October 2022 arXiv papers — page 89

Priya Tiwari, Mohit Kumar Jat, Adithi Udupa, Deepa S. Narang

Proximity-induced spin-orbit coupling in graphene has led to the observation of intriguing phenomena like time-reversal invariant $\mathbb{Z}_2$ topological phase and spin-orbital filtering effects. An understanding of the effect of spin-orbit coupling on the band structure of graphene is essential if these exciting observations are to be transformed into re

Sashideep Gutti, Akhil U Nair, Prasant Samantray

We consider a massless scalar field in Minkowski spacetime $\cal{M} $ in its vacuum state, and consider two Rindler wedges $R_1$ and $R_2$ in this space. $R_2$ is shifted to the right of $R_1$ by a distance $\Delta$. We therefore have $R_2\subset R_1 \subset \cal{M}$ with the symbol $\subset$ implying a quantum subsystem. We find the reduced state in $R_2$ u

Fumiya Okamura

We prove the irreducibility of the spaces of rational curves on del Pezzo manifolds of Picard rank 1 and dimension at least 4 by analyzing the fibers of evaluation maps. As a corollary, we prove Geometric Manin's Conjecture in these cases.

Sina Kamali, Shayan Shabihi, Mohammad Taha Fakharian, Alireza Arbabi

The consensus protocol is the core of a blockchain system which guarantees its secure and stable operation. Proof of Activity (PoA) is a consensus protocol that tries to address some of the issues pertinent to the most widely used protocols, such as Proof of Stake (PoS) and Proof of Work (PoW). However, it still needs to solve the issues regarding high energ

Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen

Knowledge graph (KG) alignment and completion are usually treated as two independent tasks. While recent work has leveraged entity and relation alignments from multiple KGs, such as alignments between multilingual KGs with common entities and relations, a deeper understanding of the ways in which multilingual KG completion (MKGC) can aid the creation of mult

Tommaso Gorni, Diego Florez-Ablan, Luca de' Medici

We compute the electronic properties of the normal state of uncollapsed LaFe$_2$As$_2$, taking into account local dynamical correlations by means of slave-spin mean-field+density-functional theory. Assuming the same local interaction strength used to model the whole electron- and hole-doped BaFe$_2$As$_2$ family, our calculations reproduce the experimental S

Li Su, Chen-Chieh Chang, Yu-Ming Lin, Li-Chun Lu

Three hundred years ago, Antonio Stradivari enjoyed the golden period of the violin making in the human history. Luthiers and researchers endeavor to study his legendary legacy. Unfortunately, a consensus that the present progress has reached his level remains lacking. Most of the instruments suffer from low sounding power and enigmatic deficit of harmonic o

Chanyong Park, Gitae Kim, Ji-seong Chae, Jae-Hyuk Oh

We study holographic entanglement entropy in 5-dimensional charged black brane geometry obtained from Einstein-SU(2)Yang-Mills theory defined in asymptotically AdS space. This gravity system undergoes second order phase transition near its critical point affected by a spatial component of the Yang-Mills fields, which is normalizable mode of the solution. Thi

Adnan Haider, Tim Ng, Zhen Huang, Xingyu Na

Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models. This paper aims to isolate, identify and bring forward the implicit modelling decisions induced by the design implementation of standard finite state transducer (FST) lattice based MMI training framework. The paper pa

Haipeng Sun, Junwei Bao, Youzheng Wu, Xiaodong He

Traditional end-to-end task-oriented dialog systems first convert dialog context into belief state and action state before generating the system response. The system response performance is significantly affected by the quality of the belief state and action state. We first explore what dialog context representation is beneficial to improving the quality of

Ronald A. Remmerswaal, Arthur E. P. Veldman

The numerical modelling of convection dominated high density ratio two-phase flow poses several challenges, amongst which is resolving the relatively thin shear layer at the interface. To this end we propose a sharp discretisation of the two-velocity model of the two-phase Navier-Stokes equations. This results in the ability to model the shear layer, rather

Daniel Gardener, Colin Snodgrass, Nicolas Ligier

67P/Churyumov-Gerasimenko is a Jupiter-family comet that was the target of the Rosetta mission, the first mission to successfully orbit and land a probe on a comet. This mission was accompanied by a large ground-based observing campaign. We have developed a pipeline to calibrate and measure photometry of comet 67P during its 2016 perihelion passage, making u

