May 2022 arXiv papers — page 89

Francisco J. Botella, Fernando Cornet-Gomez, Carlos Miró, Miguel Nebot

The experimental observations of the electron and muon anomalous magnetic moment present discrepancies with respect to the Standard Model predictions. A class of flavor conserving Two Higgs Doublet model, stable under renormalization, that is capable of explaining both anomalies simultaneously is presented. This model can also explain an excess observed by A

Rohini Ramadas

We establish an implication between two long-standing open problems in complex dynamics. The roots of the $n$-th Gleason polynomial $G_n\in\mathbb{Q}[c]$ comprise the $0$-dimensional moduli space of quadratic polynomials with an $n$-periodic critical point. $\mathrm{Per}_n(0)$ is the $1$-dimensional moduli space of quadratic rational maps on $\mathbb{P}^1$ w

Francisco M. Fernández

We apply the Frobenius (power-series) method to some simple exactly-solvable and conditionally-solvable quantum-mechanical models with supposed physical interest. We show that the supposedly exact solutions to radial eigenvalue equations derived in recent papers are not correct because they do not satisfy some well-known theorems. We also discuss the origin

Ghalib Ahmed Tahir, Chu Kiong Loo

Automatic food detection is an emerging topic of interest due to its wide array of applications ranging from detecting food images on social media platforms to filtering non-food photos from the users in dietary assessment apps. Recently, during the COVID-19 pandemic, it has facilitated enforcing an eating ban by automatically detecting eating activities fro

Utkarsh R. Patel, Yiqian Mao, Eric Michielssen

The Wigner-Smith (WS) time delay matrix relates a lossless system's scattering matrix to its frequency derivative. First proposed in the realm of quantum mechanics to characterize time delays experienced by particles during a collision, this article extends the use of WS time delay techniques to acoustic scattering problems governed by the Helmholtz equation

Mladen Kovačević, Dejan Vukobratović

Several communication models that are of relevance in practice are asymmetric in the way they act on the transmitted "objects". Examples include channels in which the amplitudes of the transmitted pulses can only be decreased, channels in which the symbols can only be deleted, channels in which non-zero symbols can only be shifted to the right (e.g., timing

Maxim Freydin, Barak Or

A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic

Ngan Ha Duong, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the costs (or budgets) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all

Yue Wang, Shaofeng Zou

This paper develops the first policy gradient method with global optimality guarantee and complexity analysis for robust reinforcement learning under model mismatch. Robust reinforcement learning is to learn a policy robust to model mismatch between simulator and real environment. We first develop the robust policy (sub-)gradient, which is applicable for any

Hengxin Tan, Daniel Kaplan, Binghai Yan

Magnetic topological insulators (MnBi$_2$Te$_4$)(Bi$_2$Te$_3$)$_n$ were anticipated to exhibit magnetic energy gaps while recent spectroscopic studies did not observe them. Thus, magnetism on the surface is under debate. In this work, we propose another symmetry criterion to probe the surface magnetism. Because of both time-reversal symmetry-breaking and inv

R. Stania, A. P. Seitsonen, H. Y. Jung, D. Kunhardt

The conformation of molecules on surfaces is decisive for their functionality. For the case of the endofullerene paramagnet Sc2TbN@C80 the conformation is linked to an electric and a magnetic dipole moment. Therefore a workfunction change of a substrate with adsorbed molecules, qualifies the system to be magnetoelectric. One monolayer of Sc2TbN@C80 has been

Ariel Zandivarez, Eugenia Diaz-Gimenez, Antonela Taverna

Compact groups of galaxies are devised as extreme environments where interactions may drive galaxy evolution. In this work, we analysed whether the luminosities of galaxies inhabiting compact groups differ from those of galaxies in loose galaxy groups. We computed the luminosity functions of galaxy populations inhabiting a new sample of 1412 Hickson-like com

