August 2022 arXiv papers — page 52

Sourodeep De, Pallabi Parui, Amruta Mishra

In-medium masses of the pseudoscalar and vector open bottom mesons ($B$, $\bar{B}$, $B_s$ and $B^*$, $\bar{B}^*$, $B_s^*$) are studied in the magnetized nuclear matter by considering the effects of Dirac sea, within the chiral effective model. The mass modifications arise due to the interactions of the open bottom mesons with the nucleons and the scalar meso

Tuomas Hytönen, Kangwei Li, Jin Tao, Dachun Yang

Let $1<q<p<\infty$, $\frac1r:=\frac1q-\frac1p$, and $T$ be a non-degenerate Calder\'on--Zygmund operator. We show that the commutator $[b,T]$ is compact from $L^p({\mathbb R}^n)$ to $L^q({\mathbb R}^n)$ if and only if the symbol $b=a+c$ with $a\in L^r({\mathbb R}^n)$ and $c$ being any constant. Since both the corresponding Hardy--Littlewood maximal operator

Amanpreet Kaur, Stephen Kerby, Abraham D. Falcone

Among the ~2157 unassociated sources in the third data release (DR3) of the fourth Fermi catalog, ~1200 were observed with the Neil Gehrels Swift Observatory pointed instruments. These observations yielded 238 high S/N X-ray sources within the 95% Fermi uncertainty regions. Recently, Kerby et al. employed neural networks to find blazar candidates among these

Wenjun Chang, Guanwen Fang, Yizhou Gu, Zesen Lin

To investigate the effects of environment in the quenching phase, we study the empirical relations for green valley (GV) galaxies between overdensity and other physical properties (i.e., effective radius $r_{\rm e}$, S\'{e}rsic indices $n$, and specific star formation rate sSFR). Based on five 3D-{\it HST}/CANDELS fields, we construct a large sample of 2126

Siyi Du, Ben Hers, Nourhan Bayasi, Ghassan Hamarneh

Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions, where lesions on darker skin types are usually underrepresented and have lower diagnosis accuracy, receives little attention. In this paper, we propose FairDisCo, a disentanglement deep learning framework with cont

Ming-Xia Ma, Shao-Feng Wu

In AdS/CFT corresponding, the UV divergence of generating functional on the field theory can be removed as the IR divergence in the gravity. This geometric process is well known as holographic renormalization. The standard method of holographic renormalization is based on the Fefferman-Graham expansion, which is strict and universal but technically cumbersom

Qilong Huang, Qing-Shan Jia, Xiang Wu, Shengyuan Xu

The large adoption of EVs brings practical interest to the operation optimization of the charging station. The joint scheduling of pricing and charging control will achieve a win-win situation both for the charging station and EV drivers, thus enhancing the operational capability of the station. We consider this important problem in this paper and make the f

Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang

While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the scalability constraint imposed by multi-hop data dependency. Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on n

Yuyang Zhou, Yinghua Wang, Yuxiao Li, Jialing Huang

Compared with image method (IM) based ray tracing (RT), shooting and bouncing ray (SBR) method is characterized by fast speed but low accuracy. In this paper, an iterative precise algorithm based on equiangular division is proposed to make rough paths accurate, allowing SBR to calculate exact channel information. Different ray launching methods are compared

Chen Wang, Yinghua Wang, Yuxiao Li, Jialing Huang

Ray tracing is an efficient channel modeling method. However, the traditional ray tracing method has high computation complexity. To solve this problem, an improved bounding volume hierarchies (BVH) algorithm is proposed in this paper. Based on surface area heuristic (SAH) and spatial distance, the proposed algorithm can effectively reduce the number of unne

Yang Wu, Yinghua Wang, Jie Huang, Cheng-Xiang Wang

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz

Yuanzhe Wang, Hao Cao, Yifan Jin, Zizhe Zhou

Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology have been proved significant for the sixth generation (6G) communication systems, and have gained global interests. In this paper, we employ the shooting and bouncing ray (SBR) method integrated with acceleration technology to model THz and massive M