Malin Altenmüller, Ross Duncan

We introduce a categorical formalism for rewriting surface-embedded graphs. Such graphs can represent string diagrams in a non-symmetric setting where we guarantee that the wires do not intersect each other. The main technical novelty is a new formulation of double pushout rewriting on graphs which explicitly records the boundary of the rewrite. Using this b

Gérard Clément, Dmitri Gal'tsov

It is well-known that traversable wormhole solutions to the Einstein equations require the existence of an exotic matter source violating the null energy condition. An apparent exception is the overcharged Kerr-Newman-NUT solution of the Einstein-Maxwell equations, which has all the properties of a geodesically complete, traversable wormhole spacetime. We sh

S. Hoyer, A. Bonfanti, A. Leleu, L. Acuña

The HD108236 system was first announced with the detection of four small planets based on TESS data. Shortly after, the transit of an additional planet with a period of 29.54d was serendipitously detected by CHEOPS. In this way, HD108236 (V=9.2) became one of the brightest stars known to host five small transiting planets (R$_p$<3R$_{\oplus}$). We characteri

Rishav Chourasia, Neil Shah

Unlearning algorithms aim to remove deleted data's influence from trained models at a cost lower than full retraining. However, prior guarantees of unlearning in literature are flawed and don't protect the privacy of deleted records. We show that when users delete their data as a function of published models, records in a database become interdependent. So,

Sreedhar Balu, Bradley Greig, Yisheng Qiu, Chris Power

We predict the 21-cm global signal and power spectra during the Epoch of Reionisation using the MERAXES semi-analytic galaxy formation and reionisation model, updated to include X-ray heating and thermal evolution of the intergalactic medium. Studying the formation and evolution of galaxies together with the reionisation of cosmic hydrogen using semi-analyti

Yutao Mou, Keqing He, Pei Wang, Yanan Wu

Discovering out-of-domain (OOD) intent is important for developing new skills in task-oriented dialogue systems. The key challenges lie in how to transfer prior in-domain (IND) knowledge to OOD clustering, as well as jointly learn OOD representations and cluster assignments. Previous methods suffer from in-domain overfitting problem, and there is a natural g

Xuri Ge, Fuhai Chen, Songpei Xu, Fuxiang Tao

Image-sentence retrieval has attracted extensive research attention in multimedia and computer vision due to its promising application. The key issue lies in jointly learning the visual and textual representation to accurately estimate their similarity. To this end, the mainstream schema adopts an object-word based attention to calculate their relevance scor

Marco Seiler, Anja Sturm

In this paper we introduce a contact process on a dynamical long range percolation (CPDLP) defined on a complete graph $(V,\mathcal{E})$. A dynamical long range percolation is a Feller process defined on the edge set $\mathcal{E}$, which assigns to each edge the state of being open or closed independently. The state of an edge $e$ is updated at rate $v_e$ an

Andrea Tocchetti, Lorenzo Corti, Agathe Balayn, Mireia Yurrita

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Robustness has been studied in many domains of AI, yet with different interpretations across domains and contexts. In this work, we systematically survey the recent progress to provide a r

Jia-Shu Niu, Jing Liu

The most significant feature in the cosmic-ray (CR) nuclei spectra is the spectral hardening at a few hundred GV. It is important to know whether the hardening of different nuclei species is the same or not for constructing CR sources and propagation models. In this work, we collect the recently released AMS-02 CR nuclei spectra of primary species (proton, h

Chong Wang, Tianyang Liu, Peng Liang, Maya Daneva

Release planning for mobile apps has recently become an area of active research. Prior research in this area concentrated on the analysis of release notes and on tracking user reviews to support app evolution with issue trackers. However, little is known about the impact of user reviews on the evolution of mobile apps. Our work explores the role of user revi

Jonas Hansson, Emma Tegling

Motivated by a need to characterize transient behaviors in large network systems in terms of relevant signal norms and worst-case input scenarios, we propose a novel approach based on existing theory for matrix pseudospectra. We extend pseudospectral theorems, pertaining to matrix exponentials, to an input-output setting, where matrix exponentials are pre- a

