October 2022 arXiv papers — page 12

Zhe Li, Man-Wai Mak

A great challenge in speaker representation learning using deep models is to design learning objectives that can enhance the discrimination of unseen speakers under unseen domains. This work proposes a supervised contrastive learning objective to learn a speaker embedding space by effectively leveraging the label information in the training data. In such a s

Enrica Duchi, Corentin Henriet

The class of fighting fish is a recently introduced model of branching surfaces generalizing parallelogram polyominoes. We can alternatively see them as gluings of cells, walks on the square lattice confined to the quadrant or shuffle of Dyck words. With these different points of view, we introduce a natural extension of fighting fish that we call \emph{gene

Yimeng Ren, Zhe Li, Xuening Zhu, Yuan Gao

The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least

P K Manoharan, Phil Perillat, C J Salter, Tapasi Ghosh

We present an occultation study of compact radio sources by the plasma tail of interstellar Comet 2I/Borisov (C/2019 Q4) both pre- and near-perihelion using the Arecibo and Green Bank radio telescopes. The interplanetary scintillation (IPS) technique was used to probe the plasma tail at P-band (302--352 MHz), 820 MHz, and L-band (1120--1730 MHz). The presenc

Liang-Liang Sun, Xingjian Zhang, Xiang Zhou, Zheng-Da Li

The unpredictable process of state collapse caused by quantum measurements makes the generation of quantum randomness possible. In this paper, we explore the quantitive connection between the randomness generation and the state collapse and provide a randomness verification protocol under the assumptions: (I) independence between the source and the measureme

Javed Akram, Khan Qasim, Lei Wei

The characteristic of the angular distributions of accelerated Bose-Einstein condensate (BEC) atoms incidence on the surface is designed using the mathematical modeling method. Here, we proposed the idea to study the retroreflection and diffraction of a BEC from an evanescent standing wave potential (ESWP). The ESWP is formed by multiple reflections of the l

Yifei Guan, Oleg V. Yazyev

Real-world samples of graphene often exhibit various types of out-of-plane disorder -- ripples, wrinkles and folds -- introduced at the stage of growth and transfer processes. These complex out-of-plane defects resulting from the interplay between self-adhesion of graphene and its bending rigidity inevitably lead to the scattering of charge carriers thus aff

Chen Liu, Yuan Gao, Xiangxiong Zhang

In this paper, we introduce second order and fourth order space discretization via finite difference implementation of the finite element method for solving Fokker-Planck equations associated with irreversible processes. The proposed schemes are first order in time and second order and fourth order in space. Under mild mesh conditions and time step constrain

Huimin Xiong, Kunle Li, Kaiyuan Tan, Yang Feng

Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. Accurate 3D tooth segmentation, which aims to precisely delineate the tooth and gingiva instances in IOS, plays a critical role in a variety of dental applications. However, segment

Xinhua Wen, Heung Kit Yip, Choonlae Cho, Jensen Li

We propose a concept called acoustic amplifying diode in combining both signal isolation and amplification in a single device. The signal is exponentially amplified in one direction with no reflection and is completely absorbed in another. In this case, the reflection is eliminated from the device in both directions due to impedance matching, preventing back

Jameel Hussain, Javed Akram, Farhan Saif

We numerically study the impact of Gaussian barrier height and width on the gray solitons population in a symmetric and asymmetric potential trap. The gray solitons are created in a double-well potential by the density engineering method. Two identical Bose-Einstein condensate fragments are confined and made to collide by switching off the Gaussian barrier i

Je Yang, JaeUk Kim, Joo-Young Kim

Multi-agent reinforcement learning (MARL) is a powerful technology to construct interactive artificial intelligent systems in various applications such as multi-robot control and self-driving cars. Unlike supervised model or single-agent reinforcement learning, which actively exploits network pruning, it is obscure that how pruning will work in multi-agent r