Dariusz Chruściński, Gen Kimura, Hiromichi Ohno, Tanmay Singal

In this paper, we introduce a one parameter generalization of the famous B\"ottcher-Wenzel (BW) inequality in terms of a $q$-deformed commutator. For $n \times n$ matrices $A$ and $B$, we consider the inequality \[ \Re\langle[B,A],[B,A]_q\rangle \le c(q) \|A\|^2 \|B\|^2, \] where $\langle A,B \rangle = {\rm tr}(A^*B)$ is the Hilbert-Schmidt inner product, $\

Muying Luo, Shunping Ji, Shiqing Wei

In this paper, we introduce a new building dataset and propose a novel domain generalization method to facilitate the development of building extraction from high-resolution remote sensing images. The problem with the current building datasets involves that they lack diversity, the quality of the labels is unsatisfactory, and they are hardly used to train a

Yuki Amano

The maximization for the independence systems defined on graphs is a generalization of combinatorial optimization problems such as the maximum $b$-matching, the unweighted MAX-SAT, the matchoid, and the maximum timed matching problems. In this paper, we consider the problem under the local oracle model to investigate the global approximability of the problem

Huijie Zhang, Anthony Opipari, Xiaotong Chen, Jiyue Zhu

Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even humans find certain transparent surfaces with little specular reflection or refraction, e.g. glass doors, difficult to perceive. A second c

Ya-Feng Jiao, Jing-Xue Liu, Ying Li, Ronghua Yang

Entanglement between distant massive mechanical oscillators is of particular interest in quantum-enabled devices due to its potential applications in distributed quantum information processing. Here we propose how to achieve nonreciprocal remote entanglement between two spatially separated mechanical oscillators within a cascaded optomechanical configuration

Masaki J. S. Yang

We consider an expansion of the type-I seesaw mechanism by the inverse of the 3-3 matrix element $1/(M_{R})_{33}$ of the mass matrix of right-handed neutrinos $M_{R}$. Conditions of such a situation are obtained for $M_{R}$ and the Dirac mass matrix $m_{D}$. In this case, a partial $Z_{2}$ symmetry such as $S m_{D} P_{} = \pm m_{D} P_{}$ with a projection ma

Rabab Abdelfattah, Xin Zhang, Zhenyao Wu, Xinyi Wu

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subs

Yihong Dong, Ge Li, Xue Jiang, Zhi Jin

Code generation focuses on the automatic conversion of natural language (NL) utterances into code snippets. The sequence-to-tree (Seq2Tree) approaches are proposed for code generation, with the guarantee of the grammatical correctness of the generated code, which generate the subsequent Abstract Syntax Tree (AST) node relying on antecedent predictions of AST

Tong Xiao, Buye Xu, Chuming Zhao

Active noise control (ANC) systems are commonly designed to achieve maximal sound reduction regardless of the incident direction of the sound. When desired sound is present, the state-of-the-art methods add a separate system to reconstruct it. This can result in distortion and latency. In this work, we propose a multi-channel ANC system that only reduces sou

Hugo Montani

We study Lagrangian and orthogonal splittings\textbf{\ }of quadratic vector spaces establishing an equivalence with complex product structures. Then we show that a Manin triple equipped with generalized metric $\mathcal{G}+% \mathcal{B}$ such that $\mathcal{B}$ is an $\mathcal{O}$-operator with extension $\mathcal{G}$ of mass -1 can be turned in another Mani

Yushi Shi, Ting Li, Jiandong Zhu

For original Kuramoto models with nonidentical oscillators, it is impossible to realize complete phase synchronization. However, this paper reveals that complete phase synchronization can be achieved for a large class of high-dimensional Kuramoto models with nonidentical oscillators. Under the topology of strongly connected digraphs, a necessary and sufficie

Stephen C. Anco

Symmetries and adjoint-symmetries are two fundamental (coordinate-free) structures of PDE systems. Recent work has developed several new algebraic aspects of adjoint-symmetries: three fundamental actions of symmetries on adjoint-symmetries; a Lie bracket on the set of adjoint-symmetries given by the range of a symmetry action; a generalized Noether (pre-symp