Julia El Zini, Mariette Awad

Contrastive explanation methods go beyond transparency and address the contrastive aspect of explanations. Such explanations are emerging as an attractive option to provide actionable change to scenarios adversely impacted by classifiers' decisions. However, their extension to textual data is under-explored and there is little investigation on their vulnerab

Xuran Pan, Tianzhu Ye, Dongchen Han, Shiji Song

Recent years have witnessed the fast development of large-scale pre-training frameworks that can extract multi-modal representations in a unified form and achieve promising performances when transferred to downstream tasks. Nevertheless, existing approaches mainly focus on pre-training with simple image-text pairs, while neglecting the semantic connections b

Sahar Diskin, Dor Elboim

Given a graph $G$, we denote by $f(G,u_0,k)$ the number of paths of length $k$ in $G$ starting from $u_0$. In graphs of maximum degree 3, with edge weights $i.i.d.$ with $exp(1)$, we provide a simple proof showing that (under the assumption that $f(G,u_0,k)=\omega(1)$) the expected weight of the heaviest path of length $k$ in $G$ starting from $u_0$ is at le

David H. Brooks, Miho Janvier, Deborah Baker, Harry P. Warren

A key goal of the Solar Orbiter mission is to connect elemental abundance measurements of the solar wind enveloping the spacecraft with EUV spectroscopic observations of their solar sources, but this is not an easy exercise. Observations from previous missions have revealed a highly complex picture of spatial and temporal variations of elemental abundances i

Vladimir Bobkov, Mieko Tanaka

We investigate qualitative properties of weak solutions of the Dirichlet problem for the equation $-\Delta_p u = \lambda m(x)|u|^{p-2}u + \eta a(x)|u|^{q-2}u + f(x)$ in a bounded domain $\Omega \subset \mathbb{R}^N$, where $q<p$. Under certain regularity and qualitative assumptions on the weights $m, a$ and the source function $f$, we identify ranges of para

Jonathan Bader, Kevin Styp-Rekowski, Leon Doehler, Soeren Becker

In Earth Systems Science, many complex data pipelines combine different data sources and apply data filtering and analysis steps. Typically, such data analysis processes are historically grown and implemented with many sequentially executed scripts. Scientific workflow management systems (SWMS) allow scientists to use their existing scripts and provide suppo

G. Bouzerar

The remarkable and fascinating properties of two-dimensional materials have raised them to the rank of most promising candidates for technological applications. In particular, the possibility of long-range ferromagnetic order in 2D materials is generating a growing excitement. Here, we demonstrate that flat bands (FBs) may pave the way to room temperature fe

Rahul Barthwal, T. Raja Sekhar

In this article, we prove the existence and regularity of a smooth solution for a supersonic-sonic patch arising in a modified Frankl problem in the study of three-dimensional axisymmetric steady isentropic relativistic transonic flows over a symmetric airfoil. We consider a general convex equation of state which makes this problem complicated as well as int

José L. Jiménez, Mourad Tighiouart

In this article, we propose a phase I-II design in two stages for the combination of molecularly targeted therapies. The design is motivated by a published case study that combines a MEK and a PIK3CA inhibitors; a setting in which higher dose levels do not necessarily translate into higher efficacy responses. The goal is therefore to identify dose combinatio

ALICE Collaboration

The production of the $\psi(2S)$ charmonium state was measured with ALICE in Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV, in the dimuon decay channel. A significant signal was observed for the first time at LHC energies down to zero transverse momentum, at forward rapidity ($2.5<y<4$). The measurement of the ratio of the inclusive production cross secti

Brendan K. Beare, Jackson D. Clarke

Given independent samples from two univariate distributions, the one-sided Wilcoxon-Mann-Whitney statistic may be used to conduct a rank-based test of first-order stochastic dominance. We broaden the scope of applicability of such tests by showing that the bootstrap may be used to conduct valid inference in a matched pairs sampling framework permitting depen

Bouchaib Azamir, Driss Bennis, Bertrand Michel

Topological data analysis has recently been applied to the study of dynamic networks. In this context, an algorithm was introduced and helps, among other things, to detect early warning signals of abnormal changes in the dynamic network under study. However, the complexity of this algorithm increases significantly once the database studied grows. In this pap

