July 2022 arXiv papers — page 115

Brian Liu, Miaolan Xie, Haoyue Yang, Madeleine Udell

ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this package first build large tree ensembles that prioritize basis functions with few features and then select a feature-sparse subset of these basis functions using a weighted lasso optimi

Elisa Marcelli, Tommaso Barbariol, Gian Antonio Susto

The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition of anomaly strictly depends on the domain framework, it is often impractical or too time consuming to obtain a fully labelled dataset. The use of unsupervised models to overcome the

Daniel Paleka, Amartya Sanyal

In supervised learning, it has been shown that label noise in the data can be interpolated without penalties on test accuracy. We show that interpolating label noise induces adversarial vulnerability, and prove the first theorem showing the relationship between label noise and adversarial risk for any data distribution. Our results are almost tight if we do

Zahra Atashgahi, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy

Change-point detection (CPD), which detects abrupt changes in the data distribution, is recognized as one of the most significant tasks in time series analysis. Despite the extensive literature on offline CPD, unsupervised online CPD still suffers from major challenges, including scalability, hyperparameter tuning, and learning constraints. To mitigate some

Chaim Even-Zohar, Joel Hass

We develop a general method for constructing random manifolds and submanifolds in arbitrary dimensions. The method is based on associating colors to the vertices of a triangulated manifold, as in recent work for curves in 3-dimensional space by Sheffield and Yadin (2014). We determine conditions on which submanifolds can arise, in terms of Stiefel-Whitney cl

Man-Yu Duan, Dian-Yong Chen, En Wang

Within the framework of the local hidden gauge approach, we have studied the near-threshold interaction of the $D^* \bar{D}^*$ channel with quantum numbers $I(J^{PC}) = 0(0^{++})$, $0(2^{++})$, $1(0^{++})$, and $1(2^{++})$, respectively. The contact interaction is taken into account alone, since it is expected to give the dominant contribution near the thres

Leo Lobski, Fabio Zanasi

We propose a categorical framework to reason about scientific explanations: descriptions of a phenomenon meant to translate it into simpler terms, or into a context that has been already understood. Our motivating examples come from systems biology, electrical circuit theory, and concurrency. We demonstrate how three explanatory models in these seemingly div

Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis

With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scient

Sheng Kuang, Jie Shi, Kiki van der Heijden, Siamak Mehrkanoon

Accurate sound localization in a reverberation environment is essential for human auditory perception. Recently, Convolutional Neural Networks (CNNs) have been utilized to model the binaural human auditory pathway. However, CNN shows barriers in capturing the global acoustic features. To address this issue, we propose a novel end-to-end Binaural Audio Spectr

Omer Bobrowski, Primoz Skraba

One of the most elusive challenges within the area of topological data analysis is understanding the distribution of persistence diagrams. Despite much effort, this is still largely an open problem. In this paper, we present a series of novel conjectures regarding the behavior of persistence diagrams arising from random point-clouds. We claim that these diag

ATLAS Collaboration

A search for a $WZ$ resonance, in the fully leptonic final state (electrons and muons), is performed using 139 fb$^{-1}$ of data collected at a centre-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The results are interpreted in terms of a singly charged Higgs boson of the Georgi$-$Machacek model, produced by $WZ$ fusion, and of

Fernando Lledó, John S. Fabila-Carrasco, Olaf Post

In this article, we develop a perturbative technique to construct families of non-isomorphic discrete graphs which are isospectral for the standard (also called normalised) Laplacian and its signless version. We use vertex contractions as a graph perturbation and spectral bracketing with auxiliary graphs which have certain eigenvalues with high multiplicity.

Alexander Esterov, Lionel Lang

A generic polynomial f(x,y,z) with a prescribed Newton polytope defines a symmetric spatial curve f(x,y,z)=f(y,x,z)=0. We study its geometry: the number, degree and genus of its irreducible components, the number and type of singularities, etc. and discuss to what extent these results generalize to higher dimension and more complicated symmetries. As an appl

Paolo Bonicatto, Giacomo Del Nin, Filip Rindler

The transport of many kinds of singular structures in a medium, such as vortex points/lines/sheets in fluids, dislocation loops in crystalline plastic solids, or topological singularities in magnetism, can be expressed in terms of the geometric (Lie) transport equation \[ \frac{\mathrm{d}}{\mathrm{d} t} T_t + \mathcal{L}_{b_t} T_t = 0 \] for a time-indexed f

