July 2022 arXiv papers — page 10

Michael Mayer

An important technique to explore a black-box machine learning (ML) model is called SHAP (SHapley Additive exPlanation). SHAP values decompose predictions into contributions of the features in a fair way. We will show that for a boosted trees model with some or all features being additively modeled, the SHAP dependence plot of such a feature corresponds to i

Yiting Lu, Xin Li, Jianzhao Liu, Zhibo Chen

Deep neural networks (DNNs) have shown great potential in non-reference image quality assessment (NR-IQA). However, the annotation of NR-IQA is labor-intensive and time-consuming, which severely limits their application especially for authentic images. To relieve the dependence on quality annotation, some works have applied unsupervised domain adaptation (UD

Yang Zhengning, Shihao Ru, Lianzhen Cao, Nikolay Zheludev

Quantum tomography is one of the major challenges of large-scale quantum information research due to the exponential time complexity. In this work, we develop and apply a Bayesian state estimation method to experimentally demonstrate quantum overlapping tomography [Phys. Rev. Lett. \textbf{124}, 100401 (2020)], a scheme intent on characterizing critical info

Adrià Gómez-Valent, Ziyang Zheng, Luca Amendola, Christof Wetterich

Some cosmological models with non-negligible dark energy fractions in particular windows of the pre-recombination epoch are capable of alleviating the Hubble tension quite efficiently, while keeping the good description of the data that are used to build the cosmic inverse distance ladder. There has been an intensive discussion in the community on whether th

Taekeun Yoon, Seon Woong Kim, Hosung Byun, Younsik Kim

A deep learning strategy is developed for fast and accurate gas property measurements using flame emission spectroscopy (FES). Particularly, the short-gated fast FES is essential to resolve fast-evolving combustion behaviors. However, as the exposure time for capturing the flame emission spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and c

Przemysław Pogorzelec, Bartłomiej Dybiec

We are exploring two archetypal noise induced escape scenarios: escape from a finite interval and from the positive half-line under the action of the mixture of L\'evy and Gaussian white noises in the overdamped regime, for the random acceleration process and higher order processes. In the case of escape from finite intervals, mixture of noises can result in

Shamim Haque, Ritam Mallick, Shashikesh Kumar Thakur

Quantum Chromodynamics predicts phase transition from hadronic matter to quark matter at high density, which is highly probable in astrophysical systems like binary neutron star mergers. To explore the critical density where such phase transition can occur, we performed numerical relativity simulations of binary neutron star mergers with various masses (equa

Jeremy M. Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal

Very little is known about the training dynamics of adaptive gradient methods like Adam in deep learning. In this paper, we shed light on the behavior of these algorithms in the full-batch and sufficiently large batch settings. Specifically, we empirically demonstrate that during full-batch training, the maximum eigenvalue of the preconditioned Hessian typic

Lei Liu, Xinglei Dou

Qubit mapping is essential to quantum computing's fidelity and quantum computers' resource utilization. Yet, the existing qubit mapping schemes meet some challenges (e.g., crosstalk, SWAP overheads, diverse device topologies, etc.), leading to qubit resource under-utilization, high error rate, and low fidelity in computing results. This paper presents QuClou

Wan-Hsuan Lin, Bochen Tan, Murphy Yuezhen Niu, Jason Kimko

With the steady progress in quantum computing over recent years, roadmaps for upscaling quantum processors have relied heavily on the targeted qubit architectures. So far, similarly to the early age of classical computing, these designs have been crafted by human experts. These general-purpose architectures, however, leave room for customization and optimiza

Dennis Shen, Peng Ding, Jasjeet Sekhon, Bin Yu

A central goal in social science is to evaluate the causal effect of a policy. One dominant approach is through panel data analysis in which the behaviors of multiple units are observed over time. The information across time and space motivates two general approaches: (i) horizontal regression (i.e., unconfoundedness), which exploits time series patterns, an

Daniel Obmann, Markus Haltmeier

One of the key assumptions in the stability and convergence analysis of variational regularization is the ability of finding global minimizers. However, such an assumption is often not feasible when the regularizer is a black box or non-convex making the search for global minimizers of the involved Tikhonov functional a challenging task. This is in particula

