November 2022 arXiv papers — page 120

Nikhil Bansal, Kartik Gupta, Kiruthika Kannan, Sivani Pentapati

Pictionary, the popular sketch-based guessing game, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings. However, some players occasionally draw atypical sketch content. While such content is occasionally relevant in the game context, it sometimes represents a rule violation and impairs the game experienc

Zonghe Chua, Allison M. Okamura

Effective force modulation during tissue manipulation is important for ensuring safe robot-assisted minimally invasive surgery (RMIS). Strict requirements for in-vivo distal force sensing have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. These limitations have made coll

Martin Pawelczyk, Himabindu Lakkaraju, Seth Neel

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected individuals, potential adversaries could also exploit these recourses to compromise privacy. In t

ATLAS Collaboration

This paper presents a statistical combination of searches targeting final states with two top quarks and invisible particles, characterised by the presence of zero, one or two leptons, at least one jet originating from a $b$-quark and missing transverse momentum. The analyses are searches for phenomena beyond the Standard Model consistent with the direct pro

Guoqing Pu, Runmin Liu, Hang Yang, Yongxin Xu

As an imperative method of investigating the internal mechanism of femtosecond lasers, traditional femtosecond laser modeling relies on the split-step Fourier method (SSFM) to iteratively resolve the nonlinear Schrodinger equation suffering from the large computation complexity. To realize inverse design and optimization of femtosecond lasers, numerous simul

Andreas A. C. Sander

From the main sequence to their late evolutionary stages, massive stars spend most of their life as hot stars. Due to their high effective temperatures, the maximum of their emitted flux falls into the ultraviolet (UV) regime. Consequently, these stars emit a significant number of photons with energies sufficiently high enough to ionize hydrogen and other el

Motahare Namakin, Modjtaba Rouhani, Mostafa Sabzekar

Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristi

Shuhei Tsujie, Ryo Uchiumi

Given a graph, we associate each edge with the transposition which exchanges the endvertices. Fixing a linear order on the edge set, we obtain a permutation of the vertices. D\'enes proved that the permutation is a full cyclic permutation for any linear order if and only if the graph is a tree. In this article, we characterize graphs having a linear order su

Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka

Deep Learning models are easily disturbed by variations in the input images that were not seen during training, resulting in unpredictable behaviours. Such Out-of-Distribution (OOD) images represent a significant challenge in the context of medical image analysis, where the range of possible abnormalities is extremely wide, including artifacts, unseen pathol

Chi-Chen Lee, Po-Tsun Paul Kuo, Chi-Han Peng

We propose a novel hematoxylin and eosin (H&E) stain normalization method based on a modified U-Net neural network architecture. Unlike previous deep-learning methods that were often based on generative adversarial networks (GANs), we take a teacher-student approach and use paired datasets generated by a trained CycleGAN to train a U-Net to perform the stain

Ulf-G. Meißner

I summarize the dispersion-theoretical analysis of the nucleon electromagnetic form factors. Special emphasis is given on the extraction of the proton charge radius and its relatives, the proton magnetic radius as well as the neutron magnetic radius. Some recent work on the hyperfine splitting in leptonic hydrogen and on radiative corrections to muon-proton

Jialin Gong, Jianhua Wang, Hongkuan Yuan, Zeying Zhang

Phonons are an ideal platform for realizing stable spinless two-dimensional (2D) Dirac points because they have a bosonic nature and hard-to-break time-reversal symmetry. It should be noted that the twofold degenerate nodal points in the phonon dispersions of almost all reported 2D materials are misclassified as 'Dirac points' owing to a historical issue. Th

Viktor Schlegel, Kamen V. Pavlov, Ian Pratt-Hartmann

State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiabili

Phuc Nguyen, Hideaki Takeda

Wikidata is the largest collaborative general knowledge graph supported by a worldwide community. It includes many helpful topics for knowledge exploration and data science applications. However, due to the enormous size of Wikidata, it is challenging to retrieve a large amount of data with millions of results, make complex queries requiring large aggregatio

