May 2022 arXiv papers — page 17

Shouvik Sadhukhan, Alokananda Kar, Surajit Chattopadhyay

In this paper we have discussed the modified gravity and scalar field DE model specifically DBI essence model with the analysis of thermodynamics during cosmological evolution. We have used the modified gravity with the form f(R,T)=R+2\Lambda T in our calculations. The basic aim behind this paper is to discuss a theory that unifies modified gravity with DE m

Marco A. Gordon, Francisco J. Vargas, Andrés A. Peters

This paper studies the mean-square stability of heterogeneous LTI vehicular platoons with inter-vehicle communication channels affected by random data loss. We consider a discrete-time platoon system with predecessor following topology and constant time-headway spacing policy. Lossy channels are modeled by Bernoulli processes allowed to be correlated in spac

Frank Freimuth, Stefan Blügel, Yuriy Mokrousov

The LDA+U method is widely used to study the properties of realistic solids with strong electron correlations. One of its main shortcomings is that it does not provide direct access to the temperature dependence of material properties such as the Dzyaloshinskii-Moriya interaction, the anomalous Hall conductivity, and the spin-orbit torque. While the method o

Akimitsu Watanabe, Hiroto Adachi, Yusuke Kato

We theoretically study the contribution of superconducting fluctuation to extrinsic spin Hall effects in two- and three-dimensional electron gas and intrinsic spin Hall effects in two-dimensional electron gas with Rashba-type spin-orbit interaction. The Aslamazov-Larkin, Density-of-States, Maki-Thompson terms have logarithmic divergence $\ln\epsilon$ in the

Dingkun Guo, Larissa Wermers, Kenn R. Oldham

Among small-scale mobile robots, multi-modal locomotion can help compensate for limited actuator capabilities. However, supporting multiple locomotion modes or gaits in small terrestrial robots typically requires complex designs with low locomotion efficiency. In this work, legged and rolling gaits are achieved by a 10~cm robot having just two degrees of fre

Gurbir Dhillon

We provide a motivated introduction to the theory of categorical actions of groups and the local geometric Langlands program. Along the way we emphasize applications, old and new, to the usual representation theory of reductive and affine Lie algebra

Andronikos Paliathanasis, Genly Leon, P. G. L. Leach

We revise recent results on the classification of the generalized three-dimensional Hamiltonian Ermakov system. We show that a statement published recently is incorrect, while the solution for the classification problem was incomplete. We present the correct classification for the three-dimensional system by using results which related the background space w

Harel Berger, Chen Hajaj, Amit Dvir

Android is the most popular OS worldwide. Therefore, it is a target for various kinds of malware. As a countermeasure, the security community works day and night to develop appropriate Android malware detection systems, with ML-based or DL-based systems considered as some of the most common types. Against these detection systems, intelligent adversaries deve

Leslie Ching Ow Tiong, Dick Sigmund, Andrew Beng Jin Teoh

Recently, the transformer model has been successfully employed for the multi-view 3D reconstruction problem. However, challenges remain on designing an attention mechanism to explore the multiview features and exploit their relations for reinforcing the encoding-decoding modules. This paper proposes a new model, namely 3D coarse-to-fine transformer (3D-C2FT)

LingHong Xing, XiangXiang Ma, GuangSheng Luo

In this paper we propose an improved mean-field inference algorithm for the fully connected paired CRFs model. The improved method Message Passing operation is changed from the original linear convolution to the present graph attention operation, while the process of the inference algorithm is turned into the forward process of the GAT model. Combined with t

Wending Yan, Lu Xu, Wenhan Yang, Robby T. Tan

Existing adherent raindrop removal methods focus on the detection of the raindrop locations, and then use inpainting techniques or generative networks to recover the background behind raindrops. Yet, as adherent raindrops are diverse in sizes and appearances, the detection is challenging for both single image and video. Moreover, unlike rain streaks, adheren

