May 2022 arXiv papers — page 103

S. H. Shabbeer Basha, Debapriya Tula, Sravan Kumar Vinakota, Shiv Ram Dubey

Transfer Learning enables Convolutional Neural Networks (CNN) to acquire knowledge from a source domain and transfer it to a target domain, where collecting large-scale annotated examples is time-consuming and expensive. Conventionally, while transferring the knowledge learned from one task to another task, the deeper layers of a pre-trained CNN are finetune

Koichi Miyamoto, Gonzalo Morrás, Takahiro S. Yamamoto, Sachiko Kuroyanagi

The speedup of heavy numerical tasks by quantum computing is now actively investigated in various fields including data analysis in physics and astronomy. In this paper, we propose a new quantum algorithm for matched filtering in gravitational wave (GW) data analysis based on the previous work by Gao et al., Phys. Rev. Research 4, 023006 (2022) [arXiv:2109.0

Son T. Huynh, Nhi Dang, Dac H. Nguyen, Phong T. Huynh

Recommender systems have been increasingly popular in entertainment and consumption and are evident in academics, especially for applications that suggest submitting scientific articles to scientists. However, because of the various acceptance rates, impact factors, and rankings in different publishers, searching for a proper venue or journal to submit a sci

Qianggang Ding, Deheng Ye, Tingyang Xu, Peilin Zhao

Graph neural networks (GNNs) have been applied into a variety of graph tasks. Most existing work of GNNs is based on the assumption that the given graph data is optimal, while it is inevitable that there exists missing or incomplete edges in the graph data for training, leading to degraded performance. In this paper, we propose Generative Predictive Network

Yiwen Chen, Sheng Guo, Zedong Zhang, Lei Zhou

Precision robotic manipulation tasks (insertion, screwing, precisely pick, precisely place) are required in many scenarios. Previous methods achieved good performance on such manipulation tasks. However, such methods typically require tedious calibration or expensive sensors. 3D/RGB-D cameras and torque/force sensors add to the cost of the robotic applicatio

Chuan Li, Cheng Fang, Zhen Li, MingDe Ding

The Chinese H{\alpha} Solar Explorer (CHASE), dubbed "Xihe" - Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration (CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the H{\alpha} waveband. The H{\alpha} Ima

Catharina Marie van Alen, Alexander Brenner, Tobias Warnecke, Julian Varghese

In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures have successfully captured biomarkers and allowed discrimination between Parkinson's disease (PD) and healthy controls (HC) or differential diagn

Junjia Liu, Yiting Chen, Zhipeng Dong, Shixiong Wang

This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly dynamic coordinated movements, which is usually difficult to learn for a chef, let alone transfer to robots. In this letter, we define a canonical stir-fry movement, and then propose a decoupled framework for

Igor Klep, Claus Scheiderer, Jurij Volčič

A noncommutative (nc) polynomial is called (globally) trace-positive if its evaluation at any tuple of operators in a tracial von Neumann algebra has nonnegative trace. Such polynomials emerge as trace inequalities in several matrix or operator variables, and are widespread in mathematics and physics. This paper delivers the first Positivstellensatz for glob

Olli Mansikkamäki, Sami Laine, Atte Piltonen, Matti Silveri

Arrays of transmons have proven to be a viable medium for quantum information science and quantum simulations. Despite their widespread popularity as qubit arrays, there remains yet untapped potential beyond the two-level approximation or, equivalently, the hard-core boson model. With the higher excited levels included, coupled transmons naturally realize th

Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang

Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene interactions mainly focus on the binding interactions without considering other relation types like agonist, antagonist, etc. In addition, existing methods either heavily rely on high-qu

Kazuma Ohi, Shohei Watabe, Tetsuro Nikuni

The phase difference of the macroscopic wave function is a unique structure of the soliton in an atomic Bose--Einstein condensate (BEC). However, experiments on ultracold atoms so far have observed the valley of the density profile to study the dynamics of solitons. We propose a method to observe the phase difference of a soliton in a BEC by using an interfe

Ben Swallow, David A. Rand, Giorgos Minas

Likelihood-based inference in stochastic non-linear dynamical systems, such as those found in chemical reaction networks and biological clock systems, is inherently complex and has largely been limited to small and unrealistically simple systems. Recent advances in analytically tractable approximations to the underlying conditional probability distributions

Frédéric Bayart

We give sufficient conditions for a general Dirichlet series to be universal with respect to translations or rearrangements.