Gerhard Jung, Giulio Biroli, Ludovic Berthier

We introduce GlassMLP, a machine learning framework using physics-inspired structural input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep neural network to atomistic models in 2D and 3D. Its performance is better than the state of the art while being more parsimonious in terms of training data and fitting parameters. Gla

Zhe Li, Man-Wai Mak, Helen Mei-Ling Meng

The challenges in applying contrastive learning to speaker verification (SV) are that the softmax-based contrastive loss lacks discriminative power and that the hard negative pairs can easily influence learning. To overcome the first challenge, we propose a contrastive learning SV framework incorporating an additive angular margin into the supervised contras

Ting Hu, Christoph Meinel, Haojin Yang

Transformer-based architectures like BERT have achieved great success in a wide range of Natural Language tasks. Despite their decent performance, the models still have numerous parameters and high computational complexity, impeding their deployment in resource-constrained environments. Post-Training Quantization (PTQ), which enables low-bit computations wit

Liu Chao

This note illustrates the Ricci flow method based on the Cao.H.D's paper[1] and Yau.S.T's paper[4], and tries to explain the method in detail, especially in some calculations. Jian Song and Weinkove's note[9] used some other estimates to obtain the result, this paper will explain some of their estimates as well. This note was a seminar lecture note in 2022 s

Georgijs Bakradze, Edmund Welter, Alexei Kuzmin

New monoclinic ($P2$/$c$) tungstates - a medium-entropy tungstate, (Mn,Ni,Cu,Zn)WO$_4$, and a high-entropy tungstate, (Mn,Co,Ni,Cu,Zn)WO$_4$ - were synthesized and characterized. Their phase purity and solid solution nature were confirmed by powder X-ray diffraction and Raman spectroscopy. X-ray absorption spectroscopy was used to probe the local structure a

Elizabeth Gutiérrez-Meza, Alejandro Vega-Flick, Eric R. Bittner, Carlos Silva-Acuña

The photophysics of $\pi$-conjugated polymers has been of considerable interest over the last three decades because of their organic semiconductor properties. Primary photoexcitations, Frenkel excitons, can be probed optically by means of numerous linear spectroscopies, providing a wealth of information on the the strength of excitonic coupling, the exciton

Huaian Diao, Hongjie Li, Hongyu Liu, Jiexin Tang

We are concerned with a coupled-physics spectral problem arising in the coupled propagation of acoustic and elastic waves, which is referred to as the acoustic-elastic transmission eigenvalue problem. There are two major contributions in this work which are new to the literature. First, under a mild condition on the medium parameters, we prove the existence

Jason J. Calvin, Amanda S. Brewer, Michelle F. Crook, Tierni M. Kaufman

Surface energy is a fundamental property of materials and is particularly important in describing nanomaterials where atoms or molecules at the surface constitute a large fraction of the material. Traditionally, surface energy is considered to be a positive quantity, where atoms or molecules at the surface are less thermodynamically stable than their counter

Manuel Arrayás, Francis Bettsworth, Richard Haley, Roch Schanen

We present the working prototype of a levitation system designed for investigation of flows in cryogenic helium fluids. The current device allows the levitation of a superconducting sphere and has several provisions made for allowing precise control over its motion. We report on progress in the detection and control systems of the prototype and demonstrate h

Guangjie Li, Elio J. König, Jukka I. Väyrynen

Multiple conduction channels interacting with a quantum impurity -- a spin in the conventional ``multi-channel Kondo effect'' or a topological mesoscopic device (``topological Kondo effect'') -- has been proposed as a platform to realize anyonic quasi-particles. However, the above implementations require either perfect channel symmetry or the use of Majorana

Abhijeet Awasthi, Ashutosh Sathe, Sunita Sarawagi

Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language queries in the new schemas. We present ReFill, a framework for synthesizing high-quality and textually diverse parallel datasets for adapting a Text-to-SQL parser to a target schema

Chunmei Wang, Junping Wang, Shangyou Zhang

This article introduces a weak Galerkin (WG) finite element method for quad-curl problems in three dimensions. It is proved that the proposed WG method is stable and accurate in an optimal order of error estimates for the exact solution in discrete norms. In addition, an $L^2$ error estimate in an optimal order except the lowest orders $k=1, 2$ is derived fo