Sakander Hayat, Muhammad Arshad, Kinkar Chandra Das

Recently in 2021, Gutman introduced the Sombor index of a graph, a novel degree-based topological index. It has been shown that the Sombor index efficiently models the thermodynamic properties of chemical compounds. Assume $\mathbb{B}_n^k$ (resp. $\mathbb{V}_n^k$) comprises all graphs with order $n$ having number of bridges (resp. vertex-connectivity) $k$. H

Karl Pierce, Edward F Valeev

We consider the problem of constructing a canonical polyadic (CP) decomposition for a tensor network, rather than a single tensor. We illustrate how it is possible to reduce the complexity of constructing an approximate CP representation of the network by leveraging its structure in the course of the CP factor optimization. The utility of this technique is d

Jing Yang, Rui-Hui Lin, Xiang-Hua Zhai

We propose a new model for the viscosity of cosmic matters, which can be applied to different epochs of the universe. Using this model, we include the bulk viscosities as practical corrections to the perfect fluid models of the baryonic and dark matters since the material fluids in the real world may have viscosities due to thermodynamics. Such inclusion is

Andrew Walker, Nahum Arav, Doyee Byun

We present the analysis of the absorption troughs of six outflows observed in quasar SDSS J1130+0411 ($z \approx 3.98$) with radial velocities ranging from $-2400$ to $-15,400$ km s$^{-1}$. These spectra were taken with the Very Large Telescope/Ultraviolet and Visual Echelle Spectrograph over the rest frame wavelength range of $1135-1890$ \r{A}. In the main

Will Lockhart, Samuel E. Gralla

The 2017 Event Horizon Telescope (EHT) observations of M87* detected a ring-shaped feature $\sim40\mu$as in diameter, consistent with the event horizon scale of a black hole of the expected mass. The thickness of this ring, however, proved difficult to measure, despite being an important parameter for constraining the observational appearance. In the first p

S. Maryam Hosseini, Milad Sikaroudi, Morteza Babaei, H. R. Tizhoosh

Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or gradients can expose private training dat

Cai-Ying Shao, Yu-Jie Tan, Cheng-Gang Shao, Kai Lin

In this paper, we study the quasinormal mode and late-time tail of charged massless scalar perturbations of a black hole in the generalized Rastall gravity. The black hole metric in question is spherically symmetric, accompanied by a power-Maxwell field surrounded by a quintessence fluid. It is shown that the massless scalar field, when {\it dressed up} with

Noah Ferich, Stanley A. Baronett, Daniel Tamayo, Jason H. Steffen

To more thoroughly study the effects of radiative forces on the orbits of small, astronomical bodies, we introduce the Yarkovsky effect into REBOUNDx, an extensional library for the N-body integrator REBOUND. Two different versions of the Yarkovsky effect (the "Full Version" and the "Simple Version") are available for use, depending on the needs of the user.

Mao Fabrice Djete

In order to deal with the question of the existence of a calibrated local stochastic volatility model in finance, we investigate a class of McKean--Vlasov equations where a minimal continuity assumption is imposed on the coefficients. Namely, the drift coefficient and, in particular, the volatility coefficient are not necessarily continuous in the measure va

Joël Lindegger, Damla Senol Cali, Mohammed Alser, Juan Gómez-Luna

Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations, and/or hardware acceleration. A promising candidate for all of the above is the recently proposed GenASM algorithm. We identify and address three inefficiencies in the GenASM algorithm: it has a h

C. F. L. Godinho, I. V. Vancea

In this paper, we propose a new model of fractional mimetic dark matter based on the fractional action-like variational approach FALVA implementation of fractionality. The model is non-local at classical level and its equations of motion are highly non-linear. We discuss two particular cases of non-fractional and fractional mimetic dark fields and solve comp

Guang Ping He

We find experimentally that when artificial neural networks are connected in parallel and trained together, they display the following properties. (i) When the parallel-connected neural network (PNN) is optimized, each sub-network in the connection is not optimized. (ii) The contribution of an inferior sub-network to the whole PNN can be on par with that of

