July 2022 arXiv papers — page 111

Mustafa Shukor, Bharath Bhushan Damodaran, Xu Yao, Pierre Hellier

We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intra and inter compression. Each frame is inverted in the latent space of StyleGAN, from which the optimal compression is learned. To do so, a diffeomorphic latent representation is l

Vitonofrio Crismale, Stefano Rossi

We compute the tail algebras of exchangeable monotone stochastic processes. This allows us to prove the analogue of de Finetti's theorem for this type of processes. In addition, since the vacuum state on the $q$-deformed $C^*$-algebra is the only exchangeable state when $|q|<1$, we draw our attention to its tail algebra, which turns out to obey a zero-one la

A. Nigoche-Netro, E. de la Fuente, R. J. Diaz, M. P. Aguero

Motivated by the challenges of calculating the dynamical masses of late-type galaxies (LTGs) and the enormous amount of data from the Sloan Digital Sky Survey (SDSS), we calculate virial masses of a sample of approximately 126,000 LTGs from the sixteenth data release of the SDSS. The virial mass estimations were made considering Newtonian mechanics, virial e

Raed Jaberi

Let $G=(V,E)$ be a strongly biconnected directed graph. In this paper we consider the problem of computing an edge subset $H \subseteq E$ of minimum size such that the directed subgraph $(V,H)$ is strongly biconnected.

J. P. J. Rodriguez

myqcs is a quantum circuit simulator using tensor. The simulator can be used with both wave functions or density matrices. The simulator also included noise that can be varied in form and that can be different for each gate.

Shihao Zou, Yuanlu Xu, Chao Li, Lingni Ma

Multi-person pose understanding from RGB videos involves three complex tasks: pose estimation, tracking and motion forecasting. Intuitively, accurate multi-person pose estimation facilitates robust tracking, and robust tracking builds crucial history for correct motion forecasting. Most existing works either focus on a single task or employ multi-stage appro

Amit Saha, Debasri Saha, Amlan Chakrabarti

Quantum walks contribute significantly to developing quantum algorithms and quantum simulations. Here, we introduce a first of its kind one-dimensional quantum walk in the $d$-dimensional quantum domain, where $d>2$, and show its equivalence for circuit realization in an arbitrary finite-dimensional quantum logic for utilizing the advantage of larger state s

Prachi Sharma, Asher Yahalom

The objective of the present paper is to investigate the constancy of the topological invariant denoted non-barotropic generalized cross helicity in the case of non-ideal magnetohydrodynamic (MHD). Existing work considers only ideal barotropic MHD and ideal non-barotropic MHD. The non-ideal MHD case was not explored probably because of its mathematical compl

Adam Brown, Aditi Laddha, Madhusudhan Pittu, Mohit Singh

Determinant maximization problem gives a general framework that models problems arising in as diverse fields as statistics \cite{pukelsheim2006optimal}, convex geometry \cite{Khachiyan1996}, fair allocations\linebreak \cite{anari2016nash}, combinatorics \cite{AnariGV18}, spectral graph theory \cite{nikolov2019proportional}, network design, and random process

Gang Liu, Zhihan Zhang, Zheng Ning, Meng Jiang

Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular. However, the tangibility and trustworthiness of the recommendations are questionable due to the complexity and lack of explainability of t

Troy Luhman, Eric Luhman

Diffusion models are a powerful class of generative models that iteratively denoise samples to produce data. While many works have focused on the number of iterations in this sampling procedure, few have focused on the cost of each iteration. We find that adding a simple ViT-style patching transformation can considerably reduce a diffusion model's sampling t

M. V. Boldin, A. R. Shabakaeva

We consider a stationary linear $AR(p)$ model with zero mean. The autoregression parameters as well as the distribution function (d.f.) $G(x)$ of innovations are unknown. We consider two situations. In the first situation the observations are a sample from a stationary solution of $AR(p)$. Interesting and essential problem is to test symmetry of $G(x)$ with