Koyal Suman Samantaray, Ruhul Amin, Saniya Ayaz, A. K. Pathak

The sol-gel prepared (1-x) Na0.5Bi0.5TiO3- (x) CaMnO3 (x=0, 0.03, 0.06, 0.12) compositions show a Rhombohedral (R3c) phase for x=0.06 while a mixed Rhombohedral (R3c) and orthorhombic (Pnma) phases for the x=0.12. The lattice volume consistently decreased with an increase in the CaMnO3 content. The phase transition temperature (Tc) decreased with an increase

Debanjan Sengupta, Paul R. Estrada, Jeffrey N. Cuzzi, Munir Humayun

Rocky bodies of the inner solar system display a systematic depletion of the "Moderately Volatile Elements" (MVEs) that correlates with the expected condensation temperature of their likely host materials under protoplanetary nebula conditions. In this paper, we present and test a new hypothesis in which open system loss processes irreversibly remove vaporiz

Thomas Spooner, Rui Silva, Joshua Lockhart, Jason Long

Solving general Markov decision processes (MDPs) is a computationally hard problem. Solving finite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-time algorithms. What drives this extreme disparity, and do problems exist that lie between these diametrically opposed complexities? In this paper we identify and analyse a sub-cla

Mahroo Bahreinian, Roberto Tron

We consider the problem of sample-based feedback motion planning from measurements affected by systematic errors. Our previous work presented output feedback controllers that use measurements from landmarks in the environment to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear Progr

G. Plante, E. Aprile, J. Howlett, Y. Zhang

As liquid xenon detectors grow in scale, novel techniques are required to maintain sufficient purity for charges to survive across longer drifts. The Xeclipse test facility at Columbia University was built to test the removal of electronegative impurities through cryogenic filtration powered by a liquid xenon pump, enabling a far higher mass flow rate than g

How Khang Lim, Avishkar Mahajan, Martin Strecker, Meng Weng Wong

The paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifiers that specify how rules interact and how they can be overridden. We then define rule transformations that eliminate these modifiers, leading in the end to a translation of rules to formulas. For reasoning with and about rules

Eduardo Ramos-Pérez, Pablo J. Alonso-González, José Javier Núñez-Velázquez

In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007-2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measu

J. D. Zamfirescu-Pereira, Jerry Chen, Emily Wen, Allison Koenecke

Algorithms provide powerful tools for detecting and dissecting human bias and error. Here, we develop machine learning methods to to analyze how humans err in a particular high-stakes task: image interpretation. We leverage a unique dataset of 16,135,392 human predictions of whether a neighborhood voted for Donald Trump or Joe Biden in the 2020 US election,

Bengt Friman, Krzysztof Redlich

We study fluctuations in the canonical ensemble, where the net baryon number is exactly conserved. The focus is on cumulants and factorial cumulants linked to the baryon and antibaryon multiplicities and their sum or difference in full phase-space as well as in subsystems. In particular, we connect the fluctuations of the net baryon number in a subsystem, re

Yiping Lu, Jose Blanchet, Lexing Ying

In this paper, we study the statistical limits in terms of Sobolev norms of gradient descent for solving inverse problem from randomly sampled noisy observations using a general class of objective functions. Our class of objective functions includes Sobolev training for kernel regression, Deep Ritz Methods (DRM), and Physics Informed Neural Networks (PINN) f

Daniel M. B. Lesko, Kristina F. Chang, Scott A. Diddams

Non-perturbative and phase-sensitive light-matter interactions have led to the generation of attosecond pulses of light and the control electrical currents on the same timescale. Traditionally, probing these effects via high harmonic generation has involved complicated lasers and apparatuses to generate the few-cycle and high peak power pulses needed to obta

Victor Tabouillot, Rahul Kumar, Paula L. Lalaguna, Maryam Hajji

Nanophotonic platforms in theory uniquely enable < femtomoles of chiral biological and pharmaceutical molecules to be detected, through the highly localised changes in the chiral asymmetries of the near-fields that they induce. However, current chiral nanophotonic based strategies are intrinsically limited because they rely on far-field optical measurements