Qianqian Xie, Jimin Huang, Tulika Saha, Sophia Ananiadou

Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization. However, in these methods, there remain limitations in the way they capture and integrate the global semantic information. In this paper, we propose a novel model, the graph contrastive topic

Sam Pattison

In this paper we prove an effective equidistribution result for both primitive and non-primitive points on certain expanding horocycle sections in ASL$(2,\mathbb{Z}) \backslash $ASL$(2,\mathbb{R})$. This provides an effective version of a result recently proven by the author and generalises recent work by Einsiedler, Luethi and Shah who prove similar results

Steven Finch

While symbolics for the equilibrium M/D/1-LIFO waiting time density are completely known, corresponding numerics for M/D/1-SIRO are derived from recursions due to Burke (1959). Implementing an inverse Laplace transform-based approach for the latter remains unworkable.

Yongwei Wang, Yong Liu, Zhiqi Shen

Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to evaluate the robustness of such CF systems in deployment. Fundamentally different from existing attacks, this work revisits the item promotion task and reformulates it from a targete

Mengying Lei, Aurelie Labbe, Lijun Sun

Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies that are nonstationary and nonseparable, developing effective and computationally efficient statistical models to accommodate nonstationary/nonseparable processes containing both lon

H. Koibuchi, F. Kato, G. Diguet, T. Uchimoto

In this paper, we numerically study Turing patterns by the Finsler geometry (FG) modeling technique on thermally fluctuating triangular lattices, which are often used for modeling cell membranes or lipid membranes, focusing on the origin of diffusion anisotropy. The FG modeling prescription allows us to assume direction-dependent diffusion described by Lapla

Filippo Stellin, Marcel Filoche, Frédéric Dias

While the properties and the shape of the ground state of a gas of ultracold bosons are well understood in harmonic potentials, they remain for a large part unknown in the case of random potentials. Here, we use the localization-landscape (LL) theory to study the properties of the solutions to the Gross-Pitaevskii equation (GPE) in one-dimensional (1D) speck

Mordechai Guri

Highly secure devices are often isolated from the Internet or other public networks due to the confidential information they process. This level of isolation is referred to as an 'air-gap .' In this paper, we present a new technique named ETHERLED, allowing attackers to leak data from air-gapped networked devices such as PCs, printers, network cameras, embed

F. Sutanto, J. Xu, S. Pereverzev, A. Bernstein

We investigate a hypothesis that energy accumulation and the subsequent release in NaI(Tl) may lead to pulse-like events in the few-keV energy regime, a phenomenon suggested by the crystal manufacturing company Saint-Gobain, who provided the crystals for DAMA-LIBRA. While we observed delayed long-lasting light emission in a 3" NaI(Tl) crystal after exposing

Ardeshir Mirbakhsh, Joyoung Lee, Dejan Besenski

This paper presents a signal-free intersection control system for CAVs by combination of a pixel reservation algorithm and a Deep Reinforcement Learning (DRL) decision-making logic, followed by a corridor-level impact assessment of the proposed model. The pixel reservation algorithm detects potential colliding maneuvers and the DRL logic optimizes vehicles'

Qian Jia, Haibo Qiu, Antonio Muñoz Mateo

We study general collisions between chiral solitons in Bose-Einstein condensates subject to combined attractive and current-dependent interatomic interactions. A simple analysis based on the linear superposition of the solitons allows us to determine the relevant time and space scales of the dynamics, which is illustrated by extensive numerical simulations.

D. Rozpedzik, L. De Keukeleere, K. Bodek, L. Hayen

For identification and 3D-tracking of low-energy electrons a new type of gas-based detector was designed that minimizes scattering and energy loss. The current version of the detector is a combination of a plastic scintillator, serving as a trigger source and energy detector, and a hexagonally structured multi-wire drift chamber (MWDC), filled with a mixture

Jeonghun J. Lee, Omar Ghattas

In this paper we propose a variant of enriched Galerkin methods for second order elliptic equations with over-penalization of interior jump terms. The bilinear form with interior over-penalization gives a non-standard norm which is different from the discrete energy norm in the classical discontinuous Galerkin methods. Nonetheless we prove that optimal a pri