Haoxiang Guo, Shilin Liu, Hao Pan, Yang Liu

We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i.e. vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized r

Rui Ai, Chang Wang, Chenchen Li, Jinshan Zhang

Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions. Although there has been a line of works concerning online bidding strategies in first-price auctions, it still remains open how to handle budget constraints in the problem. In the present paper, we initiate the study for a buyer with budge

Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang

We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a \emph{target task}. We propose a new notion of task relatedness between source and target tasks, and develop a novel approach for representational trans

Chen Zhang, Yang Yang, Qifan Wang, Jiahao Liu

Recent studies have uncovered that language model distillation is less effective when facing a large capacity gap between the teacher and the student, and introduced teacher assistant-based distillation to bridge the gap. As a connection, the scale and the performance of the teacher assistant is of vital importance to bring the knowledge from the teacher to

Ming-Song Ding, Ying Shi, Yu-jie Liu, Li Zheng

We investigate the generation of the entanglement in a cavity magnomechanical system, which consists of three modes: a magnon mode, a microwave cavity mode and a mechanical vibration mode, the couplings of the magnon-photon and the magnon-phonon are achieved by the magnetic dipole interaction and the magnetostrictive interaction, respectively. By introducing

Biprateep Dey, David Zhao, Brett H. Andrews, Jeffrey A. Newman

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable $y$ given complex inputs $\mathbf{x}$. Despite recent advances in machine learning and physics-based models, it remains challenging to assess whether an initial model is calibrated for all $\mathbf{x}$

Tamas G. Molnar, Anil Alan, Adam K. Kiss, Aaron D. Ames

Safe longitudinal control is discussed for a connected automated truck traveling behind a preceding connected vehicle. A controller is proposed based on control barrier function theory and predictor feedback for provably safe, collision-free behavior by taking into account the significant response time of the truck as input delay and the uncertainty of its d

Yuhe Ding, Lijun Sheng, Jian Liang, Aihua Zheng

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Owing to privacy concerns and heavy data transmission, source-free UDA, exploiting the pre-trained source models instead of the raw source data for target learning, has been gaining popularity in recent years. Some works attempt to reco

Ben Knudsen

A projection space is a collection of spaces interrelated by the combinatorics of projection onto tensor factors in a symmetric monoidal background category. Examples include classical configuration spaces, orbit configuration spaces, the graphical configuration spaces of Eastwood--Huggett, the simplicial configuration spaces of Cooper--de Silva--Sazdanovic,

Zhenkun Lin, Yuning Zhang, K. W. Wang, Serife Tol

We propose a nonlinear acoustic metasurface concept by exploiting the nonlinearity of the locally resonant unit cells formed by curved beams. The analytical model is established to explore the nonlinear phenomenon, specifically the second-harmonic generation (SHG) of the acoustic waveguide and validated through numerical and experimental studies. Novel nonli

Kai Yan, Ruo-Yu Liu, S. Z. Chen, Xiang-Yu Wang

The particle diffusion coefficients of three TeV pulsar halos observed so far are inferred to be significantly smaller than the typical value of the interstellar medium (ISM). The anisotropic diffusion model ascribes the slow diffusion to the cross-field diffusion assuming sub-Alfv{\'e}nic turbulence in the ISM around the pulsar if the viewing angle between

Si Li, Jie Zhou

We derive residue formulas for the regularized integrals (introduced by Li-Zhou) on configuration spaces of elliptic curves. Based on these formulas, we prove that the regularized integrals satisfy holomorphic anomaly equations, providing a mathematical formulation of the so-called contact term singularities. We also discuss residue formulas for the ordered

Vladimir Pletser

We show that Euler's relation and the Taxi-Cab relation are both solutions of the same equation. General solutions of sums of two consecutive cubes equaling the sum of two other cubes are calculated. There is an infinite number of relations to be found among the sums of two consecutive cubes and the sum of two other cubes, in the form of two families. Their