Rafal Maciula, Antoni Szczurek

We discuss production of far-forward $D$ mesons/antimesons and neutrinos/antineutrinos from their semileptonic decays in pp-collisions at the LHC. We include the gluon-gluon fusion $gg \to c\bar{c}$, the intrinsic charm (IC) $gc \to gc$ as well as the recombination $gq \to Dc$ partonic mechanisms. The calculations are performed within the $k_T$-factorization

Pietro Maria Bonetti

This thesis deals with the Hubbard model as prototypical model to describe the physics of electrons in the two-dimensional copper-oxide planes of high-$T_c$ cuprates. To get approximate solutions, we employ functional renormalization group (fRG) and dynamical mean-field theory (DMFT) methods. We deal with the problem of identifying bosonic fluctuations in th

Carl A Whitfield, University of Manchester COVID-19 Modelling Group, Ian Hall

Repeat asymptomatic testing in order to identify and quarantine infectious individuals has become a widely-used intervention to control SARS-CoV-2 transmission. In some workplaces, and in particular health and social care settings with vulnerable patients, regular asymptomatic testing has been deployed to staff to reduce the likelihood of workplace outbreaks

Petar Radanliev, David De Roure

This article presents a new design for autonomous artificial intelligence (AI), based on the state-of-the-art algorithms, and describes a new autonomous AI system called AutoAI. The methodology is used to assemble the design founded on self-improved algorithms that use new and emerging sources of data (NEFD). The objective of the article is to conceptualise

Philippe Di Francesco, Bertrand Duplantier, Olivier Golinelli, Emmanuel Guitter

We evaluate the configuration exponents of various ensembles of Hamiltonian paths drawn on random planar bicubic maps. These exponents are estimated from the extrapolations of exact enumeration results for finite sizes and compared with their theoretical predictions based on the KPZ relations, as applied to their regular counterpart on the honeycomb lattice.

Lintao Ye, Ming Chi, Ruiquan Liao, Vijay Gupta

We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization

Golam Ali Sekh, Benoy Talukdar

Satyendra Nath Bose is one of the great Indian scientists. His remarkable work on the black body radiation or derivation of Planck's law led to quantum statistics, in particular, the statistics of photon. Albert Einstein applied Bose's idea to a gas made of atoms and predicted a new state of matter now called Bose-Einstein condensate. It took 70 years to obs

Kevin Rösch, Florian Heidecker, Julian Truetsch, Kamil Kowol

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated veh

Aibek Alanov, Vadim Titov, Dmitry Vetrov

Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce a novel domain-modulation tec

Lena Mulansky, Rüdiger Pryss, Caroline Cohrdes, Harald Baumeister

With about 300 million affected people, major depressive disorder (MDD) is one of the most common diseases worldwide. During the COVID-19 pandemic, the number of cases increased even further, by 28%. Many factors may be correlated with MDD, including the excessive use of social media apps. In this paper, we investigated the relationship between the use of so

Matteo Perotti, Matheus Cavalcante, Nils Wistoff, Renzo Andri

Vector architectures are gaining traction for highly efficient processing of data-parallel workloads, driven by all major ISAs (RISC-V, Arm, Intel), and boosted by landmark chips, like the Arm SVE-based Fujitsu A64FX, powering the TOP500 leader Fugaku. The RISC-V V extension has recently reached 1.0-Frozen status. Here, we present its first open-source imple

Lorenzo Mercolli, Axel Rominger, Kuangyu Shi

The use of machine learning systems in clinical routine is still hampered by the necessity of a medical device certification and/or by difficulty to implement these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure reliable model predictions and how to appraise the quality of a model's results on

Nicolas F. Chaves-de-Plaza, Klaus Hildebrandt, Anna Vilanova

Post-translational modifications (PTMs) affecting a protein's residues (amino acids) can disturb its function, leading to illness. Whether or not a PTM is pathogenic depends on its type and the status of neighboring residues. In this paper, we present the ProtoFold Neighborhood Inspector (PFNI), a visualization system for analyzing residues neighborhoods. Th