Pablo Pérez, Ester Gonzalez-Sosa, Jesús Gutiérrez, Narciso García

Several technological and scientific advances have been achieved recently in the fields of immersive systems, which are offering new possibilities to applications and services in different communication domains, such as entertainment, virtual conferencing, working meetings, social relations, healthcare, and industry. Users of these immersive technologies can

Ernesto Arganda, Xabier Marcano, Víctor Martín Lozano, Anibal D. Medina

Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-plus-background hypothesis over the background-only one. We present here a simple method that combines the power of cur

Roman Bezrukavnikov, Pablo Boixeda Alvarez, Peng Shan, Eric Vasserot

We propose a new geometric model for the center of the small quantum group using the cohomology of certain affine Springer fibers. More precisely, we establish an isomorphism between the equivariant cohomology of affine Spaltenstein fibers for a split element and the center of the deformed graded modules for the small quantum group. We also obtain an embeddi

B. Martín-González, P. G. Ortega, D. R. Entem, F. Fernández

The properties of the $B_c$-meson family ($c\bar b$) are still not well determined experimentally because the specific mechanisms of formation and decay remain poorly understood. Unlike heavy quarkonia, i.e. the hidden heavy quark-antiquark sectors of charmonium ($c\bar c$) and bottomonium ($b\bar b$), the $B_c$-mesons cannot annihilate into gluons and they

Xinhao Mei, Xubo Liu, Mark D. Plumbley, Wenwu Wang

Automated audio captioning is a cross-modal translation task that aims to generate natural language descriptions for given audio clips. This task has received increasing attention with the release of freely available datasets in recent years. The problem has been addressed predominantly with deep learning techniques. Numerous approaches have been proposed, s

A España, X Leoncini, E Ugalde

In this paper, we introduce a codification of the paths towards synchronization for synchronizing flows defined over a network. The collection of paths toward synchronization defines a combinatorial structure: the transition diagram. We describe the transition diagram corresponding to the Laplacian flow over the completely connected graph. This applies to th

Maria Axenovich, Michael Zheng

A proper edge-coloring of a graph is an interval coloring if the labels on the edges incident to any vertex form an interval of consecutive integers. Interval thickness s(G) of a graph G is the smallest number of interval colorable graphs edge-decomposing G. We prove that s(G)=o(n) for any graph G on n vertices. This improves the previously known bound of 2n

PingFan Yang, Jian Fang, Le Fang, Alain Pumir

We derive from first principles analytic relations for the second and third order moments of the velocity gradient mij = dui/dxj in compressible turbulence, which generalize known relations in incompressible flows. These relations, although derived for homogeneous flows, hold approximately for a mixing layer. We also discuss how to apply these relations to d

Samantha Sbarra, Louis Waquier, Stephan Suffit, Aristide Lemaître

Tracking the evolution of an individual nanodroplet of liquid in real-time remains an outstanding challenge. Here a miniature optomechanical resonator detects a single nanodroplet landing on a surface and measures its subsequent evaporation down to a volume of twenty attoliters. The ultra-high mechanical frequency and sensitivity of the device enable a time

Martin Bauw, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet

Near out-of-distribution detection (OODD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OODD use case for radar targets detection extensible to other kinds of sensors and detection scenarios. We emphasize the relevance of OODD and its specific supervision requirements f

