November 2022 arXiv papers — page 22

Ali Ahmadian, Daniel Scheiber, Xuyang Zhou, Baptiste Gault

The embrittlement of metallic alloys by liquid metals leads to catastrophic material failure and severely impacts their structural integrity. The weakening of grain boundaries by the ingress of liquid metal and preceding segregation in the solid are thought to promote early fracture. However, the potential of balancing between the segregation of cohesion-enh

Tao Feng, Hangjie Yuan, Mang Wang, Ziyuan Huang

Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading k

J. Fiaschi, F. Giuli, F. Hautmann, S. Moretti

Using the 4-Dimensional Composite Higgs Model (4DCHM) realization of the minimal composite Higgs scenario, we discuss the Large Hadron Collider (LHC) sensitivity to new physics signals from multiple $Z^\prime$ and $W^\prime$ broad resonances. We illustrate the role of systematic uncertainties due to QCD effects encoded in parton distribution functions for ex

Daniela Dombrowski, Abdus Samad, Kai Mehlich, Thais Chagas

We prepare monolayers of tantalum sulfide on Au(111) by evaporation of Ta in a reactive background of H$_2$S. Under sulfur-rich conditions, monolayers of 2H-TaS$_2$ develop, whereas under sulfur-poor conditions TaS forms, a structure that can be derived from 2H-TaS$_2$ by removal of the bottom S layer. We analyse the alignment of the layers with respect to t

Giovanni Pecci, Gianni Aupetit-Diallo, Mathias Albert, Patrizia Vignolo

We consider a two-component Bose-Bose mixture at strong repulsive interactions in a tightly confining, one-dimensional ring trap and subjected to an artificial gauge field. By employing the Bethe Ansatz exact solution for the many-body wavefunction, we obtain the ground state energy and the persistent currents. For each value of the applied flux, we then det

Loïc Béthencourt

We consider a particle living in $\mathbb{R}_+$, whose velocity is a positive recurrent diffusion with heavy-tailed invariant distribution when the particle lives in $(0,\infty)$. When it hits the boundary $x=0$, the particle restarts with a random strictly positive velocity. We show that the properly rescaled position process converges weakly to a stable pr

Gilad Kutiel, Regev Cohen, Michael Elad, Daniel Freedman

Estimating uncertainty in image-to-image networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms. In this paper, we introduce a new approach to this problem based on masking. Given an existing image-to-image network, our approach computes a mask such that the distance between t

D. V. Chalin, S. B. Rochal

Spherical nanoclusters and nanoparticles are rising materials whose functional design provides many useful applications ranging from catalysis, molecular sensing, gas storage to drug targeting and delivery. Here, we develop phenomenological crystallization theory of such spherical structures with octahedral symmetries O and O_h. Within the developed theory,

Pratibha Raghupati Hegde, Gianluca Passarelli, Giovanni Cantele, Procolo Lucignano

A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory (LSTM) neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using loc

Ping Liu, Habib Ammari

This paper focuses on the fundamental aspects of super-resolution, particularly addressing the stability of super-resolution and the estimation of two-point resolution. Our first major contribution is the introduction of two location-amplitude identities that characterize the relationships between locations and amplitudes of true and recovered sources in the

Emanuele De Angelis, Fabio Fioravanti, Alberto Pettorossi, Maurizio Proietti

We address the problem of checking the satisfiability of a set of constrained Horn clauses (CHCs) possibly including more than one query. We propose a transformation technique that takes as input a set of CHCs, including a set of queries, and returns as output a new set of CHCs, such that the transformed CHCs are satisfiable if and only if so are the origina

Matthias K. Hoffmann, Willem Esterhuizen, Karl Worthmann, Kathrin Flaßkamp

We present a toolchain for solving path planning problems for concentric tube robots through obstacle fields. First, ellipsoidal sets representing the target area and obstacles are constructed from labelled point clouds. Then, the nonlinear and highly nonconvex optimal control problem is solved by introducing a homotopy on the obstacle positions where at one