Undral Byambadalai

This paper studies identification and inference of the welfare gain that results from switching from one policy (such as the status quo policy) to another policy. The welfare gain is not point identified in general when data are obtained from an observational study or a randomized experiment with imperfect compliance. I characterize the sharp identified regi

Wenyuan Sheng

We present a new model named Stacked-DETR(SDETR), which inherits the main ideas in canonical DETR. We improve DETR in two directions: simplifying the cost of training and introducing the stacked architecture to enhance the performance. To the former, we focus on the inside of the Attention block and propose the QKVA grid, a new perspective to describe the pr

Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez-Ortiz, Agnieszka Kurant

MIT wanted to commission a large scale artwork that would serve to 'illuminate a new campus gateway, inaugurate a space of exchange between MIT and Cambridge, and inspire our students, faculty, visitors, and the surrounding community to engage with art in new ways and to have art be part of their daily lives.' Among other things, the art was to reflect the f

Philip Tee, Nosratollah Jafari

The canonical approach to quantizing quantum gravity is understood to suffer from pathological non-renomalizability. Nevertheless in the context of effective field theory, a viable perturbative approach to calculating elementary processes is possible. Some non-perturbative approaches, most notably loop quantum gravity and combinatorial quantum gravity imply

Manoel M. Ferreira, Pedro D. S. Silva, Marco Schreck

We review the propagation of electromagnetic waves in continuous matter in the presence of Lorentz-violating terms. First, we briefly discuss classical electrodynamics with regard to optical properties of a dielectric medium exhibiting the Chiral Magnetic Effect (CME). Such a medium can be modeled by Maxwell-Carroll-Field-Jackiw (MCFJ) theory. Second, we des

Jack Paget, Una Alberti, Marco G. Mazza, Andrew J. Archer

Composed of microscopic layers that stack along one direction while maintaining fluid-like positional disorder within layers, smectics are excellent systems for exploring topology, defects and geometric memory in complex confining geometries. However, the coexistence of crystalline-like characteristics in one direction and fluid-like disorder within layers m

Andrea Serani, Paolo Dragone, Frederick Stern, Matteo Diez

In order to guarantee the safety of payload, crew, and structures, ships must exhibit good seakeeping, maneuverability, and structural-response performance, also when they operate in adverse weather conditions. In this context, the availability of forecasting methods to be included within model-predictive control approaches may represent a decisive factor. H

Taha Belkhouja, Yan Yan, Janardhan Rao Doppa

Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this pa

Taha Belkhouja, Janardhan Rao Doppa

Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-S

Taha Belkhouja, Yan Yan, Janardhan Rao Doppa

Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed b

Taha Belkhouja, Yan Yan, Janardhan Rao Doppa

Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-series domain due to its unique characteristics compared to images and text data. In this paper, we propose a novel algorithmic framework referred as RObust Training for Time-Serie

Sivasankar Mohankumar, Rama Raghavan

Given an infinite word, enumerating its factors is an important exercise for understanding the structure of the word. The process of finding all the factors is quite tricky for two-dimensional words. In this paper, two possible ways of enumerating the factors of the fixed point ($f_{\infty,\infty}$) of the sequence of Fibonacci arrays and a method for locati

Isibor Kennedy Ihianle, Pedro Machado, Kayode Owa, David Ada Adama

Thermal comfort in shared spaces is essential to occupants well-being and necessary in the management of energy consumption. Existing thermal control systems for indoor shared spaces adjust temperature set points mechanically, making it difficult to intelligently achieve thermal comfort for all. Recent studies have shown that thermal comfort in a shared spac

Mane Avetisyan

The present thesis represents developments in two main directions related to the simple Lie algebras. The first one is devoted to the representation theory of the simple Lie algebras. Specifically, we present recent results, which include new universal formulae in Vogel's universal description, as well as the discovery of additional properties of those formu

Silvio Sergio Cerri, Thierry Passot, Dimitri Laveder, Pierre-Louis Sulem

Using 3D gyrofluid simulations, we revisit the problem of Alfven-wave (AW) collisions as building blocks of the Alfvenic cascade and their interplay with magnetic reconnection at magnetohydrodynamic (MHD) scales. Depending on the large-scale nonlinearity parameter $\chi_0$ (the ratio between AW linear propagation time and nonlinear turnover time), different

