May 2022 arXiv papers — page 42

Lu Bai, Weixing Ji, Qinyuan Li, Xilai Yao

Deep learning is attracting interest across a variety of domains, including natural language processing, speech recognition, and computer vision. However, model training is time-consuming and requires huge computational resources. Existing works on the performance prediction of deep neural networks, which mostly focus on the training time prediction of a few

Zipeng Yan, Zichao Jiang, Yiyuan Li

This paper presents two approaches of privacy-preserving voting system: Blind Signature-based Voting (BSV) and Homorphic Encryption Based Voting (HEV). BSV is simple, stable, and scalable, but requires additional anonymous property in the communication with the blockchain. HEV simultaneously protects voting privacy against traffic-analysis attacks, prevents

Pablo Mosteiro, Jesse Kuiper, Judith Masthoff, Floortje Scheepers

Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model trained on clinical mental health data. We collected structured data related to the admission, diagnosis, and treatment of

Daniel Jaschke, Simone Montangero

The quantum advantage threshold determines when a quantum processing unit (QPU) is more efficient with respect to classical computing hardware in terms of algorithmic complexity. The "green" quantum advantage threshold $-$ based on a comparison of energetic efficiency between the two $-$ is going to play a fundamental role in the comparison between quantum a

Francesco Preti, Tommaso Calarco, Juan Mauricio Torres, József Zsolt Bernád

We propose and investigate a method to optimize recurrence entanglement purification protocols. The approach is based on a numerical search in the whole set of SU(4) matrices with the aid of a quasi-Newton algorithm. Our method evaluates average concurrences where the probabilistic occurrence of mixed entangled states is also taken into account. We show for

Mohammad Goli, Shant Shahbazian

The proton between the two oxygen atoms of the malonaldehyde molecule experiences an effective double-well potential in which the proton wavefunction is delocalized between the two wells. Herein we employed the state-of-the-art multi-component quantum theory of atoms in molecules partitioning scheme to obtain the molecular structure, i.e. atoms in molecules

Georgios Tziafas, Hamidreza Kasaei

Service robots should be able to interact naturally with non-expert human users, not only to help them in various tasks but also to receive guidance in order to resolve ambiguities that might be present in the instruction. We consider the task of visual grounding, where the agent segments an object from a crowded scene given a natural language description. M

Pardeep Kumar Tanwar, Md Shahin Alam, Mujeeb Ahmad, Dariusz Kaczorowski

The thermal conductivity (k) of the Weyl semimetal NbP was studied with the thermal gradient and magnetic field applied parallel to [0 0 1] direction. At low temperatures k(B) exhibits large quantum oscillations with frequencies matching two of several determined from the Shubnikov - de Haas effect measured on the same sample with analogous electrical curren

Peng Lv, Shi-Jie Wei, Hao-Nan Xie, Gui-Lu Long

Nucleus is a typical many-body quantum system. Full calculation of a nuclear system in a classical computer is far beyond the capacity of current classical computers. With fast development of hardware, the prospect of using quantum computers in nuclear physics is closing. Here, we report a full quantum package, QCSH, for solving nuclear shell-model in a quan

Wei You, Chao Qin, Zihao Wang, Shuoguang Yang

We consider the best-k-arm identification problem for multi-armed bandits, where the objective is to select the exact set of k arms with the highest mean rewards by sequentially allocating measurement effort. We characterize the necessary and sufficient conditions for the optimal allocation using dual variables. Remarkably these optimality conditions lead to

Bernd Finkbeiner, Niklas Metzger, Yoram Moses

Compositional synthesis relies on the discovery of assumptions, i.e., restrictions on the behavior of the remainder of the system that allow a component to realize its specification. In order to avoid losing valid solutions, these assumptions should be necessary conditions for realizability. However, because there are typically many different behaviors that