Mine Kerpicci, Van Nguyen, Shuhua Zhang, Erik Visser

Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a large number of parameters, the smallest

Luca Donno

The thesis addresses the problem of scalability in decentralized blockchains in the context of the trade-off between transaction throughput and hardware requirements to participate in the network. Rollups are presented, that is technologies to verify on-chain blocks executed off-chain by minimizing the assumptions of trust. The variant of the Optimistic Roll

Steffen Börm, Janne Henningsen

Boundary element methods for elliptic partial differential equations typically lead to boundary integral operators with translation-invariant kernel functions. Taking advantage of this property is fairly simple for particle methods, e.g., Nystrom-type discretizations, but more challenging if the supports of basis functions have to be taken into account. In t

Pedro Viegas, Tiago Cunha Dias, Chloé Fromentin, Alexander Chukalovsky

This work focuses on the benchmarking between a zero-dimensional (0D) global model (LoKI) and a one-dimensional (1D) radial fluid model for the positive column of oxygen DC glow discharges in a tube of 1 cm inner radius at pressures between 0.5 Torr and 10 Torr. The data used in the two models are the same, so that the difference between the models is reduce

Fateme Rajabi, Kourosh Nozari

We study cosmological inflation and reheating in the unimodular f(R,T) gravity. During the reheating era, which takes place just after the end of inflation, the energy density of inflaton is converted to radiation energy through, for instance, rapid oscillation about the minimum of the potential. We quantify our investigation by calculating the reheating tem

Z. Gholami, M. Amini, M. Soltani, E. Ghanbari-Adivi

The emergent integrability in a many-body localized (MBL) system can be well characterized by the existence of the complete set of local integrals of motion (LIOMs). Such exactly conserved and exponentially localized operators are often understood as quasiparticle operators which can be expanded in terms of single-particle operators dressed with different nu

Peter Belcak, Roger Wattenhofer

We propose a novel, fully explainable neural approach to synthesis of combinatorial logic circuits from input-output examples. The carrying advantage of our method is that it readily extends to inductive scenarios, where the set of examples is incomplete but still indicative of the desired behaviour. Our method can be employed for a virtually arbitrary choic

Jorge A. Borrego-Morell

We consider orthogonal polynomials with respect to a linear differential operator $$\mathcal{L}^{(M)}=\sum_{k=0}^{M}\rho_{k}(z)\frac{d^k}{dz^k}, $$ where $\{\rho_k\}_{k=0}^{M}$ are complex polynomials such that $deg[\rho_k]\leq k, 0\leq k \leq M$, with equality for at least one index. We analyze the uniqueness and zero location of these polynomials. An inter

Allison Lahnala, Charles Welch, David Jurgens, Lucie Flek

We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility. Moreover, (3) emotional empathy is overemphasized, skewing our focus to a narrow subset of simplified tasks. We believe these issues hinder

Da-Wei Yang, Zihao Xu, Yan-Quan Feng, Jaeun Lee

The spined cube $SQ_n$ is a variant of the hypercube $Q_n$, introduced by Zhou et al. in [Information Processing Letters 111 (2011) 561-567] as an interconnection network for parallel computing. A graph $\G$ is an $m$-Cayley graph if its automorphism group $\Aut(\G)$ has a semiregular subgroup acting on the vertex set with $m$ orbits, and is a Caley graph if

Deepika Saxena, Ashutosh Kumar Singh

This paper proposes a conceptual model for a secure and performance-efficient workload management model in cloud environments. In this model, a resource management unit is employed for energy and performance proficient allocation of virtual machines while ensuring the secure processing of users' applications by defending against data breaches due to unauthor