Manuel Klar, Christian Vollmann, Volker Schulz

In this work we present the mathematical foundation of an assembly code for finite element approximations of nonlocal models with compactly supported, weakly singular kernels. We demonstrate the code on a nonlocal diffusion model in various configurations and on a two-dimensional bond-based peridynamics model. The code nlfem is published under the MIT Licens

Sejin Seo, Jihong Park, Seung-Woo Ko, Jinho Choi

Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in th

Ayoub Aouina, Matteo Gatti, Siyuan Chen, Shiwei Zhang

The Kohn-Sham (KS) system is an auxiliary system whose effective potential is unknown in most cases. It is in principle determined by the ground state density, and it has been found numerically for some low-dimensional systems by inverting the KS equations starting from a given accurate density. For solids, only approximate results are available. In this wor

Yingying Wang, Kui Wang, Yao Sun, Liang Ma

The flourishing rare earth superhydrides are a class of recently discovered materials that possess near-room-temperature superconductivity at high pressures, opening a new era of superconductivity research at high pressures. Among these superhydrides, yttrium superhydrides attracted great interest owing to their abundance of stoichiometries and excellent sup

Jinpeng Li, Haibo Jin, Shengcai Liao, Ling Shao

This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection. Most facial landmark detectors focus on learning representative image features. However, these CNN-based feature representations are not robust enough to handle complex real-world scenarios due to ignoring the internal structure of landmarks, as well as the

Ricarda-Samantha Götte, Julia Timmermann

State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a sel

Johanna Vorwerk, Thierry Zufferey, Petros Aristidou, Gabriela Hug

While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors

Joanna Ramasawmy, Pamela D. Klaassen, Claudia Cicone, Tony K. Mroczkowski

The Atacama Large Aperture Submillimeter Telescope (AtLAST) is a concept for a 50m class single-dish telescope that will provide high sensitivity, fast mapping of the (sub-)millimeter sky. Expected to be powered by renewable energy sources, and to be constructed in the Atacama desert in the 2030s, AtLAST's suite of up to six state-of-the-art instruments will

Alessandro Porcarelli, Boris Kruljevic, Ivan Langella

NO$_{\rm x}$ formation in lean premixed and highly-strained pure hydrogen-air flamelets is investigated numerically. Lean conditions are established at an equivalence ratio of 0.7. Detailed-chemistry, one-dimensional simulations are performed on a reactants-to-products counter-flow configuration with an applied strain rate ranging from $a=100 \, {\rm s}^{-1}

Tianwen Zhang, Xiaoling Zhang

Most of existing synthetic aperture radar (SAR) ship in-stance segmentation models do not achieve mask interac-tion or offer limited interaction performance. Besides, their multi-scale ship instance segmentation performance is moderate especially for small ships. To solve these problems, we propose a mask attention interaction and scale enhancement network (

Christofer L. Sega, Anubis G. de M. Rossetto, Valderi R. Q. Leithardt

This work presents the design and implementation of a decentralized application (DApp) that aims to guarantee the privacy of data related to the health area, which are stored and shared within a blockchain network. For this, encryption with RSA, ECC and AES algorithms is used. The platforms, technologies, tools and libraries required for development are pres

S. Ulmer-Moll, M. Lendl, S. Gill, S. Villanueva

Long-period transiting planets provide the opportunity to better understand the formation and evolution of planetary systems. Their atmospheric properties remain largely unaltered by tidal or radiative effects of the host star, and their orbital arrangement reflects a different, and less extreme, migrational history compared to close-in objects. The sample o

Sandeep Chowdhary, Elsa Andres, Adriana Manna, Luka Blagojević

Human communication, the essence of collective social phenomena ranging from small-scale organizations to worldwide online platforms, features intense reciprocal interactions between members in order to achieve stability, cohesion, and cooperation in social networks. While high levels of reciprocity are well known in aggregated communication data, temporal p

Xing Wu, Qiulian Fang

Cancer survival prediction is important for developing personalized treatments and inducing disease-causing mechanisms. Multi-omics data integration is attracting widespread interest in cancer research for providing information for understanding cancer progression at multiple genetic levels. Many works, however, are limited because of the high dimensionality

