January 2022 arXiv papers — page 8

Michael Cox, Zahiduddin Mohammad, Sravya Kondrakunta, Ventaksamapth Raja Gogineni

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent

Pál András Papp, Roger Wattenhofer

We study and compare different Graph Neural Network extensions that increase the expressive power of GNNs beyond the Weisfeiler-Leman test. We focus on (i) GNNs based on higher order WL methods, (ii) GNNs that preprocess small substructures in the graph, (iii) GNNs that preprocess the graph up to a small radius, and (iv) GNNs that slightly perturb the graph

Henning Kirchberg, Abraham Nitzan

We consider a molecular junction immersed in a solvent where the electron transfer is dominated by Marcus-type steps. However, the successive nature of the charge transfer through the junction does not imply that the solvent reach thermal equilibrium throughout the transport. In our previous work \cite{kir2020} we have determined the nonequilibrium distribut

Duván Cardona, Michael Ruzhansky

We investigate the boundedness of oscillating singular integrals on Lie groups of polynomial growth in order to extend the classical oscillating conditions due to Fefferman and Stein for the boundedness of oscillating convolution operators. Kernel criteria are presented in terms of a fixed sub-Riemannian structure on the group induced by a sub-Laplacian asso

Jürgen Haase

Nuclear magnetic resonance (NMR) is a powerful quantum probe, but the early conclusions on the physics of the cuprates, based on a limited set of data, have to be revised in view of recent findings and results from extensive literature analyses of most NMR data. These show two coupled electronic spin components that influence the nuclei, most easily seen wit

Duván Cardona, Michael Ruzhansky

In this work, we extend the Euclidean theory of oscillating singular integrals due to Fefferman and Stein in \cite{Fefferman1970,FeffermanStein1972} to arbitrary graded Lie groups. Our approach reveals the strong compatibility between the geometric measure theory of a graded Lie group and the Fourier analysis associated with Rockland operators. Our criteria

Romaric Duvignau, Michel Raynal, Elad Michael Schiller

We study a well-known communication abstraction called Byzantine Reliable Broadcast (BRB). This abstraction is central in the design and implementation of fault-tolerant distributed systems, as many fault-tolerant distributed applications require communication with provable guarantees on message deliveries. Our study focuses on fault-tolerant implementations

Nicholas Pecka, Lotfi ben Othmane, Altaz Valani

Companies are misled into thinking they solve their security issues by using a DevSecOps system. This paper aims to answer the question: Could a DevOps pipeline be misused to transform a securely developed application into an insecure one? To answer the question, we designed a typical DevOps pipeline utilizing Kubernetes (K8s} as a case study environment and

David I. Spivak

Past work shows that one can associate a notion of Shannon entropy to a Dirichlet polynomial, regarded as an empirical distribution. Indeed, entropy can be extracted from any d:Dir by a two-step process, where the first step is a rig homomorphism out of Dir, the *set* of Dirichlet polynomials, with rig structure given by standard addition and multiplication.

Nikolaos Kidonakis, Nodoka Yamanaka

We study QCD corrections for the associated production of a single top quark and a photon ($tq\gamma$ production) at hadron colliders. We calculate the NLO cross section at LHC and future collider energies for a variety of kinematical cuts, and we estimate uncertainties from scale dependence and from parton distributions. We also calculate differential distr

Yafei Wu, Jian Shi, Peicheng Wang, Dongrui Zeng

As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi-view learning. However, they use only simple features, limiting the accuracy of these approaches in practice. In this paper, we propose DeepCatra, a multi-view learning appr

Gerrit Schierholz

Three hard problems! In this talk I investigate the long-distance properties of quantum chromodynamics in the presence of a topological theta term. This is done on the lattice, using the gradient flow to isolate the long-distance modes in the functional integral measure and tracing it over successive length scales. It turns out that the color fields produced

Robert Krauthgamer, Shay Sapir

A well-known approach in the design of efficient algorithms, called matrix sparsification, approximates a matrix $A$ with a sparse matrix $A'$. Achlioptas and McSherry [2007] initiated a long line of work on spectral-norm sparsification, which aims to guarantee that $\|A'-A\|\leq \epsilon \|A\|$ for error parameter $\epsilon>0$. Various forms of matrix appro