Shijie Bao, Qi'an Guan

In this article, we consider Bergman kernels related to modules at boundary points for singular hermitian metrics on holomorphic vector bundles, and obtain a log-subharmonicity property of the Bergman kernels. As applications, we obtain a lower estimate of weighted $L^2$ integrals on sublevel sets of plurisubharmonic functions, and reprove an effectiveness r

Hongxia Lin, Jiahong Wu, Yi Zhu

Physical experiments and numerical simulations have observed a remarkable stabilizing phenomenon: a background magnetic field stabilizes and damps electrically conducting fluids. This paper intends to establish this phenomenon as a mathematically rigorous fact on a magnetohydrodynamic (MHD) system with anisotropic dissipation in $\mathbb R^3$. The velocity e

Arkadiusz Lipiecki, Katarzyna Sznajd-Weron

Engaging with dissenting views, fostering productive disagreements or strategic anticonformity can benefit organizations as it challenges the status quo. The question arises, however, whether such strategic anticonformity ultimately leads to social polarization, which is not a desirable phenomenon. We address this question within an agent-based model of disc

Kwanhyung Lee, John Won, Heejung Hyun, Sangchul Hahn

Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important. Though many studies have proposed automatic prediction methods using Electronic Health Records (EHR), their coarse-grained time resolutions limit their practical usage in urgent environments such as the emergency department (ED) and

Poula Tadros, Iiro Vilja

String theory is the prime candidate for the theory of everything. However, it must be defined in ten dimensions to be consistent. To get 4D physics, the 6 other dimensions should be curled up in a small compact manifold, this procedure is called string compactification. In this review, we will review different compactification schemes proving that in absenc

M. J. Mehdipour, A. Rejali

In this paper, we investigate homological properties of Banach algebras. We show that retractions Banach algebras preserve biprojectivity, contractibility and biflatness. We also prove that contractibility of second dual of a Banach algebra implies contractibility of the Banach algebra. For a Banach algebra $A$ with $\Delta(A)\neq\emptyset$, let $\frak{F}(X,

Xianwang Xie, Bin Wu, Botao Hou

The Internet of Vehicles (IoV) can significantly improve transportation efficiency and ensure traffic safety. Authentication is regarded as the fundamental defense line against attacks in IoV. However, the state-of-the-art approaches suffer from several drawbacks, including bottlenecks of the single cloud server model, high computational overhead of operatio

Tsukasa Kusakabe, Sho Sakaino, Toshiaki Tsuji

Compliance control is an increasingly employed technique used in the robotic field. It is known that various mechanical properties can be reproduced depending on the design of the stiffness matrix, but the design theory that takes advantage of this high degree of design freedom has not been elucidated. This paper, therefore, discusses the non-diagonal elemen

Marina David, James T. Liu

T-duality has been shown to constrain the higher derivative corrections of string theory. We revisit the problem of understanding the T-duality constraints imposed on the $\alpha'$ corrections using the language of a torsionful connection. We find a convenient way to express the $O(d,d)$ invariants in terms of linear combinations of the metric and B-field fo

Xianxin Song, Tony Xiao Han, Jie Xu

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communication (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one extended target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assis

Yifang Qin, Yifan Wang, Fang Sun, Wei Ju

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead

Wei-Wei Du, Wei-Yao Wang, Wen-Chih Peng

Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors. Despite the effectiveness of recommending accurately, there exist several factors that are essential but unexplored for evaluating various facets of recommendation systems, e.g., fairness, diversity, and limited resources.

Zheyang Xiong, Fangshuo Liao, Anastasios Kyrillidis

The strong Lottery Ticket Hypothesis (LTH) claims the existence of a subnetwork in a sufficiently large, randomly initialized neural network that approximates some target neural network without the need of training. We extend the theoretical guarantee of the strong LTH literature to a scenario more similar to the original LTH, by generalizing the weight chan

Henri Lombardi, Thierry Coquand

We give a constructive proof that $R[X]$ is normal when $R$ is normal. We apply this result to an operation needed for studying the henselization of a local ring. Our proof is based on the case where $R$ is without zero divisors, which is more involved than the case where $R$ is an integral domain. We have to use a constructive deciphering technique that rep