Claudio Bravo

Let C be a smooth, projective and geometrically integral curve defined over a finite field F. For each closed point P of C, let R be the ring of functions that are regular outside P, and let K be the completion at P of the function field of C. In order to study groups of the form GL2(R), Serre describes the quotient graph GL2(R)\t, where t is the Bruhat-Tits

Maciej Błaszak, Krzysztof Marciniak

This is the third article in our series of articles exploring connections between dynamical systems of St\"ackel-type and of Painlev\'e-type. In this article we present a method of deforming of minimally quantized quasi-St\"ackel Hamiltonians, considered in Part I to self-adjoint operators satisfying the quantum Frobenius condition, thus guaranteeing that th

Tomas Fullana, Vincent Le Chenadec, Taraneh Sayadi

A range of optimization cases of two-dimensional Stefan problems, solved using a tracking-type cost-functional, is presented. A level set method is used to capture the interface between the liquid and solid phases and an immersed boundary (cut cell) method coupled with an implicit time-advancement scheme is employed to solve the heat equation. A conservative

Athanasios Papaioannou, Rami Vainio, Osku Raukunen, Piers Jiggens

The Probabilistic Solar Particle Event foRecasting (PROSPER) model predicts the probability of occurrence and the expected peak flux of Solar Energetic Particle (SEP) events. Predictions are derived for a set of integral proton energies (i.e. E$>$10, $>$30 and $>$100 MeV) from characteristics of solar flares (longitude, magnitude), coronal mass ejections (wi

Yue Guan, Zhengyi Li, Jingwen Leng, Zhouhan Lin

Transformer architecture has become the de-facto model for many machine learning tasks from natural language processing and computer vision. As such, improving its computational efficiency becomes paramount. One of the major computational inefficiency of Transformer-based models is that they spend the identical amount of computation throughout all layers. Pr

M. Andrecut

Intrusion detection systems (IDS) are used to monitor networks or systems for attack activity or policy violations. Such a system should be able to successfully identify anomalous deviations from normal traffic behavior. Here we discuss the machine learning approach to building an anomaly-based IDS using the CSE-CIC-IDS2018 dataset. Since the publication of

Tewodros Amdeberhan, George E. Andrews, Cristina Ballantine

The dimension of an irreducible representation of $GL(n,\mathbb{C})$, $Sp(2n)$, or $SO(n)$ is given by the respective hook-length and content formulas for the corresponding partition. The first author, inspired by the Nekrasov-Okounkov formula, conjectured combinatorial interpretations of analogous expressions involving hook-lengths and symplectic/orthogonal

Hans Rabus, Maria Zankl, Jose Maria Gomez-Ros, Carmen Villagrasa

Organized by Working Group 6 "Computational Dosimetry" of the European Radiation Dosimetry Group (EURADOS), a group of intercomparison exercises was conducted in which participants were asked to solve predefined problems in computational dosimetry. The results of these comparisons were published in a series of articles in this virtual special issue of Radiat

Keitaro Sakamoto, Issei Sato

The lottery ticket hypothesis (LTH) has attracted attention because it can explain why over-parameterized models often show high generalization ability. It is known that when we use iterative magnitude pruning (IMP), which is an algorithm to find sparse networks with high generalization ability that can be trained from the initial weights independently, call

Tracy Qian, Jackson Kaunismaa, Tony Chung

Analysing music in the field of machine learning is a very difficult problem with numerous constraints to consider. The nature of audio data, with its very high dimensionality and widely varying scales of structure, is one of the primary reasons why it is so difficult to model. There are many applications of machine learning in music, like the classifying th

Geoffrey R. Grimmett

This celebratory article contains a personal and idiosyncratic selection of a few open problems in discrete probability theory. These include certain well known questions concerning Lorentz scatterers and self-avoiding walks, and also some problems of percolation-type. The author hopes the reader will find something to leaven winter evenings, and perhaps eve

Andreea Bobu, Andi Peng

As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging. A central challenge remains that often, it is difficult (perhaps even impossible) to capture the full complexity of the deployment