Ghazi Felhi, Joseph Le Roux, Djamé Seddah

We propose a generative model for text generation, which exhibits disentangled latent representations of syntax and semantics. Contrary to previous work, this model does not need syntactic information such as constituency parses, or semantic information such as paraphrase pairs. Our model relies solely on the inductive bias found in attention-based architect

Putian Yang, Shiqing Zhang

This paper we consider for the N-body problem with potential 1/r{\alpha} (0 < {\alpha} < 1) the existence of hyperbolic motions for any prescribed limit shape and any given initial configuration of the bodies. Here E is the Euclidean space where the bodies moving and is the norm induced by the inner product. The energy level h > 0 of the motion can also be c

Tomasz Wasak, Matteo Sighinolfi, Johannes Lang, Francesco Piazza

Despite the paradigmatic nature of the Fermi-polaron model, the theoretical description of its nonlinear dynamics poses challenges. Here, we apply a quantum kinetic theory of driven polarons to recent experiments with ultracold atoms, where Rabi oscillations between a Fermi-polaron state and a non-interacting level were reported. The resulting equations sepa

Duc H. Le, Tram T. Doan, Son T. Huynh, Binh T. Nguyen

The recommendation system plays a vital role in many areas, especially academic fields, to support researchers in submitting and increasing the acceptance of their work through the conference or journal selection process. This study proposes a transformer-based model using transfer learning as an efficient approach for the paper submission recommendation sys

Ruixin Fan, Xin Du

In wireless positioning systems, non-line-of-sight (NLOS) is a challenging problem. NLOS causes great ranging bias and location error, so NLOS mitigation is essential for high accuracy positioning. In this letter, we propose the Weighted-Least-Squares Robust Kalman Filter (WLS-RKF) for NLOS identification and mitigation. WLS-RKF employs a hypothesis test bas

Joachim Winther Pedersen, Sebastian Risi

Organisms in nature have evolved to exhibit flexibility in face of changes to the environment and/or to themselves. Artificial neural networks (ANNs) have proven useful for controlling of artificial agents acting in environments. However, most ANN models used for reinforcement learning-type tasks have a rigid structure that does not allow for varying input s

Mayank Patel, Minal Bhise

The paper aims to find an efficient way for processing large datasets having different types of workload queries with minimal replication. The work first identifies the complexity of queries best suited for the given data processing tool . The paper proposes Query Complexity Aware partitioning technique QCA with a lightweight query identification and partiti

Anatol Slissenko

In mathematics information is a number that measures uncertainty (entropy) based on a probabilistic distribution, often of an obscure origin. In real life language information is a datum, a statement, more precisely, a formula. But such a formula should be justified by a proof. I try to formalize this perception of information. The measure of informativeness

Yuheng Jia, Sirui Tao, Ran Wang, Yongheng Wang

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the pe

Liyun Zhang, Zhao Wang, Yucheng Wang, Junhua Zhang

Synchronizing a few-level quantum system is of fundamental importance to understanding synchronization in deep quantum regime. Whether a two-level system, the smallest quantum system, can be synchronized has been theoretically debated for the past several years. Here, for the first time, we demonstrate that a qubit can indeed be synchronized to an external d

Chen Lan, Yi-Fan Wang

We use the monodromy method to investigate the asymptotic quasinormal modes of regular black holes based on the explicit Stokes portraits. We find that, for regular black holes with spherical symmetry and a single shape function, the analytical forms of the asymptotic frequency spectrum are not universal and do not depend on the multipole number but on the p

Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu

Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance selection coarsely; thus neglecting the latent fine-grained interaction between feature space and instance space. To address this chal

Yuki Shiraito, James Lo, Santiago Olivella

A common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item-response theory (IRT) model to a common set of stimuli. Despite being exposed to the same stimuli, voters and legislators may not share a common understanding of how these stimuli map onto the