Daniel Poh, Stephen Roberts, Stefan Zohren

Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures. While these strategies have been applied successfully to data-rich settings involving mature assets with long histories, deploying them on instruments with limited samples generally produce over-fitted m

Vasisht Duddu, Antoine Boutet

Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to its corresponding model prediction. The dependency of explanations on input raises privacy concerns for sensitive user data. However, current literature has limited discussion on pr

Przemyslaw Biecek

The data revolution has generated a huge demand for data-driven solutions. This demand propels a growing number of easy-to-use tools and training for aspiring data scientists that enable the rapid building of predictive models. Today, weapons of math destruction can be easily built and deployed without detailed planning and validation. This rapidly extends t

Mehdi Khalloufi, Jesse Capecelatro

We perform particle-resolved simulations of subsonic and transonic flows past random arrays of spherical particles. The Reynolds number is held at $Re{\approx}300$ to ensure the flow remains in the continuum regime. At low volume fractions, the drag force increases sharply near a critical Mach number due to the formation of shock waves and reaches a maximum

Peter W. Shor

I recount some of my memories of the early development of quantum computation, including the discovery of the factoring algorithm, of error correcting codes, and of fault tolerance.

Barry Mazur, Karl Rubin, Alexandra Shlapentokh

Let $K$ be a number field, let $L$ be an algebraic (possibly infinite degree) extension of $K$, and let $O_K$ $\subset$ $O_L$ be their rings of integers. Suppose $A$ is an abelian variety defined over $K$ such that $A(K)$ is infinite and $A(L)/A(K)$ is a torsion group. If at least one of the following conditions is satisfied: 1. $L$ is a number field, 2. $L$

Siyan Daniel Li-Huerta

Using a mixed-characteristic incarnation of fusion, we prove an analog of Nekov\'a\v{r}-Scholl's plectic conjecture for local Shimura varieties. We apply this to obtain results on the plectic conjecture for (global) Shimura varieties after restricting to a decomposition group. Along the way, we prove a $p$-adic uniformization theorem for the basic locus of a

John S. Caughman, Charles L. Dunn, Joshua D. Laison, Nancy Ann Neudauer

A rectangle visibility graph (RVG) is represented by assigning to each vertex a rectangle in the plane with horizontal and vertical sides in such a way that edges in the graph correspond to unobstructed horizontal and vertical lines of sight between their corresponding rectangles. To discretize, we consider only rectangles whose corners have integer coordina

Myeongju Chae, Gunhee Cho, Maria Gordina, Guang Yang

We first provide a stochastic formula for the Carath\'eodory distance in terms of general Markovian couplings and prove a comparison result between the Carath\'eodory distance and the complete K\"ahler metric with a negative lower curvature bound using the Kendall-Cranston coupling. This probabilistic approach gives a version of the Schwarz lemma on complete

Yuanyuan Feng, Abhilasha Ravichander, Yaxing Yao, Shikun Zhang

We present a study of privacy attitudes and behaviors of people who are blind or low vision. Our study involved in-depth interviews with 21 US participants. The study explores their risk perceptions and also whether and how they go about obtaining information about the data practices of digital technologies with which they interact. One objective of the stud

Parand Akbari, Masoud Zamani, Amir Mostafaei

Predicting mechanical properties in metal additive manufacturing (MAM) is essential for ensuring the performance and reliability of printed parts, as well as their suitability for specific applications. However, conducting experiments to estimate mechanical properties in MAM processes can be laborious and expensive, and they are often limited to specific mat

Duyu Chen, Yu Liu, Yu Zheng, Houlong Zhuang

Carbon nanotubes are quasi-one-dimensional systems that possess superior transport, mechanical, optical, and chemical properties. In this work, we generalize the notion of disorder hyperuniformity, a recently discovered exotic state of matter with hidden long-range order, to quasi-one-dimensional materials. As a proof of concept, we then apply the generalize

Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang

Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph data. However, real-world graphs are always heterogeneous, which poses three critical challenges that existing methods ignore: 1) how to capture complex graph structure? 2) how to in