Sarthak Anand, Jens-Joris Decorte, Niels Lowie

In this paper, we describe our method for ranking the skills required for a given job title. Our analysis shows that important/relevant skills appear more frequently in similar job titles. We train a Language-agnostic BERT Sentence Encoder (LaBSE) model to predict the importance of the skills using weak supervision. We show the model can learn the importance

Xiang Zheng, Xingjun Ma, Cong Wang

Intrinsic motivation is a promising exploration technique for solving reinforcement learning tasks with sparse or absent extrinsic rewards. There exist two technical challenges in implementing intrinsic motivation: 1) how to design a proper intrinsic objective to facilitate efficient exploration; and 2) how to combine the intrinsic objective with the extrins

ALICE Collaboration

The transverse-momentum ($p_{\rm T}$) spectra and coalescence parameters $B_2$ of (anti)deuterons are measured in pp collisions at $\sqrt{s} = 13$ TeV for the first time in and out of jets. In this measurement, the direction of the leading particle with the highest $p_{\rm T}$ in the event ($p_{\rm T}^{\rm{ lead}} > 5$ GeV/$c$) is used as an approximation fo

Luigi Ernesto Ghezzer, Francesco Nozzoli, Riccardo Nicolaidis, Cristian De Santis

The nuclide $^{176}$Lu is one of the few naturally occurring isotopes that are potentially unstable with respect to electron capture (EC). Although experimental evidence for $^{176}$Lu EC decay is still missing, this isotope is instead well known to $\beta^-$ decay into $^{176}$Hf with an half-life of about 38 Gyr. The precise investigation of all $^{176}$Lu

Witold Sosnowski, Anna Wróblewska, Karolina Seweryn, Piotr Gawrysiak

Distance Metric Learning (DML) has attracted much attention in image processing in recent years. This paper analyzes its impact on supervised fine-tuning language models for Natural Language Processing (NLP) classification tasks under few-shot learning settings. We investigated several DML loss functions in training RoBERTa language models on known SentEval

Hyuga Yoshizaki

We survey Weber's class number problem and its variants in the spirit of arithmetic topology; we recollect some history, present a relation to certain units and generalized Pell's equation, and overview a study of the $p$-adic limits of class numbers in $\mathbb{Z}_p$-towers together with numerical investigation for knots and elliptic curves.

Imam Mustafa Kamal, Hyerim Bae

Deep metric learning (DML) aims to automatically construct task-specific distances or similarities of data, resulting in a low-dimensional representation. Several significant metric-learning methods have been proposed. Nonetheless, no approach guarantees the preservation of the ordinal nature of the original data in a low-dimensional space. Ordinal data are

Eylon Gueta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman

We present a new pre-trained language model (PLM) for modern Hebrew, termed AlephBERTGimmel, which employs a much larger vocabulary (128K items) than standard Hebrew PLMs before. We perform a contrastive analysis of this model against all previous Hebrew PLMs (mBERT, heBERT, AlephBERT) and assess the effects of larger vocabularies on task performance. Our ex

Willem Esterhuizen, Gyula Molnár, Tim Aschenbruck, Franz Rußwurm

This paper is concerned with estimating critical clearing times in the transient stability problem of power grids without extensive time-domain simulations. We consider a highdimensional post-fault system (the grid after the fault is cleared) which we decouple into many smaller subsystems. Then, for each subsystem, we find the so-called safety sets and simul

Imam Mustafa Kamal, Hyerim Bae, Ling Liu

With the emergence of deep learning, metric learning has gained significant popularity in numerous machine learning tasks dealing with complex and large-scale datasets, such as information retrieval, object recognition and recommendation systems. Metric learning aims to maximize and minimize inter- and intra-class similarities. However, existing models mainl

Raunak Joshi, Abhishek Gupta, Nandan Kanvinde, Pandharinath Ghonge

The advancement in the area of computer vision has been brought using deep learning mechanisms. Image Forensics is one of the major areas of computer vision application. Forgery of images is sub-category of image forensics and can be detected using Error Level Analysis. Using such images as an input, this can turn out to be a binary classification problem wh