J. W. E. Mallinson, K. Lind, A. M. Amarsi, P. S. Barklem

The titanium abundances of late-type stars are important tracers of Galactic formation history. However, abundances inferred from Ti I and Ti II lines can be in stark disagreement in very metal-poor giants. Departures from local thermodynamic equilibrium (LTE) have a large impact on the minority neutral species and thus influences the ionisation imbalance, b

Anthony Favier, Shashank Shekhar, Rachid Alami

We consider the human-aware task planning problem where a human-robot team is given a shared task with a known objective to achieve. Recent approaches tackle it by modeling it as a team of independent, rational agents, where the robot plans for both agents' (shared) tasks. However, the robot knows that humans cannot be administered like artificial agents, so

Hadeel Moustafa, Peder Meisner Lyngby, Jens Jørgen Mortensen, Kristian S. Thygesen

We use a generative neural network model to create thousands of new, one-dimensional materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of the new materials are shown with density functional theory calculations to be dynamically stable and with heats of format

Timofey Grigoryev, Polina Verezemskaya, Mikhail Krinitskiy, Nikita Anikin

Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient in this task. Many works have exploited different deep learning models alongside cl

Seungyun Han, Hyun-Woo Lee, Kyoung-Whan Kim

Many interesting spin and orbital transport phenomena originate from orbital textures, referring to $\vec{k}$-dependent orbital states. Most of previous works are based on symmetry analysis to model the orbital texture and analyze its consequences. However the microscopic origins of orbital texture and its strength are largely unexplored. In this work, we de

Yousef Alqasrawi

This paper addresses the problem of semantic-based image retrieval of natural scenes. A typical content-based image retrieval system deals with the query image and images in the dataset as a collection of low-level features and retrieves a ranked list of images based on the similarities between features of the query image and features of images in the image

Ang Li, Judea Pearl

Probabilities of causation play a crucial role in modern decision-making. Pearl defined three binary probabilities of causation, the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN). These probabilities were then bounded by Tian and Pearl using a combination of experimental and observa

Weihao Zeng, Keqing He, Zechen Wang, Dayuan Fu

Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals. However, such systems rely on costly manually labeled dialogs which are not available in practical scenarios. In this paper, we present our models for Track 2 of the SereTOD 2022 challenge, which is the first challenge of bu

Yiqun Chen, Hangyu Mao, Jiaxin Mao, Shiguang Wu

Centralized Training with Decentralized Execution (CTDE) has emerged as a widely adopted paradigm in multi-agent reinforcement learning, emphasizing the utilization of global information for learning an enhanced joint $Q$-function or centralized critic. In contrast, our investigation delves into harnessing global information to directly enhance individual $Q

Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to

Jialiang Sun, Tingsong Jiang, Wen Yao, Donghua Wang

Recent studies reveal that deep neural network (DNN) based object detectors are vulnerable to adversarial attacks in the form of adding the perturbation to the images, leading to the wrong output of object detectors. Most current existing works focus on generating perturbed images, also called adversarial examples, to fool object detectors. Though the genera

Jiakang Zheng, Zhuoyi Zhao, Jiayi Zhang, Julian Cheng

Cell-free (CF) massive multiple-input multiple-output (MIMO) is considered as a promising technology for achieving the ultimate performance limit. However, due to its distributed architecture and low-cost access points (APs), the signals received at user equipments (UEs) are most likely asynchronous. In this paper, we investigate the performance of CF massiv

Zhanqiang Guo, Yao Luan, Jianjiang Feng, Wangsheng Lu

Accurate cerebrovascular segmentation from Magnetic Resonance Angiography (MRA) and Computed Tomography Angiography (CTA) is of great significance in diagnosis and treatment of cerebrovascular pathology. Due to the complexity and topology variability of blood vessels, complete and accurate segmentation of vascular network is still a challenge. In this paper,

Stéphane Chevalier, Marine Garcia, Alain Sommier, Jean-Christophe Batsale

In this paper we report a semianalytical model of the mass transfer impedance in microfluidic electrochemical chips (MEC). It is based on the molar advection diffusion equation in a microfluidic channel with a Poiseuille flow and an electrochemical reaction at the interface of deposited electrodes. Using the Fourier-Laplace integral transforms and the quadru