Andrey Shternshis, Piero Mazzarisi

Shannon entropy is the most common metric to measure the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems may be in general non stationary, with an entropy value that is not constant in time. The goal of this paper is to propose a hypothesis testing procedure to test the null hyp

Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey

flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling. It is built on top of the machine learning libraries JAX and Flax. At its core, flowMC uses a local sampler and a learnable global sampler in tandem to efficiently sample posterior distributions. While multiple chains of the local sampler generate

Ke Yang, Charles Yu, Yi Fung, Manling Li

Several works have proven that finetuning is an applicable approach for debiasing contextualized word embeddings. Similarly, discrete prompts with semantic meanings have shown to be effective in debiasing tasks. With unfixed mathematical representation at the token level, continuous prompts usually surpass discrete ones at providing a pre-trained language mo

Pei Yuan, Jonathan Allcock, Shengyu Zhang

Some physical implementation schemes of quantum computing can apply two-qubit gates only on certain pairs of qubits. These connectivity constraints are commonly viewed as a significant disadvantage. For example, compiling an unrestricted $n$-qubit quantum circuit to one with poor qubit connectivity, such as a 1D chain, usually results in a blowup of depth by

Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh, Weng-Fai Wong

Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance. A major advantage of SNNs is their binary information transfer through spike trains which eliminates multiplication operations. The training of SNNs has, however, been a challenge, since n

Igor Kuzmenko, Tetyana Kuzmenko, Y. Avishai, Y. B. Band

Using the Dirac equation, radiative corrections and finite nuclear size and mass corrections, we calculate the $1s$-$2s$ quantum transition frequency $f_{1s,2s}$ of hydrogen and its uncertainty due to the uncertainties $\delta m_e, \delta m_p, \delta \alpha, \delta r_p, \delta R_{\infty}$ of the electron mass $m_e$, proton mass $m_p$, fine structure constant

Jaechul Roh, Yajun Fang

Over the past few years, the field of adversarial attack received numerous attention from various researchers with the help of successful attack success rate against well-known deep neural networks that were acknowledged to achieve high classification ability in various tasks. However, majority of the experiments were completed under a single model, which we

Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim

Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods hav

Heishiro Kanagawa, Alessandro Barp, Arthur Gretton, Lester Mackey

Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. Notable applications include the diagnosis of approximate MCMC samplers and goodness-of-fit tests for unnormalized statistical models. The present work analyzes the convergence contr

Nghia Hieu Nguyen, Duong T. D. Vo, Kiet Van Nguyen

Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwriting recognition methods. Moreover, as a

Boris Kunyavskii, Vadim Z. Ostapenko

The Tate-Shafarevich set of a group G defined by Takashi Ono coincides, in the case where G is finite, with the group of outer class-preserving automorphisms of G introduced by Burnside. We consider analogues of this important group-theoretic object for Lie algebras and associative algebras and establish some new structure properties thereof. We also discuss

Nghia Hieu Nguyen, Duong T. D. Vo, Minh-Quan Ha

Image captioning is currently a challenging task that requires the ability to both understand visual information and use human language to describe this visual information in the image. In this paper, we propose an efficient way to improve the image understanding ability of transformer-based method by extending Object Relation Transformer architecture with A

Qu Cao, Jin Dong, Song He, Yao-Qi Zhang

Based on the covariant color-kinematics duality, we investigate combinatorial and algebraic structures underlying their Bern-Carrasco-Johansson (BCJ) numerators of tree-level amplitudes in Yang-Mills-scalar (YMS) theory. The closed-formulae for BCJ numerators of YMS amplitudes and the pure-Yang-Mills (YM) ones exhibit nice quasi-shuffle Hopf algebra structur

Saimon Amanuel Tsegai, Xinyu Yang, Haoyuan Liu, Peng Gao

System auditing is a vital technique for collecting system call events as system provenance and investigating complex multi-step attacks such as Advanced Persistent Threats. However, existing attack investigation methods struggle to uncover long attack sequences due to the massive volume of system provenance data and their inability to focus on attack-releva