Hui-Hui Qin, Shao-Ming Fei

We investigate the optimal approximation to triple incompatible quantum measurements within the framework of statistical distance and joint measurability. According to the lower bound of the uncertainty inequality presented in [Physical Review A 99, 312107 (2019)], we give the analytical expressions of the optimal jointly measurable approximation to two kind

Weizhang Huang, Ruo Li, Jianxian Qiu, Min Zhang

A well-balanced moving mesh discontinuous Galerkin (DG) method is proposed for the numerical solution of the Ripa model -- a generalization of the shallow water equations that accounts for effects of water temperature variations. Thermodynamic processes are important particularly in the upper layers of the ocean where the variations of sea surface temperatur

Dimitris Vardakis, Alexander Volberg

We consider a model of randomness for self-similar Cantor sets of finite and positive $1$-Hausdorff measure. We find the sharp rate of decay of the probability that a Buffon needle lands $\delta$-close to a Cantor set of this particular randomness. Two quite different models of randomness for Cantor sets, by Peres and Solomyak, and by Shiwen Zhang, appear to

Yu-Chien Lin, Ta-Sung Lee, Zhi Ding

Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed sol

Qiang He, Huangyuan Su, Jieyu Zhang, Xinwen Hou

Deep reinforcement learning (DRL) gives the promise that an agent learns good policy from high-dimensional information, whereas representation learning removes irrelevant and redundant information and retains pertinent information. In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, sa

Jun Gao, Jie Ma

We prove that for $n>k\geq 3$, if $G$ is an $n$-vertex graph with chromatic number $k$ but any its proper subgraph has smaller chromatic number, then $G$ contains at most $n-k+3$ copies of cliques of size $k-1$. This answers a problem of Abbott and Zhou and provides a tight bound on a conjecture of Gallai.

Zhengyi Jiang, Hanxu Hou, Yunghsiang S. Han, Patrick P. C. Lee

Piggybacking codes are a special class of MDS array codes that can achieve small repair bandwidth with small sub-packetization by first creating some instances of an $(n,k)$ MDS code, such as a Reed-Solomon (RS) code, and then designing the piggyback function. In this paper, we propose a new piggybacking coding design which designs the piggyback function ove

Qing-Hua Zhu, Yu-Xuan Han, Qing-Guo Huang

The supertranslated black hole proposed by Hawking, Perry, and Strominger might provide a resolution to the information paradox, which is usually defined by a complicated space-time metric with even less space-time symmetries compared to Kerr black hole. In this paper, we figure out the shadow for the supertranslated Schwarzschild black hole by making use of

Thomas Laurent, James H. von Brecht, Xavier Bresson

We propose a simple data model inspired from natural data such as text or images, and use it to study the importance of learning features in order to achieve good generalization. Our data model follows a long-tailed distribution in the sense that some rare subcategories have few representatives in the training set. In this context we provide evidence that a

R. Doria, L. S. Mendes

Electromagnetism is the energy originating from an electric charge. Our purpose is to enlarge Maxwell. Include the charge transfer phenomenology. A four bosons electromagnetism is derived. An EM completeness is achieved. The charge's set $\{+,0,-\}$ is intermediated by a quadruplet $\{A_{\mu}, U_{\mu}, V^{\pm}_{\mu}\}$. They are the usual photon $A_{\mu}$, m

Mayleen Cortez, Matthew Eichhorn, Christina Lee Yu

Randomized experiments are widely used to estimate causal effects across a variety of domains. However, classical causal inference approaches rely on critical independence assumptions that are violated by network interference, when the treatment of one individual influences the outcomes of others. All existing approaches require at least approximate knowledg

Mo Zhou, Kai Pan, Tian-Cheng Yi, Xing-Fen Jiang

By simulating the X-ray paths and the Chapman Model of a flat multi-channel plate and a curved multi-channel plate in the MATLAB, the field of view, local reflection efficiency, spherical aberration, point-spread function, collection efficiency of incident X-ray and peak-to-background ratio on the focal plane of the two devices were compared. At the same tim