Fang Wan, Wei Liu, Frank Bretz

Regression modeling is the workhorse of statistics and there is a vast literature on estimation of the regression function. It is realized in recent years that in regression analysis the ultimate aim may be the estimation of a level set of the regression function, instead of the estimation of the regression function itself. The published work on estimation o

Zewei Lin, Dungang Liu

Model diagnostics is an indispensable component of regression analysis, yet it is not well addressed in standard textbooks on generalized linear models. The lack of exposition is attributed to the fact that when outcome data are discrete, classical methods (e.g., Pearson/deviance residual analysis and goodness-of-fit tests) have limited utility in model diag

Zachary Bradshaw, Chen-Chih Lai, Tai-Peng Tsai

We first prove decay estimates and spacetime integral bounds for Stokes flows in amalgam spaces $E^r_q$ which connect the classical Lebesgue spaces to the spaces of uniformly locally $r$-integrable functions. Using these estimates, we construct mild solutions of the Navier-Stokes equations in the amalgam spaces satisfying the corresponding spacetime integral

Qi Wang, Jingwu Mei

In actual industrial production, the assessment of the steel plate welding effect is an important task, and the segmentation of the weld section is the basis of the assessment. This paper proposes an industrial weld segmentation network based on a deep learning semantic segmentation algorithm fused with heatmap detail guidance and Image Matting to solve the

Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin

Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present T

Guang Yang, Arvind Rao, Christine Fernandez-Maloigne, Vince Calhoun

Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning mod

Mikhail I. Fraiman, Evgenij V. Troitsky

Among restricted wreath products $G\wr \mathbb Z^k $, where $G$ is a finite Abelian group, we find three large classes of groups admitting an automorphism $\varphi$ with finite Reidemeister number $R(\varphi)$ (number of $\varphi$-twisted conjugacy classes). In other words, groups from these classes do not have the $R_\infty$ property. If a general automorph

Lev V. Utkin, Andrei V. Konstantinov

New models of random forests jointly using the attention and self-attention mechanisms are proposed for solving the regression problem. The models can be regarded as extensions of the attention-based random forest whose idea stems from applying a combination of the Nadaraya-Watson kernel regression and the Huber's contamination model to random forests. The s

Kirby Schmidt, Anthony Trofe, Tetyana Ignatova

It is common to measure a single sample using multiple different microscopy methods that have variable scales, rotation and translation. Registering hyperspectral images of two dimensional materials is particularly difficult due to the lack of keypoints on unprepared substrates. Identifying variations in the strain of these samples can assist in the registra

Deren Han, Yansheng Su, Jiaxin Xie

The Douglas-Rachford (DR) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). However, the method converges weakly and the associated rate of convergence is hard to analyze in general. In addition, the direct extension of the DR method for solving more-than-two-sets feasibility problems, cal

T. J. Meijer, V. S. Dolk, M. S. Chong, W. P. M. H. Heemels

This paper considers the design of robust state observers for a class of slope-restricted nonlinear descriptor systems with unknown time-varying parameters belonging to a known set. The proposed design accounts for process disturbances and measurement noise, while allowing for a trade-off between transient performance and sensitivity to noise and parameter m

Jesse Elliott, Mark Giesbrecht, Eric Schost

We analyze the bit complexity of an algorithm for the computation of at least one point in each connected component of a smooth real algebraic set. This work is a continuation of our analysis of the hypersurface case (On the bit complexity of finding points in connected components of a smooth real hypersurface, ISSAC'20). In this paper, we extend the analysi

Bin Li, Yufeng Liu, Ling Tan, Heng Pan

Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) asso

A. D. Kapustin, S. A. Paston

We propose an analytical method to describe a matter density profile near a galaxy center. The description is based on the study of the distribution function of particles over possible trajectories. We establish a relation between the central slope of density profile and the near-origin behavior of the angular momentum distribution function. We consider both