Nikolas McNeal, Jennifer Huang, Aniekan Umoren, Shuqi Dai

We explore the use of a neural network inspired by predictive coding for modeling human music perception. This network was developed based on the computational neuroscience theory of recurrent interactions in the hierarchical visual cortex. When trained with video data using self-supervised learning, the model manifests behaviors consistent with human visual

Jinning Li, Shubhanshu Mishra, Ahmed El-Kishky, Sneha Mehta

On social media, additional context is often present in the form of annotations and meta-data such as the post's author, mentions, Hashtags, and hyperlinks. We refer to these annotations as Non-Textual Units (NTUs). We posit that NTUs provide social context beyond their textual semantics and leveraging these units can enrich social media text representations

Slava Pimenov

We study the cohomology of Lie superalgebra of vector fields on affine super-spaces $\mathbb{A}^{m,n}$ with trivial coefficients. Previously known results covered the classical case $\mathbb{A}^m = \mathbb{A}^{m,0}$ as well as the case $\mathbb{A}^{m,n}$ with $m \leq n$. In this paper we perform the computation for super-spaces $\mathbb{A}^{m,1}$.

Shengchao Chen, Sufen Ren, Guanjun Wang, Mengxing Huang

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability red

Matteo Cucchi, Daniela Parker, Paschalis Gkoupidenis, Eleni Stavrinidou

Next-generation implantable computational devices require long-term stable electronic components capable of operating in, and interacting with, electrolytic surroundings without being damaged. Organic electrochemical transistors (OECTs) emerged as fitting candidates. However, while single devices feature impressive figures of merit, integrated circuits (ICs)

Wu-Zhen Li, Chen Yang, Zhi-Yuan Zhou, Yan Li

The change in the relative phase between two light fields serves as a basic principle for the measurement of the physical quantity that guides this change. It would therefore be highly advantageous if the relative phase could be amplified to enhance the measurement resolution. One well-known method for phase amplification involves the use of the multi-photon

Yudai Suzuki, Hideaki Kawaguchi, Naoki Yamamoto

Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) that measure the similarity between data. Despite the potential quantum advantage of the method, the commonly used fidelity-based QK suffers from a detrimental issue, which we call the vanishing similarity issue; detecting the difference betw

Nadime Francis, Amélie Gheerbrant, Paolo Guagliardo, Leonid Libkin

The development of practical query languages for graph databases runs well ahead of the underlying theory. The ISO committee in charge of database query languages is currently developing a new standard called Graph Query Language (GQL) as well as an extension of the SQL Standard for querying property graphs represented by a relational schema, called SQL/PGQ.

Bipasha Sen, Aditya Agarwal, Vinay P Namboodiri, C. V. Jawahar

Generating videos is a complex task that is accomplished by generating a set of temporally coherent images frame-by-frame. This limits the expressivity of videos to only image-based operations on the individual video frames needing network designs to obtain temporally coherent trajectories in the underlying image space. We propose INR-V, a video representati

Brahim Amghar, Abdallah Slaoui, Jamal Elfakir, Mohammed Daoud

Comprehending the connections between the geometric, topological, and dynamical structures of integrable quantum systems with quantum phenomena exploitable in quantum information tasks, such as quantum entanglement, is a major problem in geometric information science. In this work we investigate these issues in a physical system of $\mathcal{N}$ interacting

Jean-Baptiste Camps, Julien Randon-Furling

How did written works evolve, disappear or survive down through the ages? In this paper, we propose a unified, formal framework for two fundamental questions in the study of the transmission of texts: how much was lost or preserved from all works of the past, and why do their genealogies (their ``phylogenetic trees'') present the very peculiar shapes that we

Simon Santschi

We prove a representation theorem for totally ordered idempotent monoids via a nested sum construction. Using this representation theorem we obtain a characterization of the subdirectly irreducible members of the variety of semilinear idempotent distributive l-monoids and a proof that its lattice of subvarieties is countably infinite. For the variety of comm