Alexey Piunovskiy, Bakhti Vasiev

Following the ideas of L.N.Gumilev, we introduce the mathematical model of ethnogenesis which describes the dynamics of subgroups in the developing polity in terms of ordinary differential equations. The bust dynamics associated with the rise and fall of civilisations is modelled as an excitation process, which is the non-linear phenomenon, well known in mat

Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately, the leading rationalization models often rely on data biases, especially shortcut features, to compose rationales and make predictions without probing the critical and causal patt

Ahmad Barhoumi, Pavel M. Bleher, Alfredo Deaño, Maxim L. Yattselev

We investigate the phase diagram of the complex cubic unitary ensemble of random matrices with the potential $V(M)=-\frac{1}{3}M^3+tM$ where $t$ is a complex parameter. As proven in our previous paper, the whole phase space of the model, $t\in\mathbb C$, is partitioned into two phase regions, $O_{\mathsf{one-cut}}$ and $O_{\mathsf{two-cut}}$, such that in $O

Qianghui Xiao

In this paper, some preliminaries about signal flow graph, linear time-invariant system on F(z) and computational complexity are first introduced in detail. In order to synthesize the necessary and sufficient condition on F(z) for a general 2-path problem, the sufficient condition on F(z) or R and necessary conditions on F(z) for a general 2-path problem are

Kanstantsin Pashkovich, Xinyue Xie

Online markets are a part of everyday life, and their rules are governed by algorithms. Assuming participants are inherently self-interested, well designed rules can help to increase social welfare. Many algorithms for online markets are based on prices: the seller is responsible for posting prices while buyers make purchases which are most profitable given

Zihao Liu

In this paper, we present a simple analytic proof of Siegel's theorem that concerns the lower bound of $L(1,\chi)$ for primitive quadratic $\chi$. Our new method compares an elementary lower bound with an analytic upper bound obtained by the inverse Mellin transform of $\Gamma(s)$.

Chih-Chiang Chang, Shun-Po Chuang, Hung-yi Lee

Simultaneous machine translation (SimulMT) speeds up the translation process by starting to translate before the source sentence is completely available. It is difficult due to limited context and word order difference between languages. Existing methods increase latency or introduce adaptive read-write policies for SimulMT models to handle local reordering

Arsham Gholamzadeh Khoee, Kimia Mohammadi Mohammadi, Mostafa Jani, Kourosh Parand

Anisotropic diffusion filtering for signal smoothing as a low-pass filter has the advantage of the edge-preserving, i.e., it does not affect the edges that contain more critical data than the other parts of the signal. In this paper, we present a numerical algorithm based on least squares support vector regression by using Legendre orthogonal kernel with the

Grant Kopitzke

The Gini index is a number that attempts to measure how equitably a resource is distributed throughout a population, and is commonly used in economics as a measurement of inequality of wealth or income. The Gini index is often defined as the area between the "Lorenz curve" of a distribution and the line of equality, normalized to be between zero and one. In

Sushant Saryal, Deepak Dhar

We study the entropy of a set of identical hard objects, of general shape, with each object pivoted on the vertices of a d-dimensional regular lattice of lattice spacing a, but can have arbitrary orientations. When the pivoting point is situated asymmetrically on the object, we show that there is a range of lattice spacings a, where in any orientation, a par

Nicola Gnecco, Edossa Merga Terefe, Sebastian Engelke

Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme value theory can be used to extrapolate beyond the range of the data, and several approaches exist that use linear regression, kernel methods or generalized additive models. Most

A. Atmani, A. Boukarou, D. Benterki, Kh. Zennir

The solution of a coupled system consisting of generalized Korteweg-de Vries-type equations is obtained for all time where the initial data are analytic on a band in the complex plane. We show that the width of this band decreases algebraically with time.