Maximilian Thees, Min-Han Lee, Rosa Luca Bouwmeester, Pedro H. Rezende-Gonçalves

In solids, strong repulsion between electrons can inhibit their movement and result in a "Mott" metal-to-insulator transition (MIT), a fundamental phenomenon whose understanding has remained a challenge for over 50 years. A key issue is how the wave-like itinerant electrons change into a localized-like state due to increased interactions. However, observing

Wending Mai, Jingwei Xu, Douglas H. Werner

Time-varying materials bring an extra degree of design freedom compared to their conventional time-invariant counterparts. However, few discussions have focused on the underlying physical difference between spatial and temporal boundaries. In this letter, we thoroughly investigate those differences from the perspective of conservation laws. By doing so, the

Wending Mai, Ronald P. Jenkins, Yifan Chen, Douglas H. Werner

Inspired by the epidemic contact tracing technique, we propose a method to efficiently solve electromagnetics by tracing the energy distribution. The computational domain is adaptively decomposed, and the available computational resources are focused on those energy-active (infections) and their adjacent (exposed) domains, while avoiding the unnecessary comp

Yaoxian Li, Shiyi Qi, Cuiyun Gao, Yun Peng

Transformer-based models have demonstrated state-of-the-art performance in many intelligent coding tasks such as code comment generation and code completion. Previous studies show that deep learning models are sensitive to the input variations, but few studies have systematically studied the robustness of Transformer under perturbed input code. In this work,

Patrick Ebel, Moritz Berger, Christoph Lingenfelder, Andreas Vogelsang

With ever-improving driver assistance systems and large touchscreens becoming the main in-vehicle interface, drivers are more tempted than ever to engage in distracting non-driving-related tasks. However, little research exists on how driving automation affects drivers' self-regulation when interacting with center stack touchscreens. To investigate this, we

Ryutaro Nishimura, Shunji Kishimoto, Yasuo Arai, Toshinobu Miyoshi

We are developing a new readout board with a newer generation field-programmable gate array (FPGA) and the 10-gigabit ethernet to improve the performance and usability of the current readout board based on the 1-gigabit Ethernet. In this new readout board, the SiTCP-XG network processor supporting 10-gigabit Ethernet was implemented. SiTCP is a network proce

Wei Ding, Tianning Chen, Chen Chen, Dimitrios Chronopoulos

Chiral phononic crystals provide unique properties not offered by conventional phononic material based on Bragg scattering and local resonance. However, it is insufficient to only consider the inertial amplification effect in chiral phononic crystals. Here, we theoretically and experimentally introduce the analogy with Thomson scattering to characterize the

Mario Coccia, Saeed Roshani

The goal of this study is to explore emerging trends in cloud computing technology that can support an economic and social change. We apply the methods of entity linking, which links word strings to entities from a knowledge base, to extract main keywords in cloud computing from accumulated publications from 2004 to 2021. Results suggest that in cloud comput

Yingxiang Hu, Haizhong Li

In this paper, we prove a family of identities for closed and strictly convex hypersurfaces in the sphere and hyperbolic/de Sitter space. As applications, we prove Blaschke-Santal\'o type inequalities in the sphere and hyperbolic/de Sitter space, which generalizes the previous work of Gao, Hug and Schneider \cite{GHS03}. We also prove the quermassintegral in

Giulio Peruginelli

We show that every Dedekind domain $R$ lying between the polynomial rings $\mathbb Z[X]$ and $\mathbb Q[X]$ with the property that its residue fields of prime characteristic are finite fields is equal to a generalized ring of integer-valued polynomials, that is, for each prime $p\in\mathbb Z$ there exists a finite subset $E_p$ of transcendental elements over

Yogendra Srivastava, Giorgio Immirzi, John Swain, Orland Panella

A very general class of axially-symmetric metrics in general relativity (GR) that includes rotations is used to discuss the dynamics of rotationally-supported galaxies. The exact vacuum solutions of the Einstein equations for this extended Weyl class of metrics allow us to deduce rigorously the following: (i) GR rotational velocity always exceeds the Newtoni