Resul Dagdanov, Halil Durmus, Nazim Kemal Ure

In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the div

Youngseok Kim, Sanmin Kim, Sangmin Sim, Jun Won Choi

Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accurate depth to objects than other methods thanks to the depth estimation network trained on a large-scale dataset. However, depth map approaches can be limited by the accuracy of the

Chandni Menapara, Chetan Lodha, Ajay Kumar Rai

In contrast to past studies, the current paper is focused on baryons, and all four isospin states have been independently generated using u and d quarks with various constituent masses. The hypercentral Constituent Quark Model (hCQM) serves as the theoretical foundation for computing the resonance masses. The spin-dependent and first order correction terms a

Zhong-Min Tsai, Yu-Ju Tsai, Chien-Yao Wang, Hong-Yuan Liao

The paper presents a new method, SearchTrack, for multiple object tracking and segmentation (MOTS). To address the association problem between detected objects, SearchTrack proposes object-customized search and motion-aware features. By maintaining a Kalman filter for each object, we encode the predicted motion into the motion-aware feature, which includes b

Debesh Bhattacharjee, Prasad Subramanian, Angelos Vourlidas, Teresa Nieves-Chinchilla

The pressure and energy density of the gas and magnetic field inside solar coronal mass ejections (in relation to that in the ambient solar wind) is thought to play an important role in determining their dynamics as they propagate through the heliosphere. We compare the specific energy (${\rm erg\,g^{-1}}$) [comprising kinetic ($H_{\rm k}$), thermal ($H_{\rm

M. M. Slob, J. R. Callingham, H. J. A. Röttgering, W. L. Williams

We present a sample of 373 peaked-spectrum (PS) sources with spectral peaks around 150MHz, selected using a subset of two LOFAR all-sky surveys, the LOFAR Two Meter Sky Survey and the LOFAR LBA Sky Survey. These surveys are the most sensitive low-frequency widefield surveys to date, allowing us to select low-luminosity PS sources. Our sample increases the nu

Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar

Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent approaches, such as XR-Transformer and LightXML, leverage a transformer instance to achieve state-of-the-art performance. However, in this process, these approaches need to make var

Junghoon Kim, Seyyedali Hosseinalipour, Taejoon Kim, David J. Love

We consider linear coding for Gaussian two-way channels (GTWCs), in which each user generates the transmit symbols by linearly encoding both its message and the past received symbols (i.e., the feedback information) from the other user. In Gaussian one-way channels (GOWCs), Butman has proposed a well-developed model for linear encoding that encapsulates feed

Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt

Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, chemicals locked in the ice display quasi-periodic cycles that delineate annual layers. Manually counting these noisy seasonal patterns to inf

Resul Dagdanov, Feyza Eksen, Halil Durmus, Ferhat Yurdakul

Safely navigating through an urban environment without violating any traffic rules is a crucial performance target for reliable autonomous driving. In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction spots and re-constructing mini-scenarios o

Gang Liu, Aiqing Guo, Fengli Cao, Weiwei Ju

Combined with first-principles calculations and semiclassical Boltzmann transport theory, Janus {\alpha}-STe2 and {\alpha}-SeTe2 monolayers are investigated systematically. Janus {\alpha}-STe2 and {\alpha}-SeTe2 monolayers are indirect semiconductors with band gaps of 1.20 and 0.96 eV. It is found they possess ultrahigh figure of merit (ZT) values of 3.9 and

V. P. Burichenko

By an {\em isotropy group} of a tensor $t\in V_1 \otimes V_2\otimes V_3=\widetilde V$ we mean the group of all invertible linear transformations of $\widetilde V$ that leave $t$ invariant and are compatible (in an obvious sense) with the structure of tensor product on~$\widetilde V$. We consider the case where $t$ is the structure tensor of multiplication ma