Vanessa A. Godoy, Gian F. Napa-García, J. Jaime Gómez-Hernández

The ensemble random forest filter (ERFF) is presented as an alternative to the ensemble Kalman filter (EnKF) for the purpose of inverse modeling. The EnKF is a data assimilation approach that forecasts and updates parameter estimates sequentially in time as observations are being collected. The updating step is based on the experimental covariances computed

Job D. Rock, Shijie Zhu

We introduce continuous analogues of Nakayama algebras. In particular, we introduce the notion of (pre-)Kupisch functions, which play a role as Kupisch series of Nakayama algebras, and view continuous Nakayama representations as a special type of representation of $\mathbb{R}$ or $\mathbb{S}^1$. We investigate equivalences and connectedness of the categories

Sayan Banerjee, Satoshi Ikegaya, Andreas P. Schnyder

Noise spectroscopy is a key technique to investigate the nature and dynamics of charge carriers in superconductors. The recently discovered superconducting hybrids with Bogoliubov Fermi surfaces exhibit a particularly intriguing and rich charge dynamics, as their charge carriers consist of both Cooper pairs and an extensive number of Bogoliubov quasiparticle

Torben Villadsen, Niels F. W. Ligterink, Mie Andersen

The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with time-consuming experiments, computationally expensive quantum chemical calculations, or the inexpensive, but inaccurate,

Bhagyashree Prabhune, Krishnan Suresh

If a finite element mesh contains concave elements, it is said to tangled. Tangled meshes can occur during mesh generation, mesh optimization, and large deformation simulations, and will lead to erroneous results during finite element analysis. Recently, the authors introduced the tangled finite element method (TFEM) to accurately handle tangled 2D concave q

Chao Yang, Yuqing Ni, Wen Yang, Hongbo Shi

In this paper, we study the privacy preservation problem in a cooperative networked control system, which has closed-loop dynamics, working for the task of linear quadratic Guassian (LQG) control. The system consists of a user and a server: the user owns the plant to control, while the server provides computation capability, and the user employs the server t

José Cernicharo, Raúl Fuentetaja, Carlos Cabezas, Marcelino Agúndez

We report the discovery of five cyano derivatives of propene towards TMC-1 with the QUIJOTE line survey: $trans$ and $cis$-crotononitrile ($t$-CH$_3$CHCHCN, $c$-CH$_3$CHCHCN), methacrylonitrile (CH$_2$C(CH$_3$)CN), and $gauche$ and $cis$-allyl cyanide ($g$-CH$_2$CHCH$_2$CN and $c$-CH$_2$CHCH$_2$CN). The observed transitions allowed us to derive a common rota

Shunyu Liu, Jie Song, Yihe Zhou, Na Yu

Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent learning mainly focus on value decomposition while leaving entity interactions still intertwined, which easily leads to over-fitting on noisy interactions between entities. In this

Benjamin Mitchell, Yuto Yokoyama, Ali Nassiri, Yoshiyuki Tagawa

Hertzian contact of a rigid sphere and a highly deformable soft solid is investigated using integrated photoelasticity. The experiments are performed by pressing a styrene sphere of 15 mm diameter against a 44 x 44 x 47 mm$^3$ cuboid made of 5% wt. gelatin, inside a circular polariscope, and with a range of forces. The emerging light rays are processed by co

Yulai Cong, Miaoyun Zhao

Recent advances in big/foundation models reveal a promising path for deep learning, where the roadmap steadily moves from big data to big models to (the newly-introduced) big learning. Specifically, the big learning exhaustively exploits the information inherent in its large-scale complete/incomplete training data, by simultaneously modeling many/all joint/c

Dakeyo, Maksimovic, Démoulin, Halekas

Statistical classification of the Helios solar wind observations into several populations sorted by bulk speed has revealed an outward acceleration of the wind. The faster the wind is, the smaller is this acceleration in the 0.3 - 1 au radial range (Maksimovic et al. 2020). In this article we show that recent measurements from the Parker Solar Probe (PSP) ar

André Costa, Vincent Grandjean, Maria Michalska

Real or complex polynomial mappings between affines spaces admitting a Lipschitz-trivial value are completely characterized.

Roland Speicher

We provide a short proof for the twisted multiplicativity property of the operator-valued S-transform. This is my contribution to the topical collection ''Multivariable Operator Theory. The J\"org Eschmeier Memorial'' of Complex Analysis and Operator Theory.