Satoru Odake, Ryu Sasaki

A new interpretation and applications of the ``Diophantine'' and factorisation properties of {\em finite} orthogonal polynomials in the Askey scheme are explored. The corresponding twelve polynomials are the ($q$-)Racah, (dual, $q$-)Hahn, Krawtchouk and five types of $q$-Krawtchouk. These ($q$-)hypergeometric polynomials, defined only for the degrees of $0,1

Hongfei Zhang, Shu Zhang

We consider the minimizers of $L^{2}$-critical inhomogeneous variational problems with a spatially decaying nonlinear term in an open bounded domain $\Omega$ of $\mathbb{R}^{N}$ which contains $0$. We prove that there is a threshold $a^{*}>0$ such that minimizers exist for $0<a<a^{*}$ and the minimizer does not exist for any $a>a^{*}$. In contrast to the hom

Young Seok Jeon, Hongfei Yang, Mengling Feng

The encoder-decoder model is a commonly used Deep Neural Network (DNN) model for medical image segmentation. Conventional encoder-decoder models make pixel-wise predictions focusing heavily on local patterns around the pixel. This makes it challenging to give segmentation that preserves the object's shape and topology, which often requires an understanding o

Ganlong Zhao, Guanbin Li, Yipeng Qin, Feng Liu

Deep models trained with noisy labels are prone to over-fitting and struggle in generalization. Most existing solutions are based on an ideal assumption that the label noise is class-conditional, i.e., instances of the same class share the same noise model, and are independent of features. While in practice, the real-world noise patterns are usually more fin

Raziyeh Zaregonbadi, Nasim Saba, Mehrdad Farhoudi

While considering the chameleon scalar field model with the spatially flat FLRW background, we investigate the late-time acceleration phase of the universe, wherein we apply the typical potential usually used in this model. Through setting some constraints on the free parameters of the model, we indicate that the non-minimal coupling between the matter and t

Adrian Braemer, Titus Franz, Matthias Weidemüller, Martin Gärttner

Strongly interacting quantum systems subject to quenched disorder exhibit intriguing phenomena such as glassiness and many-body localization. Theoretical studies have mainly focused on disorder in the form of random potentials, while many experimental realizations naturally feature disorder in the interparticle interactions. Inspired by cold Rydberg gases, w

Chih-Chen Chen, William Chen

Little research has been done on Neural Machine Translation (NMT) for Azerbaijani. In this paper, we benchmark the performance of Azerbaijani-English NMT systems on a range of techniques and datasets. We evaluate which segmentation techniques work best on Azerbaijani translation and benchmark the performance of Azerbaijani NMT models across several domains o

Shuchao Pang, Anan Du, Mehmet A. Orgun, Yan Wang

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect sy

Anshuman Verma, Ritam Mallick

Numerical simulation of hydrodynamic equations forms the central part of solving various modern astrophysical problems. In the case of shocks, one can have either dynamical equations or jump conditions (the conservation equations without any time evolution). The solution of the jump condition in curve space-time is derived and analyzed in detail in the prese

Yannick Roy, Jocelyn Faubert

Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on multiple object tracking (MOT) tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at the electro

Calum MacRury, Erlang Surya

The $\mathcal{D}$-process is a single player game in which the player is initially presented the empty graph on $n$ vertices. In each step, a subset of edges $X$ is independently sampled according to a distribution $\mathcal{D}$. The player then selects one edge $e$ from $X$, and adds $e$ to its current graph. For a fixed monotone increasing graph property $

Thien Van Luong, Nir Shlezinger, Chao Xu, Tiep M. Hoang

Non-orthogonal communications are expected to play a key role in future wireless systems. In downlink transmissions, the data symbols are broadcast from a base station to different users, which are superimposed with different power to facilitate high-integrity detection using successive interference cancellation (SIC). However, SIC requires accurate knowledg

Jian Yang, Yuwei Yin, Liqun Yang, Shuming Ma

Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. However, vanilla Transformer mainly exploits the top-layer representation, assuming the lower layers provide trivial or redundant information and thus ignoring the bottom-layer feature that is potentially valuabl

Guangkai Xu, Wei Yin, Jianming Zhang, Oliver Wang

Existing depth completion methods are often targeted at a specific sparse depth type and generalize poorly across task domains. We present a method to complete sparse/semi-dense, noisy, and potentially low-resolution depth maps obtained by various range sensors, including those in modern mobile phones, or by multi-view reconstruction algorithms. Our method l