Witold Sosnowski, Karolina Seweryn, Anna Wróblewska, Piotr Gawrysiak

This paper presents an analysis regarding an influence of the Distance Metric Learning (DML) loss functions on the supervised fine-tuning of the language models for classification tasks. We experimented with known datasets from SentEval Transfer Tasks. Our experiments show that applying the DML loss function can increase performance on downstream classificat

ALICE Collaboration

Femtoscopic correlations of non-identical charged kaons ($\rm K^+ K^-$) are studied in Pb$-$Pb collisions at a center-of-mass energy per nucleon$-$nucleon collision $\sqrt{s_{\mathrm{NN}}} =2.76$ TeV by ALICE at the LHC. One-dimensional $\rm K^+ K^-$ correlation functions are analyzed in three centrality classes and eight intervals of particle-pair transvers

Yiwen Lu, Tao Wei, Xuejun Guo

We improve S.-C. Chen's result on the parity of Schur's partition function. Let $A(n)$ be the number of Schur's partitions of $n$, i.e., the number of partitions of $n$ into distinct parts congruent to $1, 2 \mod{3}$. S.-C. Chen \cite{MR3959837} shows $\small \frac{x}{(\log{x})^{\frac{47}{48}}} \ll \sharp \{0\le n\le x:A(2n+1)\; \text{is odd}\}\ll \frac{x}{(

Huy-Dung Nguyen, Michaël Clément, Boris Mansencal, Pierrick Coupé

Accurate diagnosis and prognosis of Alzheimer's disease are crucial to develop new therapies and reduce the associated costs. Recently, with the advances of convolutional neural networks, methods have been proposed to automate these two tasks using structural MRI. However, these methods often suffer from lack of interpretability, generalization, and can be l

Zhimin Liu, Shenglin Zhu

For a semisimple quasi-triangular Hopf algebra $\left( H,R\right) $ over a field $k$ of characteristic zero, and a strongly separable quantum commutative $H$-module algebra $A$ over which the Drinfeld element of $H$ acts trivially, we show that $A\#H$ is a weak Hopf algebra, and it can be embedded into a weak Hopf algebra $\operatorname{End}A^{\ast}\otimes H

Rhombik Roy, Barnali Chakrabarti, Arnaldo Gammal

We solve the Schr\"odinger equation from first principles to investigate the many-body effects in the expansion dynamics of one-dimensional repulsively interacting bosons released from a harmonic trap. We utilize the multiconfigurational time-dependent Hartree method for bosons (MCTDHB) to solve the many-body Schr\"odinger equation at high level of accuracy.

Marta Z. Cieplak, I. Zajcewa, A. Lynnyk, K. M. Kosyl

The normal state in iron chalcogenides is metallic but highly unusual, with orbital and spin degrees of freedom partially itinerant or localized depending on temperature, leading to many unusual features. In this work, we report on the observations of two of such features, the orbital selective Mott phase (OSMP) and spin nematicity, evidenced in magnetizatio

Robert Joseph George, Jiawei Zhao, Jean Kossaifi, Zongyi Li

Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows. At the core of FNO is a spectral layer that leverages a discretization-convergent representation in the Fourier domain, and learns weights over a fixed set of frequencies. However, training FNO presents two significa

Jorge Bruno, Paul Szeptycki

We prove the Tree Alternative Conjecture for the topological minor relation: letting $[T]$ denote the equivalence class of $T$ under the topological minor relation we show that: $|[T]| = 1$ or $|[T]|\geq \aleph_0$ and $\forall r\in V(T)$, $|[(T,r)]| = 1$ or $|[(T,r)]|\geq \aleph_0$. In particular, by means of curtailing trees, we show that for any tree $T$ w

Baishun Yang, Yang Li, Hongjun Xiang, Haiqing Lin

Besides moir\'e superlattice, twisting can also generate moir\'e magnetic exchange interactions (MMEIs) in van der Waals magnets. However, due to the extreme complexity and twist-angle-dependent sensitivity, all existing models fail to capture the MMEIs, preventing the understanding of MMEIs-induced new physics. Here, we develop a microscopic moir\'e spin Ha