Maurice Margenstern

The present paper is a new version of the arXiv paper revisiting the proof given in a previous paper of the author published in 2008 proving that the general tiling problem of the hyperbolic plane is undecidable by proving a slightly stronger version using only a regular polygon as the basic shape of the tiles. The problem was raised by a paper of Raphael Ro

Lauri Lahti

We propose and experimentally motivate a new methodology to support decision-making processes in healthcare with artificial intelligence based on personal rankings of care decision making steps that can be identified with our methodology, questionnaire data and its statistical patterns. Our longitudinal quantitative cross-sectional three-stage study gathered

Amin Aboubrahim, Pran Nath

An analysis of a tower of hidden sectors coupled to each other, with one of these hidden sectors coupled to the visible sector, is given and the implications of such couplings on physics in the visible sector are investigated. Thus the analysis considers $n$ number of hidden sectors where the visible sector couples only to hidden sector 1, while the latter c

Siddhartha Datta

Recent work in black-box adversarial attacks for NLP systems has attracted much attention. Prior black-box attacks assume that attackers can observe output labels from target models based on selected inputs. In this work, inspired by adversarial transferability, we propose a new type of black-box NLP adversarial attack that an attacker can choose a similar d

Raghav Dalmia, Aryaman Sinha, Ruchi Verma, P. K. Gupta

CPU scheduling is the reason behind the performance of multiprocessing and in time-shared operating systems. Different scheduling criteria are used to evaluate Central Processing Unit Scheduling algorithms which are based on different properties of the system. Round Robin is known to be the most recurrent pre-emptive algorithm used in an environment where pr

Lan V. Truong

This paper presents novel generalization bounds for the multi-kernel learning problem. Motivated by applications in sensor networks and spatial-temporal models, we assume that the dataset is mixed where each sample is taken from a finite pool of Markov chains. Our bounds for learning kernels admit $O(\sqrt{\log m})$ dependency on the number of base kernels a

Ruojun Huang

For a second-order particle system in $\mathbb R^d$ subject to locally-in-space pairwise annihilation, we prove a scaling limit for its empirical measure on position and velocity towards a degenerate elliptic partial differential equation. Crucial ingredients are Green's function estimates for the associated hypoelliptic operator and an It\^o-Tanaka trick.

Sumit K. Mandal, Gokul Krishnan, A. Alper Goksoy, Gopikrishnan Ravindran Nair

Graph convolutional networks (GCNs) have shown remarkable learning capabilities when processing graph-structured data found inherently in many application areas. GCNs distribute the outputs of neural networks embedded in each vertex over multiple iterations to take advantage of the relations captured by the underlying graphs. Consequently, they incur a signi

Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu

While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods across dataset. In this paper, we observe the performance of two of the latest state-of-the-art trajectory pred

Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang

Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design problem -- generating a small "linker" to physically attach two independent molecules with their distinct functions. The main computational challenges include: 1) the generation of l

Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan

We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily. However, it is a significant challenge to set personalized neighborhood sizes for different nodes. Further, for other homophilous nodes excluded in the neighborhood, they are ignor

Alexey Mironov, Ilnur Khuziev

Decision forest (decision tree ensemble) is one of the most popular machine learning algorithms. To use large models on big data, like document scoring with learning-to-rank models, we need to evaluate these models efficiently. In this paper, we explore MatrixNet, the ancestor of the popular CatBoost library. Both libraries use the SSE instruction set for sc

Nujood M. Alshehri, Zinaida A. Lykova

In this paper we prove a Schwarz lemma for the pentablock. The set \[ \mathcal{P}=\{(a_{21}, \text{tr} \ A, \det A) : A=[a_{ij}]_{i,j=1}^2 \in \mathbb{B}^{2\times 2}\} \] where $\mathbb{B}^{2\times 2}$ denotes the open unit ball in the space of $2\times 2$ complex matrices, is called the pentablock. The pentablock is a bounded nonconvex domain in $\Bbb{C}^3$