Le Ngoc Tram, Lars Bonne, Yue Hu, Enrique Lopez-Rodriguez

The heart of the Large Magellanic Cloud, 30 Doradus, is a complex region with a clear core-halo structure. Feedback from the stellar cluster R$\,$136 has been shown to be the main source of energy creating multiple pc-scale expanding-shells in the outer region, and carving a nebula core in the proximity of the ionization source. We present the morphology and

Jia Wang, Ming Zeng, Dazhang Li, Xiaoning Wang

A new injection scheme using the interference of two coaxial laser pulses is proposed for generating high quality beams in laser wakefield accelerators. In this scheme, a relatively loosely focused laser pulse drives the plasma wakefield, and a tightly focused laser pulse with similar intensity triggers interference ring pattern which creates onion-like mult

Vinícius R. Máximo, Jean-François Cordeau, Mariá C. V. Nascimento

A recent study on the classical Capacitated Vehicle Routing Problem (CVRP) introduced an adaptive version of the widely used Iterated Local Search (ILS) paradigm, hybridized with a path-relinking strategy (PR). The solution method, called AILS-PR, outperformed existing meta-heuristics for the CVRP on benchmark instances. However, tests on large-scale instanc

Salim Lardjane

The author establishes a new mathematical expression for the Frequency Polygon. He uses it to prove the strong uniform consistency of the Frequency Polygon marginal density estimator for non-anticipative stationary stochastic processes which are stable in the sense of Wu. He gives examples of several times series models for which this result is relevant.

Edward Chen, Han Bao, Tate Shorthill, Carl Elks

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. In this work, we develop an approach c

Yiru Wang, Pengxin Guan, Hongkang Yu, Yuping Zhao

In this letter, we study the reconfigurable intelligent surfaces (RIS) aided full-duplex (FD) communication system. By jointly designing the active beamforming of two multi-antenna sources and passive beamforming of RIS, we aim to maximize the energy efficiency of the system, where extra self-interference cancellation power consumption in FD system is also c

Lunyiu Nie, Shulin Cao, Jiaxin Shi, Jiuding Sun

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified interme

Ariel Nikas, Hasan Ahmed, Veronika I. Zarnitsyna

Developing accurate and reliable methods to estimate vaccine protection is a key goal in immunology and public health. While several statistical methods have been proposed, their potential inaccuracy in capturing fast intra-seasonal waning of vaccine-induced protection needs to be rigorously investigated. To compare statistical methods for vaccine effectiven

Xiuxiu Ma, Abla Kammoun, Ayed M. Alrashdi, Tarig Ballal

This paper focuses on the performance analysis of a class of limited peak-to-average power ratio (PAPR) precoders for downlink multi-user massive multiple-input multiple-output (MIMO) systems. Contrary to conventional precoding approaches based on simple linear precoders such as maximum ratio transmission (MRT) and regularized zero-forcing (RZF), the precode

Yuling Wang, Hao Xu, Yanhua Yu, Mengdi Zhang

It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able to flexibly remedy the weakness of GNNs. However, this optimization objective is only proved for GNNs wit

Edward Chen, Han Bao, Tate Shorthill, Carl Elks

In recent years, there has been considerable effort to modernize existing and new nuclear power plants with digital instrumentation and control systems. However, there has also been considerable concern both by industry and regulatory bodies for the risk and consequence analysis of these systems. Of concern are digital common cause failures specifically due

Carlos Manuel de Armas-Morejón, Luis A. Montero-Cabrera, Angel Rubio, Joaquim Jornet-Somoza

Spectroscopic properties of molecules holds great importance for the description of the molecular response under the effect of an UV/Vis electromagnetic radiation. Computationally expensive ab initio (e.g. MultiConfigurational SCF, Coupled Cluster) or TDDFT methods are commonly used by the quantum chemistry community to compute these properties. In this work

Peihao Dong, Qihui Wu, Xiaofei Zhang, Guoru Ding

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated. To this end, this article discusses the concepts of edge intelligence from the semantic cognitive perspective. Two instructive theoretical models for