Kausthubh Chandramouli, William Sethares

The mridangam is a double-headed percussion instrument that plays a key role in Carnatic music concerts. This paper presents a novel automatic transcription algorithm to classify the strokes played on the mridangam. Onset detection is first performed to segment the audio signal into individual strokes, and feature vectors consisting of the DFT magnitude spec

Giulia Lupi, Karol Mikula, Seol Ah Park

When analyzing cell trajectories, we often have to deal with noisy data due to the random motion of the cells and possible imperfections in cell center detection. To smooth these trajectories, we present a mathematical model and numerical method based on evolving open-plane curve approach in the Lagrangian formulation. The model contains two terms: the first

Colin Troisemaine, Joachim Flocon-Cholet, Stéphane Gosselin, Sandrine Vaton

In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In this paper, we propose TabularNCD, a new m

Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat

Non-parametric episodic memory can be used to quickly latch onto high-rewarded experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning approaches in which reward signals need to be back-propagated slowly, these methods only need to discover the solution once, and may then repeatedly solve the task. However, episodic

Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, f

Fu-En Wang, Chien-Yi Wang, Min Sun, Shang-Hong Lai

Although significant progress has been made in face recognition, demographic bias still exists in face recognition systems. For instance, it usually happens that the face recognition performance for a certain demographic group is lower than the others. In this paper, we propose MixFairFace framework to improve the fairness in face recognition models. First o

Xiaoyue Duan, Guoliang Kang, Runqi Wang, Shumin Han

Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to adversarial attacks. The conventional solution for robust MAML is to introduce robustness-promoting regularization during meta-training stage. With such a regularization, previous r

Kostya Druzhkov

A notion of internal Lagrangian for a system of differential equations is introduced. A spectral sequence related to internal Lagrangians is obtained. A connection between internal Lagrangians and presymplectic structures is investigated. An interpretation of the term $E^{3,\, n-2}_2$ of Vinogradov's $\mathcal{C}$-spectral sequence is given for irreducible g

D. W. Boukhvalov, V. Yu. Osipov, B. T. Hogan, A. Baldycheva

We propose and demonstrate a novel range of models to accurately determine the optical properties of nitrogen-free carbon quantum dots (CQDs) with ordered graphene layered structures. We confirm the results of our models against the full range of experimental results for CQDs available from an extensive review of the literature. The models can be equally app

Yu. M. Shabelski, A. G. Shuvaev

The complete Glauber calculation of the differential cross sections of $^{12}$C--$^{12}$C and halo nuclei on $^{12}$C scattering was performed using the previously proposed in Refs.~\cite{Shabelski:2021iqk, Shabelski:2022xkw} method of generating function. The results are different as compared with the similar calculations in the optical model and the rigid

Keita Sakai, Shoichi Sasaki

Removing ultraviolet noise from the gauge fields is necessary for glueball spectroscopy in lattice QCD. It is known that the Yang-Mills gradient flow method is an alternative approach instead of link smearing or link fuzzing in various aspects. In this work we study the application of the gradient flow technique to the construction of the extended glueball o

Shreesha G. Bhat, Kartik Nagar

Distributed protocols are generally parametric and can be executed on a system with any number of nodes, and hence proving their correctness becomes an infinite state verification problem. The most popular approach for verifying distributed protocols is to find an inductive invariant which is strong enough to prove the required safety property. However, find

Yi Zhang, Yichi Zhang, Zhiliang Xu, Zhongxu Wang

Thermal transient measurement based on source-drain voltage is a standard method to characterize thermal properties of silicon semiconductors but is doubtful to be directly applied to silicon carbide (SiC) devices. To evaluate its feasibility and limitations, this paper conducts a comprehensive investigation into its accuracy, resolution, and stability towar