Haripriya Harikumar, Santu Rana, Kien Do, Sunil Gupta

Adversarial attacks on deep learning-based models pose a significant threat to the current AI infrastructure. Among them, Trojan attacks are the hardest to defend against. In this paper, we first introduce a variation of the Badnet kind of attacks that introduces Trojan backdoors to multiple target classes and allows triggers to be placed anywhere in the ima

Sreekala S., Vineeth Paleri

Constant propagation and copy propagation are code transformations that may avoid some load operations and can enable other optimizations. In literature, constant and copy propagations are considered two independent transformations requiring two different data flow analyses. Here we give a generic definition for copy propagation which enables us to view cons

Christian Vitale, Panayiotis Kolios, Georgios Ellinas

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing t

Guillermo Badia, Petr Cintula, Libor Behounek, Andrew Tedder

We generalize the notion of consequence relation standard in abstract treatments of logic to accommodate intuitions of relevance. The guiding idea follows the \emph{use criterion}, according to which in order for some premises to have some conclusion(s) as consequence(s), the premises must each be \emph{used} in some way to obtain the conclusion(s). This rel

Roland Speicher

This article contains some thoughts on Wilhelm von Waldenfels and on universal second order constructions. It is my contribution to the Special Commemorative Issue of IDAQP in Honour of Professor Robin Lyth Hudson and Professor Wilhelm von Waldenfels.

Clinton Cao, Annibale Panichella, Sicco Verwer, Agathe Blaise

NetFlow data is a popular network log format used by many network analysts and researchers. The advantages of using NetFlow over deep packet inspection are that it is easier to collect and process, and it is less privacy intrusive. Many works have used machine learning to detect network attacks using NetFlow data. The first step for these machine learning pi

Andrey D. Sladkov, Artem V. Korzhimanov

A brightness of a betatron radiation generated by laser wakefield accelerated electrons can be increased by utilizing the laser driver with shorter duration at the same energy. Such shortening is possible by pulse compression after its nonlinear self-phase modulation in thin plate. However, this method can lead to a rather complex femtosecond-scale time stru

Pedro D. S. Silva, Manoel M. Ferreira

Bi-isotropic media constitute a proper scenario for scrutinizing optical effects stemming from magnetoelectric parameters. Chiral magnetic current is a macroscopic effect arising from the chiral magnetic effect that enriches the phenomenology of a classical dielectric medium. This work examines optical aspects of bi-isotropic media in the presence of magneti

Markus Wenzel

Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods, and has proven to be hig

Konstantinos Deligiannis, Quentin Fontaine, Davide Squizzato, Maxime Richard

The statistics of the fluctuations of quantum many-body systems are highly revealing of their nature. In driven-dissipative systems displaying macroscopic quantum coherence, as exciton polariton condensates under incoherent pumping, the phase dynamics can be mapped to the stochastic Kardar-Parisi-Zhang (KPZ) equation. However, in two dimensions (2D), it was

Roland Roller, Laura Seiffe, Ammer Ayach, Sebastian Möller

Background: In the information extraction and natural language processing domain, accessible datasets are crucial to reproduce and compare results. Publicly available implementations and tools can serve as benchmark and facilitate the development of more complex applications. However, in the context of clinical text processing the number of accessible datase

Manish Goyal, Miheer Dewaskar, Parasara Sridhar Duggirala

We propose a new technique for performing state space exploration of closed loop control systems with neural network feedback controllers. Our approach involves approximating the sensitivity of the trajectories of the closed loop dynamics. Using such an approximator and the system simulator, we present a guided state space exploration method that can generat

Kevin Kamm

In this paper, we model the rating process of an entity as a piecewise homogeneous continuous time Markov chain. We focus specifically on calibrating the model to both historical data (rating transition matrices) and market data (CDS quotes), relying on a simple change of measure to switch from the historical probability to the risk-neutral one. We overcome

Nikita A. Mikheytsev, Artem V. Korzhimanov

It is shown that when relativistically intense ultrashort laser pulses are reflected from the boundary of a plasma with a near-critical density, Doppler frequency shift leads to generation of intense radiation both in the high-frequency, up to the X-ray, range, and in the low-frequency, mid-infrared, range. The efficiency of energy conversion into the wavele

Zahraa Al Sahili, Mariette Awad

Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Dom

Mark Chevallier, Matthew Whyte, Jacques D. Fleuriot

We introduce a theorem proving approach to the specification and generation of temporal logical constraints for training neural networks. We formalise a deep embedding of linear temporal logic over finite traces (LTL$_f$) and an associated evaluation function characterising its semantics within the higher-order logic of the Isabelle theorem prover. We then p