Astamur Bagapsh, Konstantin Fedorovskiy

We consider the Dirichlet problem for second-order elliptic systems with constant coefficients. We prove that non-reducible strongly elliptic systems of this type do not admits non-negatively defined energy functionals of the form $f\mapsto\int_{D}\varPhi(u_x,v_x,u_y,v_y)\,dxdy$, where $D$ is the domain where the problem we are interested in is considered, $

Robin A. Heinonen, Luca Biferale, Antonio Celani, Massimo Vergassola

In many practical scenarios, a flying insect must search for the source of an emitted cue which is advected by the atmospheric wind. On the macroscopic scales of interest, turbulence tends to mix the cue into patches of relatively high concentration over a background of very low concentration, so that the insect will only detect the cue intermittently and ca

Asim Ghosh, Soumyajyoti Biswas, Bikas K. Chakrabarti

Statistical physicists and social scientists both study extensively some characteristic features of the unequal distributions of energy, cluster or avalanche sizes and of income, wealth etc among the particles (or sites) and population respectively. While physicists concentrate on the self-similar (fractal) structure (and the characteristic exponents) of the

Nguyen Bin

Surfaces of general type with canonical map of degree d bigger than 8 have bounded geometric genus and irregularity. In particular the irregularity is at most 2 if d>= 10. In the present paper, the existence of surfaces with d=10 and all possible irregularities, surfaces with d = 12 and irregularity 1 and 2, and surfaces with d = 14 and irregularity 0 and 1

Xing Huang, Xucheng Wang

In this paper, the well-posedness for one-dimensional path dependent McKean-Vlasov SDEs with $\alpha$($\alpha\geq \frac{1}{2}$)-H\"{o}lder continuous diffusion is investigated. Moreover, the associated quantitative propagation of chaos in the sense of Wasserstein distance, total variation distance as well as relative entropy is studied.

Justin Kang, Wei Yu

Massive machine-type communications protocols have typically been designed under the assumption that coordination between users requires significant communication overhead and is thus impractical. Recent progress in efficient activity detection and collision-free scheduling, however, indicates that the cost of coordination can be much less than the naive sch

Liren Yang, Hang Zhang, Jean-Baptiste Jeannin, Necmiye Ozay

Backward reachability analysis is essential to synthesizing controllers that ensure the correctness of closed-loop systems. This paper is concerned with developing scalable algorithms that under-approximate the backward reachable sets, for discrete-time uncertain linear and nonlinear systems. Our algorithm sequentially linearizes the dynamics, and uses const

Chiaki Kobayashi

Thanks to the long-term collaborations between nuclear and astrophysics, we have good understanding on stellar nucleosynthesis, except for the elements around Ti and some neutron-capture elements. From the comparison between observations and Galactic chemical evolution models, it is necessary to have the rapid neutron-capture process associated with core-col

Daniel Camazón, Santiago Encinas

We associate a combinatorial object to sequences of point blow-ups over perfect fields, the weighted directed graph, and another one to the composition of all blow-ups, which we call associated sequential morphisms, the $d-$ary intersection form. Then, in order to consider different fields extensions, we introduce the concepts of algebraically and combinator

Mihwa Jin, Ka Ho Lam, Melissa K. McClure, Jeroen Terwisscha van Scheltinga

Chemical models and experiments indicate that interstellar dust grains and their ice mantles play an important role in the production of complex organic molecules (COMs). To date, the most complex solid-phase molecule detected with certainty in the ISM is methanol, but the James Webb Space Telescope (JWST) may be able to identify still larger organic species

Hong Zhang, Hongying Huang, Jue Yan

In this paper, we introduce cell-average based neural network (CANN) method to solve high-dimensional parabolic partial differential equations. The method is based on the integral or weak formulation of partial differential equations. A feedforward network is considered to train the solution average of cells in neighboring time. Initial values and approximat