Debargha Banerjee, Priyanka Majumder, Chitrabhanu Chaudhuri

We compute intersection matrices for modular curves of the form $X_0(p^r)$ with $r \in \{3,4\}$ and as an application, we compute an asymptotic expression for the Arakelov self-intersection number of the relative dualizing sheaf of Edixhoven's minimal regular model for the modular curve $X_0(p^r)$ over $\qq$ with $r$ as above. This computation will be useful

Wai Meng Kwok, Sarat Chandra Dass, George Streftaris

Parameter estimation and associated uncertainty quantification is an important problem in dynamical systems characterized by ordinary differential equation (ODE) models that are often nonlinear. Typically, such models have analytically intractable trajectories which result in likelihoods and posterior distributions that are similarly intractable. Bayesian in

Chenning Yu, Sicun Gao

Sampling-based motion planning is a popular approach in robotics for finding paths in continuous configuration spaces. Checking collision with obstacles is the major computational bottleneck in this process. We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perfor

Annie S. Chen, Archit Sharma, Sergey Levine, Chelsea Finn

Reinforcement learning algorithms are typically designed to learn a performant policy that can repeatedly and autonomously complete a task, usually starting from scratch. However, in many real-world situations, the goal might not be to learn a policy that can do the task repeatedly, but simply to perform a new task successfully once in a single trial. For ex

Yuta Shingu, Tetsuro Nikuni, Shiro Kawabata, Yuichiro Matsuzaki

Quantum annealing (QA) is one of the efficient methods to calculate the ground-state energy of a problem Hamiltonian. In the absence of noise, QA can accurately estimate the ground-state energy if the adiabatic condition is satisfied. However, in actual physical implementation, systems suffer from decoherence. On the other hand, much effort has been paid int

Jiang Zhu, Xiangming Meng, Xupeng Lei, Qinghua Guo

We consider the problem of recovering an unknown signal ${\mathbf x}\in {\mathbb R}^n$ from general nonlinear measurements obtained through a generalized linear model (GLM), i.e., ${\mathbf y}= f\left({\mathbf A}{\mathbf x}+{\mathbf w}\right)$, where $f(\cdot)$ is a componentwise nonlinear function. Based on the unitary transform approximate message passing

Shu Zhao, Almıla Akdağ Salah, Albert Ali Salah

The way the human body is depicted in classical and modern paintings is relevant for art historical analyses. Each artist has certain themes and concerns, resulting in different poses being used more heavily than others. In this paper, we propose a computer vision pipeline to analyse human pose and representations in paintings, which can be used for specific

Mingqi Gao, Xiaojun Wan

Many studies have revealed that word embeddings, language models, and models for specific downstream tasks in NLP are prone to social biases, especially gender bias. Recently these techniques have been gradually applied to automatic evaluation metrics for text generation. In the paper, we propose an evaluation method based on Word Embeddings Association Test

Hao Li, Gabriel Sanchez-Santolino, Sergio Puebla, Riccardo Frisenda

We study the effect of uniaxial strain on the band structure of ZrSe$_3$, a semiconducting material with a marked in-plane structural anisotropy. By using a modified 3-point bending test apparatus, thin ZrSe$_3$ flakes were subjected to uniaxial strain along different crystalline orientations monitoring the effect of strain on their optical properties throug

Marc Grosjean, Elise Lorenceau

Bubbles and droplets are ubiquitous in many areas of engineering, including microfluidics where they can serve as microreactors for screening of chemical reactions. They are often formed out of a constriction (a microfluidic channel or a cylindrical tube) by blowing a given volume of gas into a liquid phase. It is obviously crucial to be able to control thei

Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos, Emmanouil-Vasileios Vlatakis-Gkaragkounis

Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game itself evolves over time, possibly in a very complicated manner. Because of this, the convergence properties of popular learning algorithms - like policy gradient and its variants

Wenhe Jia, Lu Yang, Zilong Jia, Wenyi Zhao

Since first proposed, Video Instance Segmentation(VIS) task has attracted vast researchers' focus on architecture modeling to boost performance. Though great advances achieved in online and offline paradigms, there are still insufficient means to identify model errors and distinguish discrepancies between methods, as well approaches that correctly reflect mo