Shijie Wang, Jianlong Chang, Zhihui Wang, Haojie Li

Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise similarities on the semantic embedding spaces or design a localization sub-network to continually fine-tune the entire model in limited data scenarios, thus resulting in convergence to

Gilchan Park, Kun Zhang, Kwangmin Yu, Vladimir Korepin

Quantum multi-programming is a method utilizing contemporary noisy intermediate-scale quantum computers by executing multiple quantum circuits concurrently. Despite early research on it, the research remains on quantum gates or small-size quantum algorithms without correlation. In this paper, we propose a quantum multi-programming (QMP) algorithm for Grover'

D. F. G. Coelho, R. J. Cintra, F. M. Bayer, S. Kulasekera

This paper introduced a matrix parametrization method based on the Loeffler discrete cosine transform (DCT) algorithm. As a result, a new class of eight-point DCT approximations was proposed, capable of unifying the mathematical formalism of several eight-point DCT approximations archived in the literature. Pareto-efficient DCT approximations are obtained th

Shashwat Sharma, Piero Triverio

The electrostatic modeling of conductors is a fundamental challenge in various applications, including the prediction of parasitic effects in electrical interconnects, the design of biasing networks, and the modeling of biological, microelectromechanical, and sensing systems. The boundary element method (BEM) can be an effective simulation tool for these pro

Zheng Li, Yiming Huang, Yui-Pan Yau, Pan Hui

Research attention on natural user interfaces (NUIs) for drone flights are rising. Nevertheless, NUIs are highly diversified, and primarily evaluated by different physical environments leading to hard-to-compare performance between such solutions. We propose a virtual environment, namely VRFlightSim, enabling comparative evaluations with enriched drone fligh

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The singly Cabibbo suppressed decay $\Lambda^{+}_{c}\to p\eta^{\prime}$ is measured using 4.5 $\mathrm{fb}^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies between 4.600 and 4.699 GeV with the BESIII detector at BEPCII. Evidence for $\Lambda^{+}_{c}\to p\eta^{\prime}$ with a statistical significance of $3.6\sigma$ is reported with a doub

Xinjie Yao, Ji Zhang, Jean Oh

Under shared autonomy, wheelchair users expect vehicles to provide safe and comfortable rides while following users high-level navigation plans. To find such a path, vehicles negotiate with different terrains and assess their traversal difficulty. Most prior works model surroundings either through geometric representations or semantic classifications, which

Vu-Duc Ngo, Thien Van Luong, Nguyen Cong Luong, Minh-Tuan Le

This paper analyzes the bit error rate (BER) of multicarrier index keying - orthogonal frequency division multiplexing (MCIK-OFDM) with selection combining (SC) diversity reception. Particularly, we propose a generalized framework to derive the BER for both the low-complexity greedy detector (GD) and maximum likelihood (ML) detector. Based on this, closedfor

Xinyu Fu, Yitong Yin, Chaodong Zheng

Distributed graph coloring is one of the most extensively studied problems in distributed computing. There is a canonical family of distributed graph coloring algorithms known as the locally-iterative coloring algorithms, first formalized in the seminal work of [Szegedy and Vishwanathan, STOC'93]. In such algorithms, every vertex iteratively updates its own

Tian Han, Rajitha Senanayake, Peter Smith, Jamie Evans

In this paper, we propose a new upper bound on the error probability performance of maximum-likelihood (ML) detection. The proposed approach provides a much tighter upper bound when compared to the traditionally used union bound, especially when the number of pairwise error probabilities (PEPs) is large. In fact, the proposed approach tightens the union boun

Xin Zhang, Wenliang Lu, Zun Liang, Yashen Wang

We present a classical molecular-dynamics study of the collective dynamical properties of the coexisting liquid phase at equilibrium body-centered cubic (BCC) Fe crystal-melt interfaces. For the three interfacial orientations (100), (110), and (111), the collective dynamics are characterized through the calculation of the intermediate scattering functions, d