N. Lewandowska, P. B. Demorest, M. A. McLaughlin, P. Kilian

We investigate the use of bright single pulses from the Crab pulsar to determine separately the dispersion measure (DM) for the Main Pulse and Interpulse components. We develop two approaches using cross correlation functions (CCFs). The first method computes the CCF of the total intensity of each of 64 frequency channels with a reference channel and convert

Jinhui Hou, Zhiyu Zhu, Hui Liu, Junhui Hou

This paper tackles the challenging problem of hyperspectral (HS) image denoising. Unlike existing deep learning-based methods usually adopting complicated network architectures or empirically stacking off-the-shelf modules to pursue performance improvement, we focus on the efficient and effective feature extraction manner for capturing the high-dimensional c

Sergey Bakhurin, Roland Hildebrand, Mohammad Alkousa, Alexander Titov

We propose a second-order method for unconditional minimization of functions $f(z)$ of complex arguments. We call it the Mixed Newton Method due to the use of the mixed Wirtinger derivative $\frac{\partial^2f}{\partial\bar z\partial z}$ for computation of the search direction, as opposed to the full Hessian $\frac{\partial^2f}{\partial(z,\bar z)^2}$ in the c

Yichi Zhang, Chao Zhang, Cai Zhang, Lixin Fan

This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that is designed to provide highly secure and efficient aggregation of local model parameters for federated learning. The scheme is secure in protecting private model parameters from being disclosed to semi-honest attackers by utilizing quantum bits i.e. qubits to represent model parame

Wending Mai, Yifan Chen

We propose a system to visualize the chirality of the protein in brains, which would be helpful to diagnose early neurological degenerative diseases in vivo. These neurological degenerative diseases often occur along with some mark proteins. By nanoparticle instilling and metamaterial technique, the chiral effect of the mark proteins is assumed to be manifes

Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu

In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imput

Yu Pan, Yifan Tong, Shibei Xue, Guofeng Zhang

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by incr

Yihan Wu, Min Xia, Xiuzhu Wang, Yangsong Zhang

Schizophrenia (SZ) is a serious mental disorder that could seriously affect the patient's quality of life. In recent years, detection of SZ based on deep learning (DL) using electroencephalogram (EEG) has received increasing attention. In this paper, we proposed an end-to-end recurrent auto-encoder (RAE) model to detect SZ. In the RAE model, the raw data was

David Masis, Esteban Segura, Javier Trejos, Adilson Xavier

We propose a novel method for building fuzzy clusters of large data sets, using a smoothing numerical approach. The usual sum-of-squares criterion is relaxed so the search for good fuzzy partitions is made on a continuous space, rather than a combinatorial space as in classical methods \cite{Hartigan}. The smoothing allows a conversion from a strongly non-di

Carlo F. Barenghi

Near absolute zero, superfluid liquid helium displays quantum properties at macroscopic length scales. One property, superfluidity, means flow with zero viscosity. Another property, the existence of a complex wavefunction, constrains the rotation to thin, discrete vortex lines carrying one quantum of circulation each. Therefore, if liquid helium is stirred,

Benedito Leandro, Jeferson Poveda

We prove that a steady gradient Ricci soliton is either Ricci flat with a constant potential function or a quotient of the product steady soliton $N^{n-1}\times\mathbb{R}$, where $N^{n-1}$ is Ricci flat, or isometric to the Bryant soliton (up to scalings), provided that a couple of geometric conditions inspired by the cigar soliton hold. As an application, w

Jeroen G. S. Overschie

Both in the domains of Feature Selection and Interpretable AI, there exists a desire to `rank' features based on their importance. Such feature importance rankings can then be used to either: (1) reduce the dataset size or (2) interpret the Machine Learning model. In the literature, however, such Feature Rankers are not evaluated in a systematic, consistent

Antonio F. Costa, Cam Van Quach Hongler

A knot $K$ in $S^3$ is $q$-periodic if it admits a symmetry that is conjugate to a rotation of order $q$ of $S^3$. If $K$ admits a symmetry which is a homeomorphism without fixed point of period $q$ of $S^3$, then $K$ is called freely $q$-periodic. In a previous paper, we obtained, as a consequence of Flyping Theorem due to Menasco and Thislethwaite, that th

