strategy to extract explicit knowledge from a data set, and the problem of symbolic classification of mu

Parikshit Bansal, Amit Sharma

To address the problem of NLP classifiers learning spurious correlations between training features and target labels, a common approach is to make the model's predictions invariant to these features. However, this can be counter-productive when the features have a non-zero causal effect on the target label and thus are important for prediction. Therefore, us

Otto Mikkonen, Alec Wright, Eloi Moliner, Vesa Välimäki

The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consis

Atandrita Bhattacharyya, Amitav Sahu, Sanjoy Patra, Vivek Tiwari

Singlet exciton fission (SEF) is initiated by ultrafast internal conversion of a singlet exciton into a correlated triplet pair (TT)1. The `reaction coordinates' for ultrafast SEF even in archetypal systems such as pentacene thin film remain unclear with synthetic design principles broadly relying on tailoring electronic couplings to achieve new templates fo

Joe Benton, George Deligiannidis, Arnaud Doucet

Score-based generative models are a popular class of generative modelling techniques relying on stochastic differential equations (SDE). From their inception, it was realized that it was also possible to perform generation using ordinary differential equations (ODE) rather than SDE. This led to the introduction of the probability flow ODE approach and denois

Juraj Vladika, Florian Matthes

The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the variation of the task concerned with verifying claims rooted in scientific knowledge. This task has received significant attention due to the growing importance of scientific and hea

Rinkila Bhutia, RB Yadav, Namita Behera

In this paper, we consider compatible Hom-Leibniz algebra where the Hom map twists the operations in the compatible system. We consider a suitably graded Lie algebra whose Maurer-Cartan elements characterize the structure of compatible Hom-Leibniz algebras. Using this, we study cohomology, infinitesimal deformations, the Nijenhuis operator, and their relatio

Aida Abiad, Jozefien D'haeseleer, Willem H. Haemers, Robin Simoens

We provide three infinite families of graphs in the Johnson and Grassmann schemes that are not uniquely determined by their spectrum. We do so by constructing graphs that are cospectral but non-isomorphic to these graphs.

Shengbing Deng, Xingliang Tian, Minbo Yang, Shunneng Zhao

In this note we study a nonlocal version of the Sobolev inequality \begin{equation*} \int_{\mathbb{R}^N}|\nabla u|^2 dx \geq S_{HLS}\left(\int_{\mathbb{R}^N}\big(|x|^{-\alpha} \ast u^{2_\alpha^{\ast}}\big)u^{2_\alpha^{\ast}} dx\right)^{\frac{1}{2_\alpha^{\ast}}}, \quad \forall u\in \mathcal{D}^{1,2}(\mathbb{R}^N), \end{equation*} where $S_{HLS}$ is the best

Eldad Bettelheim

We employ a mathematical framework based on the Riemann-Hilbert approach developed in Ref. [1] to study logarithmic negativity of two intervals of free fermions in the case where the size of the intervals as well as the distance between them is macroscopic. We find that none of the eigenvalues of the density matrix become negative, but rather they develop a

Guillaume Deffuant, Marijn A. Keijzer, Sven Banisch

We live in an age of information abundance but know little about how this influences our opinions or attitudes. A common expectation is that people consulting numerous pieces of information, well balancing the different sides of an issue, will adopt a moderate attitude about the issue. We claim that this expectation is deceitful and suggest that people tend

Yuchang Sun, Zehong lin, Yuyi Mao, Shi Jin

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of ra

Keiichiro Imura, Yuki Yoneyama, Hideyuki Ando, Noriyuki Kabeya

We have synthesized new Tsai-type Yb-based intermediate-valence approximant crystals (ACs) with different degree of approximation to quasicrystal, Zn--Au--Yb 1/1 and 2/1 AC, and studied the external pressure effect on their Yb mean-valence $\nu$. Whereas 1/1 AC distinctly exhibits a first-order-like jump in $\nu$ at a transition pressure $P_{\rm v}$, 2/1 AC