Itsuki Ueda, Yoshihiro Fukuhara, Hirokatsu Kataoka, Hiroaki Aizawa

The success of neural fields for 3D vision tasks is now indisputable. Following this trend, several methods aiming for visual localization (e.g., SLAM) have been proposed to estimate distance or density fields using neural fields. However, it is difficult to achieve high localization performance by only density fields-based methods such as Neural Radiance Fi

Vu-Duc Ngo, Thien Van Luong, Nguyen Cong Luong, Mai Xuan Trang

This paper proposes a novel spread spectrum and sub-carrier index modulation (SS-SIM) scheme, which is integrated to orthogonal frequency division multiplexing (OFDM) framework to enhance the diversity over the conventional IM schemes. Particularly, the resulting scheme, called SS-SIMOFDM, jointly employs both spread spectrum and sub-carrier index modulation

H. S. Xu, L. Jin

The reflectionless coherent light transport in the coupled resonator array is investigated in the presence of intra-resonator intermodal coupling between the clockwise and counterclockwise modes, which plays a constructive role for modulating the light flow rather than inducing the unwanted backscattering. The interplay between the intra-resonator intermodal

Yunjie Peng, Saihui Hou, Chunshui Cao, Xu Liu

Occluded person re-identification (Re-ID) aims at addressing the occlusion problem when retrieving the person of interest across multiple cameras. With the promotion of deep learning technology and the increasing demand for intelligent video surveillance, the frequent occlusion in real-world applications has made occluded person Re-ID draw considerable inter

Yinghui Xing, Shuyuan Yang, Song Wang, Yan Zhang

The fusion of multispectral and panchromatic images is always dubbed pansharpening. Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network. However, remote sensing images always have large variations, as a result, these on

Nathan Shettell, Majid Hassani, Damian Markham

Networks of quantum sensors are a central application of burgeoning quantum networks. A key question for the use of such networks will be their security, particularly against malicious participants of the network. We introduce a protocol to securely evaluate linear functions of parameters over a network of quantum sensors, ensuring that all parties only have

Yueke Hu, Philippe Michel, Paul Nelson

In this paper we study the subconvexity problem for the Rankin-Selberg L-function and triple product L-function, allowing joint ramifications and conductor dropping range. We first extend the method of Michel-Venkatesh to reduce the bounds for L-functions to local conjectures on test vectors, then verify these local conjectures under certain conditions, givi

Maxim Mozgovoy, Marina Purgina

This paper introduces a collection of board games specifically chosen to serve as a basis for programming exercises. We examine the attractiveness of board games in this context as well as features that make a particular game a good exercise. The collection is annotated across several dimensions to assist choosing a game suitable for the target topic and stu

Dexie Lin

In this paper, we focus on the moduli space of Seiberg-Witten equation on non-compact manifold with periodic end. Suppose that the scalar curvature on the periodic end is identically zero and the topological conditions: the first de-Rham cohomology and the self-dual cohomology restricting on the periodic end vanish. Then, we will show that the moduli space o

Colin Conwell, Tomer Ullman

Relations are basic building blocks of human cognition. Classic and recent work suggests that many relations are early developing, and quickly perceived. Machine models that aspire to human-level perception and reasoning should reflect the ability to recognize and reason generatively about relations. We report a systematic empirical examination of a recent t

Huihui Fang, Fei Li, Huazhu Fu, Junde Wu

Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma.OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures. To promote the research of AI technology in the field of OCT-assisted diagnosis of glaucoma, we held a Glaucoma OCT Analys

Fang Qi, Kaitlin N. Smith, Travis LeCompte, Nianfeng Tzeng

While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the s

Yi-Cheng Tai, Weijing Wang, Martin T. Wells, National Yang Ming Chiao Tung U.

We consider a Kendall's tau measure between a binary group indicator and the continuous variable under investigation to develop a thorough two-sample comparison procedure. The measure serves as a useful alternative to the hazard ratio whose applicability depends on the proportional hazards assumption. For right censored data, we propose a weighted log-rank s

Theo Steiner, Rui Zhang

Comments, or natural language descriptions of source code, are standard practice among software developers. By communicating important aspects of the code such as functionality and usage, comments help with software project maintenance. However, when the code is modified without an accompanying correction to the comment, an inconsistency between the comment

Xiaofeng Cao, Weixin Bu, Shengjun Huang, Minling Zhang

Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI, which requires machines to recognize objectives and scenarios relying on small data as humans. A series of