Boris Mocialov, Graham Turner, Helen Hastie

Signers compose sign language phonemes that enable communication by combining phonological parameters such as handshape, orientation, location, movement, and non-manual features. Linguistic research often breaks down signs into their constituent parts to study sign languages and often a lot of effort is invested into the annotation of the videos. In this wor

Inge S. Helland

The well known Bell experiment with two actors Alice and Bob is considered. First the simple deduction leading to the CHSH inequality under local realism is reviewed, and some arguments from the literature are recapitulated. Then I take up certain background themes before I enter a discussion of Alice's analysis of the situation. An important point is that h

Jenny Yang, Rasheed El-Bouri, Odhran O'Donoghue, Alexander S. Lachapelle

With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanc

Daniel Geiß, Klaus Kroy, Viktor Holubec

Retardation between sensation and action is an inherent biological trait. Here we study its effect in the Vicsek model, which is a paradigmatic swarm model. We find that: (i) a discrete time delay in the orientational interactions diminishes the ability of strongly aligned swarms to follow a leader and, in return, increases their stability against random ori

Peng Yang, Xiang Wang, Yonghai Li

A family of quadratic finite volume method (FVM) schemes are constructed and analyzed over tetrahedral meshes. In order to prove stability and error estimate, we propose the minimum V-angle condition on tetrahedral meshes, and the surface and volume orthogonal conditions on dual meshes. Through the element analysis technique, the local stability is equivalen

Janet Talvacchia

In this paper we define a canonical Poisson structure on a normal generalized contact metric space and use this structure to define a generalized Sasakian structure. We show also that this canonical Poisson structure enables us to distinguish generalized Sasakian structures from generalized coK\"ahler structures.

Gilbert Reinisch

We define a new quantum Hermitian operator (namely, the energy variance operator) which is simply duplicated from the statistical definition of energy variance in classical physics. Its expectation value yields the standard deviation of the energy about the mean value of this latter. We show by use of an exact Hamiltonian description that this standard devia

Zixuan Huang, Yunfeng Wang, Zhiwen Chen, Xin Gao

Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image. In recent years many attractive works in skeleton extraction have been made. But as far as we know, there is little research on how to utilize the context information in the binary shape of objects. In this pa

Graciela Boente, Juan Carlos Pardo-Fernández

This paper focuses on the problem of testing the null hypothesis that the regression functions of several populations are equal under a general nonparametric homoscedastic regression model. It is well known that linear kernel regression estimators are sensitive to atypical responses. These distorted estimates will influence the test statistic constructed fro

Yinzheng Zhong, John Y. Goulermas, Alexei Lisitsa

In this paper, we consider the applications of process mining in intrusion detection. We propose a novel process mining inspired algorithm to be used to preprocess data in intrusion detection systems (IDS). The algorithm is designed to process the network packet data and it works well in online mode for online intrusion detection. To test our algorithm, we u

Sandra M. Conde C, Rekha Jain, Vera Jatenco-Pereira

We present measurements of periodicity for transverse loop oscillations during the periods of activity of two remote and separated (both temporally and spatially) flares. The oscillations are observed in the same location more than 100 Mm away from the visible footpoints of the loops. Evidence for several possible excitation sources is presented. After close

Sihao Xue, Qianyao Shen, Guoqing Li

In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving the performance of Realtime Custom Keyword Spotting (RCKS) that is still an industrial challenge for demanding powerful classification ability with limited computation resources. Inspired by Brain Science that a brain is only partly activated for a nerve simulation and numerous mac

Hunter Belanger, Davide Mancusi, Andrea Zoia

The Monte Carlo method is often used to simulate systems which can be modeled by random walks. In order to calculate observables, in many implementations the "walkers" carry a statistical weight which is generally assumed to be positive. Some random walk simulations, however, may require walkers to have positive or negative weights: it has been shown that th

Mor M. Dahan, Evelyn T. Breyer, Stefan Slesazeck, Thomas Mikolajick

Ferroelectric field effect transistor (FeFET) memory has shown the potential to meet the requirements of the growing need for fast, dense, low-power, and non-volatile memories. In this paper, we propose a memory architecture named crossed-AND (C-AND), in which each storage cell consists of a single ferroelectric transistor. The write operation is performed u