Adina Bianca Barba, Giulio Maria Bianco, Luca Fiore, Fabiana Arduini

Flexible and epidermal sensing devices are becoming vital to enable precision medicine and telemonitoring systems. The NFC (Near Field Communication) protocol is also becoming increasingly important for this application since it is embedded in most smartphones that can be used as pervasive and low-cost readers. Furthermore, the responder can be passive and c

Wei-Ming Chen, Ming-Zhi Chung, Yu-tin Huang, Jung-Wook Kim

Effects of massive object's spin on massive-massless $2 \to 2$ classical scattering is studied. Focus is set on the less-considered dimensionless expansion parameter $\lambda/b$, where $\lambda$ is the massless particle's wavelength and $b$ is the impact parameter. Corrections in $\lambda/b$ start to appear from $\mathcal{O}(G^2)$, with leading correction te

S. D. Odintsov, V. K. Oikonomou

In this paper we shall consider an axionic Chern-Simons corrected $f(R)$ gravity theoretical framework, and we shall study the chirality of the generated primordial gravitational waves. Particularly, we shall consider two main axion models, the canonical misalignment axion model and the kinetic axion model, both of which provide an interesting particle pheno

Yuan Sun, Sisi Liu, Junjie Deng, Xiaobing Zhao

The pre-trained language model is trained on large-scale unlabeled text and can achieve state-of-the-art results in many different downstream tasks. However, the current pre-trained language model is mainly concentrated in the Chinese and English fields. For low resource language such as Tibetan, there is lack of a monolingual pre-trained model. To promote t

Wei Lan, Xuerong Chen, Tao Zou, Chih-Ling Tsai

Advancements in data collection techniques and the heterogeneity of data resources can yield high percentages of missing observations on variables, such as block-wise missing data. Under missing-data scenarios, traditional methods such as the simple average, $k$-nearest neighbor, multiple, and regression imputations may lead to results that are unstable or u

Valeria Efimova, Ivan Jarsky, Ilya Bizyaev, Andrey Filchenkov

Generative Adversarial Networks (GAN) have motivated a rapid growth of the domain of computer image synthesis. As almost all the existing image synthesis algorithms consider an image as a pixel matrix, the high-resolution image synthesis is complicated.A good alternative can be vector images. However, they belong to the highly sophisticated parametric space,

Zhen Wang, Yong Zhang, Hong Zhao

We propose a more general setup for prethermalization in the system of interacting waves. The idea lies in dividing the multi-wave interactions into trivial and nontrivial ones. The trivial interactions will dress waves and lead to a less strongly interacting system which is statistically equivalent to the original one. With this in mind, we find that prethe

Rui-Jie Yew, Alice Xiang

Harms resulting from the development and deployment of facial processing technologies (FPT) have been met with increasing controversy. Several states and cities in the U.S. have banned the use of facial recognition by law enforcement and governments, but FPT are still being developed and used in a wide variety of contexts where they primarily are regulated b

Juri Fiaschi, Benjamin Fuks, Michael Klasen, Alexander Neuwirth

Motivated by the increased precision expected from LHC Run 3, equally accurate theory predictions are mandatory. As supersymmetry mass limits increase, predictions can be improved by threshold resummation. We examine the effects of including next-to-leading logarithms on associated squark-electroweakino production at the LHC and find a significant reduction

Yujia Wu, Wei Lan, Tao Zou, Chih-Ling Tsai

Measuring heterogeneous influence across nodes in a network is critical in network analysis. This paper proposes an Inward and Outward Network Influence (IONI) model to assess nodal heterogeneity. Specifically, we allow for two types of influence parameters; one measures the magnitude of influence that each node exerts on others (outward influence), while we

Ilya D. Shkredov

We develop the theory of the additive dimension ${\rm dim} (A)$, i.e. the size of a maximal dissociated subset of a set $A$. It was shown that the additive dimension is closely connected with the growth of higher sumsets $nA$ of our set $A$. We apply this approach to demonstrate that for any small multiplicative subgroup $\Gamma$ the sequence $|n\Gamma|$ gro