Prashasti Singh, Vivek Kumar Singh, Hiran H. Lathabai

Future Generation Computer Systems (FGCS), published by Elsevier, is a prestigious peer reviewed journal that started in 1984. As on date of writing this article, the journal is in its 137th volume. Owing to its publication quality and continued academic standards, it has been indexed by major academic databases such as Web of Science, Scopus, DOAJ etc. It i

Lixiu Duan, Qing Guo

We consider the following fractional prescribed curvature problem $$(-\Delta)^s u= K(y)u^{2^*_s-1},\ \ u>0,\ \ y\in \mathbb{R}^N,\qquad (0.1)$$ where $s\in(0,\frac{1}{2})$ for $N=3$, $s\in(0,1)$ for $N\geqslant4$ and $2^*_s=\frac{2N}{N-2s}$ is the fractional critical Sobolev exponent, $K(y)$ has a local maximum point in $r\in(r_0-\delta,r_0+\delta)$. First,

Amalan Mahendran, Helen Thompson, James M. McGree

In today's modern era of Big data, computationally efficient and scalable methods are needed to support timely insights and informed decision making. One such method is sub-sampling, where a subset of the Big data is analysed and used as the basis for inference rather than considering the whole data set. A key question when applying sub-sampling approaches i

Kevin Jiang, Yang Ning

The estimation of the treatment effect is often biased in the presence of unobserved confounding variables which are commonly referred to as hidden variables. Although a few methods have been recently proposed to handle the effect of hidden variables, these methods often overlook the possibility of any interaction between the observed treatment variable and

Angus Lowe, Ashwin Nayak

We study the problems of quantum tomography and shadow tomography using measurements performed on individual, identical copies of an unknown $d$-dimensional state. We first revisit a known lower bound due to Haah et al. (2017) on quantum tomography with accuracy $\epsilon$ in trace distance, when the measurements choices are independent of previously observe

V. V. Flambaum, B. T. McAllister, I. B. Samsonov, M. E. Tobar

We establish new experiments to search for dark matter based on a model of a light scalar field with a dilaton-like coupling to the electromagnetic field, which is strongly motivated by superstring theory. We estimate the power of the photon signal in the process of a non-resonant scalar-photon transition and in a cavity resonator permeated by electric and m

Seung Yeon Shin, Sungwon Lee, Ronald M. Summers

We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the contact between parts of itself along its course, which makes the path tracking difficult together with the indistinct appearance of the wall. It causes the tracked path to easily

Elias R. Most, Alexander A. Philippov

It is an open question whether and how gravitational wave events involving neutron stars can be preceded by electromagnetic counterparts. This work shows that the collision of two neutron stars with magnetic fields well below magnetar-level strengths can produce millisecond Fast-Radio-Burst-like transients. Using global force-free electrodynamics simulations

Diptanil Roy

Partons in heavy-ion collisions interact strongly with the Quark-Gluon Plasma (QGP), and hence have their energy and shower structure modified compared to those in vacuum. Theoretical calculations predict that the radiative energy loss, which is the dominant mode of energy loss for gluons and light quarks in the QGP, is suppressed for heavy quarks at low tra

Avinash Surendran, Jacques R. Delorme, Carlos M. Correia, Steve Doyle

The development of the Keck All sky Precision Adaptive optics (KAPA) project was initiated in September 2018 to upgrade the Keck I adaptive optics (AO) system to enable laser tomography adaptive optics (LTAO) with a four laser guide star (LGS) asterism. The project includes the replacement of the existing LMCT laser with a Toptica laser, the implementation o

Keping Huang, Aaron Levin

We prove inequalities involving greatest common divisors of functions at integral points with respect to numerically parallel divisors, generalizing a result of Wang and Yasufuku (after work of Bugeaud-Corvaja-Zannier, Corvaja-Zannier, and the second author). After applying a result of Vojta on integral points on subvarieties of semiabelian varieties, we use

Lai Wei, Shiteng Liu, Rigui Zhou, Changming Zhu

The critical point for the successes of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices which can faithfully reveal the subspace structures of data sets. An ideal reconstruction coefficient matrix should have two properties: 1) it is block diagonal with each block indicating a subspace; 2) each block is fully conne