Yaoshu Xie, Kiyou Shibata, Teruyasu Mizoguchi

Crystalline interfaces are of highly importance in many practical applications. To conduct effective simulation and analysis for coincident site lattice (CSL) interfaces, effective programmes are of high demand in building their CSL bicrystal models to apply periodic boundary condition. The existing reported programmes capable suffer from problems including

Antonio Ros

For any Riemannian metric $ds^2$ on a compact surface of genus $g$, Yang and Yau proved that the normalized first eigenvalue of the Laplacian $\lambda_1(ds^2)Area(ds^2)$ is bounded in terms of the genus. In particular, if $\Lambda_1(g)$ is the supremum for each $g$, it follows that the asymptotic growth of the sequence ${\Lambda_1(g)}$ is no larger than the

Alex M. Garcia, Sophie Le Bras, Wolfgang Polifke

This study presents a numerical analysis of the impact of hydrogen addition on the consumption speed of premixed lean methane-air laminar flames exposed to combined strain and heat loss. Equivalence ratios of 0.9, 0.7, and 0.5 with fuel mixture composition ranging from pure methane to pure hydrogen are considered to cover a wide range of conditions in the le

Sorin G. Gal, Constantin P. Niculescu

By extending the classical quantitative approximation results for positive and linear operators in $L^{p}([0, 1]), 1\le p \le +\infty$ of Berens and DeVore in 1978 and of Swetits and Wood in 1983 to the more general case of sublinear, monotone and strongly translatable operators, in this paper we obtain quantitative estimates in terms of the second order and

Shreepranav Varma Enugandla

We consider degenerations of all simple Lie algebras of exceptional type obtained by embedding into affine Lie algebras. We give a filtration to consider this as an abelianisation of the original Lie algebra. We then show that the associated graded of simple, finite-dimensional modules are isomorphic to Demazure modules.

Sayani Bera, Kaushal Verma

It has been conjectured that every stable manifold arising from a holomorphic automorphism, that acts hyperbolically on a compact invariant set, is biholomorphic to complex Euclidean space. Such stable manifolds are known to be biholomorphic to the basin of a uniformly attracting family of holomorphic maps. It is shown that the basin of a uniformly attractin

Erlend Grong, Stefan Sommer

Optimal paths for the classical Onsager-Machlup function determining most probable paths between points on a manifold are only explicitly identified for specific processes, for example the Riemannian Brownian motion. This leaves out large classes of manifold-valued processes such as processes with parallel transported non-trivial diffusion matrix, processes

Arnaud Eteve

Let $G$ be a reductive group over a finite field with a maximal unipotent subgroup $U$, we consider certain sheaves on $G/U$ defined by Kazhdan and Laumon and show that their cohomology produces the cohomology of the Deligne-Lusztig varieties. We then use this comparison to give a new proof of a result of Dudas.

Xuechao Zhang, Xuda Ding, Yi Ren, Yu Zheng

The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized globally and simultaneously. We first propose to use the number

Shōta Inoue, Junxian Li

We consider the joint value distribution of Dirichlet $L$-functions in the critical strip $\frac{1}{2} < \sigma < 1$. We show that the values of distinct Dirichlet $L$-functions are dependent in the sense that they do not behave like independently distributed random variables and they prevent each other from obtaining large values. Nevertheless, we show that

Niklas Reisz, Vito D. P. Servedio, Stefan Thurner

Forecasting the popularity of new songs has become a standard practice in the music industry and provides a comparative advantage for those that do it well. Considerable efforts were put into machine learning prediction models for that purpose. It is known that in these models, relevant predictive parameters include intrinsic lyrical and acoustic characteris

Ziliang Lai, Chris Liu, Eric Lo

Private blockchain as a replicated transactional system shares many commonalities with distributed database. However, the intimacy between private blockchain and deterministic database has never been studied. In essence, private blockchain and deterministic database both ensure replica consistency by determinism. In this paper, we present a comprehensive ana

Zijun Gao, Jun Wang, Guoxian Yu, Zhongmin Yan

Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail hashing methods have been proposed but they can not adapt for multi-modal data, due to the complex interplay between labels and individuality and commonality information of multi-