Joseph D. Dietz, Kai Nan, Robert S. Hoy

Semiflexible polymer glasses (SPGs), including those formed by the recently synthesized semiflexible conjugated polymers (SCPs), are expected to be brittle because classical formulas for their craze extension ratio $\lambda_{\rm craze}$ and fracture stretch $\lambda_{\rm frac}$ predict that systems with $N_e = C_\infty$ have $\lambda_{\rm craze} = \lambda_{\

Sebastian Forster, Tijn de Vos

In this paper, we bring the main tools of the Laplacian paradigm to the Broadcast Congested Clique. We introduce an algorithm to compute spectral sparsifiers in a polylogarithmic number of rounds, which directly leads to an efficient Laplacian solver. Based on this primitive, we consider the linear program solver of Lee and Sidford (FOCS 2014). We show how t

Yanting Dong, Zhu Liu, Youli Tuo, James F. Steiner

We report results on the joint-fit of the NuSTAR and HXMT data for the black hole X-ray binary candidate MAXI J1535-571. The observations were obtained in 2017 when the source evolved through the hard, hard-intermediate and soft-intermediate states over the rising phase of the outburst. After subtracting continuum components, X-ray reflection signatures are

Ivan Polekhin

The planar inverted pendulum with a vibrating pivot point in the presence of an additional horizontal force field is studied. The horizontal force is not assumed to be small or rapidly oscillating. We assume that the pivot point of the pendulum rapidly oscillates in the vertical direction and the period of these oscillations is commensurable with the period

Nitzan Ragoler, Ealeal Bear, Ron Schreier, Shlomi Hillel

Using a one-dimensional stellar evolution code we simulate the response of a red supergiant (RSG) star to injection of energy and to mass removal. We take the values of the energy that we inject and the mass that we remove according to our previous three-dimensional hydrodynamical simulations of a neutron star (NS) on a highly eccentric orbit that enters the

Alessio Squarcini, Enzo Marinari, Gleb Oshanin, Luca Peliti

We discuss the statistical properties of a single-trajectory power spectral density $S(\omega,\mathcal{T})$ of an arbitrary real-valued centered Gaussian process $X(t)$, where $\omega$ is the angular frequency and $\mathcal{T}$ the observation time. We derive a double-sided inequality for its noise-to-signal ratio and obtain the full probability density func

Guobin Shen, Dongcheng Zhao, Yi Zeng

High-quality and challenging event stream datasets play an important role in the design of an efficient event-driven mechanism that mimics the brain. Although event cameras can provide high dynamic range and low-energy event stream data, the scale is smaller and more difficult to obtain than traditional frame-based data, which restricts the development of ne

Jack Poole, Paul Gardner, Nikolaos Dervilis, Lawrence Bull

The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adaptation (DA) - gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. Ty

Karim Khanaki

We give several new characterizations of $IP$ (the independence property) and $SOP$ (the strict order property) for continuous first order logic and study their relations to the function theory and the Banach space theory. We suggest new dividing lines of unstable theories by the study of subclasses of Baire-1 functions and argue why one should not expect a

Julius Lohmann, Bernhard Schmitzer, Benedikt Wirth

In recent work arXiv:2109.07820 we have shown the equivalence of the widely used nonconvex (generalized) branched transport problem with a shape optimization problem of a street or railroad network, known as (generalized) urban planning problem. The argument was solely based on an explicit construction and characterization of competitors. In the current arti

Daniel Lacker, Luc Le Flem

We prove the optimal rate of quantitative propagation of chaos, uniformly in time, for interacting diffusions. Our main examples are interactions governed by convex potentials and models on the torus with small interactions. We show that the distance between the $k$-particle marginal of the $n$-particle system and its limiting product measure is $O((k/n)^2)$