Jing Peng, Pengyu Wei, Zuo Quan Xu

This paper studies a continuous-time optimal portfolio selection problem in the complete market for a behavioral investor whose preference is of the prospect type with probability distortion. The investor concerns about the terminal relative growth rate (log-return) instead of absolute capital value. This model can be regarded as an extension of the classica

Fan Zhang

By incorporating feedback loops, that engender amplification and damping so that output is not proportional to input, the biological neural networks become highly nonlinear and thus very likely chaotic in nature. Research in control theory reveals that strange attractors can be approximated by collection of cycles, and be collapsed into a more coherent state

Andrzej Góźdź, Marcin Kisielowski, Włodzimierz Piechocki

The quantum dynamics of a self-gravitating thin matter shell in vacuum has been considered. Quantum Hamiltonian of the system is positive definite. Within chosen set of parameters, the quantum shell bounces above the horizon. Considered quantum system does not collapse to the gravitational singularity of the corresponding classical system.

Haoming Zhang, Felix Widmayer, Lars Lünnemann, Dirk Abel

Accurate and consistent vehicle localization in urban areas is challenging due to the large-scale and complicated environments. In this paper, we propose onlineFGO, a novel time-centric graph-optimization-based localization method that fuses multiple sensor measurements with the continuous-time trajectory representation for vehicle localization tasks. We gen

Matteo Aldovardi, Jacopo Bellazzini

We give a direct proof of fractional Hardy inequality by means of Littlewood-Paley decomposition and properties of singular homogeneous kernels of degree -$d$. A refinement when $q>2$ is proved.

Xiaofei Shi, Terry Liu, Anton Artemyev, Vassilis Angelopoulos

Thermalization and heating of plasma flows at shocks result in unstable charged-particle distributions which generate a wide range of electromagnetic waves. These waves, in turn, can further accelerate and scatter energetic particles. Thus, the properties of the waves and their implication for wave-particle interactions are critically important for modeling

Giovanni Domenico Di Salvo, Tyson Ritter, Erlend F. Wold

We solve the problem of simultaneously embedding properly holomorphically into $\Bbb C^2$ a whole family of $n$-connected domains $\Omega_r\subset\Bbb P^1$ such that none of the components of $\Bbb P^1\setminus\Omega_r$ reduces to a point, by constructing a continuous mapping $\Xi\colon\bigcup_r\{r\}\times\Omega_r\to\Bbb C^2$ such that $\Xi(r,\cdot)\colon\Om

Arghadeep Pal, Alekhya Ghosh, Shuangyou Zhang, Toby Bi

The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate

Xiaoteng Zhou, Changli Yu, Shihao Yuan, Xin Yuan

Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering. Although deep learning brings opportunities for target recognition on land and in the air, underwater target recognition techniques based on deep learning have lagged due to sensor performance and the size of trainable data. This letter proposed a frame

Giovanni Domenico Di Salvo

We display four approximation theorems for manifold-valued mappings. The first one approximates holomorphic embeddings on pseudoconvex domains in $\Bbb C^n$ with holomorphic embeddings with dense images. The second theorem approximates holomorphic mappings on complex manifolds with bounded images with holomorphic mappings with dense images. The last two theo

Yuzuru Inahama

In this paper we prove a support theorem of Stroock-Varadhan type for pinned diffusion processes. To this end we use two powerful results from stochastic analysis. One is quasi-sure analysis for Brownian rough path. The other is Aida-Kusuoka-Stroock's positivity theorem for the densities of weighted laws of non-degenerate Wiener functionals.