Adam Rouhiainen, Moritz Münchmeyer

Fields in cosmology, such as the matter distribution, are observed by experiments up to experimental noise. The first step in cosmological data analysis is usually to de-noise the observed field using an analytic or simulation driven prior. On large enough scales, such fields are Gaussian, and the de-noising step is known as Wiener filtering. However, on sma

Zhao-Feng Wu, Lok W. L. Chan, Martin Hendry, Otto A. Hannuksela

The mergers of supermassive black hole binaries (SMBHBs) can serve as standard sirens: the gravitational wave (GW) analog of standard candles. The upcoming space-borne GW detectors will be able to discover such systems and estimate their luminosity distances precisely. Unfortunately, weak gravitational lensing can induce significant errors in the measured di

Henry N. Adorna

Several studies have been reported in the literature about SN P system and its variants. Often, the results provide universality of various variants and the classes of languages that these variants generate and recognize. The state of SN P system is its configuration. We refer to our previous result on reachability of configuration as the {\it Fundamental st

Sein Kim, Namkyeong Lee, Junseok Lee, Dongmin Hyun

Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly com

Matthias Gohlke, Alberto Corticelli, Roderich Moessner, Paul A. McClarty

Linear spin wave theory (LSWT) is the standard technique to compute the spectra of magnetic excitations in quantum materials. In this paper, we show that LSWT, even under ordinary circumstances, may fail to implement the symmetries of the underlying ordered magnetic Hamiltonian leading to spurious degeneracies. In common with pseudo-Goldstone modes in cases

Henry N. Adorna

In the 2010, matrix representation of SN P system without delay was presented while in the case of SN P systems with delay, matrix representation was suggested in the 2017. These representations brought about series of simulation of SN P systems using computer software and hardware technology. In this work, we revisit these representation and provide some ob

Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao

Neural architectures can be naturally viewed as computational graphs. Motivated by this perspective, we, in this paper, study neural architecture search (NAS) through the lens of learning random graph models. In contrast to existing NAS methods which largely focus on searching for a single best architecture, i.e, point estimation, we propose GraphPNAS a deep

JunHao Chen

In this paper, we modify the proof methods of some previously weakly consistent variants of random forests into strongly consistent proof methods, and improve the data utilization of these variants in order to obtain better theoretical properties and experimental performance. In addition, we propose a data-driven multinomial random forest (DMRF), which has t

Quan Feng, Jiayu Yao, Zhison Pan, Guojun Zhou

In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. However, obtaining these annotated data has to afford expensive costs in reality. Therefore, a more realistic strategy is to leverage semi-supervised learning (SSL) with a small amount o

Xiangping Kang, Guoxian Yu, Jun Wang, Wei Guo

Crowdsourcing is a favorable computing paradigm for processing computer-hard tasks by harnessing human intelligence. However, generic crowdsourcing systems may lead to privacy-leakage through the sharing of worker data. To tackle this problem, we propose a novel approach, called iFedCrowd (incentive-boosted Federated Crowdsourcing), to manage the privacy and

Yang Li, Fan Wang, Rong Li, Yifan Sun

In the genomic analysis, it is significant while challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets as well, this paper proposes a novel integrative interaction approach under the semiparametric model, in which the genetic factors and environmental f

Imam Mustafa Kamal, Hyerim Bae

Binary classification (BC) is a practical task that is ubiquitous in real-world problems, such as distinguishing healthy and unhealthy objects in biomedical diagnostics and defective and non-defective products in manufacturing inspections. Nonetheless, fully annotated data are commonly required to effectively solve this problem, and their collection by domai

Yifan Sun, Zhengyang Sun, Yu Jiang, Yang Li

In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a s

Yifan Sun, Yu Jiang, Yang Li, Shuangge Ma

The analysis of cancer omics data is a "classic" problem, however, still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consid