Penghui Wei, Weimin Zhang, Ruijie Hou, Jinquan Liu

Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration techniques aim to post-process model predictions to posterior probabilities. Field-level calibration -- which performs calibration w.r.t. to a specific field value

Xinyan Fan, Wei Lan, Tao Zou, Chih-Ling Tsai

In this article, we propose the mutual influence regression model (MIR) to establish the relationship between the mutual influence matrix of actors and a set of similarity matrices induced by their associated attributes. This model is able to explain the heterogeneous structure of the mutual influence matrix by extending the commonly used spatial autoregress

Ligong Bian, Jing Shu, Bo Wang, Qiang Yuan

We search for stochastic gravitational wave background emitted from cosmic strings using the Parkes Pulsar Timing Array data over 15 years. While we find that the common power-law excess revealed by several pulsar timing array experiments might be accounted for by the gravitational wave background from cosmic strings, the lack of the characteristic Hellings-

Yukun Yang, Peng Li

Several recent studies attempt to address the biological implausibility of the well-known backpropagation (BP) method. While promising methods such as feedback alignment, direct feedback alignment, and their variants like sign-concordant feedback alignment tackle BP's weight transport problem, their validity remains controversial owing to a set of other unso

Ruipeng Zhu

We prove a version of a theorem of Auslander for finite group actions or coactions on noetherian polynomial identity Artin-Schelter regular algebra.

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt

The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commonly used in statistical learning theory to upper bound the generalisation risk of a hypothesis in terms of its empirical risk on held-out data, for the case of a binary-valued loss

Dawei Zhu, Xiaoyu Shen, Michael A. Hedderich, Dietrich Klakow

Training deep neural networks (DNNs) under weak supervision has attracted increasing research attention as it can significantly reduce the annotation cost. However, labels from weak supervision can be noisy, and the high capacity of DNNs enables them to easily overfit the label noise, resulting in poor generalization. Recent methods leverage self-training to

Nuno J. Alves

In this article, the weak-strong uniqueness principle is proved for an Euler-Poisson system in the whole space, with initial data so that the strong solution exists. Some results on Riesz potentials are used to justify the considered weak formulation. Then, one follows the relative energy methodology and, in order to handle the solution of Poisson's equation

D. Matos, L. Kantorovich, I. J. Ford

We investigate the total stochastic entropy production of a two-level bosonic open quantum system under protocols of time dependent coupling to a harmonic environment. These processes are intended to represent the measurement of a system observable, and consequent selection of an eigenstate, whilst the system is also subjected to thermalising environmental n

Lindsay N. Childs

We give a self-contained proof that a skew left brace yields a solution of the Yang-Baxter equation.

Thiyanga S. Talagala, Randi Shashikala

Dashboards are the most common visualization method for displaying COVID-19 data and informing the public. We examined 15 different dashboards to see how various visualization techniques were used. This paper describes the creation and implementation of a dashboard for COVID-19 epidemic and vaccination administration data in Sri Lanka.

Ziteng Wang, Xiangdong Wang, Zhichan Hu, Domenico Bongiovanni

A hallmark of symmetry-protected topological phases (SPTs) are topologically protected boundary states, which are immune to perturbations that respect the protecting symmetry. It is commonly believed that any perturbation that destroys an SPT phase simultaneously destroys the boundary states. However, by introducing and exploring a weaker sub-symmetry (SubSy

Roman S. Puzko, Alexander M. Merzlikin

The propagation of light through a disordered layered system is studied. It is shown that distribution function of the transmission coefficient phase tends to stationary non-uniform distribution as the number of layers increases. The exponential convergence to the stationary distribution allows unambiguous definition of the phase randomization length scale.