Carlo Mantegazza, Matteo Novaga, Alessandra Pluda

The motion by curvature of networks is the generalization to finite union of curves of the curve shortening flow. This evolution has several peculiar features, mainly due to the presence of junctions where the curves meet. In this paper we show that whenever the length of one single curve vanishes and two triple junctions coalesce, then the curvature of the

M. R. Pelicer, D. P. Menezes

Based on the assumption that the QCD phase diagram gives a realistic picture of hadronic and quark matter under different regimes, it is possible to claim that a quark core may be present inside compact objects commonly named hybrid neutron stars or even that a pure strange star may exist. In this work we explore how the phase transition is modified by the p

T. Amano, Y. Kawakami, H. Itoh, K. Konno

In a honeycomb-lattice spin-orbit assisted Mott insulator {\alpha}-RuCl3, an ultrafast magnetization is induced by circularly polarized excitation below the Mott gap. Photo-carriers play an important role, which are generated by turning down the synergy of the on-site Coulomb interaction and the spin-orbit interaction realizing the insulator state. An ultraf

Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin

TSP is a classical NP-hard combinatorial optimization problem with many practical variants. LKH is one of the state-of-the-art local search algorithms for the TSP. LKH-3 is a powerful extension of LKH that can solve many TSP variants. Both LKH and LKH-3 associate a candidate set to each city to improve the efficiency, and have two different methods, $\alpha$

Andrei Okounkov

This is a popular article about the work of June Huh, 2022 Fields medalist.

Andrei Okounkov

This is a popular article about the work of Hugo Duminil-Copin, 2022 Fields medalist.

Ingo Blechschmidt, Peter Schuster

The existence of a maximal ideal in a general nontrivial commutative ring is tied together with the axiom of choice. Following Berardi, Valentini and thus Krivine but using the relative interpretation of negation (that is, as "implies 0 = 1") we show, in constructive set theory with minimal logic, how for countable rings one can do without any kind of choice

V. Kalinova, D. Colombo, S. F. Sánchez, E. Rosolowsky

It has been suggested that the gravitational potential can have a significant role in suppressing the star formation in the nearby galaxies. To establish observational constrains on this scenario, we investigate the connection between the dynamics, through the circular velocity curves (CVCs) as a proxy of the inner gravitational potential, and star formation

Andrei Okounkov

This is a popular article about the work of James Maynard, 2022 Fields medalist.

Andrei Okounkov

This is a popular article about the work of Maryna Viazovska, 2022 Fields medalist.

Taichi Fukuda, Kotaro Hasegawa, Shinya Ishizaki, Shohei Nobuhara

We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual driver or a self-driving system to potential causes of accidents (e.g., draw attention to a road region from which a child may spring out).

Joshua Harrelson

For a graph $G$, we show that if $mad(G)<m$, then $\chi'_\ell(G)\leq \Delta+1$ where $m$ depends upon $\Delta$ and $\chi'_\ell(G)$ is the list-chromatic index of $G$. When $\Delta\leq 20$ the value of $m$ is close to $\frac{1}{2}\Delta$, but as $\Delta$ increases $m$ becomes asymptotic to about $\frac{1}{4}\Delta+5$.

Riccardo Mereu, Gabriele Trivigno, Gabriele Berton, Carlo Masone

In robotics, Visual Place Recognition is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This task requires robust, scalable, and efficient techniques for real applications. This work proposes a detailed taxonomy of techniques using sequential descriptors, highli

Yash Sharma, Yi Zhu, Chris Russell, Thomas Brox

While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision. To address this, we propose Pixel-level Correspondence (PiCo), a method for dense contrastive learning from video. By tracking points with optical flow, we obtain a correspondence map wh

Swamit Tannu, Prashant J. Nair

Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides made in scaling this technology can bear significant environmental consequences. On a global scale, a notable portion of semiconductor manufacturing relies on electricity derived from

Xavier Claeys

In the present contribution we propose a new proof of the so-called fictitious space lemma. For the proof, we exhibit an explicit expression for the inverse of additive Schwarz preconditioners in terms of Moore-Penrose pseudo inverse of the map associated to the decomposition over the subdomain partition.