Ziye Luo, Xinyue Yao, Yifan Sun, Xinyan Fan

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing

Di Wu

In this paper, we investigate the topological numbers for singly rotating Kerr black holes in arbitrary dimensions and four-dimensional Kerr-Newman black hole. We show that for uncharged black holes, the rotation parameter has a significant effect on the topological number, and for rotating black holes, the dimension of spacetime has a remarkable effect on t

Xiang Zhang, Yong Shen, Xiaokang Tang, Qu Liu

From the point of view of the differential phase delay experienced by the two counterpropagating optical fields, the self-starting of the mode-locked fiber laser with a nonlinear amplifying loop mirror (NALM) is theoretically studied. Although it is generally believed that NALM shows a saturable absorption effect on both continuous wave (CW) light and pulses

Floris van der Tak

While ALMA and JWST are revolutionizing our view of star and planet formation with their unprecedented sensitivity and resolution at submillimeter and near-IR wavelengths, many outstanding questions can only be answered with observations in the thermal (mid- and far-) infrared domain. Many of these questions require space-based observations, to achieve the n

Lanling Xu, Zhen Tian, Gaowei Zhang, Lei Wang

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update requests. This motivates us to make some significant improvements on our library, so as to meet the user requirements and contribute to the research community. In order to show the recent

Wu-yi Pan

We give a survey, known and new results on the beingness of fixed points of the maximal operator in the more general settings of metric measure space. In particular, we prove that the fixed points of the uncentered one must be the constant function if the measure satisfies a mild continuity assumption and its support is connected.

Richard Santiago, Ivan Sergeev, Rico Zenklusen

Random order online contention resolution schemes (ROCRS) are structured online rounding algorithms with numerous applications and links to other well-known online selection problems, like the matroid secretary conjecture. We are interested in ROCRS subject to a matroid constraint, which is among the most studied constraint families. Previous ROCRS required

Ricardo Vinuesa, Steve Brunton

The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess areas tha

Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker

The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP. However, recent works argue that offline RL methods encounter unique challenges to scaling up model capacity. Drawing on the learnings from th

Bin Wang, Wenbin Pei, Bing Xue, Mengjie Zhang

Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as the most dominant method for image classification. However, a severe drawback of deep convolutional neural networks is poor explainability. Unfortunately, in many real-world applications, users need to understand the rationale behind the predictions of deep conv

Zhen-Xia Niu, Qian Wang

Identifying dynamical signatures of excited state quantum phase transitions (ESQPTs) in experimentally realizable quantum many-body systems is helpful for understanding the dynamical effects of ESQPTs. In such systems, the highly controllable spinor Bose-Einstein condensates (BECs) offer an exceptional platform to study ESQPTs. In this work, we investigate t

Yiqian Shi, Chunhui Wei, Bin Xu

We consider the ${\rm SU}(n+1)$ Toda system on a simply connected domain $\Omega$ in ${\Bbb C}$, the $n=1$ case of which coincides with the Liouville equation $\Delta u+8e^u=0$. A classical result by Liouville says that a solution of this equation on $\Omega$ can be represented by some non-degenerate meromorphic function on $\Omega$. We construct a family of

D. R. Yahne, B. Placke, R. Schäfer, O. Benton

The dipolar-octupolar (DO) pyrochlores, R$_2$M$_2$O$_7$ (R = Ce, Sm, Nd), are key players in the search for realizable novel quantum spin liquid (QSL) states as a large parameter space within the DO pyrochlore phase diagram is theorized to host QSL states of both dipolar and octupolar nature. New single crystals and powders of Ce$_2$Sn$_2$O$_7$, synthesized

Wuhyun Sohn, Arman Shafieloo, Dhiraj Kumar Hazra

While the simplest inflationary models predict the primordial perturbations to be near scale-invariant, the primordial power spectrum (PPS) can exhibit oscillatory features in many physically well-motivated models. We search for hints of such features via free-form reconstructions of the PPS based on \textit{Planck} 2018 CMB temperature and polarization anis

Wojciech Roga, Rikizo Ikuta, Tomoyuki Horikiri, Masahiro Takeoka

We analyze the generation of entanglement in a multipartite optical network. We generalize the twin-field strategy to the multipartie case and show that our protocol has advantageous rate-loss scalings of distributing W states and Dicke states over the star networks. We give precise theoretical formulas and quantitative performance analyses. Also analysis of