Hadi Zolfaghari, Mervyn Andiapen, Andreas Baumbach, Anthony Mathur

Spatial patterns of elevated wall shear stress and pressure due to blood flow past aortic stenosis (AS) are studied using GPU-accelerated patient-specific computational fluid dynamics. Three cases of moderate AS, one with a dilated ascending aorta and two within the normal range (root diameter less than 4cm) are simulated for physiological beat cycle wavefor

Haoming Ding, Youbing Li, Mian Li, Ke Chen

Intercalation of non-van der Waals (vdW) layered materials can produce new 2D and 3D materials with unique properties, but it is difficult to achieve. Here, we describe a structural editing protocol for 3D non-vdW layered ternary carbides and nitrides (MAX phases) and their 2D vdW derivatives (MXenes). Gap-opening and species-intercalating stages were mediat

Hao Wang, Guosheng Lin, Steven C. H. Hoi, Chunyan Miao

This paper investigates an open research problem of generating text-image pairs to improve the training of fine-grained image-to-text cross-modal retrieval task, and proposes a novel framework for paired data augmentation by uncovering the hidden semantic information of StyleGAN2 model. Specifically, we first train a StyleGAN2 model on the given dataset. We

Yu Sheng, Weiyang Wang, Yongcheng Deng, Yang Ji

More-versatile memory is strongly desired for end-users to protect their information in the information era. In particular, bit-level switchable memory, from rewritable to read-only function, allows end-users to prevent any important data from being tampered with. However, no such switchable memory has been reported. We demonstrated the rewritable function c

Haijin Huang, Xu Han, Armandas Balčytis, Aditya Dubey

High efficiency and a compact footprint are desired properties for electro-optic modulators. In this paper, we propose, theoretically investigate and experimentally demonstrate a recirculating phase modulator, which increases the modulation efficiency by modulating the optical field several times in a non-resonant waveguide structure. The 'recycling' of ligh

Hao Wang, Wenhao Shen, Guosheng Lin, Steven C. H. Hoi

In this paper, we investigate an open research task of generating 3D cartoon face shapes from single 2D GAN generated human faces and without 3D supervision, where we can also manipulate the facial expressions of the 3D shapes. To this end, we discover the semantic meanings of StyleGAN latent space, such that we are able to produce face images of various exp

Xingyuan Xu, Guanghui Ren, Aditya Dubey, Tim Feleppa

Programmable photonic integrated circuits (PICs), offering diverse signal processing functions within a single chip, are promising solutions for applications ranging from optical communications to artificial intelligence. While the scale and complexity of programmable PICs is increasing, the characterization, and thus calibration, of them becomes increasingl

Dicong Liang, Rui Xu, Xuchen Lu, Lijing Shao

Lorentz violation modifies the dispersion relation of gravitational waves (GWs), and induces birefringence and anisotropy in propagation. Our study shows that Lorentz violation can also activate multiple polarizations of GWs. We use the gauge invariants to investigate the polarizations of GWs in the bumblebee gravity model, and obtain the following results.

Simon Odense, Kamal Gupta, William G. Macready

The past decade has amply demonstrated the remarkable functionality that can be realized by learning complex input/output relationships. Algorithmically, one of the most important and opaque relationships is that between a problem's structure and an effective solution method. Here, we quantitatively connect the structure of a planning problem to the performa

P. Kagerer, C. I. Fornari, S. Buchberger, T. Tschirner

Inducing a magnetic gap at the Dirac point of the topological surface state (TSS) in a 3D topological insulator (TI) is a route to dissipationless charge and spin currents. Ideally, magnetic order is present only at the surface and not in the bulk, e.g. through proximity of a ferromagnetic (FM) layer. However, such a proximity-induced Dirac mass gap has not

Weicheng Huang, Dongze He, Yanbin Li, Dahai Zhang

In this paper, a flexible tether-net system is applied to capture the space debris and a numerical framework is established to explore its nonlinear dynamic behaviors, which comprises four principal phases: folding, spreading, contacting, and closing. Based on the discretization of the whole structure into multiple nodes and connected edges, elastic force ve

Wenhao Luo, Wen Sun, Ashish Kapoor

Reinforcement Learning (RL) and continuous nonlinear control have been successfully deployed in multiple domains of complicated sequential decision-making tasks. However, given the exploration nature of the learning process and the presence of model uncertainty, it is challenging to apply them to safety-critical control tasks due to the lack of safety guaran