Massimiliano d'Aquino, Riccardo Hertel

We present efficient numerical methods for the simulation of small magnetization oscillations in three-dimensional micromagnetic systems. Magnetization dynamics is described by the Landau-Lifshitz-Gilbert (LLG) equation, linearized in the frequency domain around a generic equilibrium configuration, and formulated in a special operator form that allows levera

Richard Post, Edwin van den Heuvel

In recent years, the field of causal inference from observational data has emerged rapidly. The literature has focused on (conditional) average causal effect estimation. When (remaining) variability of individual causal effects (ICEs) is considerable, average effects may be uninformative for an individual. The fundamental problem of causal inference preclude

Sushant Ghimire, Kevin Oldenburg, Stephan Bartling, Rostyslav Lesyuk

We report a structural reconstruction-induced high photoluminescence quantum yield of 25% in colloidal two-dimensional tin iodide nanosheets that are synthesized by a hot-injection method. The as-synthesized red-colored nanosheets of octylammonium tin iodide perovskites at room temperature transform to white hexagonal nanosheets upon washing or exposure to l

Zhiheng Hu, Yongzhen Wang, Peng Li, Jie Qin

Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detection. On the one hand, to fully exploit

Liwei Dai, Abderrezak Torche, Christian Strelow, Tobias Kipp

Mn2+-doped semiconductor nanocrystals with tuned location and concentration of Mn2+ ions can yield diverse coupling regimes, which can highly influence their optical properties such as emission wavelength and photoluminescence (PL) lifetime. However, investigation on the relationship between the Mn2+ concentration and the optical properties is still challeng

Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man Cheung

Few-shot image generation (FSIG) aims to learn to generate new and diverse samples given an extremely limited number of samples from a domain, e.g., 10 training samples. Recent work has addressed the problem using transfer learning approach, leveraging a GAN pretrained on a large-scale source domain dataset and adapting that model to the target domain based

Henri Lombardi, Assia Mahboubi

The first part of the present article consists in a survey about the dynamical constructive method designed using dynamical theories and dynamical algebraic structures. Dynamical methods uncovers a hidden computational content for numerous abstract objects of classical mathematics, which seem a priori inaccessible constructively, e.g., the algebraic closure

Shulin Huang, Shirong Ma, Yinghui Li, Yangning Li

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e.,

Shikhar Jaiswal, Rahul Kiran Kranti Goli, Aayan Kumar, Vivek Seshadri

Running machine learning inference on tiny devices, known as TinyML, is an emerging research area. This task requires generating inference code that uses memory frugally, a task that standard ML frameworks are ill-suited for. A deployment framework for TinyML must be a) parametric in the number representation to take advantage of the emerging representations

Richard Post, Edwin van den Heuvel, Hein Putter

Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, the hazard difference has been shown to remain unaffected by the selection of frailty factors over time. Therefore, observed hazard differences can be used as an unbiased estimator for the causal hazard differences in the absence of confounding. However,

Guangzhi Sun, Chao Zhang, Philip C. Woodland

End-to-end spoken language understanding (SLU) suffers from the long-tail word problem. This paper exploits contextual biasing, a technique to improve the speech recognition of rare words, in end-to-end SLU systems. Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model component, is studied, which leverages a slo

Oleg N. German

Diophantine exponents are ones of the simplest quantitative characteristics responsible for the approximation properties of linear subspaces of a Euclidean space. This survey is aimed at describing the current state of the area of Diophantine approximation which studies Diophantine exponents and relations they satisfy. We discuss classical Diophantine expone

Natalie J. Briscoe, Shane D. Morris, Paul D. Mathewson, Lauren B. Buckley

A challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are

Manju Maan, Asoka Biswas, Shubhrangshu Dasgupta

We propose an entanglement criterion, specially designed for mixed states, based on uncertainty relation and the Wigner-Yanase skew information. The variances in this uncertainty relation does not involve any classical mixing uncertainty, and thus turns out to be purely of quantum mechanical nature. We show that any mixed entangled state can be characterized