Swapnil Sayan Saha, Sandeep Singh Sandha, Mani Srivastava

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of

Mohammad Amin Zarrabian, Ni Ding, Parastoo Sadeghi

This paper proposes a novel watchdog privatization scheme by generalizing local information privacy (LIP) to enhance data utility. To protect the sensitive features $S$ correlated with some useful data $X$, LIP restricts the lift, the ratio of the posterior belief to the prior on $S$ after and before accessing $X$. For each $x$, both maximum and minimum lift

Chunwei Tian, Yixuan Yuan, Shichao Zhang, Chia-Wen Lin

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusi

Takumi Nagasawa, Naoki Numadate, Tetsuya Hama

Infrared (IR) multiple-angle incidence resolution spectrometry (IR-MAIRS) is a recently developed spectroscopic technique that combines oblique incidence transmission measurements and chemometrics (multivariate analysis) to obtain both pure in-plane (IP) and out-of-plane (OP) vibration spectra for a thin sample. IR-MAIRS is established for analyzing the mole

Dongsung Huh, Avinash Baidya

Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training. Recently, the technique of Invariant Risk Minimization (IRM) was proposed to learn predictors that only use invariant features by conserving the feature-conditioned label expectati

Qian Zhang, Anuran Makur, Kamyar Azizzadenesheli

The estimation of cumulative distribution functions (CDF) is an important learning task with a great variety of downstream applications, such as risk assessments in predictions and decision making. In this paper, we study functional regression of contextual CDFs where each data point is sampled from a linear combination of context dependent CDF basis functio

Ran Rubin, Haim Sompolinsky

When neural circuits learn to perform a task, it is often the case that there are many sets of synaptic connections that are consistent with the task. However, only a small number of possible solutions are robust to noise in the input and are capable of generalizing their performance of the task to new inputs. Finding such good solutions is an important goal

Nathan Beckmann, Phillip B Gibbons, Charles McGuffey

Caches exploit temporal and spatial locality to allow a small memory to provide fast access to data stored in large, slow memory. The temporal aspect of locality is extremely well studied and understood, but the spatial aspect much less so. We seek to gain an increased understanding of spatial locality by defining and studying the Granularity-Change Caching

Olga Maryeva, Sergey Karpov, Alexei Kniazev, Vasilii Gvaramadze

Luminous Blue Variables (LBVs) are massive stars that show strong spectral and photometric variability. The question of what evolutionary stages they represent and what exactly drives their instability is still open, and thus is it important to understand whether LBVs without significant ongoing activity exist, and for how long such dormant LBVs may ``sleep'

Aladji Babacar Niang, Gane Samb Lo, Chérif Mamadou Moctar Traoré, Amadou Ball

The simple L\'evy Poisson process and scaled forms are explicitly constructed from partial sums of independent and identically distributed random variables and from sums of non-stationary independent random variables. For the latter, the weak limits are scaled Poisson processes. The method proposed here prepares generalizations to dependent data, to associat

Feng Liang, Yangguang Li, Diana Marculescu

Recently, self-supervised Masked Autoencoders (MAE) have attracted unprecedented attention for their impressive representation learning ability. However, the pretext task, Masked Image Modeling (MIM), reconstructs the missing local patches, lacking the global understanding of the image. This paper extends MAE to a fully supervised setting by adding a supervi

Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber

Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the representations learned by VAE are less competitive than other non-latent variable models. This has led to some speculations that latent variable models may be fundamentally unsui

Gregorio Landi, Giovanni E. Landi

The approach to heteroscedasticity of ref.1(Instruments 2022, 6(1), 10) contains a sketchy application of a sub-optimal method of very easy implementation: the lucky model. The supporting proof of this method could not be inserted in ref.1. The proof requires the analytical forms of the probability of ref.2 for the two strip center of gravity. However, those