George-Eduard Zaharia, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel, Mihai Dascalu

Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a robust model that generalizes across a w

J. C. Andrade, C. G. Best

We give an analytic proof of the asymptotic behaviour of the moments of moments of the characteristic polynomials of random symplectic and orthogonal matrices. We therefore obtain alternate, integral expressions for the leading order coefficients previously found by Assiotis, Bailey and Keating. We also discuss the conjectures of Bailey and Keating for the c

Shuai Zhang, Shiyu Peng, Xi Dai, Hongming Weng

Chevrel phase materials form a family of ternary molybdenum chalcogenides with a general chemical formula $A_x{\rm Mo}_6X_8$ ($A$ = metal elements, $X$ = chalcogen). The variety of $A$ atoms makes a large number of family members and leads to many tunable physical properties, such as the superconductivity, thermoelectricity and the ionic conductivity. In thi

Zhijun Liu, Jiang Hu, Zhidong Bai, Haiyan Song

In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSS) of large-dimensional sample covariance matrix when the population covariance matrices are not uniformly bounded, which is a nontrivial extension of the Bai-Silverstein theorem (BST) (2004). The latter has strongly stimulated the development of high-dimensional st

Fan Wang, Adams Wai-Kin Kong

Model attributions are important in deep neural networks as they aid practitioners in understanding the models, but recent studies reveal that attributions can be easily perturbed by adding imperceptible noise to the input. The non-differentiable Kendall's rank correlation is a key performance index for attribution protection. In this paper, we first show th

Juliana Roberta Theodoro de Lima

In this work we extend Goldberg result \cite{Goldberg} for generalized string links over closed, connected and orientable surfaces of genus $g \geq 1$, i.e., different from the sphere (up to link-homotopy).

Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen H. Bach

As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across various population subgroups including the minority groups. For instance, it should not be the case that explanations associated with insta

Askhat Sitdikov, Nikita Balagansky, Daniil Gavrilov, Alexander Markov

This paper proposes a simple method for controllable text generation based on weighting logits with a free-form classifier, namely CAIF sampling. Using an arbitrary text classifier, we adjust a small part of a language model's logits and guide text generation towards or away from classifier prediction. We experimented with toxicity avoidance and sentiment co

Jochen Blath, Felix Hermann, Michel Reitmeier

In this paper, we introduce a type switching mechanism for the Contact Process on the lattice $\mathbb{Z}^d$. That is, we allow the individual particles/sites to switch between two (or more) types independently of one another, and the different types may exhibit specific infection and recovery dynamics. Such type switches can eg.\ be motivated from biology,

Amir H Gandomi, Kalyanmoy Deb, Ronald C Averill, Shahryar Rahnamayan

To solve complex real-world problems, heuristics and concept-based approaches can be used in order to incorporate information into the problem. In this study, a concept-based approach called variable functioning Fx is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subset o

Yalong Jiao, Xu-Tao Zeng, Cong Chen, Zhen Gao

Two-dimensional (2D) magnetic materials hosting nontrivial topological states are interesting for fundamental research as well as practical applications. Recently, the topological state of 2D Weyl half-semimetal (WHS) was proposed, which hosts fully spin polarized Weyl points robust against spin-orbit coupling in a 2D ferromagnetic system, and single-layer P

Zhongwei Tang, Ning Zhou

Given $(M, g)$ a smooth compact $(n+1)$-dimensional Riemannian manifold with boundary $\partial M$. Let $\rho$ be a defining function of $M$ and $\sigma \in(0,1)$. In this paper we study a weighted Sobolev-Poincar\'e type trace inequality corresponding to the embedding of $W^{1,2}(\rho^{1-2 \sigma}, M) \hookrightarrow L^{p}(\partial M)$, where $p=\frac{2 n}{

Shimeng Huang, Elisabeth Ailer, Niki Kilbertus, Niklas Pfister

The compositionality and sparsity of high-throughput sequencing data poses a challenge for regression and classification. However, in microbiome research in particular, conditional modeling is an essential tool to investigate relationships between phenotypes and the microbiome. Existing techniques are often inadequate: they either rely on extensions of the l

Hao-Guang Li, Chao-Jiang Xu

In this work, we study the linear Landau equation with soft potential and show that the solution to the Cauchy problem with initial datum in $L^{2}(\mathbb{R}^3)$ enjoys an analytic regularizing effect, and the evolution of analytic radius is same as heat equations.