Georgios P. Koudouridis, Henrik Lundqvist, Hong Li, Xavier Gelabert

Energy efficiency becomes increasingly important due to the limited battery capacity in wireless devices while at the same time user throughput requirements are relentlessly increasing. In this paper, we study an energy efficient cooperation scheme which employs network coding to enhance the energy efficiency for mobile devices. Herein we propose that the mo

Laetitia Della Maestra, Marc Hoffmann

We establish the local asymptotic normality (LAN) property for estimating a multidimensional parameter in the drift of a system of $N$ interacting particles observed over a fixed time horizon in a mean-field regime $N \rightarrow \infty$. By implementing the classical theory of Ibragimov and Hasminski, we obtain in particular sharp results for the maximum li

Gennadiy Kalyabin

Assuming the validity of Riemann Hypothesis (RH), we derive the explicit bilateral estimates ("narrow passage") of the remainder in the modified Mertens asymptotic formula for the sums of primes' reciprocals. These results are reversable, thus yielding some new criteria for RH.

Paolo Eugenio Demagistris, Sandro Petruzzi, Rodolfo Pampaloni, Milan Šmigić

Construction project governance relies on agreements between the actors along the construction industry value chain. The mutual obligations arising from these contracts rely on timely monetary transactions. Despite the advantages of automation in payment systems and improved access to digital progress data, several payment applications rely nonetheless on in

Michel Chipot, Mingmin Zhang

The purpose of this note is to study the existence of a nontrivial solution for an elliptic system which comes from a newly introduced mathematical problem so called Field-Road model. Specifically, it consists of coupled equations set in domains of different dimensions together with some interaction of non classical type. We consider a truncated problem by i

Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi

Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient and real-time reduced order models to be used as virtual twin

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Jocelyn Chanussot

Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detectors struggle with effective features extraction for small-scale objects. To address this challenge, we propose image pyramid single-shot detector (IPSSD). In IPSSD, single-shot de

Xueji Wang, Tyler Sentz, Sathwik Bharadwaj, Subir Ray

Spinning thermal radiation is a unique phenomenon observed in condensed astronomical objects including the Wolf-Rayet star EZ-CMa and the red degenerate star G99-47, due to existence of strong magnetic fields. Here, by designing symmetry-broken metasurfaces, we demonstrate that spinning thermal radiation with a non-vanishing optical helicity can be realized

Guocai Cai, Boqiang Lü, Yi Peng

The nonhomogeneous Navier-Stokes equations with density-dependent viscosity is studied in three-dimensional (3D) exterior domains with nonslip or slip boundary conditions. We prove that the strong solutions exists globally in time provided that the gradient of the initial velocity is suitably small. Here the initial density is allowed to contain vacuum state

Gökhan Yücel, Volkan Bakış

49 new eclipsing twin binary candidates are identified and analyzed based on Kepler eclipsing binary light curves. Their colours and spectral types are calculated according to our classification. A comparison of the spectral type distribution of eclipsing twin binary systems showed that F-type twins dominate among others, which agrees well with recent studie

Dariush Kiani, Sara Saeedi Madani, Saeed Tafazolian

In this paper, we study the Hankel edge ideals of graphs. We determine the minimal prime ideals of the Hankel edge ideal of labeled Hamiltonian and semi-Hamiltonian graphs, and we investigate radicality, being a complete intersection, almost complete intersection and set theoretic complete intersection for such graphs. We also consider the Hankel edge ideal

Jian Zhang, Yuanqing Zhang, Huan Fu, Xiaowei Zhou

Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images. A main limitation of conventional NeRFs is that they often fail to produce high-quality renderings under novel viewpoints that are significantly different from the training viewpoints. In this paper, instead of exploiting few-shot

Remigiusz Durka, Krzysztof M. Graczyk

We present new superalgebra for $\mathcal{N}=2$ $D=3,4$ supergravity theory endowed with the $U(1)$ generator. The superalgebra is rooted in the so-called Soroka-Soroka algebra and spanned by the Lorentz $J_{ab}$ and Lorentz-like $Z_{ab}$, translation $P_a$ and $T$ generators, as well as two supercharges $Q^I_\alpha$. It is the only possible realization for