Christian Schroeder de Witt

A physically-motivated genetic algorithm (GA) and full enumeration for a tile-based model of self-assembly (JaTAM) is implemented using a graphics processing unit (GPU). We observe performance gains with respect to state-of-the-art implementations on CPU of factor 7.7 for the GA and 2.9 for JaTAM. The correctness of our GA implementation is demonstrated usin

Davide Buoso, Paolo Luzzini, Luigi Provenzano, Joachim Stubbe

We compute three-term semiclassical asymptotic expansions of counting functions and Riesz-means of the eigenvalues of the Laplacian on spheres and hemispheres, for both Dirichlet and Neumann boundary conditions. Specifically for Riesz-means we prove upper and lower bounds involving asymptotically sharp shift terms, and we extend them to domains of $\mathbb S

Matteo Gori

In the present work, we present a detailed discussion of a Riemannian metric structure originally introduced in [Gori et al., \textit{J. Stat. Mech.}, \textbf{9} 093204 (2018)] on the configuration space and on phase space allowing us to interpret the derivatives of the configurational microcanonical entropy and of the canonical entropy in terms of integrals

Reza Sazeedeh

Let $R$ be a commutative noetherian ring, $\frak a$ be an ideal of $R$, $\cS$ be an arbitrary Serre subcategory of $R$-modules and let $\cN$ be the subcategory of finitely generated $R$-modules. In this paper, we study $\cN\cS$-$\frak a$-cofinite modules with respect to the extension subcategory $\cN\cS$ when $\dim R/\frak a\leq 2$. We also study $\frak a$-c

Alexander Davie, Fabian Germ, István Gyöngy

A partially observed jump diffusion $Z=(X_t,Y_t)_{t\in[0,T]}$ given by a stochastic differential equation driven by Wiener processes and Poisson martingale measures is considered when the coefficients of the equation satisfy appropriate Lipschitz and growth conditions. Under general conditions it is shown that the conditional density of the unobserved compon

Negar Alipour, Reza Sazeedeh

Let $R$ be a commutative noetherian ring, $\frak a$ be an ideal of $R$, $\mathcal{S}$ be an arbitrary Serre subcategory of $R$-modules satisfying the condition $C_{\frak a}$ and let $\mathcal{N}$ be the subcategory of finitely generated $R$-modules. In this paper, we define and study $\mathcal{NS}$-$\frak a$-cofinite modules with respect to the extension sub

Ying Zhang, John S. Preisser, Fan Li, Elizabeth L. Turner

Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) are commonly used to provide population-averaged inference in CRTs, there is a gap of general methods and statistical software tools for power calculation based on multi-parameter,

Dinesh Kumar Baghel, Vadim E. Levit, Erel Segal-Halevi

In many developing countries, the total electricity demand is larger than the limited generation capacity of power stations. Many countries adopt the common practice of routine load shedding - disconnecting entire regions from the power supply - to maintain a balance between demand and supply. Load shedding results in inflicting hardship and discomfort on ho

Lei Yan, Zhijin Qin, Rui Zhang, Yongzhao Li

With the aim of accomplishing intelligence tasks, semantic communications transmit task-related information only, yielding significant performance gains over conventional communications. To guarantee user requirements for different types of tasks, we perform the semantic-aware resource allocation in a multi-cell multi-task network in this paper. Specifically

Hanlin Li, Brent Hecht, Stevie Chancellor

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities.

Hanlin Li, Brent Hecht, Stevie Chancellor

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its mo

Octavio Arizmendi, José Guerrero

We give a Coulson integral formula and a Coulson-Jacobs formula for the $p$-Schatten energy. We use this formulas to compare the $p$-Schatten energy of different trees by using a quasiorder, and establish the maximality of paths among all trees.