Gi-Chol Cho, Chikako Idegawa, Eibun Senaha

We examine CP-violating effects on electroweak phase transition (EWPT) in the standard model with a complex singlet scalar focusing particularly on a scenario where additional scalars have masses close to 125 GeV. Such a high mass degeneracy makes collider signatures in the scenario standard model like, and current experimental data cannot distinguish them f

Fengge Zhang, Jia-Xi Feng, Xian Gao

We investigate the scalar induced gravitational waves (SIGWs) in the Chern-Simons (CS) modified gravity during the radiation dominated era. The SIGWs are circularly polarized, which provide us a tool to test the possible parity violation in the early universe. We derive the semianalytic expressions to evaluate the fractional energy density of the SIGWs, $\Om

Yaqi Deng, Chaozhen Wei, Rholee Xu, Luis Vidali

There is a growing interest in measuring the cell wall mechanical property at different locations in single walled cells. We present an inference scheme that maps relative surface elastic modulus distributions along the cell wall based on tracking the location of material marker points along the turgid and relaxed cell wall outline. A primary scheme provides

Jun Deng, Hua Zong, Yun Wang

This article analytically characterizes the impermanent loss of concentrated liquidity provision for automatic market makers in decentralised markets such as Uniswap. We propose two static replication formulas for the impermanent loss by a combination of European calls or puts with strike prices supported on the liquidity provision price interval. It facilit

Fan Wang, Weiming Liu, Chaochao Chen, Mengying Zhu

The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming distance of learned binary representations of users and items to accelerate recommendations. However, Hash-CF often faces two challenging pr

Zheng Qi, AprilPyone MaungMaung, Yuma Kinoshita, Hitoshi Kiya

In this paper, we propose a privacy-preserving image classification method that is based on the combined use of encrypted images and the vision transformer (ViT). The proposed method allows us not only to apply images without visual information to ViT models for both training and testing but to also maintain a high classification accuracy. ViT utilizes patch

Matthieu Arnhem, Célia Griffet, Nicolas J. Cerf

Distinguishing quantum states that admit a classical counterpart from those that exhibit nonclassicality has long been a central issue in quantum optics. Finding an implementable criterion certifying optical nonclassicality (i.e, the incompatibility with a statistical mixture of coherent states) is of major importance as it often is a prerequisite to quantum

Helena Jonsson, Volodymyr Mazorchuk, Elin Persson Westin, Shraddha Srivastava

We study various aspects of the structure and representation theory of singular Artin monoids. This includes a number of generalizations of the desingularization map and explicit presentations for certain finite quotient monoids of diagrammatic nature. The main result is a categorification of the classical desingularization map for singular Artin monoids ass

Zhiwei Ling, Zhihao Yue, Jun Xia, Ming Hu

Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy. However, due to the biased data d

Zhaosheng Li, Wenhui Yu, Yongqi Lu, Yuanyue Pan

Type-I X-ray burst oscillations are powered by thermonuclear released on the neutron star (NS) surface in low mass X-ray binaries (LMXBs), where the burst oscillation frequencies are close to the NS spin rates. In this work, we report the detection of oscillation at 584.65 Hz during the cooling tail of a type-I X-ray bursts observed from the accreting NS LMX

Joseph Ntahompagaze, Amare Abebe, Manasse R. Mbonye

This work deals with the computation of the power spectrum of large-scale structure using the dynamical system approach for a multi-fluid universe in scalar-tensor theory of gravity. We use the $1+3$ covariant approach to obtain evolution equations and study the behavior of the matter power spectrum of perturbation equations. The study is based on the equiva

Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao

Despite pre-training's progress in many important NLP tasks, it remains to explore effective pre-training strategies for dense retrieval. In this paper, we propose RetroMAE, a new retrieval oriented pre-training paradigm based on Masked Auto-Encoder (MAE). RetroMAE is highlighted by three critical designs. 1) A novel MAE workflow, where the input sentence is