C. J. A. P. Martins, J. S. J. S. Oliveira, D. A. R. Pinheiro

The search for a physical model which explains the observed recent acceleration of the universe is a compelling task of modern fundamental cosmology. Recently Fernandes \textit{et al.} presented low redshift observational constraints on a scale invariant model by Maeder. Phenomenologically this can be interpreted as a bimetric theory with a time-dependent co

Wei Ren, Junlin Xiong

This paper studies input-to-state stability for hybrid systems with memory, which models hybrid dynamics affected by time delays. Using both Lyapunov-Razumikhin functions and Lyapunov-Krasovskii functionals, Lyapunov-based sufficient conditions are established for input-to-state stability. In addition, further extensions and relaxations are proposed for spec

Weidong Cao, Yilong Zhao, Adith Boloor, Yinhe Han

Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices provide a promising hardware substrate to build PIM accelerators due to their abilities to realize efficient in-situ vector-matrix multiplications (VMMs). However, existing PIM ac

Han-Fei Li, Artem R. Oganov, Haixu Cui, Xiang-Feng Zhou

The origin of water on the Earth is a long-standing mystery, requiring a comprehensive search for hydrous compounds, stable at conditions of the deep Earth and made of Earth-abundant elements. Previous studies usually focused on the current range of pressure-temperature conditions in the Earth's mantle and ignored a possible difference in the past, such as t

Menachem Shlossberg

The Addition Theorem for the algebraic entropy of group endomorphisms of torsion abelian groups was proved in [4]. Later, this result was extended to all abelian groups [3] and, recently, to all torsion finitely quasihamiltonian groups [7]. In contrast, when it comes to metabelian groups, the additivity of the algebraic entropy fails [8]. Continuing the rese

Jens Oliver Gutsfeld, Markus Müller-Olm, Christoph Ohrem

We introduce a novel logic for asynchronous hyperproperties with a new mechanism to identify relevant positions on traces. While the new logic is more expressive than a related logic presented recently by Bozzelli et al., we obtain the same complexity of the model checking problem for finite state models. Beyond this, we study the model checking problem of o

Andrea Grimaldi, Luis Sánchez-Tejerina1, Navid Anjum Aadit, Stefano Chiappini

The search of hardware-compatible strategies for solving NP-hard combinatorial optimization problems (COPs) is an important challenge of today s computing research because of their wide range of applications in real world optimization problems. Here, we introduce an unconventional scalable approach to face maximum satisfiability problems (Max-SAT) which comb

Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato

Relative entropy coding (REC) algorithms encode a sample from a target distribution $Q$ using a proposal distribution $P$, such that the expected codelength is $\mathcal{O}(D_{KL}[Q \,||\, P])$. REC can be seamlessly integrated with existing learned compression models since, unlike entropy coding, it does not assume discrete $Q$ or $P$, and does not require

Chunfeng Huang, Ao Li

The link between Gaussian random fields and Markov random fields is well established based on a stochastic partial differential equation in Euclidean spaces, where the Mat\'ern covariance functions are essential. However, the Mat\'ern covariance functions are not always positive definite on circles and spheres. In this manuscript, we focus on the extension o

Marlon Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella

AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs an

Hyunjun Kim, JeongGil Ko

We introduce Monte-Carlo Attention (MCA), a randomized approximation method for reducing the computational cost of self-attention mechanisms in Transformer architectures. MCA exploits the fact that the importance of each token in an input sequence varies with respect to their attention scores; thus, some degree of error can be tolerable when encoding tokens

Bernard Pire, Kirill M. Semenov-Tian-Shansky, Alisa A. Shaikhutdinova, Lech Szymanowski

The exclusive photoproduction off nucleon of a large invariant mass lepton pair in the backward region specified by the small Mandelstam variable $-u$ is discussed in the framework of collinear QCD factorization. The amplitude is factorized in terms of photon-to-nucleon Transition Distribution Amplitudes (TDAs) which encode the photon content of the nucleon.