Yujie Nie, Nansen Zhou, Li Tao, Jinlong Zhu

The physical properties of two-dimensional (2D) materials may drastically vary with their thickness profiles. Current thickness profiling methods for 2D material (e.g., atomic force microscopy and ellipsometry) are limited in measurement throughput and accuracy. Here we present a novel high-speed and high-precision thickness profiling method, termed Transmis

Sami Douba

For each integer $n \geq 3$, we exhibit a nonuniform arithmetic lattice in $\mathrm{SO}(n,1)$ containing Zariski-dense surface subgroups.

Fábio Botler, Lucas Colucci, Yoshiharu Kohayakawa

The $\!{}\bmod k$ chromatic index of a graph $G$ is the minimum number of colors needed to color the edges of $G$ in a way that the subgraph spanned by the edges of each color has all degrees congruent to $1\!\!\pmod k$. Recently, the authors proved that the $\!{}\bmod k$ chromatic index of every graph is at most $198k-101$, improving, for large $k$, a resul

Gurpreet Singh, J. C. Pandey

We present a detailed X-ray analysis and imaging of stellar coronae of five coronally connected eclipsing binaries, namely, 44 Boo, DV Psc, ER Vul, XY UMa, and TX Cnc. Both components of these binaries are found to be active. The X-ray light curves of detached and semidetached type systems show eclipsed-like features, whereas no evidence for coronal eclipsin

Steven Rendon Restrepo, Pierre Barge, Radim Vavrik

The diversity of the structures recently observed in protoplanetary discs (PPDs) with the new generation of high-resolution instruments have made more acute the challenging questions that planet-formation models must answer. The challenge is in the theoretical side but also in the numerical one with the need to significantly improve the performances of the c

Rémi Catellier, Romain Duboscq

We consider the rough differential equation with drift driven by a Gaussian geometric rough path. Under natural conditions on the rough path, namely non-determinism, and uniform ellipticity conditions on the diffusion coefficient, we prove path-by-path well-posedness of the equation for poorly regular drifts. In the case of the fractional Brownian motion $B^

Florian Kadner, Tobias Thomas, David Hoppe, Constantin A. Rothkopf

The human prioritization of image regions can be modeled in a time invariant fashion with saliency maps or sequentially with scanpath models. However, while both types of models have steadily improved on several benchmarks and datasets, there is still a considerable gap in predicting human gaze. Here, we leverage two recent developments to reduce this gap: t

Mingzhao Zhuo, Niall Kirkaldy, Tom Maull, Timothy Engstrom

Traditional equivalent circuit models (ECMs) have difficulties in estimating battery internal states due to the lack of relevant physics, such as the lithium diffusion in active particles. Here we configure a circuit network to describe the lithium diffusion and define it as a new high-level circuit element called diffusion-aware voltage source. The circuit

Andrew McInerney, Kevin Burke

Feedforward neural networks (FNNs) can be viewed as non-linear regression models, where covariates enter the model through a combination of weighted summations and non-linear functions. Although these models have some similarities to the approaches used within statistical modelling, the majority of neural network research has been conducted outside of the fi

Tatsuki Yamaguchi

Utilizing ultraproducts, Schoutens constructed a big Cohen-Macaulay algebra $\mathcal{B}(R)$ over a local domain $R$ essentially of finite type over $\mathbb{C}$. We show that if $R$ is normal and $\Delta$ is an effective $\mathbb{Q}$-Weil divisor on $\operatorname{Spec} R$ such that $K_R+\Delta$ is $\mathbb{Q}$-Cartier, then the BCM test ideal $\tau_{\hat{\

A. Zuevsky

For a foliation $\F$ defined on a smooth complex manifold $M$ we introduce the category of vertex operator algebra $V$ bundles with sections provided by vectors of elements of the space of algebraically extended $V$-module $W$-valued differentials. An intrinsic coordinate-independent formulation for such bundles is given. Finally, we identify the cohomology