Kashinath T Chavan, Sharat Chandra

We report the first principle theory-based study of stability, electronic structure and optical properties of cluster assembled materials in various 1D, 2D and 3D nanostructures using a cage-like Cd9Te9 cluster as the super-atom. The bulk 3D self-assemblies form in 2D stacked structures for different cubic lattices. The face centered stacking is the most sta

Jack Woollam, Vincent Van der Heiden, Andreas Rietbrock, Bernd Schurr

Machine Learning (ML) methods have demonstrated exceptional performance in recent years when applied to the task of seismic event detection. With numerous ML techniques now available for detecting seismicity, applying these methods in practice can help further highlight their advantages over more traditional approaches. Constructing such workflows also enabl

Katharina Hoedt, Arthur Flexer, Gerhard Widmer

Adversarial attacks can drastically degrade performance of recommenders and other machine learning systems, resulting in an increased demand for defence mechanisms. We present a new line of defence against attacks which exploit a vulnerability of recommenders that operate in high dimensional data spaces (the so-called hubness problem). We use a global data s

Dustin Morrill, Ryan D'Orazio, Marc Lanctot, James R. Wright

Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior among multiple agents with mediated equilibria. To develop hindsight rational learning in sequential decision-making settings, we formalize beh

Renata Ferrero, Martin Reuter

Quantum Einstein Gravity (QEG), nonperturbatively renormalized by means of a certain asymptotically safe renormalization group (RG) trajectory, is explored by solving its scale dependent effective field equations and embedding the family of emerging 4-dimensional spacetimes into a single 5-dimensional manifold, which thus encodes the complete information abo

Souhail Bakkali, Zuheng Ming, Mickael Coustaty, Marçal Rusiñol

Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relation

Yansel Gónzalez Tejeda, Helmut A. Mayer

Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be

Yue Chen, Peng Yi

The decision making and management of many engineering networks involves multiple parties with conflicting interests, while each party is constituted with multiple agents. Such problems can be casted as a multi-cluster game. Each cluster is treated as a self-interested player in a non-cooperative game where agents in the same cluster cooperate together to op

İbrahim Halil Efendioğku

With the rapid increase in the use of social media in the last decade, the conspicuous consumption lifestyle within society has been now transferred to the social media. Along with the changing culture of consumption, the consumer who witnesses such portrayals on social media aspires to and desires the same products and services. Having regard to this situat

Xianghong Gong

We study regularity of solutions $u$ to $\overline\partial u=f$ on a relatively compact $C^2$ domain $D$ in a complex manifold of dimension $n$, where $f$ is a $(0,q)$ form. Assume that there are either $(q+1)$ negative or $(n-q)$ positive Levi eigenvalues at each point of boundary $\partial D$. Under the necessary condition that a locally $L^2$ solution exi

Bruno Sokal, Paulo R. B. Gomes, André L. F. de Almeida, Behrooz Makki

In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum IRS phase-shift vector for reducing the feedback overhead. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off

Thomas Frachon, Peter Hansbo, Erik Nilsson, Sara Zahedi

We study cut finite element discretizations of a Darcy interface problem based on the mixed finite element pairs $\textbf{RT}_k\times Q_k$, $k\geq 0$. Here $Q_k$ is the space of discontinuous polynomial functions of degree less or equal to $k$ and $\textbf{RT}$ is the Raviart-Thomas space. We show that the standard ghost penalty stabilization, often added in

Jianhan Wu, Shijing Si, Jianzong Wang, Jing Xiao

With the rapid development of the Metaverse, virtual humans have emerged, and human image synthesis and editing techniques, such as pose transfer, have recently become popular. Most of the existing techniques rely on GANs, which can generate good human images even with large variants and occlusions. But from our best knowledge, the existing state-of-the-art

Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich

Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in imaging. Our regularizer, called patch normalizing flow regularizer (patchNR), involves a normalizing flow learn

Emmanuel Daucé

The capability to widely sample the state and action spaces is a key ingredient toward building effective reinforcement learning algorithms. The variational optimization principles exposed in this paper emphasize the importance of an occupancy model to synthesizes the general distribution of the agent's environmental states over which it can act (defining a