Shilong Zhang, Zhuoran Yu, Liyang Liu, Xinjiang Wang

We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding boxes and a large set of weakly-labeled images with only a single point annotated for each instance. The core of this task is to train a point-to-box regressor on well-labeled images

Sabri Bahrouni, Hichem Ounaies, Olfa Elfalah

In this paper we prove compact embedding of a subspace of the fractional Orlicz-Sobolev space $W^{s, G}\left(\mathbb{R}^{N}\right)$ consisting of radial functions, our target embedding spaces are of Orlicz type. Also, we prove a Lions and Lieb type results for $W^{s,G}\left(\mathbb{R}^{N}\right)$ that works together in a particular way to get a sequence whos

Thao V. Ha, Hoang Nguyen, Son T. Huynh, Trung T. Nguyen

In recent years, the occurrence of falls has increased and has had detrimental effects on older adults. Therefore, various machine learning approaches and datasets have been introduced to construct an efficient fall detection algorithm for the social community. This paper studies the fall detection problem based on a large public dataset, namely the UP-Fall

Jihong Wang, Yizhou Yang, Jie Jiang, Liuhua Mu

For more than a century, electricity and magnetism have been believed to always exhibit inextricable link due to the symmetry in electromagnetism. At the interface, polar groups that have polar charges, are indispensable to be considered, which interact directly with other polar charges/external charges/external electric fields. However, there is no report o

Shihao Shen, Yilin Cai, Jiayi Qiu, Guangzhao Li

We propose a dense dynamic RGB-D SLAM pipeline based on a learning-based visual odometry, TartanVO. TartanVO, like other direct methods rather than feature-based, estimates camera pose through dense optical flow, which only applies to static scenes and disregards dynamic objects. Due to the color constancy assumption, optical flow is not able to differentiat

Henrik Lundqvist, George P. Koudouridis, Xavier Gelabert

In cellular networks user equipment (UE) need to be tracked so that they can be reached by incoming data and to keep context information such as encryption keys available for UE originated transmission. Typically UEs measure reference signal transmissions that are broadcasted by the network, and report to the network based on some criterion that allows the n

Amirreza Zaman, Wolfgang Birk, Khalid Tourkey Atta

This paper presents the distributed pinning consensus solution for nonlinear positive multi-agent systems with nonlinear control input by applying observer-based control protocols. The network topology is considered as a directed and fully connected structure. By considering sector input nonlinearities and various forms of topologies, two kinds of state obse

Mamadou N'diaye, Francois P. Hamon, Hamdi A. Tchelepi

This work focuses on the development of a two-step field-split nonlinear preconditioner to accelerate the convergence of two-phase flow and transport in heterogeneous porous media. We propose a field-split algorithm named Field-Split Multiplicative Schwarz Newton (FSMSN), consisting in two steps: first, we apply a preconditioning step to update pressure and

Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay

Building facade parsing, which predicts pixel-level labels for building facades, has applications in computer vision perception for autonomous vehicle (AV) driving. However, instead of a frontal view, an on-board camera of an AV captures a deformed view of the facade of the buildings on both sides of the road the AV is travelling on, due to the camera perspe

Klaus Scherer, Edin Husidic, Marian Lazar, Horst Fichtner

Given their uniqueness, the Ulysses data can still provide us with valuable new clues about the properties of plasma populations in the solar wind, and, especially, about their variations with heliographic coordinates. We revisit the electron data reported by by the SWOOPS instrument on-board of the Ulysses spacecraft between 1990 to early 2008. These observ

Marco Scutari

Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks" by David Atienza, Pedro Larranaga and Concha Bielza (TEST, 2022).