Michail Kalntis, George Iosifidis

The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we propose an onl

Emanuele Gallorini, Maurizio Quadrio, Davide Gatti

The effect of streamwise-traveling waves of spanwise wall velocity (StTW) on the quasi-streamwise vortices (QSV) populating the near-wall region of turbulent channels is studied via a conditional averaging technique applied to flow snapshots obtained via Direct Numerical Simulation. The analysis by A. Yakeno, Y. Hasegawa, N. Kasagi, "Modification of quasi-st

Manuel Ettengruber, Matteo Agostini, Allen Caldwell, Philipp Eller

We evaluate the discovery probability of a combined analysis of proposed neutrinoless double-beta decay experiments in a scenario with normal ordered neutrino masses. The discovery probability strongly depends on the value of the lightest neutrino mass, ranging from zero in case of vanishing masses and up to 80-90\% for values just below the current constrai

J. Lian, K. Choi, B. Veeramani, A. Hu

The quality of Artificial Intelligence (AI) algorithms is of significant importance for confidently adopting algorithms in various applications such as cybersecurity, healthcare, and autonomous driving. This work presents a principled framework of using a design-of-experimental approach to systematically evaluate the quality of AI algorithms, named as Do-AIQ

Yun-Feng Wei, Tong Liu

Gamma-ray bursts (GRBs) are classified into long and short populations (i.e., LGRBs and SGRBs) based on the observed bimodal distribution of duration $T_{90}$. Multimessenger observations indicated that most SGRBs and LGRBs should be powered by ultrarelativistic jets launched from black hole (BH) hyperaccretion in compact object mergers and massive collapsar

Atasi Panda, Anand Louis, Prajakta Nimbhorkar

We study the probabilistic assignment of items to platforms that satisfies both group and individual fairness constraints. Each item belongs to specific groups and has a preference ordering over platforms. Each platform enforces group fairness by limiting the number of items per group that can be assigned to it. There could be multiple optimal solutions that

Diego Frias

In this article, the conversion of color RGB images to grayscale is covered by characterizing the mathematical operators used to project 3 color channels to a single one. Based on the fact that most operators assign each of the $256^3$ colors a single gray level, ranging from 0 to 255, they are clustering algorithms that distribute the color population into

Satyabrata Datta, Rome Samanta

A long-lived scalar field ($\Phi$) which couples weakly to the right-handed (RH) neutrinos ($N_{Ri}$), generates small RH neutrino masses ($M_i$) in Low-Scale-Leptogenesis (LSL) mechanisms, despite having a large vacuum expectation value $v_\Phi$. In this case, the correlation shared by the $M_i$s and the duration of the non-standard cosmic history driven by

Jin-Yi Cai, Ashwin Maran

We study the problem of counting all cycles or self-avoiding walks (SAWs) on triangulated planar graphs. We present a subexponential $2^{O(\sqrt{n})}$ time algorithm for this counting problem. Among the technical ingredients used in this algorithm are the planar separator theorem and a delicate analysis using pairs of Motzkin paths and Motzkin numbers. We ca

Pravin Khanal, Bowei Zhou, Magda Andrade, Christopher Mastrangelo

Perpendicular magnetic tunnel junction with MgAl2O4 barrier is investigated. It is found that reactive RF sputtering with O2 is essential to obtain strong perpendicular magnetic anisotropy and large tunneling magnetoresistance in MgAl2O4-based junctions. An interfacial perpendicular magnetic anisotropy energy density of 2.25 mJ/m2 is obtained for the samples

Ivan Chajda, Helmut Länger

It is widely accepted that the logic of quantum mechanics is based on orthomodular posets. However, such a logic is not dynamic in the sense that it does not incorporate time dimension. To fill this gap, we introduce certain tense operators on such a logic in an inexact way, but still satisfying requirements asked on tense operators in the classical logic ba

Glib Yevkin, Olexandr Yevkin

The advantages and difficulties of application of Pad\'e approximants to two-dimensional regression analysis are discussed. New formulation of residuals is suggested in the method of least squares. It leads to a system of linear equations in case of rational functions. The possibility of using Tikhonov regularization technique to avoid overfitting is demonst