Yannick Wack, Martine Baelmans, Robbe Salenbien, Maarten Blommaert

In the presented study, a pipe penalization approach for the economic topology optimization of District Heating Networks is proposed, drawing inspiration from density-based topology optimization. For District Heating Networks, the upfront investment is a crucial factor for the rollout of this technology. Today, the pipe routing is usually designed relying on

S. P. Baranov, A. V. Lipatov, A. A. Prokhorov

We consider the process of double $J/\psi$ production in $pp$ collisions at the LHC in the framework of $k_T$-factorization approach. We focus on the gluon fragmentation mechanism which is related to multiple gluon emission in the initial state. The initial state emission is treated according to Catani-Ciafaloni-Fiorani-Marchesini evolution equation, and app

Simone Costa, Stefano Della Fiore

A subset $A$ of an abelian group $G$ is sequenceable if there is an ordering $(a_1, \ldots, a_k)$ of its elements such that the partial sums $(s_0, s_1, \ldots, s_k)$, given by $s_0 = 0$ and $s_i = \sum_{j=1}^i a_i$ for $1 \leq i \leq k$, are distinct, with the possible exception that we may have $s_k = s_0 = 0$. In the literature there are several conjectur

C. Athorne, L. Cruzeiro, J. C. Eilbeck

The Davydov model describes amide I energy transfer in proteins without dispersion or dissipation. In spite of five decades of study, there are few exact analytical results, especially for the discrete version of this model. Here we develop two methods to determine the exact orthonormal, multi-quanta, eigenstates of the Davydov dimer. The first method involv

N. Vishnu Priya, S. Monisha, M. Senthilvelan, Govindan Rangarajan

In this paper, we investigate smooth positon and breather-positon solutions of a generalized nonlinear Schr\"{o}dinger (GNLS) equation which contains higher order nonlinear effects. With the help of generalized Darboux transformation (GDT) method we construct $N$th order smooth positon solutions of GNLS equation. We study the effect of higher order nonlinear

F. Johnson, J. Kimák, J. Zemen, Z. Šobáň

The intrinsic anomalous Nernst effect in a magnetic material is governed by the Berry curvature at the Fermi energy and can be realized in non-collinear antiferromagnets with vanishing magnetization. Thin films of (001)-oriented Mn$_{3}$NiN have their chiral antiferromagnetic structure located in the (111) plane facilitating the anomalous Nernst effect unusu

Tomer Barak, Yonatan Loewenstein

In this paper, we aimed to help bridge the gap between human fluid intelligence - the ability to solve novel tasks without prior training - and the performance of deep neural networks, which typically require extensive prior training. An essential cognitive component for solving intelligence tests, which in humans are used to measure fluid intelligence, is t

John Pavlopoulos, Vanessa Lislevand

In this project, we tackle emotion classification for the Greek language, presenting and releasing a new dataset in Greek. We fine-tune and assess Transformer-based masked language models that were pre-trained on monolingual and multilingual resources, and we present the results per emotion and by aggregating at the sentiment and subjectivity level. The pote

Andre Opris

We show that a real analytic restricted log-exp-analytic function has a holomorphic extension which is again restricted log-exp-analytic. We also establish a parametric version of this result.

Yaoyao Zhong, Weihong Deng, Jiani Hu, Dongyue Zhao

Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions. The existing algorithms devote to realizing an ideal idea: minimizing the intra-class distance and maximizing the inter-class distance. However, they may neglect that there are also low quality training images which should not be opti

Hassan Fawaz, Julien Lesca, Pham Tran Anh Quang, Jérémie Leguay

In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gol

Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang

Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing pre-trained models suffer from the probl

Hsiu-Chuan Hsu, Ion Cosma Fulga, Jhih-Shih You

The stability of three-dimensional relativistic semimetals to disorder has recently attracted great attention, but the effect of disorder remains elusive for multifold fermions, that are not present in the framework of quantum field theory. In this paper, we investigate one type of multifold fermions, so-called triple-point fermions (TPFs), which have pseudo