A Das, S R Das, J P Panda, A Dey

In recent times Mechanical and Production industries are facing increasing challenges related to the shift toward sustainable manufacturing. In this article, machining was performed in dry cutting condition with a newly developed coated insert called AlTiSiN coated carbides coated through scalable pulsed power plasma technique in dry cutting condition and a

Mahmood Roshan, Bahram Mashhoon

Nonlocal gravity (NLG) is a classical nonlocal generalization of Einstein's theory of gravitation that has been constructed in close analogy with the nonlocal electrodynamics of media. According to NLG, what appears as dark matter in astrophysics and cosmology is in reality the nonlocal aspect of the universal gravitational interaction. We focus here on two

Yongqing Cai, Jianfeng Wang, Yuan Wang, Zhanyang Hao

Lorentz-violating type-II Dirac nodal line semimetals (DNLSs), hosting curves of band degeneracy formed by two dispersion branches with the same sign of slope, represent a novel states of matter. While being studied extensively in theory, convincing experimental evidences of type-II DNLSs remain elusive. Recently, Vanadium-based kagome materials have emerged

Yaser Alizadeh, Sandi Klavžar

The transmission ${\rm Tr}_G(v)$ of a vertex $v$ of a connected graph $G$ is the sum of distances between $v$ and all other vertices in $G$. $G$ is a stepwise transmission irregular (STI) graph if $|{\rm Tr}_G(u) - {\rm Tr}_G(v)| =1$ holds for each edge $uv \in E(G)$. In this paper, extremal results on STI graphs with respect to the size and different metric

Anbu Arjunan, Sruthymurali, S. Sundar

Let $C_c^{*}(\mathbb{N}^{2})$ be the universal $C^{*}$-algebra generated by a semigroup of isometries $\{v_{(m,n)}: m,n \in \mathbb{N}\}$ whose range projections commute. We analyse the structure of KMS states on $C_{c}^{*}(\mathbb{N}^2)$ for the time evolution determined by a homomorphism $c:\mathbb{Z}^{2} \to \mathbb{R}$. In contrast to the reduced version

Axel Brando, Joan Gimeno, Jose A. Rodríguez-Serrano, Jordi Vitrià

Quantile Regression (QR) provides a way to approximate a single conditional quantile. To have a more informative description of the conditional distribution, QR can be merged with deep learning techniques to simultaneously estimate multiple quantiles. However, the minimisation of the QR-loss function does not guarantee non-crossing quantiles, which affects t

Alejandro Jiménez-Cano

This thesis covers several developments performed in metric-affine gravity. This alternative framework extends General Relativity by considering a more general connection than the one induced by the metric (i.e., arbitrary torsion and nonmetricity participate in the dynamics). We start by revising some mathematical aspects of the metric-affine framework, the

Maxime Savoy

This thesis proposes a framework based on a notion of combinatorial cell complex (cc) whose cells are defined simply as finite sets of vertices. The cells of a cc are subject to four axioms involving a rank function that assigns a rank (or a dimension) to each cell. Our framework focuses on classes of cc admitting an inclusion-reversing duality map. We intro

Guilong Li, Yixian Chen, Qionghua Liao, Zhaocheng He

Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this pap

Yaser Rowshan, Mostafa Gholami

For bipartite graphs $G$ and $H$ and a positive integer $m$, the $m$-bipartite Ramsey number $BR_m(G, H)$ of $G$ and $H$ is the smallest integer $n$, such that every red-blue coloring of $K_{m,n}$ results in a red $G$ or a blue $H$. Zhenming Bi, Gary Chartrand and Ping Zhang in \cite{bi2018another} evaluate this numbers for all positive integers $m$ when $G=

Maxim Fishman, Chaim Baskin, Evgenii Zheltonozhskii, Almog David

Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance. We tackle this problem with the kernel regression (KR) approach, using KR loss as the primary loss in self-supervised settings or as a re

Alexey Basalaev

We investigate the solutions to open WDVV equation, associated to type A and D Dubrovin-Frobenius manifolds. We show that these solutions satisfy some stabilization condition and associate to both of them the systems of commuting PDEs. In the type A we show that the system of PDEs constructed coincides with the dispersionless modifiled KP hierarchy written i

Teng Huang

In this article, we study harmonic symmetries on the compact locally conformally K\"{a}hler manifold $M$ of $dim_{\mathbb{C}}=n$. The space of harmonic symmetries is a subspace of harmonic differential forms which defined by the kernel of a certain Laplacian-type operator $\square$. We observe that the spaces $\ker(\square)\cap\Omega^{l}=\{0\}$ for any $|l-n