Alef Iury Siqueira Ferreira, Gustavo dos Reis Oliveira

This paper presents our efforts to build a robust ASR model for the shared task Automatic Speech Recognition for spontaneous and prepared speech & Speech Emotion Recognition in Portuguese (SE&R 2022). The goal of the challenge is to advance the ASR research for the Portuguese language, considering prepared and spontaneous speech in different dialects. Our me

Muqsit Azeem, Alexandros Evangelidis, Jan Křetínský, Alexander Slivinskiy

While value iteration (VI) is a standard solution approach to simple stochastic games (SSGs), it suffered from the lack of a stopping criterion. Recently, several solutions have appeared, among them also "optimistic" VI (OVI). However, OVI is applicable only to one-player SSGs with no end components. We lift these two assumptions, making it available to gene

Rui Lou, Oleksandr Suvorov, Hans-Joachim Grafe, Andrii Kuibarov

The nematicity in multilayer FeSe/SrTiO$_3$ films has been previously suggested to be enhanced with decreasing film thickness. Motivated by this, there have been many discussions about the competing relation between nematicity and superconductivity. However, the criterion for determining the nematicity strength in FeSe remains highly debated. The understandi

David R. Guichard

We use a dynamic programming algorithm to establish a new lower bound on the domination number of complete cylindrical grid graphs of the form $C_n\square P_m$, that is, the Cartesian product of a path and a cycle, when $n\equiv 2\pmod{5}$, and we establish a new upper bound equal to the lower bound, thus computing the exact domination number for these graph

Samuel Tripp, Zachary Winkeler

Given a link $L$, Dowlin constructed a filtered complex inducing a spectral sequence with $E_2$-page isomorphic to the Khovanov homology $\overline{Kh}(L)$ and $E_\infty$-page isomorphic to the knot Floer homology $\widehat{HFK}(m(L))$ of the mirror of the link. In this paper, we prove that the $E_k$-page of this spectral sequence is also a link invariant, f

Thomas Huettemann, Luke Steers

Let $R$ be a strongly $\mathbb{Z}^2$-graded ring, and let $C$ be a bounded chain complex of finitely generated free $R$-modules. The complex $C$ is $R_{(0,0)}$-finitely dominated, or of type FP over $R_{(0,0)}$, if it is chain homotopy equivalent to a bounded complex of finitely generated projective $R_{(0,0)}$-modules. We show that this happens if and only

David R. Guichard

We use a dynamic programming algorithm to establish a lower bound on the domination number of complete grid graphs of the form $C_n\square P_m$, that is, the Cartesian product of a cycle $C_n$ and a path $P_m$, for $m$ and $n$ sufficiently large.

Frederic Sauvage, Martin Larocca, Patrick J. Coles, M. Cerezo

Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very litt

Xiang Chen, Enrico Schierle, Yu He, Mayia Vranas

The quasi-two-dimensional van der Waals magnet Fe$_{5-\delta}$GeTe$_2$ has emerged as a promising platform for electronic and spintronic functionalities at room temperature, owing to its large ferromagnetic ordering temperature $T_{\text{C}}$ $\sim$ 315 K. Interestingly, by cobalt (Co) substitution of iron in F5GT, $i.e.$ $({\text{Fe}}_{1-x}{\text{Co}}_x)_{5

Kenneth Sharman, Omid Golami, Stephen Wein, Hadi Zadeh-Haghighi

Quantum emitters in two-dimensional hexagonal boron nitride (h-BN) have generated significant interest due to observations of ultra-bright emission made at room temperature. The expectation that solid-state emitters exhibit broad zero-phonon lines at elevated temperatures has been put in question by recent observations of Fourier transform (FT) limited photo

Mario Ballardini, Fabio Finelli

The {\em Planck} DR3 measurements of the temperature and polarization anisotropies power spectra of the cosmic microwave background (CMB) show an excess of smoothing of the acoustic peaks with respect to $\Lambda$CDM, often quantified by a phenomenological parameter $A_{\rm L}$. A specific feature superimposed to the primordial power spectrum has been sugges