Dongjie Wang, Yanjie Fu, Kunpeng Liu, Xiaolin Li

Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extracting a good representation space is critical to address the curse of dimensionality, improve model generalization, overcome data sparsity, and increase the availability of classic

Charles W. Champ, Andrew V. Sills

A generalization of the law of total covariance is presented and proved.

Marco Bravin, Francesco Fanelli

In this paper, we perform the fast rotation limit $\varepsilon\rightarrow0^+$ of the density-dependent incompressible Navier-Stokes-Coriolis system in a thin strip $\Omega_\varepsilon\,:=\,\mathbb{R}^2\times\,]-\ell_\varepsilon,\ell_\varepsilon[\,$, where $\varepsilon\in\,]0,1]$ is the size of the Rossby number and $\ell_\varepsilon>0$ for any $\varepsilon>0

Lars Lindemann, Lejun Jiang, Nikolai Matni, George J. Pappas

The wide availability of data coupled with the computational advances in artificial intelligence and machine learning promise to enable many future technologies such as autonomous driving. While there has been a variety of successful demonstrations of these technologies, critical system failures have repeatedly been reported. Even if rare, such system failur

Puyuan Liu, Xiang Zhang, Lili Mou

Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation. In previous work, researchers have developed various approaches to improve the ROUGE score, which is the main evaluation metric for summarization, whereas controlling the summary length h

Puyuan Liu, Chenyang Huang, Lili Mou

Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as pseudo-groundtruth. Then, we train an en

Michael D. Vander Wal, Ryan G. McClarren, Kelli D. Humbird

Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fidelity such that the opacity calculations can contribute ninety-five percent of the total computation time. This proportion can even reach large pro

Jon Schneider, Kiran Vodrahalli

We study the problem of full-information online learning in the "bounded recall" setting popular in the study of repeated games. An online learning algorithm $\mathcal{A}$ is $M$-$\textit{bounded-recall}$ if its output at time $t$ can be written as a function of the $M$ previous rewards (and not e.g. any other internal state of $\mathcal{A}$). We first demon

Krutik Patel, Geyue Cai, Henry Ando, Cheng Chin

Particle-like excitations, or quasi-particles, emerging from interacting fermionic and bosonic quantum fields underlie many intriguing quantum phenomena in high energy and condensed matter systems. Computation of the properties of these excitations is frequently intractable in the strong interaction regime. Quantum degenerate Bose-Fermi mixtures offer promis

Glenn Dawson, Muhammad Umer, Robi Polikar

Deep neural networks for image classification are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor attack, which has demonstrated the capability to perform targeted misclassification of specific examples. In particular, backdoor attacks attempt to force a model to learn spuriou

Chang Su, Wenbo Lyu, Yueting Liu

By comparing the historical patterns of currency development, this paper pointed out the inevitability of the development of digital currency and the relationship between digital currency and the digital economy. With the example of China, this paper predicts the future development trend of digital currency. In the context of the rapid development of private

Yuan Yao, Ziwen Zhao

We compute the product and coproduct structures on the fixed point Floer homology of iterations on the single Dehn twist, subject to some mild topological restrictions. We show that the resulting product and coproduct structures are determined by the product and coproduct on Morse homology of the complement of the twist region, together with certain sectors

David Rügamer

In the age of big data and interpretable machine learning, approaches need to work at scale and at the same time allow for a clear mathematical understanding of the method's inner workings. While there exist inherently interpretable semi-parametric regression techniques for large-scale applications to account for non-linearity in the data, their model comple

Julio Delgado

In this work we introduce a Poincar\'e determinant type for operators on the torus $\To^n$. As an application we establish the existence of nontrivial solutions for elliptic equations of the form $(-\Delta)^{\frac{\nu}{2}}u+Qu=0$ on $\To^n$ by using the Hill's method.