Xiaopei Zhu, Zhanhao Hu, Siyuan Huang, Jianmin Li

Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated the process from cloth to clothing in the digital world and t

Jiaqi Lin, Feng Wang, Linhua Deng, Hui Deng

The statistical study of the Coronal Mass Ejections (CMEs) is a hot topic in solar physics. To further reveal the temporal and spatial behaviors of the CMEs at different latitudes and heights, we analyzed the correlation and phase relationships between the occurrence rate of CMEs, the Coronal Brightness Index (CBI), and the 10.7-cm solar radio flux (F10.7).

Louise Bloch, Christoph M. Friedrich

Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) black-box models a workflow based on Shapley values was developed. All models were trained on the Alzheimer's Disease Neuroimag

Abdul Ghaffar, Peng Song, Kenta Hongo, Ryo Maezono

Recent progress on theoretical predictions of ternary superhydrides and their subsequent experimental confirmations have introduced us with a new generation of superconductors, having the potential to realize the synthesis of the most anticipated room temperature superconductors. Motivated by the recent high pressure experiment on YH$_6$ and EuH$_6$, we have

Arjun K. Rathie, John M. Campbell

We extend the Reed Dawson identity for Knuth's old sum with a complex parameter, and we offer two separate hypergeometric series-based proofs of this generalization, and we apply this generalization to introduce binomial-harmonic sum identities. We also provide another ${}_{2}F_{1}(2)$-generalization of the Reed Dawson identity involving a free parameter. We

Anubhav Shrimal, Avi Jain, Kartik Mehta, Promod Yenigalla

NER has been traditionally formulated as a sequence labeling task. However, there has been recent trend in posing NER as a machine reading comprehension task (Wang et al., 2020; Mengge et al., 2020), where entity name (or other information) is considered as a question, text as the context and entity value in text as answer snippet. These works consider MRC b

Hao Yu, Yu-Xiao Liu, Jin Li

In this work, we study the entropies of photons, dust (baryonic matter), dark matter, and dark energy in the context of cosmology. When these components expand freely with the universe, we calculate the entropy and specific entropy of each component from the perspective of statistics. Under specific assumptions and conditions, the entropies of these componen

Priyanka Yadav, Sumit Sarkar, Manju Sharma, Rajamani Raghunathan

Na2IrO3 (NIO) is known to be a spin-orbit (SO) driven j=1/2 pseudo-spin Mott-Hubbard (M-H) insulator. However, the microscopic origin of the pseudo-spin state and the role of local structural distortions have not been clearly understood. Using a combination of theoretical calculations and x-ray spectroscopy, we show that the energetics in the vicinity of Fer

Neeraja Kirtane, Tanvi Anand

As the use of natural language processing increases in our day-to-day life, the need to address gender bias inherent in these systems also amplifies. This is because the inherent bias interferes with the semantic structure of the output of these systems while performing tasks like machine translation. While research is being done in English to quantify and m

Sophia Elia, Donghyun Kim, Mariel Supina

Equivariant Ehrhart theory generalizes the study of lattice point enumeration to also account for the symmetries of a polytope under a linear group action. We present a catalogue of techniques with applications in this field, including zonotopal decompositions, symmetric triangulations, combinatorial interpretation of the $h^\ast$-polynomial, and certificate

Tony N. Mavely, Viji Z. Thomas

We give a sharp bound on the number of triangles in a graph with fixed number of edges. We also characterize graphs that achieve the maximum number of triangles. Using the upper bound on number of triangles, we prove that if $G$ is a special $p$-group of rank $2 \leq k \leq \binom{d}{2}$, then $|\mathcal{M}(G)| \leq p^{\frac{d(d+2k-1)}{2} - k- \binom{d}{3}+

Ho Hin Lee, Yucheng Tang, Riqiang Gao, Qi Yang

Non-contrast computed tomography (NCCT) is commonly acquired for lung cancer screening, assessment of general abdominal pain or suspected renal stones, trauma evaluation, and many other indications. However, the absence of contrast limits distinguishing organ in-between boundaries. In this paper, we propose a novel unsupervised approach that leverages pairwi