Jingcai Guo, Song Guo, Jie Zhang, Ziming Liu

Federated learning (FL) has emerged as a promising privacy-preserving distributed machine learning framework recently. It aims at collaboratively learning a shared global model by performing distributed training locally on edge devices and aggregating local models into a global one without centralized raw data sharing in the cloud server. However, due to the

Alexander Kensert, Gert Desmet, Deirdre Cabooter

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecular descriptors or fingerprints. Since relatively recently, graph neural network (GNN) algorithms have been implemented for molecular ML, showing comparable or superior performance to descriptor or fingerprint-b

Jonathan H. Brown, Adam H. Fuller, David R. Pitts, Sarah A. Reznikoff

Let $B \subseteq A$ be an inclusion of C$^*$-algebras. We study the relationship between the regular ideals of $B$ and regular ideals of $A$. We show that if $B \subseteq A$ is a regular C$^*$-inclusion and there is a faithful invariant conditional expectation from $A$ onto $B$, then there is an isomorphism between the lattice of regular ideals of $A$ and in

Maksim E. Eren, Nick Solovyev, Manish Bhattarai, Kim Rasmussen

As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents. One popular topic modeling approach is non-negative matrix factorization (NMF), an unsupervised machine learning (ML) method. Recently, Semantic NMF with automatic model selection (SeNMFk) ha

Richard James Petti

This work interprets the quantum terms in a Lagrangian, and consequently of the wave equation and momentum tensor, in terms of a modified spacetime metric. Part I interprets the quantum terms in the Lagrangian of a Klein Gordon field as scalar curvature of conformal dilation covector nm that is proportional to hbar times the gradient of wave amplitude R. Par

Cuong Nguyen, Daniel Nkemelu, Ankit Mehta, Michael Best

Hate speech and misinformation, spread over social networking services (SNS) such as Facebook and Twitter, have inflamed ethnic and political violence in countries across the globe. We argue that there is limited research on this problem within the context of the Global South and present an approach for tackling them. Prior works have shown how machine learn

Liya Gaynutdinova, Martin Ladecký, Aleš Nekvinda, Ivana Pultarová

A numerical procedure providing guaranteed two-sided bounds on the effective coefficients of elliptic partial differential operators is presented. The upper bounds are obtained in a standard manner through the variational formulation of the problem and by applying the finite element method. To obtain the lower bounds we formulate the dual variational problem

Xiaoyu Lin

The past few years have witnessed the burst of drone-based applications where computer vision plays an essential role. However, most public drone-based vision datasets focus on detection and tracking. On the other hand, the performance of most existing image super-resolution methods is sensitive to the dataset, specifically, the degradation model between hig

Rustam Sadykov, Stanislav Trunov

Introduced by Seifert and Threlfall, cylindrical neighborhoods of isolated critical points of smooth functions is an essential tool in the Lusternik- Schnirelmann theory. We conjecture that every isolated critical point of a smooth function admits a cylindrical ball neighborhood. We show that the conjecture is true for cone-like critical points, Cornea reaso

Hae Beom Lee, Dong Bok Lee, Sung Ju Hwang

In this paper, we introduce a novel approach for systematically solving dataset condensation problem in an efficient manner by exploiting the regularity in a given dataset. Instead of condensing the dataset directly in the original input space, we assume a generative process of the dataset with a set of learnable codes defined in a compact latent space follo

Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher

Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood due to the min-max optimization of the generator and discriminator. This paper analyzes these joint dynamics when the true samples, as well as the generated samples, are discrete,

Hai Li, Xingrui Yang, Hongjia Zhai, Yuqian Liu

Virtual content creation and interaction play an important role in modern 3D applications such as AR and VR. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. We propose Vox-Surf, a voxel-based implicit surface represen