Weiwen Xu, Xin Li, Yang Deng, Wai Lam

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates th

Gregor Gantner, Martin Vohralík

We consider isogeometric discretizations of the Poisson model problem, focusing on high polynomial degrees and strong hierarchical refinements. We derive a posteriori error estimates by equilibrated fluxes, i.e., vector-valued mapped piecewise polynomials lying in the $\boldsymbol{H}({\rm div})$ space which appropriately approximate the desired divergence co

Fl. Stancu

We generalize to five distinct flavors the flavor-spin hyperfine interaction introduced previously for four flavors and used in the study of $uudc\overline{c}$ pentaquark. As a particular case here we study the lowest states of the pentaquark $uudb\overline{b}$, of either positive or negative parity, in a constituent quark model with linear confinement and t

Sayyed Heidar Jafari, Samir Zahirović

The directed power graph $\vec{\mathcal G}(\mathbf G)$ of a group $\mathbf G$ is the simple digraph with vertex set $G$ such that $x\rightarrow y$ if $y$ is a power of $x$. The power graph $\mathcal G(\mathbf G)$ of the group $\mathbf G$ is the underlying simple graph. In this paper, we prove that Pr\"ufer group is the only nilpotent group whose power graph

The Tien Mai

In this paper, we present a theoretical study of a low-rank matrix single index model. This model is recently introduced in biostatistics however its theoretical properties on estimating together the link function and the coefficient matrix are not yet carried out. Here, we advance on using PAC-Bayesian bounds technique to provide a rigorous theoretical unde

Pierre Andreoletti, Pierre Debs

We study a simple random walk on Z^2 with constraints on the axis. Motivation comes from physics when particles (a gas for example, see [Dal88]) are submitted to a local field. In our case we assume that the particle evolves freely in the cones but when touching the axis a force pushes it back progressively to the origin. The main result proves that this for

Kaoru Teranishi, Kiminao Kogiso

Encrypted control is a promising method for the secure outsourcing of controller computation to a public cloud. However, a feasible method for security proofs of control has not yet been developed in the field of encrypted control systems. Additionally, cryptography does not consider certain types of attacks on encrypted control systems; therefore, the secur

Jean-Paul Décamps, Fabien Gensbittel, Thomas Mariotti

We study a generic family of two-player continuous-time nonzero-sum stopping games modeling a war of attrition with symmetric information and stochastic payoffs that depend on an homogeneous linear diffusion. We first show that any Markovian mixed strategy for player $i$ can be represented by a pair $(\mu^i,S^i)$, where $\mu^i$ is a measure over the state sp

Simona Bernardi, José Merseguer, Raúl Javierre

This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can identify anomalous epochs, i.e., time intervals where the observations are above a given percentile of a baseline distribution, defined by a dis

Kaoru Teranishi, Kiminao Kogiso

A sample identifying complexity has been introduced in the previous study to capture an adversary's estimation error of system identification. The complexity plays a crucial role in defining the security of encrypted control systems and designing a controller and security parameter for the systems. This study proposes a novel sample identifying complexity of

Vincent Beck, Cédric Lecouvey

We establish analogues in the context of group actions or group representations of some classical problems and results in additive combinatorics of groups. We also study the notion of left invariant submodular function defined on power sets which plays a central role in our proofs.This new version is a completely reorganized version of the preceding one.

Ulysse Pavloff, Tristan Cazenave, Jérôme Lang

Voting by sequential elimination is a low-communication voting protocol: voters play in sequence and eliminate one or more of the remaining candidates, until only one remains. While the fairness and efficiency of such protocols have been explored, the impact of strategic behaviour has not been addressed. We model voting by sequential elimination as a game. G

G. H. Sargsyan, K. D. Launey, R. M. Shaffer, S. T. Marley

We calculate single-neutron spectroscopic overlaps for lithium isotopes in the framework of the \textit{ab initio} symmetry-adapted no-core shell model. We report the associated neutron-nucleus asymptotic normalization coefficients (ANCs) and spectroscopic factors (SFs) that are important ingredients in many reaction cross section calculations. While spectro

Lucas Drumetz, Alexandre Reiffers-Masson, Naoufal El Bekri, Franck Vermet

In many applications, one encounters signals that lie on manifolds rather than a Euclidean space. In particular, covariance matrices are examples of ubiquitous mathematical objects that have a non Euclidean structure. The application of Euclidean methods to integrate differential equations lying on such objects does not respect the geometry of the manifold,

Weikang Lin, Tsutomu T. Yanagida

Recently, arXiv:2210.05659 shows that a photon-axion like particle (ALP) oscillation can boost the survival rate of the high energy photons associated with the observed GRB221009A event. Here, we show that the proposed ALP is consistent with the electroweak axion with an anomaly free $Z_{10}$ Froggatt-Nielsen symmetry.