Changxing Miao, Jiye Yuan, Tengfei Zhao

In this paper, we obtain the maximal estimate for the Weyl sums on the torus $\mathbb{T}^d$ with $d\geq 2$, which is sharp up to the endpoint. We also consider two variants of this problem which include the maximal estimate along the rational lines and on the generic torus. Applications, which include some new upper bound on the Hausdorff dimension of the se

Shao-Hsuan Wang, Ray Bai, Hsin-Hsiung Huang

We introduce a Bayesian framework for mixed-type multivariate regression using continuous shrinkage priors. Our framework enables joint analysis of mixed continuous and discrete outcomes and facilitates variable selection from the $p$ covariates. Theoretical studies of Bayesian mixed-type multivariate response models have not been conducted previously and re

Sølve Selstø

Complex absorbing potentials are frequently imposed when simulating unbound quantum systems. While this is usually done solely in order to avoid artifacts at the numerical boundary, we show how absorbers may also be used to probe the characteristics of the particle undergoing absorption. This way, information about the removed particles may be retained. More

Zhenxiang Hong, Ya Wang, Haisheng Ji

With high-resolution narrowband He I 10830 \r{A} filtergrams from GST, we give an extensive analysis for 4 granular sized microeruptions which appear as the gentle ejection of material in He I 10830 \r{A} band. The analysis was aided with the EUV data from AIA and line-of-sight magnetograms from HMI on board SDO. The microeruptions are situated on magnetic p

Jiayin Dong, Chelsea X. Huang, George Zhou, Rebekah I. Dawson

Close-in gas giants present a surprising range of stellar obliquity, the angle between a planet's orbital axis and its host star's spin axis. It is unclear whether the obliquities reflect the planets' dynamical history (e.g., aligned for in situ formation or disk migration versus misaligned for high-eccentricity tidal migration) or whether other mechanisms (

Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim

Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias). Furthermore, the distortions persist despite model fine-tuning on images biased by different factors (blur,

Yueling Yang, Xule Zhao, Lan Lang, Jinshu Huang

Considering the $B$ mesonic wave function ${\phi}_{B2}$, the $B$ ${\to}$ $PV$ decays are restudied at the leading order for three scenarios using the perturbative QCD approach within the standard model, where $P$ $=$ ${\pi}$ and $K$, and $V$ denotes the ground $SU(3)$ vector mesons. It is found that contributions from ${\phi}_{B2}$ can enhance most branching

Jingwen Li, Leander Girrbach

We describe our participation in the Word Segmentation and Morphological Parsing (WSMP) for Sanskrit hackathon. We approach the word segmentation task as a sequence labelling task by predicting edit operations from which segmentations are derived. We approach the morphological analysis task by predicting morphological tags and rules that transform inflected

Subhendu B. Ghosh, Tathagata Gupta, Ardra A. V., Anandamay Das Bhowmik

Apart from the Bell nonlocality, which deals with the correlations generated from the local input-output statistics, quantum theory exhibits another kind of nonlocality that involves the indistiguishability of the locally preparable set of multipartite states. While Bell-type nonlocality cannot be distilled from a given local correlation, it is already repor

David A. Stephens, Widemberg S. Nobre, Erica E. M. Moodie, Alexandra M. Schmidt

We study Bayesian approaches to causal inference via propensity score regression. Much of the Bayesian literature on propensity score methods have relied on approaches that cannot be viewed as fully Bayesian in the context of conventional `likelihood times prior' posterior inference; in addition, most methods rely on parametric and distributional assumptions

Fang Sun

Over-smoothing is a severe problem which limits the depth of Graph Convolutional Networks. This article gives a comprehensive analysis of the mechanism behind Graph Convolutional Networks and the over-smoothing effect. The article proposes an upper bound for the occurrence of over-smoothing, which offers insight into the key factors behind over-smoothing. Th

Peter Bishop, Andrey Povyakalo

This short paper describes a numerical method for optimising the conservative confidence bound on the reliability of a system based on tests of its individual components. This is an alternative to the algorithmic approaches identified in Bishop and Povyakalo (RESS 2020). For a given maximum number of component tests, the numerical method can derive an optima

Harshit Singh Chhabra, Koteswar Rao Jerripothula

Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion has been one of the promising ways to carry it out. However, prior works either fuse saliency maps of the same image or saliency maps of different images to extract the expected fore