Yankai Li, John R. Royer, Jin Sun, Christopher Ness

Colloidal gels formed from small attractive particles are commonly used in formulations to keep larger components in suspension. However, despite extensive work characterizing unfilled gels, little is known about how larger inclusions alter the phase behavior and microstructure of the colloidal system. Here we use numerical simulations to examine how larger

Chien-Chung Huang, François Sellier

We consider the maximum weight $b$-matching problem in the random-order semi-streaming model. Assuming all weights are small integers drawn from $[1,W]$, we present a $2 - \frac{1}{2W} + \varepsilon$ approximation algorithm, using a memory of $O(\max(|M_G|, n) \cdot poly(\log(m),W,1/\varepsilon))$, where $|M_G|$ denotes the cardinality of the optimal matchin

Pramod Sharma, Ambresh Shivaji

The couplings of the Higgs boson ($H$) with massive gauge bosons of weak interaction ($V= W, Z$), can be probed in single Higgs boson production at the proposed future Large Hadron-Electron Collider (LHeC). In the collision of an electron with a proton, single Higgs production takes place via so-called charged-current ($e^-p \to \nu_e H j$) and neutral-curre

Dan Mendels, Fabian Byléhn, Timothy W. Sirk, Juan J. de Pablo

Advances in manufacturing and characterization of complex molecular systems have created a need for new methods for design at molecular length scales. Emerging approaches are increasingly relying on the use of Artificial Intelligence (AI), and the training of AI models on large data libraries. This paradigm shift has led to successful applications, but short

Tong Zhang, Peng Gao, Hao Dong, Yin Zhuang

Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning. It has reached the status of consensus solution for task-aware model training in remote sensing domain (RSD). Unfortunately, due to differen

Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines

This paper studies the Variational Inference (VI) used for training Bayesian Neural Networks (BNN) in the overparameterized regime, i.e., when the number of neurons tends to infinity. More specifically, we consider overparameterized two-layer BNN and point out a critical issue in the mean-field VI training. This problem arises from the decomposition of the l

Somdatta Goswami, Aniruddha Bora, Yue Yu, George Em Karniadakis

Standard neural networks can approximate general nonlinear operators, represented either explicitly by a combination of mathematical operators, e.g., in an advection-diffusion-reaction partial differential equation, or simply as a black box, e.g., a system-of-systems. The first neural operator was the Deep Operator Network (DeepONet), proposed in 2019 based

Yukyung Lee, Takyoung Kim, Hoonsang Yoon, Pilsung Kang

Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past research efforts focused on enhancing DST performance through alterations to the model structure or integrating additional features like graph relations, they often require addition

Eugene Bulyak, Louis Rinolfi, Junji Urakawa

Electron storage rings of GeV energy with laser pulse stacking cavities are promising intense sources of polarised hard photons which, via pair production, can be used to generate polarised positron beams. Dynamics of electron bunches circulating in a storage ring and interacting with high-power laser pulses is studied both analytically and by simulation. Co

Andreas Buchmüller, Gillian Kant, Christoph Weisser, Benjamin Säfken

We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-tagged Tweets from Twitter and and provides a comprehensive and user-friendly toolbox to generate topic distributions from Latent Dirichlet Allocations (LDA), correlated topic model

Anna Jaskulska, Kinga Skorupska, Zuzanna Bubrowska, Kinga Kwiatkowska

To understand and begin to address the challenge of air pollution in Europe we conducted participatory research, art and design activities with the residents of one of the areas most affected by smog in Poland. The participatory research events, described in detail in this article, centered around the theme of ecology and served to design an application that

Aaron D. Kaplan, Mel Levy, John P. Perdew

Ground-state Kohn-Sham density functional theory provides, in principle, the exact ground-state energy and electronic spin-densities of real interacting electrons in a static external potential. In practice, the exact density functional for the exchange-correlation (xc) energy must be approximated in a computationally efficient way. About twenty mathematical

Bruno Alexandre, Joao Magueijo

The recent transition from decelerated to accelerated expansion can be seen as a reflection (or "bounce") in the connection variable, defined by the inverse comoving Hubble length ($b=\dot a$, on-shell). We study the quantum cosmology of this process. We use a formalism for obtaining relational time variables either through the demotion of the constants of N