The Critical Assessment of Genome Interpretation Consortium

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state of the art for computational prediction of genetic variant impact, particularly those relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and thes

G. Howitt, A. Melatos

Three sudden spin-down events, termed `anti-glitches', were recently discovered in the accreting pulsar NGC 300 ULX-1 by the \textit{Neutron Star Interior Composition Explorer} (NICER) mission. Unlike previous anti-glitches detected in decelerating magnetars, these are the first anti-glitches recorded in an accelerating pulsar. One standard theory is that pu

Keren Ye, Adriana Kovashka

Videos are more well-organized curated data sources for visual concept learning than images. Unlike the 2-dimensional images which only involve the spatial information, the additional temporal dimension bridges and synchronizes multiple modalities. However, in most video detection benchmarks, these additional modalities are not fully utilized. For example, E

Somnath Pradhan, Serdar Yuksel

In control theory, typically a nominal model is assumed based on which an optimal control is designed and then applied to an actual (true) system. This gives rise to the problem of performance loss due to the mismatch between the true model and the assumed model. A robustness problem in this context is to show that the error due to the mismatch between a tru

Efthimios Kappos

An analysis of necessary conditions for the existence of controlled dynamics with an attractor of a specified topological type is given. It uses the Hopf classification by degree for Gauss maps of manifolds to spheres of the same dimension, in this case noting that by a linear homotopy, the degree is the same for the negative of the gradient flow of a Lyapun

Aaron Glanville, Cullan Howlett, Tamara M. Davis

With recent evidence for a possible "curvature tension" among early and late universe cosmological probes, Effective Field Theories of Large Scale Structure (EFTofLSS) have emerged as a promising new framework to generate constraints on $\Omega_k$ that are independent of both CMB measurements, and some of the assumptions of flatness that enter into other lar

Shiraz Khan, S. A. Mardan, M. A. Rehman

The main theme of this work is the development of complexity induced generalized frameworks for static cylindrical polytropes. We consider two different definitions of generalized polytopes with charged anisotropic inner fluid distribution. A new methodology based on complexity factor for the generation of consistent sets of differential equations will be pr

Xiaoxiang Chai

A marginally outer trapped hypersurface is a generalization of minimal hypersurfaces originated from general relativity. We show a curvature estimate for stable marginally outer trapped hypersurfaces up to the free boundary satisfying a uniform area bound. Our proof is based on an iteration argument. The curvature estimate was previously known via a blowup a

Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems from the unreasonable benchmark constru

Hang Li, Ahmed Mourad, Bevan Koopman, Guido Zuccon

Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF method

Matthew J. Katz, Micha Sharir

We present an algorithm for computing a bottleneck matching in a set of $n=2\ell$ points in the plane, which runs in $O(n^{\omega/2}\log n)$ deterministic time, where $\omega\approx 2.37$ is the exponent of matrix multiplication.

Changhong Yu, Chunhong Zhang, Qi Sun

The goal of building intelligent dialogue systems has largely been separately pursued under two motives: task-oriented dialogue (TOD) systems, and open-domain systems for chit-chat (CC). Although previous TOD dialogue systems work well in the testing sets of benchmarks, they would lead to undesirable failure when being exposed to natural scenarios in practic

Xiao Qi

As social network analysis (SNA) has drawn much attention in recent years, one bottleneck of SNA is these network data are too massive to handle. Furthermore, some network data are not accessible due to privacy problems. Therefore, we have to develop sampling methods to draw representative sample graphs from the population graph. In this paper, Metropolis-Ha

Manisha Arora, Rashi Sachdeva, Sankalpa Ghosh

It has been shown that Bernal stacked bilayer graphene (BLG) in a uniform magnetic field demonstrates integer quantum Hall effect with a zero Landau-level anomaly \cite{Geimbilayer}. In this article we consider such system in a two dimensional periodic magnetic modulation with square lattice symmetry. It is shown algebraically that the resulting Hofstadter s

Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger, Zülal Bingöl

A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e., sequence-to-sequence mapping). Recent works replace the linear reference sequence with a graph-based representation of the reference genome, which captures the genetic variations a

Arpit Khare, Sudhakar Singh, Richa Mishra, Shiv Prakash

In this paper, a workflow for designing a bot using Robotic Process Automation (RPA), associated with Artificial Intelligence (AI) that is used for information extraction, classification, etc., is proposed. The bot is equipped with many features that make email handling a stress-free job. It automatically login into the mailbox through secured channels, dist

Souvik Sasmal, Gourav Dwari, Bishal Baran Maity, Vikas Saini

Anisotropic transport, Shubnikov-de Haas (SdH), and de Haas-van Alphen (dHvA) quantum oscillations studies are reported on a high-quality CoSi single crystal grown by the Czochralski method. Temperature-dependent resistivities indicate the dominating electron-electron scattering. Magnetoresistance (MR) at 2 K reaches 610% for I||[111] and B||[01-1], whereas

Qiuping Jiang, Jiawu Xu, Yudong Mao, Wei Zhou

Blind image quality assessment (BIQA), which aims to accurately predict the image quality without any pristine reference information, has been extensively concerned in the past decades. Especially, with the help of deep neural networks, great progress has been achieved. However, it remains less investigated on BIQA for night-time images (NTIs) which usually

Masaki Ogawa

In this paper, we consider decompositions of closed orientable 3-manifolds with more than 3 handlebodies, where the union of intersections of handlebodies is a multibranched surface. We define stabilization operations for such decompositions and show the stable equivalence.

Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou

Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities. In particular, they tend to be overconfident. We begin to address this problem in the context of multi-class classification by developing a novel training alg

Santanu Das, Sumedha

We study a $p$-spin model with ferromagnetic coupling and quenched random-crystal fields for $p \ge 3$ for spin-1 systems. We find that the model has lines of first order transitions at finite temperature $(T)$ for all $p \ge 3$. For bimodal distribution of the random-crystal field these lines meet at a \emph{triple point} for weak strength of the crystal fi

Abdullah Alqarni, Wei Wen, Ben C. P. Lam, John D. Crawford

Objective: To examine the moderation effects of hormonal factors on the associations between vascular risk factors and white matter hyperintensities (WMH) in men and women, separately. Methods: WMH were automatically segmented and quantified in the UK Biobank dataset (N = 18,294). Generalised linear models were applied to examine 1) the main effects of vascu

Yijia Zhang, Hua Feng

We propose a new method to identify rapid X-ray transients observed with focusing telescopes. They could be statistically significant if three or more photons are detected with Chandra in a single CCD frame within a point-spread-function region out of quiescent background. In the Chandra archive, 11 such events are discovered from regions without point-like

David Macêdo, Cleber Zanchettin, Teresa Ludermir

Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classification accuracy in some situations. On the other hand, some methods simultaneously increase classification accuracy, uncertainty estimation, and out-of-distribution detection at the expense

Qing Liu, Kai Wang, Jia-Xiao Dai, Y. X. Zhao

Recently, real topological phases protected by $PT$ symmetry have been actively investigated. In two dimensions, the corresponding topological invariant is the Stiefel-Whitney number. A recent theoretical advance is that in the presence of the sublattice symmetry, the Stiefel-Whitney number can be equivalently formulated in terms of Takagi's factorization. T

Atsuo Shitade

The spin Nernst effect is a phenomenon in which the spin current flows perpendicular to a temperature gradient. Similar to the spin Hall effect, this phenomenon also causes spin accumulation at the boundaries. Here, we study the spin response to the gradient of the temperature gradient with the use of Green's functions. Our formalism predicts physically obse

Yin-Jyun Luo, Sebastian Ewert, Simon Dixon

Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static latent variable. The former encode information at a frame rate identical to the observation, while the latter globally governs the entire sequence. This introduces an inductive bias an