Deepankar Sri Gyan, Danny Mannix, Dina Carbone, James L. Sumpter

Time-resolved x-ray diffraction has been used to measure the low-temperature thermal transport properties of a Pt/Gd3Fe5O12//Gd3Ga5O12 metal/oxide heterostructure relevant to applications in spin caloritronics. A pulsed femtosecond optical signal produces a rapid temperature rise in the Pt layer, followed by heat transport into the Gd3Fe5O12 (GdIG) thin film

Helena Liz, Javier Huertas-Tato, Manuel Sánchez-Montañés, Javier Del Ser

Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to

N. M. Alvarenga, E. Cavalcanti, C. A. Linhares, J. A. Lourenço

We investigate the non-perturbative effects of a deformation of the Mathieu differential equation consistent with $\mathcal{PT}$ symmetry. First, we develop a connection between the non-Hermitian and Hermitian scenarios by a reparameterization in the complex plane, followed by a restriction of the $\mathcal{PT}$ deformation parameter. The latter is responsib

Kevin Zhang, Neha Patki, Kalyan Veeramachaneni

The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data. This includes a novel neural network-based machine learning model, conditional p

Donato Cianci, Chris Judge, Samuel Lin, Craig Sutton

Let a torus $T$ act freely on a closed manifold $M$ of dimension at least two. We demonstrate that, for a generic $T$-invariant Riemannian metric $g$ on $M$, each real $\Delta_g$-eigenspace is an irreducible real representation of $T$ and, therefore, has dimension at most two. We also show that, for the generic $T$-invariant metric on $M$, if $u$ is a non-in

Jennafer Shae Roberts, Laura N Montoya

This research examines the impact of digital neo-colonialism on the Global South and encourages the development of legal and economic incentives to protect Indigenous cultures globally. Data governance is discussed in an evolutionary context while focusing on data sharing and data mining. Case studies that exemplify the need to steer global data law towards

Piotr Mironowicz

Rendezvous is an old problem of assuring that two or more parties, initially separated, not knowing the position of each other, and not allowed to communicate, meet without pre-agreement on the meeting point. This problem has been extensively studied in classical computer science and has vivid importance to modern applications like coordinating a fleet of dr

Shikib Mehri, Yulan Feng, Carla Gordon, Seyed Hossein Alavi

The ultimate goal of dialog research is to develop systems that can be effectively used in interactive settings by real users. To this end, we introduced the Interactive Evaluation of Dialog Track at the 9th Dialog System Technology Challenge. This track consisted of two sub-tasks. The first sub-task involved building knowledge-grounded response generation m

Pascal Beckedorf, Angelika Rohde

We study Edgeworth expansions in limit theorems for self-normalized sums. Non-uniform bounds for expansions in the central limit theorem are established while only imposing minimal moment conditions. Within this result, we address the case of non-integer moments leading to a reduced remainder. Furthermore, we provide non-uniform bounds for expansions in loca

Keisuke Nakagahara, Katsunori Wakabayashi

Graphene on hBN (G/hBN) has a long period moir\'{e} superstructure owing to the lattice mismatch between two materials. Long periodic potential caused by the moir\'{e} superstructure induces modulation of electronic properties of the system. In this paper, we numerically calculate optical conductivity of G/hBN under circularly polarized light irradiation. Th

Rafael Arce, Carlos Hernández, José Ortiz, Ivelisse Rubio

Linear complexity is an important parameter for arrays that are used in applications related to information security. In this work we survey constructions of two and three dimensional arrays, and present new results on the multidimensional linear complexity of periodic arrays obtained using the definition and method proposed in \cite{ArCaGoMoOrRuTi,GoHoMoRu,

Michail Savvas

This is the third in a series of works devoted to constructing virtual structure sheaves and $K$-theoretic invariants in moduli theory. The central objects of study are almost perfect obstruction theories, introduced by Y.-H. Kiem and the author as the appropriate notion in order to define invariants in $K$-theory for many moduli stacks of interest, includin

Diego Ayala, Danilo Chavez, Leopoldo Altamirano Robles

This paper describes the development of an embedded vision system for detection, location, and tracking of a color object; it makes use of a single 32-bit microprocessor to acquire image data, process, and perform actions according to the interpreted data. The system is intended for applications that need to make use of artificial vision for detection, locat