Jakob Schyga, Swantje Plambeck, Johannes Hinckeldeyn, Görschwin Fey

Absolute position accuracy is the key performance criterion of an Indoor Localization System (ILS). Since ILS are heterogeneous and complex cyber-physical systems, the localization accuracy depends on various influences from the environment, system configuration, and the application processes. To determine the position accuracy of a system in a reproducible,

Xianrui Zheng, Chao Zhang, Philip C. Woodland

Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diarisation and speech recognition using a single model, a tandem multitask training (TMT) method is proposed to fine-tune W2V2. For speaker diarisation, the tasks of voice activity de

Julen Cestero, Marco Quartulli, Alberto Maria Metelli, Marcello Restelli

Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or are in great need of manual interaction. Here is where Reinforcement Learning techniques come into play, providing automatization and adaptability to current optimization policies

A. Karki, D. Biswas, F. A. Gonzalez, W. Henry

The nuclear dependence of the inclusive inelastic electron scattering cross section (the EMC effect) has been measured for the first time in $^{10}$B and $^{11}$B. Previous measurements of the EMC effect in $A \leq 12$ nuclei showed an unexpected nuclear dependence; $^{10}$B and $^{11}$B were measured to explore the EMC effect in this region in more detail.

Mikko Salo, Hjørdis Schlüter

The aim of hybrid inverse problems such as Acousto-Electric Tomography or Current Density Imaging is the reconstruction of the electrical conductivity in a domain that can only be accessed from its exterior. In the inversion procedure, the solutions to the conductivity equation play a central role. In particular, it is important that the Jacobian of the solu

Lexin Ding

Entanglement plays a central role in numerous fields of quantum science. However, as one departs from the typical "Alice versus Bob" setting into the world of indistinguishable fermions, it is not immediately clear how the concept of entanglement is defined among these identical particles. Our endeavor to recover the notion of subsystems, or mathematically s

Franck Barthe, Dario Cordero-Erausquin

A positive correlation inequality is established for circular-invariant plurisubharmonic functions, with respect to complex Gaussian measures. The main ingredients of the proofs are the Ornstein-Uhlenbeck semigroup, and another natural semigroup associated to the Gaussian $\partial$-Laplacian.

Anastasiia Sheveleva, Pierre Colman, John M Dudley, Christophe Finot

The dynamics of ideal four-wave mixing in optical fiber is reconstructed by taking advantage of the combination of experimental measurements with supervised machine learning strategies. The training data consist of power-dependent spectral phase and amplitude recorded at the output of a short segment of fiber. The neural network is able to accurately predict

Stephan Hessenberger, Wolfgang Hollik

We present the currently most precise predictions for the $W$-boson mass and the leptonic effective mixing angle $\sin^2\theta_{\text{eff}}$ in the aligned Two-Higgs-Doublet Model. The evaluation includes the full one-loop result, all known higher-order corrections of the Standard Model, and the non-standard two-loop contributions that increase with mass spl

Antonio Alcántara, Carlos Ruiz

When dealing with real-world optimization problems, decision-makers usually face high levels of uncertainty associated with partial information, unknown parameters, or complex relationships between these and the problem decision variables. In this work, we develop a novel Chance Constraint Learning (CCL) methodology with a focus on mixed-integer linear optim

Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi, Lionel Mathelin

This paper proposes a novel approach to domain translation. Leveraging established parallels between generative models and dynamical systems, we propose a reformulation of the Cycle-GAN architecture. By embedding our model with a Hamiltonian structure, we obtain a continuous, expressive and most importantly invertible generative model for domain translation.

Bruno Barracosa, Julien Bect, Héloïse Dutrieux Baraffe, Juliette Morin

This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use probabilistic models to make predictions about the functions to be optimized. The proposed approach is an ext

Valerio Faraoni, Pierre-Antoine Graham, Alexandre Leblanc

Analytical solutions of nonminimally coupled scalar field cosmology corresponding to critical scalar field values constitute a potential challenge to the recent first-order thermodynamics of scalar-tensor gravity (a formalism picturing general relativity as the zero-temperature equilibrium state for modified gravity). The critical solutions are unstable with

MicroBooNE collaboration, P. Abratenko, J. Anthony, L. Arellano

We present a search for long-lived Higgs portal scalars (HPS) and heavy neutral leptons (HNL) decaying in the MicroBooNE liquid-argon time projection chamber. The measurement is performed using data collected synchronously with the NuMI neutrino beam from Fermilab's Main Injector with a total exposure corresponding to $7.01 \times 10^{20}$ protons on target.