M. C. Araújo, R. V. Maluf

This paper investigates how the nonzero temperature affects the differential cross-section for mesons scattering in a Lorentz-violating extension of the scalar electrodynamics. We initially discuss some features of the model and extract the zero temperature Feynman rules. Temperature effects are introduced using the Thermo Field Dynamics (TFD) formalism. It

Jiaxi Wang, Yanxi Hou, Xingchi Li, Tiandong Wang

Modeling heterogeneity on heavy-tailed distributions under a regression framework is challenging, and classical statistical methodologies usually place conditions on the distribution models to facilitate the learning procedure. However, these conditions are likely to overlook the complex dependence structure between the heaviness of tails and the covariates.

Vadim R. Munirov, Lazar Friedland, Jonathan S. Wurtele

We present a method for constructing multiphase excitations in the generally non-integrable system of warm fluid equations describing plasma oscillations. It is based on autoresonant excitation of nonlinear electron plasma waves by phase locking with small amplitude chirped-frequency ponderomotive drives. We demonstrate the excitation of these multiphase wav

Heming Sun, Lu Yu, Jiro Katto

Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra. However, the large network complexity prohibits the usage of LIC on resource-limited embedded systems. Network quantization is an efficient way to reduce the network burden. This paper presents a quantized LIC (QLIC) by channel splitt

Adam Earnst, Silas Alben

Many previous studies of sliding locomotion have assumed that body inertia is negligible. Here we optimize the kinematics of a three-link body for efficient locomotion and include among the kinematic parameters the temporal period of locomotion, or equivalently, the body inertia. The optimal inertia is non-negligible when the coefficient of friction for slid

Sagnik Dakshit, Barbara Mukami Maweu, Sristi Dakshit, Balakrishnan Prabhakaran

The adoption of deep learning-based healthcare decision support systems such as the detection of irregular cardiac rhythm is hindered by challenges such as lack of access to quality data and the high costs associated with the collection and annotation of data. The collection and processing of large volumes of healthcare data is a continuous process. The perf

Daniel S. Karls, Steven M. Clark, Brendon A. Waters, Ryan S. Elliott

The Open Knowledgebase of Interatomic Models (OpenKIM) is an NSF Science Gateway that archives fully functional computer implementations of interatomic models (potentials and force fields) and simulation codes that use them to compute material properties. Interatomic models are coupled with compatible simulation codes and executed in a fully automated manner

Kaitlin Gili, Mykolas Sveistrys, Chris Ballance

Due to the linearity of quantum mechanics, it remains a challenge to design quantum generative machine learning models that embed non-linear activations into the evolution of the statevector. However, some of the most successful classical generative models, such as those based on neural networks, involve highly non-linear dynamics for quality training. In th

Federica Ferretti, Simon Grosse-Holz, Caroline Holmes, Jordan L Shivers

Flocking in $d=2$ is a genuine non-equilibrium phenomenon for which irreversibility is an essential ingredient. We study a class of minimal flocking models whose only source of irreversibility is self-propulsion and use the entropy production rate (EPR) to quantify the departure from equilibrium across their phase diagrams. The EPR is maximal in the vicinity

Chong Zhong, Jin Yang, Junshan Shen, Catherine Liu

This article tackles the old problem of prediction via a nonparametric transformation model (NTM) in a new Bayesian way. Estimation of NTMs is known challenging due to model unidentifiability though appealing because of its robust prediction capability in survival analysis. Inspired by the uniqueness of the posterior predictive distribution, we achieve effic

Tahsin Reza, Geoffrey Sanders, Roger Pearce

Given an edge-weighted graph and a set of known seed vertices, a network scientist often desires to understand the graph relationships to explain connections between the seed vertices. When the seed set is 3 or larger Steiner minimal tree - min-weight acyclic connected subgraph (of the input graph) that contains all the seed vertices - is an attractive gener