Adela Gorczynska, Peter Fule, Christoph Treude

Constructing complex queries on data which combines spatial, temporal, and spectral aspects is a challenging and error-prone process. Query interfaces of general-purpose database management systems fall short in providing intuitive support for users to effectively and efficiently construct queries. To address this situation, we developed GraphicalQueryBuilde

Diego Antognini

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, with more data available than ever before, it has become increasingly important to explain automated pr

Verity Allan

A discussion of the history of scientific computing for Radio Astronomy in the Cavendish Laboratory of the University of Cambridge in the decades after the Second World War. This covers the development of the aperture synthesis technique for Radio Astronomy and how that required using the new computing technology developed by the University's Mathematical La

Fang Wu, Siyuan Li, Stan Z. Li

Graph neural networks (GNNs) rely mainly on the message-passing paradigm to propagate node features and build interactions, and different graph learning problems require different ranges of node interactions. In this work, we explore the capacity of GNNs to capture node interactions under contexts of different complexities. We discover that GNNs usually fail

Dong-Dong Dong, Geng-Biao Wei, Xue-Ke Song, Dong Wang

In quantum resource theories (QRTs), there exists evidences of intrinsic connections among different measures of quantum resources, including entanglement, coherence, quantum steering, and so on. However, building the relations among different quantum resources is a vital yet challenging task in multipartite quantum systems. Here, we focus on a unified frame

Run-Dong Zhao, Jia-Hui Huang

The superradiant stability of higher dimensional non-extremal Reissner-Nordstrom black hole under charged massive scalar perturbation is analytically studied. We extend our previous studies of four- and five-dimensional non-extremal Reissner-Nordstrom black hole cases to six-dimensional case. By analyzing the derivative of the effective potential with an ana

N. Aizawa, S. Doi

Irreducible representations (irreps) of $\mathbb{Z}_2^2$-graded supersymmetry algebra of ${\cal N}=2$ are obtained by the method of induced representation and they are used to derive $\mathbb{Z}_2^2$-graded supersymmetric classical actions. The irreps are four dimensional for $ \lambda = 0$ where $ \lambda $ is an eigenvalue of the Casimir element, and eight

Koichi Arashi

We investigate the multiplicity-freeness property for the holomorphic multiplier representations of affine transformation groups of a Siegel domain of the second kind. We deal with the generalized Heisenberg group and its subgroups. Necessary and sufficient conditions for a specific representation to be multiplicity-free are provided. We study the multiplici

Ruth King, Blanca Sarzo, Víctor Elvira

We consider the challenges that arise when fitting complex ecological models to 'large' data sets. In particular, we focus on random effect models which are commonly used to describe individual heterogeneity, often present in ecological populations under study. In general, these models lead to a likelihood that is expressible only as an analytically intracta

Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Dong Gu Lee

L2 regularization for weights in neural networks is widely used as a standard training trick. However, L2 regularization for gamma, a trainable parameter of batch normalization, remains an undiscussed mystery and is applied in different ways depending on the library and practitioner. In this paper, we study whether L2 regularization for gamma is valid. To ex

Vasudeva Raju Sangaraju, Bharath Kumar Bolla, Deepak Kumar Nayak, Jyothsna Kh

Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated with topics/aspects to provide efficient customer assistance. LDA and LSA fail to capture the semantic relationship and are not specific to any domain. In this

Chang Li, Qi Meng, Dong Wei, Wenzhong Shi

Studies on rapid change detection of large area urgently need to be extended from 2D image to digital elevation model (DEM) due to the challenge of changes caused by disasters. This research investigates positional uncertainty of digital elevation change detection (DECD) caused by different degrees of DEM complexity and DEM misregistration. Unfortunately, us

Shaoli Wang, Tengfei Wang, Ya-nen Qi, Fei Xu

Recent evidences show that individuals who recovered from COVID-19 can be reinfected. However, this phenomenon has rarely been studied using mathematical models. In this paper, we propose a SEIRE epidemic model to describe the spread of the epidemic with reinfection. We obtain the important thresholds $R_0$ (the basic reproduction number) and Rc (a threshold