Richard Post, Edwin van den Heuvel, Hein Putter

It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from built-in selection bias as, over time, the individuals at risk in the exposed and unexposed are no longer exchangeable. In this work, we formalize how the observed hazard ratio evolves and deviates from the c

Johan Liedgren, Pieter Desmet, Andrea Gaggioli

As ubiquitous technology is increasingly mediating our relationships with the world and others, we argue that the sublime is struggling to find room in product design primarily aimed at commercial and transactional goals such as speed and efficiency. We suggest a new category of products to promote deeper and more meaningful experiences, specifically those o

J. Hok, S. Kucherenko

In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, unlike MC QMC lacks a practical error estimate. Randomized QMC (RQMC) method combines the best of two methods. Application of scrambled LDS allow to compute confidence intervals aro

Richard Post, Isabel van den Heuvel, Marko Petkovic, Edwin van den Heuvel

Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning (ML) methods can be used to study complex forms of causal effect heterogeneity. Recently, several ML methods were developed to estimate the conditional average treatment effect (CATE). If the features at hand cannot explain al

Evgeny Yu. Panov

We find an explicit form of weak solutions to a Riemann problem for a degenerate semilinear parabolic equation with piecewise constant diffusion coefficient. It is demonstrated that the phase transition lines (free boundaries) correspond to the minimum point of some strictly convex function of a finite number of variables. In the limit as number of phases te

Ran Damari, Amit Beer, Eli Flaxer, Sharly Fleischer

THz fields induce orientation in gas phase molecules via resonant dipole-field interaction. The degree of orientation however remains severely limited due to the practical shortage of high THz-field amplitudes. In this paper, we experimentally demonstrate a concerted Near-IR and THz excitation scheme that provides significant increase in the degree of orient

Zhilin Lu, Xudong Zhang, Rui Zeng, Jintao Wang

The channel state information (CSI) needs to be fed back from the user equipment (UE) to the base station (BS) in frequency division duplexing (FDD) multiple-input multiple-output (MIMO) system. Recently, neural networks are widely applied to CSI compressed feedback since the original overhead is too large for the massive MIMO system. Notably, lightweight fe

Tingting Zhang, Yongjie Sun, Pengpeng Wang, Yufeng Qiu

This paper proposes a novel calibration-free wavelength modulated spectroscopy (WMS) spectral fitting technique based on the firefly algorithm. The technique by simulating the information interaction behavior between fireflies to achieve the retrieval of gas concentration and laser parameters. Contrasted with the spectral fitting technique based on the class

Junyi Ji, Changsong Xu, H. J. Xiang

Two-dimensional (2D) ferroelectrics, which is rare in nature, enable high-density non-volatile memory with low energy consumption. Here, we propose a theory of bilayer stacking ferroelectricity (BSF), in which, two stacked layers of the same 2D material, with different rotation and translation, exhibits ferroelectricity. By performing systematic group theory

Fengqi Wang, Fei Li, Hao Fei, Jingye Li

Relation Extraction (RE) is a fundamental task of information extraction, which has attracted a large amount of research attention. Previous studies focus on extracting the relations within a sentence or document, while currently researchers begin to explore cross-document RE. However, current cross-document RE methods directly utilize text snippets surround

Yunzhe Zheng, Hemant Sharma, Johannes Borregaard

Generating multiple entangled qubit pairs between distributed nodes is a prerequisite for a future quantum internet. To achieve a practicable generation rate, standard protocols based on photonic qubits require multiple long-term quantum memories, which remains a significant experimental challenge. In this paper, we propose a novel protocol based on $2^m$-di

Yi Wang, Jiajun Deng, Tianzi Wang, Bo Zheng

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Textual embedding features produced by pre-trained language models (PLMs) such as BERT are widely use

Stephane Kenmoe, Obinna Abah

We present the status of the research in the field of atomic and molecular physics in Africa as well as some challenges hindering the efforts being made by the African scientists. We further report the discussions and progress of the African Strategy for Fundamental and Applied Physics (ASFAP) working group on Atomic and Molecular physics with the view of pr