S. Yu. Orevkov

Let $f(m,n)$ be the number of primitive lattice triangulations of $m\times n$ rectangle. We compute the limits $\lim_n f(m,n)^{1/n}$ for $m=2$ and $3$. For $m=2$ we obtain the exact value of the limit which is equal to $(611+\sqrt{73})/36$. For $m=3$, we express the limit in terms of certain Fredholm's integral equation on generating functions. This provides

Yuxin Zhang, Mingbao Lin, Mengzhao Chen, Fei Chao

Network sparsity receives popularity mostly due to its capability to reduce the network complexity. Extensive studies excavate gradient-driven sparsity. Typically, these methods are constructed upon premise of weight independence, which however, is contrary to the fact that weights are mutually influenced. Thus, their performance remains to be improved. In t

Eric Qu, Dongmian Zou

With the recent advance of geometric deep learning, neural networks have been extensively used for data in non-Euclidean domains. In particular, hyperbolic neural networks have proved successful in processing hierarchical information of data. However, many hyperbolic neural networks are numerically unstable during training, which precludes using complex arch

Xiaokai He, Xiaoning Wu, Naqing Xie

The supertranslation ambiguity issue of angular momentum is a long-standing problem in general relativity. Recently, there appeared the first definition of angular momentum at null infinity that is supertranslation invariant. However, in the compact binary coalescence community, supertranslation ambiguity is often ignored. This paper demonstrates that we hav

Rui Hong, Ya-Xuan Xiao, Jie Hu, An-Chun Ji

Schr\"odinger equation belongs to the most fundamental differential equations in quantum physics. However, the exact solutions are extremely rare, and many analytical methods are applicable only to the cases with small perturbations or weak correlations. Solving the many-body Schr\"odinger equation in the continuous spaces with the presence of strong correla

Michael Aupetit, Ahmed Ali

In multiclass classification of multidimensional data, the user wants to build a model of the classes to predict the label of unseen data. The model is trained on the data and tested on unseen data with known labels to evaluate its quality. The results are visualized as a confusion matrix which shows how many data labels have been predicted correctly or conf

Victor V. Senkevich

As is known, AGI (Artificial General Intelligence), unlike AI, should operate with meanings. And that's what distinguishes it from AI. Any successful AI implementations (playing chess, unmanned driving, face recognition etc.) do not operate with the meanings of the processed objects in any way and do not recognize the meaning. And they don't need to. But for

Mao-Sheng Li, Hanbin Zhang

Let $G$ be a finite abelian group. For any positive integers $d$ and $m$, let $\varphi_G(d)$ be the number of elements in $G$ of order $d$ and $\mathsf M(G,m)$ be the set of all zero-sum sequences of length $m$. In this paper, for any finite abelian group $H$, we prove that $$|\mathsf M(G,|H|)|=|\mathsf M(H,|G|)|$$ if and only if $\varphi_G(d)=\varphi_H(d)$

Amadou Bah

Let $C=A(r, r')$ be a closed annulus of radii $r$ and $r'$ ($r < r' \in \mathbb{Q}_{\geq 0}$) over a complete discrete valuation field with algebraically closed residue field of characteristic $p>0$. To an \'etale sheaf of $\mathbb{F}_{\ell}$-modules $\mathcal{F}$ on $C$, ramified at most at a finite set of rigid points of $C$, we associate an Abbes-Saito Sw

Andrei Aksjonov, Ville Kyrki

While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from both methods they must be joined in a s

Jianqing Cai, Huasheng Xie, Yang Li, Michel Tuszewski

Most tokamak devices including ITER exploit the D-T reaction due to its high reactivity, but the wall loading caused by the associated 14MeV neutrons will limit the further development of fusion performance at high beta. To explore p-11B fusion cycle, a tokamak system code is extended to incorporate the relativistic bremsstrahlung since the temperature of el

J. H. Martel

We study the topology of singularities of $c$-optimal semicouplings in unequal dimension. Our main results describe homotopy-reductions from a source space $(X,\sigma)$ onto the singularities $Z_j$, $j\geq 0$ of $c$-optimal semicouplings whenever $(Y, \tau)$ is a Riemannian target space and $c$ is a cost on $X\times Y$ satisfying some general assumptions (A0