Nurettin Turan, Michael Koller, Benedikt Fesl, Samer Bazzi

We propose a precoder codebook construction and feedback encoding scheme which is based on Gaussian mixture models (GMMs). In an offline phase, the base station (BS) first fits a GMM to uplink (UL) training samples. Thereafter, it designs a codebook in an unsupervised manner by exploiting the GMM's clustering capability. We design one codebook entry per GMM

Dongfen Bian, Emmanuel Grenier

The aim of this paper is to give a detailed presentation of long wave instabilities of shear layers for Navier Stokes equations, and in particular to give a simple and easy to read presentation of the study of Orr Sommerfeld equation and to detail the analysis of its adjoint. Using these analyses we prove the existence of long wave instabilities in the case

Shijie Dong, Kuijie Li, Yue Ma, Xu Yuan

In this paper, we are interested in the two-dimensional Dirac-Klein-Gordon system, which is a basic model in particle physics. We investigate the global behaviors of small data solutions to this system in the case of a massive scalar field and a massless Dirac field. More precisely, our main result is twofold: 1) we show sharp time decay for the pointwise es

Yi-Hao Ding, Chao-Ping Dong, Ping-Yuan Li

Using the sharpened Helgason-Johnson bound, this paper classifies all the irreducible unitary representations with non-zero Dirac cohomology of $E_{7(-5)}$. As an application, we find that the cancellation between the even part and the odd part of the Dirac cohomology continues to happen for certain unitary representations of $E_{7(-5)}$. Assuming the infini

Yuting Yang, Binbin Du, Yuke Li

The choice of modeling units is crucial for automatic speech recognition (ASR) tasks. In mandarin scenarios, the Chinese characters represent meaning but are not directly related to the pronunciation. Thus only considering the writing of Chinese characters as modeling units is insufficient to capture speech features. In this paper, we present a novel method

Christopher Sims

This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. There have been several breakthroughs in the analysis of fMRI via 3D convolutional neural networks (CNNs). With these new techniques it is possible to preserve the 3D spatial data of fMRI data. Additionally there have been recent advances in the use of 3D generat

Shourya Bose, Sifat Chowdhury, Yu Zhang

Mobile energy storage systems (MESS) offer great operational flexibility to enhance the resiliency of distribution systems in an emergency condition. The optimal placement and sizing of those units are pivotal for quickly restoring the curtailed loads. In this paper, we propose a model for load restoration in a microgrid while concurrently optimizing the MES

Đorđe Žikelić, Mathias Lechner, Krishnendu Chatterjee, Thomas A. Henzinger

In this work, we address the problem of learning provably stable neural network policies for stochastic control systems. While recent work has demonstrated the feasibility of certifying given policies using martingale theory, the problem of how to learn such policies is little explored. Here, we study the effectiveness of jointly learning a policy together w

Martin Gonzalez, Thibault Defourneau, Hatem Hajri, Mihaly Petreczky

In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems. This embedding preserves input-output behavior and can suitably be extended to other neural DE architectures. We then use realization theory of polynomial systems to provid

Dmitry Nikolaev, Sebastian Padó

The capabilities and limitations of BERT and similar models are still unclear when it comes to learning syntactic abstractions, in particular across languages. In this paper, we use the task of subordinate-clause detection within and across languages to probe these properties. We show that this task is deceptively simple, with easy gains offset by a long tai

Hiroki Harakawa, Takuya Takarada, Yui Kasagi, Teruyuki Hirano

We report the near-infrared radial-velocity (RV) discovery of a super-Earth planet on a 10.77-day orbit around the M4.5 dwarf Ross 508 ($J_\mathrm{mag}=9.1$). Using precision RVs from the Subaru Telescope IRD (InfraRed Doppler) instrument, we derive a semi-amplitude of $3.92^{+0.60}_{-0.58}$ ${\rm m\,s}^{-1}$, corresponding to a planet with a minimum mass $m