Benjamin Towle, Ke Zhou

Smart Reply (SR) systems present a user with a set of replies, of which one can be selected in place of having to type out a response. To perform well at this task, a system should be able to effectively present the user with a diverse set of options, to maximise the chance that at least one of them conveys the user's desired response. This is a significant

Paola Mejia-Domenzain, Eva Laini, Seyed Parsa Neshaei, Thiemo Wambsganss

Flipped Classrooms (FC) are a promising teaching strategy, where students engage with the learning material before attending face-to-face sessions. While pre-class activities are critical for course success, many students struggle to engage effectively in them due to inadequate of self-regulated learning (SRL) skills. Thus, tools enabling teachers to monitor

Partha Bagchi, Oindrila Ganguly, Biswanath Layek, Anjishnu Sarkar

It is believed that cores of neutron stars provide a natural laboratory where exotic high baryon density QCD phases may exist.The theoretically well established {\it neutron superfluid phase} is also believed to be found only inside neutron stars. Focus on neutron stars has intensified in recent years with the direct detection of gravitational waves (GWs) fr

Jai Kannan, Scott Barnett, Anj Simmons, Taylan Selvi

Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation. However, their popularity raises concerns about environmental impact and inefficient model selection. This paper introduces GreenRunnerGPT, a novel tool for efficiently selecting deep learning models based on specif

Willian Carvalho, M. Dias, A. C. Lehum, J. M. Hoff da Silva

This paper addresses perturbative aspects of the renormalization of a fermion with mass dimension one non-minimally coupled to the electromagnetic field. Specifically, we calculate the one-loop corrections to the propagators and vertex functions of the model and determine the one-loop beta function of the non-minimal electromagnetic coupling. Additionally, w

Jingyin Huang

We show that for a large class of Artin groups with Dynkin diagrams being a tree, the $K(\pi,1)$-conjecture holds. We also establish the $K(\pi,1)$-conjecture for another class of Artin groups whose Dynkin diagrams contain a cycle, which applies to some hyperbolic type Artin groups. This is based on a new approach to the $K(\pi,1)$-conjecture for Artin group

F. Arend Torres, Marcello Massimo Negri, Marco Inversi, Jonathan Aellen

We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time. By construction, the proposed LFlows satisfy the continuity equation, a PDE describing mass conservation in its differentiable form. Our model is based on the insight that solutions to the continuity equation can be expressed as time-dep

Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis

New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning. Here we present an end-to-end strategy for recovering a free-form analytical potential from a mere snapshot of stellar positions and velocities. First we show how

Liyan Xu, Chenwei Zhang, Xian Li, Jingbo Shang

We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Our supervision comes from a high-quality seed attribute set bootstrapped from existing resources, and we aim to expand the attribute vocabulary of existing seed types, and also to d

Riccardo Cavuoto, Pietro Lenarda, Anna Tampieri, Davide Bigoni

Many stiff biological materials exhibiting outstanding compressive strength/weight ratio are characterized by high porosity, spanning different size-scales, typical examples being bone and wood. A successful bio-mimicking of these materials is provided by a recently-obtained apatite, directly produced through a biomorphic transformation of natural wood and t

Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya

Transformers have impressive generalization capabilities on tasks with a fixed context length. However, they fail to generalize to sequences of arbitrary length, even for seemingly simple tasks such as duplicating a string. Moreover, simply training on longer sequences is inefficient due to the quadratic computation complexity of the global attention mechani

Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt

Partitioning a set of elements into an unknown number of mutually exclusive subsets is essential in many machine learning problems. However, assigning elements, such as samples in a dataset or neurons in a network layer, to an unknown and discrete number of subsets is inherently non-differentiable, prohibiting end-to-end gradient-based optimization of parame

Amarnath R, Vinay Kumar

Recognizing handwritten digits is a challenging task primarily due to the diversity of writing styles and the presence of noisy images. The widely used MNIST dataset, which is commonly employed as a benchmark for this task, includes distorted digits with irregular shapes, incomplete strokes, and varying skew in both the training and testing datasets. Consequ