Lai Wei, Ryan McCloy, Jie Bao

Driven by the flexible manufacturing trend in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems (e.g., using process models with parametric uncertainties) with adaptable performance. The proposed adaptive control approach incorporates

Kaustav Moni Bora, Adarsh Mishra, Cheruvu Siva Kumar

The realistic modelling of medical interventions is the key requirement for development of high-fidelity medical simulators. In this work, a parameterized model suitable for real-time haptic feedback of needle interaction with tissue has been developed. The FE based numerical simulations are performed in ABAQUS/CAE and Comsol and then the results are post-pr

H. R. Christiansen, Milko Estrada, M. S. Cunha, J. Furtado

We obtain new regular black hole solutions for an action in 2+1 dimensions with bilocal Ricci scalar and negative cosmological constant. Besides their connection to the cosmological constant, these solutions depend on a fundamental length due to their non-local nature. The effective profile densities that result from the non-local geometries have quasi-local

André Correia, Luís A. Alexandre

This paper presents a framework for learning visual representations from unlabeled video demonstrations captured from multiple viewpoints. We show that these representations are applicable for imitating several robotic tasks, including pick and place. We optimize a recently proposed self-supervised learning algorithm by applying contrastive learning to enhan

Ryan McCloy, Yifeng Li, Jie Bao, Maria Skyllas-Kazacos

In this article, an electrolyte flow rate control approach is developed for an all-vanadium redox flow battery (VRB) system based on the linear parameter varying (LPV) framework. The electrolyte flow rate is regulated to provide a trade-off between stack voltage efficiency and pumping energy losses, so as to achieve optimal battery energy efficiency. The non

Tony T. Lee, Bojun Lu, Hanli Chu

In this paper, we propose a depth-first search (DFS) algorithm for searching maximum matchings in general graphs. Unlike blossom shrinking algorithms, which store all possible alternative alternating paths in the super-vertices shrunk from blossoms, the newly proposed algorithm does not involve blossom shrinking. The basic idea is to deflect the alternating

Soumitri Chattopadhyay, Aritra Marik, Rishav Pramanik

Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfully provided near-optimal or optimal solutions to several engineering tasks. This chapter focuses on meta-heur

Vijeth Aradhya, Seth Gilbert, Aquinas Hobor

Blockchains use peer-to-peer networks for disseminating information among peers, but these networks currently do not have any provable guarantees for desirable properties such as Byzantine fault tolerance, good connectivity and small diameter. This is not just a theoretical problem, as recent works have exploited unsafe peer connection policies and weak netw

Danko D. Georgiev, James F. Glazebrook

Protein$\alpha$-helices provide an ordered biological environment that is conducive to soliton-assisted energy transport. The nonlinear interaction between amide I excitons and phonon deformations induced in the hydrogen-bonded lattice of peptide groups leads to self-trapping of the amide I energy, thereby creating a localized quasiparticle (soliton) that pe

Alexander Sherman

We discuss several points regarding symmetries of the Duflo-Serganova functor. In particular we give new constructions of Lie superalgebras, Lie supergroups, and associative superalgebras which act on the Duflo-Serganova functor. We connect our work to a computation of Heidersdorf and Weissauer which computed $DS_x$ for a maximal rank $x$ on Kac-modules for

Hiroaki Yamamoto, Ryuji Okamoto, Yasuhiro Murata, Hiroyuki Nakanishi

We conducted observations and analyses of the molecular cloud, N4, which is located at ~40 pc from SS433 and the same line of sight as that of the radio shell, in 12CO(J=1-0), 12CO(J=3-2), 13CO(J=3-2), and grand-state OH emissions.N4 has a strong gradient of the integrated intensity of 12CO(J=1-0, 3-2) emission at the northern, eastern and western edges. The

Minglun Han, Linhao Dong, Zhenlin Liang, Meng Cai

Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on phrase-level contextual modeling and attention-based relevance modeling, they may encounter confusion between similar context-specific phrases, which hurts predictions at the token l