Peng Gao, Liangyi Zhao

We develope the $L$-functions ratios conjecture with one shift in the numerator and denominator in certain ranges for the family of quadratic Hecke $L$-functions in the Gaussian field using multiple Dirichlet series under the generalized Riemann hypothesis. We also obtain an asymptotical formula for the first moment of central values of the same family of $L

Eda Oktay, Erin Carson

Using lower precision in algorithms can be beneficial in terms of reducing both computation and communication costs. Motivated by this, we aim to further the state-of-the-art in developing and analyzing mixed precision variants of iterative methods. In this work, we focus on the block variant of low-synchronization classical Gram-Schmidt with reorthogonaliza

Onur Çakıroğlu, Joshua O. Island, Yong Xie, Riccardo Frisenda

This work presents an automated three-point bending apparatus that can be used to study strain engineering and straintronics in two-dimensional materials. We benchmark the system by reporting reproducible strain tuned micro-reflectance, Raman, and photoluminescence spectra for monolayer molybdenum disulfide (MoS2). These results are in good agreement with re

Byungki Ryu, Jaywan Chung, Masaya Kumagai, Tomoya Mato

A thermoelectric device is a heat engine that directly converts heat into electricity. Many materials with a high figure of merit ZT have been discovered in anticipation of a high thermoelectric efficiency. However, there has been a lack of investigations on efficiency-based material evaluation, and little is known about the achievable limit of thermoelectri

Peirong Zhang, Jiajia Jiang, Yuliang Liu, Lianwen Jin

Although online handwriting verification has made great progress recently, the verification performances are still far behind the real usage owing to the small scale of the datasets as well as the limited biometric mediums. Therefore, this paper proposes a new handwriting verification benchmark dataset named Multimodal Signature and Digit String (MSDS), whic

Yiming Shan, Aiguo Xu, Yudong Zhang, Lifeng Wang

A Discrete Boltzmann Model(DBM) based on the Shakhov model for detonation is proposed. Compared with the DBM based on the Bhatnagar-Gross-Krook (BGK) model, the current model has a flexible Prandtl numbers and consequently can be applied to a much wider range of detonation phenomena. Besides the Hydrodynamic Non-Equilibrium (HNE) behaviors usually investigat

Sandipana Dowerah, Romain Serizel, Denis Jouvet, Mohammad Mohammadamini

Speaker verification (SV) suffers from unsatisfactory performance in far-field scenarios due to environmental noise andthe adverse impact of room reverberation. This work presents a benchmark of multichannel speech enhancement for far-fieldspeaker verification. One approach is a deep neural network-based, and the other is a combination of deep neural network

DAMPE Collaboration

Boron nuclei in cosmic rays (CRs) are believed to be mainly produced by the fragmentation of heavier nuclei, such as carbon and oxygen, via collisions with the interstellar matter. Therefore, the boron-to-carbon flux ratio (B/C) and the boron-to-oxygen flux ratio (B/O) are very essential probes of the CR propagation. The energy dependence of the B/C ratio fr

Xin Zhang, Shenghui Song

The Rayleigh-product channel model is utilized to characterize the rank deficiency caused by keyhole effects. However, the finite blocklength analysis for Rayleigh product channels is not available in the literature. In this paper, we will characterize the mutual information density (MID) and perform the FBL analysis to reveal the impact of rank-deficiency i

Vivien Thiney, Pierre-André Mortemousque, Konstantinos Rogdakis, Romain Thalineau

We investigate experimentally the capacitive coupling between a two-electron singlet-triplet spin qubit and flying electrons propagating in quantum Hall edge channels. After calibration of the spin qubit detector, we assess its charge sensibility and demonstrate experimentally the detection of less than five flying electrons with average measurement. This ex