Eiichi Matsuhashi, Yoshiyuki Oshima

We introduce the new class of continua; $D^{**}$-$continua$. The classes of Wilder continua and $D^{*}$-continua are strictly contained in the class of $D^{**}$-continua. Also, the class of $D$-continua is bigger than the class of $D^{**}$-continua. Using $D^{**}$-continua, we give the negative answer to a Question. Furthermore, we prove that being Wilder, b

Dailan He, Ziming Yang, Hongjiu Yu, Tongda Xu

In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate reconstructions of LIC suffer from artifacts such as blurring, color drifting and texture missing. Moreover, those varied artifacts make image quality metrics correlate badly with hu

Alexandre M. Florio, Pedro Martins, Maximilian Schiffer, Thiago Serra

Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth. Accordingly, decision diagrams are usually less prone to data fragmentation in internal nodes. However, the inherent complexity of training

A. Deconto-Machado, A. del Olmo Orozco, P. Marziani

Much effort has been done in order to better understand the active galactic nuclei mechanisms behind the relativistic jets observed in radio-loud sources. These phenomena are commonly seen in luminous objects with intermediate/high redshift such as quasars, so that the analysis of the spectroscopic properties of these sources may be a way to clarify this iss

Diego Alonso-Orán, Ángel D. Martínez

In this note we show finite time blow-up for a class of non-local active scalar equations on compact Riemannian manifolds. The strategy we follow was introduced by Silvestre and Vicol to deal with the one dimensional C\'ordoba-C\'ordoba-Fontelos equation and might be regarded as an instance of De Giorgi's method.

Francesco Minna, Fabio Massacci, Katja Tuma

Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on ``trial and error'' experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a knowledge, AND/OR, graphs approach to model cloud deployment security objects and vulnerabilities. In this way, we can capture

Ángel D. Martínez

In this paper we prove an exponential covering lemma implying the three dimensional case of a well-known conjecture formulated by A. Zygmund circa 1935 and solved by A. C\'ordoba in 1978. Our approach avoids a subtle argument involving the power series of the exponential function.

Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang

Deep learning models have been deployed in numerous real-world applications such as autonomous driving and surveillance. However, these models are vulnerable in adversarial environments. Backdoor attack is emerging as a severe security threat which injects a backdoor trigger into a small portion of training data such that the trained model behaves normally o

Hanqing Guo, Qiben Yan, Nikolay Ivanov, Ying Zhu

Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attackers synthesize the voice of a victim or simply replay it, have brought growing security concerns. Existing speaker verification techniques distingui

Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin

Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks while addressing the limitations of standard deep learning approaches, such as catastrophic forgetting. In this work, we investigate the factors that contribute to the performance differences between task-agnostic CL and multi-task (MTL) agents. We pose t

Ernst Schulte-Geers, Bo Waggoner

Concentration bounds are given for throwing balls into bins independently according to a distribution $p$. The probability of a $k$-loaded bin after $m$ balls is shown to be controlled on both sides by $\rho_{m,k} := m \|p\|_k / k$. This gives concentration inequalities for the maximum load as well as for the waiting time until a $k$-loaded bin.

Ángel D. Martínez, Francisco Torres de Lizaur

We prove {\em sign equidistribution} of Legendre polynomials: the ratio between the lengths of the regions in the interval $[-1, 1]$ where the Legendre polynomial assumes positive versus negative values, converges to one as the degree grows. The proof method also has application to the symmetry conjecture for a basis of eigenfunctions in the sphere.

Ciprian Pungila, Darius Galis, Viorel Negru

We are presenting a fast and innovative approach to performing approximate pattern-matching for plagiarism detection, using an NDFA-based approach that significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). We present, formaliz

Ángel D. Martínez, Francisco Torres de Lizaur

In this paper we study a number of conjectures on the behavior of the value distribution of eigenfunctions. On the two dimensional torus we observe that the symmetry conjecture holds in the strongest possible sense. On the other hand we provide a counterexample for higher dimensional tori, which relies on a computer assisted argument. Moreover we prove a the