Hiroshi Onuki

In 2015, Abatzoglou, Silverberg, Sutherland, and Wong presented a framework for primality proving algorithms for special sequences of integers using an elliptic curve with complex multiplication. They applied their framework to obtain algorithms for elliptic curves with complex multiplication by imaginary quadratic field of class numbers one and two, but, th

So Kuroki, Tatsuya Matsushima, Jumpei Arima, Hiroki Furuta

While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative 2D push manipulations using mobile robots. Although conventional works demonstrate potential solutions for the probl

Piyush Agarwal, Yingshu Yang, Rohit Medwal, Hironori Asada

Femtosecond laser-induced photoexcitation of ferromagnet (FM)/heavy metal (HM) heterostructures have attracted attention by emitting broadband terahertz frequencies. The phenomenon relies on the formation of ultrafast spin current, which is largely attributed to the direct photoexcitation of the FM layer. However, we reveal that during the process, the FM la

A. Pal, S. Santra, A. Kundu, D. Chattopadhyay

Mass distributions of fission fragments arising from the slow quasi-fission process have been derived by comparing the measured distributions with the theoretical distributions based on compound nuclear fission model for several reactions. The mass-distributions corresponding to quasi-fission events for all the systems show the following common features: (1)

Can Lei, Huigang Wang, Juan Lei

The existing sonar image classification methods based on deep learning are often analyzed in Euclidean space, only considering the local image features. For this reason, this paper presents a sonar classification method based on improved Graph Attention Network (GAT), namely SI-GAT, which is applicable to multiple types imaging sonar. This method quantifies

W. X. Wang, Z. X. Ling, C. Zhang, W. M. Yuan

In recent years, tremendous progress has been made on complementary metal-oxide-semiconductor (CMOS) sensors for applications as X-ray detectors. To shield the visible light in X-ray detection, a blocking filter of aluminum is commonly employed. We designed three types of aluminum coating layers, which are deposited directly on the surface of back-illuminate

G. Martinelli, M. Naviglio, S. Simula, L. Vittorio

We present the results of the application of the Dispersion Matrix approach to semileptonic heavy-to-heavy and heavy-to-light $B$-meson decays. This method allows to determine the hadronic form factors in a non-perturbative and model-independent way. Starting from the available lattice results at large values of the momentum transfer, we obtain the behaviour

Hao Yang, Yan-Gang Miao

Regular black holes, as an important attempt to eliminate the singularities in general relativity, have been widely concerned. Due to the fact that the superradiance associated with rotating regular black holes plays an indispensable role in black hole physics, we calculate the superradiance related effects, i.e., the superradiance instability and the energy

Alessio Russo, Alexandre Proutiere

We investigate the sample complexity of learning the optimal arm for multi-task bandit problems. Arms consist of two components: one that is shared across tasks (that we call representation) and one that is task-specific (that we call predictor). The objective is to learn the optimal (representation, predictor)-pair for each task, under the assumption that t

Kristian Hovde Liland, Joakim Skogholt, Ulf Geir Indahl

In the present paper, we prove a new theorem, resulting in an update formula for linear regression model residuals calculating the exact k-fold cross-validation residuals for any choice of cross-validation strategy without model refitting. The required matrix inversions are limited by the cross-validation segment sizes and can be executed with high efficienc

Xi Zheng, Weisong Wen, Li-Ta Hsu

Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The visual odometry-based method can provide accurate positioning in a short period but is subjected to drift over time. Moreover, the quantification of the safety of the loc

M. C. Suster, J. Derlikiewicz, K. Krajewska, F. Cajiao Vélez

A general method for solving numerically the time-dependent Schr\"odinger equation, that is based on the Suzuki-Trotter scheme with the split-step Fourier approach, is introduced. The method relies on a Hamiltonian decomposition, where except of the components depending exclusively on the momentum or on the position operators, there are also terms depending