Garvit Chhabra, J. H. Gagan, J. R. Harish Kumar

Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to deter

Sreekanth Harikumar, Marek Biesiada

Scalar Tensor Vector Gravity(STVG) is a fully covariant Lorentz invariant alternative theory of gravity also called as MOdified Gravity(MOG) which modifies General Relativity(GR) with dynamical massive vector field and scalar fields. In STVG the mass $\mu$ of the vector field $\phi$ and the Universal gravitational constant G enjoys the status of a dynamical

Nayara Fonseca, Veronica Guidetti

This work uncovers an interplay among data density, noise, and the generalization ability in similarity learning. We consider Siamese Neural Networks (SNNs), which are the basic form of contrastive learning, and explore two types of noise that can impact SNNs, Pair Label Noise (PLN) and Single Label Noise (SLN). Our investigation reveals that SNNs exhibit do

Dror Varolin

Drawing on work of Berndtsson and of Lempert and Sz\H{o}ke, we define a kind of complex analytic structure for families of (possibly finite-dimensional) Hilbert spaces that might not fit together to form a holomorphic vector bundle but nevertheless have a reasonable definition of curvature that agrees with the curvature of the Chern connection when the famil

Sjoerd van der Heide, Bram van Esch, Menno van den Hout, Thomas Bradley

Angular resolved digital holography is presented as a technique for real-time characterization of the full optical field (amplitude and phase) of space-division multiplexing components and fibers, here a 6-mode photonic-lantern is characterized.

Yizhu Zhang, Zhi-Hong Jiao, Tao He, Jingjing Zhao

The two-color strong-field mixing in gas medium is a widely-used approach to generate bright broadband terahertz (THz) radiation. Here, we present a new and counterintuitive method to promote THz performance in two-color scheme. Beyond our knowledge that the maximum THz generation occurs with two-color foci overlapped, we found that, when the foci of two-col

Haiyu Meng, Lingling Wang, Ziran Liu, Jianghua Chen

Active control and manipulation of electromagnetic waves are highly desirable for advanced photonic device technology, such as optical cloaking, active camouflage and information processing. Designing optical resonators with high ease-of-control and reconfigurability remains a open challenge thus far. Here we propose a novel mechanism to continuously reconfi

Scott Pezanowski, Prasenjit Mitra

Analyzing the geographic movement of humans, animals, and other phenomena is a growing field of research. This research has benefited urban planning, logistics, animal migration understanding, and much more. Typically, the movement is captured as precise geographic coordinates and time stamps with Global Positioning Systems (GPS). Although some research uses

Mubashir Mansoor, Mehya Mansoor, Maryam Mansoor, Zuhal Er

Functional materials that can serve as high-pressure transducers are limited, making such sensor material sought after. It has been reported that hydrostatic pressures highly influence Raman shifts of ZrSiO4. Therefore, zirconium silicate has been suggested as a Raman spectroscopic pressure sensor. However, mass applications of a Raman-based sensor technolog

Huaiqian Li, Bingyao Wu

The asymptotic behaviour of empirical measures has been studied extensively. In this paper, we consider empirical measures of given subordinated processes on complete (not necessarily compact) and connected Riemannian manifolds with possibly nonempty boundary. We obtain rates of convergence for empirical measures to the invariant measure of the subordinated

Yuan Wu, Diana Inkpen, Ahmed El-Roby

Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains. Recently, many MDTC methods adopt adversarial learning, shared-private paradigm, and entropy minimization to yield state-of-the-art results. However, these approaches face three issues:

Carles Riera, Camilo Rey, Thiago Serra, Eloi Puertas

Neural networks are more expressive when they have multiple layers. In turn, conventional training methods are only successful if the depth does not lead to numerical issues such as exploding or vanishing gradients, which occur less frequently when the layers are sufficiently wide. However, increasing width to attain greater depth entails the use of heavier

T. J. Hicken, M. N. Wilson, S. J. R. Holt, R. Khassanov

We present magnetization and muon-spin spectroscopy measurements of N\'{e}el skyrmion-host GaV$_4$S$_8$ under the application of hydrostatic pressures up to $P=2.29$ GPa. Our results suggest that the magnetic phase diagram is altered with pressure via a reduction in the crossover temperature from the cycloidal (C) to ferromagnetic-like state with increasing