Mohammad Amini, Orlando J. Silveira, Viliam Vaňo, Jose L. Lado

Two-dimensional (2D) ferroelectric materials provide a promising platform for the electrical control of quantum states. In particular, due to their 2D nature, they are suitable for influencing the quantum states of deposited molecules via the proximity effect. Here, we report electrically controllable molecular states in phthalocyanine molecules adsorbed on

Yang Zhang, Yang Wang, Haifeng Du, Shlomo Havlin

Interdisciplinary research increasingly fuels innovation, and is considered to be a key to tomorrow breakthrough. Yet little is known about whether interdisciplinary research manifests delayed impact. Here, we use the time to reach the citation peak to quantify the highest impact time and citation dynamics, and examine its relationship with interdisciplinari

Hemanta Kalita, Salvador Sánchez Perales, Bipan Hazarika

In this paper we extend the theory of Henstock-Orlicz spaces with respect to vector measure. We study the integral representation of operators. Lastly we study Uniformly convexity, reflexivity and the Radon-Nikodym property of the Henstock-Orlicz spaces

Bin Ren, Hao Tang, Yiming Wang, Xia Li

For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images. Hence, one not only needs to encode the long-range dependencies

Tai-Xing Liu, Rui Peng, Songlin Lyu, Bingwei Long

Renormalization of proton-proton fusion is studied in the framework of chiral effective field theory. Strict perturbative treatment of subleading corrections is applied in the analysis. Possible enhancement of two-nucleon contact axial current operators is the focus of the study. We find evidence that supports a previous proposal in the literature to promote

Hannes Hagmar, Le Anh Tuan, Robert Eriksson

Deep reinforcement learning (DRL) is a machine learning-based method suited for complex and high-dimensional control problems. In this study, a real-time control system based on DRL is developed for long-term voltage stability events. The possibility of using system services from demand response (DR) and energy storage systems (ESS) as control measures to st

Weihang Liu, Yi Jiao, Yu Zhao, Jianliang Chen

In the fourth-generation storage ring light sources (4GLSs), associated with the extremely strong nonlinearities inherent in the multi-bend achromat design, the dynamic acceptance is usually small and it is difficult to implement traditional off-axis local-bump injection. To release the requirement on dynamic acceptance, on-axis longitudinal injection scheme

Matan Gilboa, Noam Nisan

We study the computational complexity of "public goods games on networks". In this model, each vertex in a graph is an agent that needs to take a binary decision of whether to "produce a good" or not. Each agent's utility depends on the number of its neighbors in the graph that produce the good, as well as on its own action. This dependence can be captured b

Toufique Ahmed, Premkumar Devanbu

Very large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A particularly exciting aspect of LLMs is their knack for few-shot and zero-shot learning: they can learn to perform a task with very few examples. Few-shotting has particular synergies

Inseung Hwang, Daniel S. Jeon, Adolfo Muñoz, Diego Gutierrez

Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent tec

Davide Perego, Matteo Tarocchi

A group $G$ is invariably generated if there exists a subset $S \subseteq G$ such that, for every choice $g_s \in G$ for $s \in S$, the group $G$ is generated by $\{ s^{g_s} \mid s \in S \}$. In [GGJ16] Gelander, Golan and Juschenko showed that Thompson groups $T$ and $V$ are not invariably generated. Here we generalize this result to the larger setting of r

A. D. Shkop

Thermal transmission in a molecular transistor with fully spin-polarized electrodes subjected to a temperature gradient is considered. The problem has been solved by using density matrix method in perturbation approach over small tunneling width. It has been found that due to the vibronic effects spintronic molecular transistor is characterized by negative d

Alberto Facchini, Carmelo Antonio Finocchiaro

The goal of this paper is to deepen the study of multiplicative lattices in the sense of Facchini, Finocchiaro and Janelidze. We provide a sort of Prime Ideal Principle that guarantees that maximal implies prime in a variety of cases (among them the case of commutative rings with identity). This result is used to study the lattice theoretic counterpart of mu