Iman Vakilinia, Weihong Wang, Jiajun Xin

The dominance of a few big companies in the storage market arising various concerns including single point of failure, privacy violation, and oligopoly. To eliminate the dependency on such a centralized storage architecture, several Decentralized Storage Network (DSN) schemes such as Filecoin, Sia, and Storj have been introduced. DSNs leverage blockchain tec

Arsalan Heidarpanah

During the last three decades, many studies have been conducted in the field of treatment with non-invasive methods. In this way, researchers try to use alternative methods including the use of electromagnetic waves in the treatment of diseases. As a result, the therapeutic use of electromagnetic waves in the treatment of neurological diseases has made signi

Baian Liu

$\DeclareMathOperator{\IntR}{Int{}^\text{R}}$Integer-valued rational functions are a natural generalization of integer-valued polynomials. Given a domain $D$, the collection of all integer-valued rational functions over $D$ forms a ring extension $\IntR(D)$ of $D$. For a valuation domain $V$, we characterize when $\IntR(V)$ is a Pr\"ufer domain and when $\In

Mehmet Aktukmak, Haonan Zhu, Marc G. Chevrette, Julia Nepper

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to

Ashkan Farhangi, Jiang Bian, Arthur Huang, Haoyi Xiong

Time series models often deal with extreme events and anomalies, both prevalent in real-world datasets. Such models often need to provide careful probabilistic forecasting, which is vital in risk management for extreme events such as hurricanes and pandemics. However, it is challenging to automatically detect and learn to use extreme events and anomalies for

Harsh Rangwani, Naman Jaswani, Tejan Karmali, Varun Jampani

Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples. There has been a large body of work to train discriminative models for visual recognition on long-tailed distribution. In contrast, we aim to train conditional Generative Ad

Łukasz Struski, Jacek Tabor, Bartosz Zieliński

Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilistic approach to this problem, which has at least three advantages compared to the existing approaches: it simplifies the training process, i

Richard D. Gill, Justo Pastor Lambare

In a sequence of papers, Marian Kupczynski has argued that Bell's theorem can be circumvented if one takes correct account of contextual setting-dependent parameters describing measuring instruments. We show that this is not true. Despite first appearances, Kupczynksi's concept of a contextual locally causal probabilistic model is mathematically a special ca

Jordan Aiko Deja, Sven Mayer, Klen Čopič Pucihar, Matjaž Kljun

Humans have been developing and playing musical instruments for millennia. With technological advancements, instruments were becoming ever more sophisticated. In recent decades computer-supported innovations have also been introduced in hardware design, usability, and aesthetics. One of the most commonly digitally augmented instruments is the piano. Besides

Enes Altuncu, Virginia N. L. Franqueira, Shujun Li

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term ``deepfake''. Based on both the research literature and resources in English and in Chinese, this p

Bernhard Heim, Markus Neuhauser

We construct a new parametrization of double sequences $\{A_{n,k}(s)\}_{n,k}$ between $A_{n,k}(0)= \binom{n-1}{k-1}$ and $A_{n,k}(1)= \frac{1}{n!}\stirl{n}{k}$, where $\stirl{n}{k}$ are the unsigned Stirling numbers of the first kind. For each $s$ we prove a central limit theorem and a local limit theorem. This extends the de\,Moivre--Laplace central limit t

Evan D. Gorman, Manuel E. Lladser

Ultrametric matrices have a rich structure that is not apparent from their definition. Notably, the subclass of strictly ultrametric matrices are covariance matrices of certain weighted rooted binary trees. In applications, these matrices can be large and dense, making them difficult to store and handle. In this manuscript, we exploit the underlying tree str

Mansoor Ali, Gilberto Ochoa-Ruiz, Sharib Ali

Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods which require large fully labeled data to train supervised models and suffer from pseudo label bias because of class imbalance issues. However large image datasets with bounding box annotatio

Pauliina Ilmonen, Nourhan Shafik, Tommi Sottinen, Germain Van Bever

This paper considers the problem of reconstructing missing parts of functions based on their observed segments. It provides, for Gaussian processes and arbitrary bijective transformations thereof, theoretical expressions for the $L^2$-optimal reconstruction of the missing parts. These functions are obtained as solutions of explicit integral equations. In the