Xiaoni Wang, Lijie Wang, Yixin Liu, Fan Chen

Quantum Griffiths singularity (QGS), which is closely correlated with the quenched disorder, is characterized by the divergence of the dynamical critical exponent and the presence of activated scaling behavior. Typically such a quantum phenomenon is rather rare and only observed in extremely low temperatures. Here we report the experimental observation of a

Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk, Eduard Hovy

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we a

Pritam Das, Krishnamohan Thekkepat, Young-Su Lee, Seung-Cheol Lee

Finding a suitable material for hydrogen storage at ambient atmospheric conditions is challenging for material scientists and chemists. In this work, using a first principles based cluster expansion approach, the hydrogen storage capacity of Ti2AC (A = Al,Ti, Cr, Mn, Fe, Co, Ni, Cu, and Zn) MAX phase and its alloys were studied. We found that hydrogen is ene

Zai-Hui Wu, Hai-Bing Fu, Tao Zhong, Dong Huang

In this paper, moments of $a_0(980)$-meson twist-2 light-cone distribution amplitudes were deeply researched by using QCD sum rules approach within background field theory. Up to 9th-order accuracy, we present $\langle\xi_{2;a_0}^n\rangle|_{\mu_0}$ at the initial scale $\mu_0 = 1~{\rm GeV}$, i.e. $\langle\xi^1_{2;a_0}\rangle|_{\mu_0} = -0.307(43)$, $\langle\

Martin Balko, Máté Vizer

For an integer $k \geq 2$, an ordered $k$-uniform hypergraph $\mathcal{H}=(H,<)$ is a $k$-uniform hypergraph $H$ together with a fixed linear ordering $<$ of its vertex set. The ordered Ramsey number $\overline{R}(\mathcal{H},\mathcal{G})$ of two ordered $k$-uniform hypergraphs $\mathcal{H}$ and $\mathcal{G}$ is the smallest $N \in \mathbb{N}$ such that ever

Peng Wang, Qidong Fu, Ruihan Peng, Yaroslav V. Kartashov

Continuous and quantized transports are profoundly different. The latter is determined by the global rather than local properties of a system, it exhibits unique topological features, and its ubiquitous nature causes its occurrence in many areas of science. Here we report the first observation of fully-two-dimensional Thouless pumping of light by bulk modes

Masoomeh Mahmoodi-Darian, Paul Martini, Lukas Tiefenthaler, Jaroslav Kočišek

We use a novel technique to solvate silver cations in small clusters of noble gases. The technique involves formation of large, superfluid helium nanodroplets that are subsequently electron ionized, mass-selected by deflection in an electric field, and doped with silver atoms and noble gases (Ng) in pickup cells. Excess helium is then stripped from the doped

Bo Mu, Gong Cheng, Jing Liu, Zong-Kuan Guo

The nonattractor evolution in ultra-slow-roll (USR) inflation results in the amplification of superhorizon curvature perturbations and then induces a strong and detectable stochastic gravitational wave background. In this letter, we search for such a stochastic gravitational wave background in data from the third LIGO-Virgo observing run and place constraint

Gaku Narita, Junichi Shimizu, Taketo Akama

We propose GANStrument, a generative adversarial model for instrument sound synthesis. Given a one-shot sound as input, it is able to generate pitched instrument sounds that reflect the timbre of the input within an interactive time. By exploiting instance conditioning, GANStrument achieves better fidelity and diversity of synthesized sounds and generalizati

Yaroslav V. Kartashov

We address the formation of \{chi}(2) topological edge solitons emerging in topologically nontrivial phase in Su-Schrieffer-Heeger (SSH) waveguide arrays. We consider edge solitons, whose fundamental frequency (FF) component belongs to the topological gap, while phase mismatch determines whether second harmonic (SH) component falls into topological or trivia

Paul Martini, Lorenz Kranabetter, Marcelo Goulart, Bilal Rasul

High-resolution mass spectra of helium droplets doped with gold and ionized by electrons reveal He$_n$Au$^+$ cluster ions. Additional doping with heavy noble gases results in NenAu+, Ar$_n$Au$^+$, Kr$_n$Au$^+$, and Xe$_n$Au$^+$ cluster ions. The high stability predicted for covalently bonded Ar$_2$Au$^+$, Kr$_2$Au$^+$, and Xe$_2$Au$^+$ is reflected in their

Steven R. Costenoble, Thomas Hudson, Sean Tilson

B\'ezout's theorem, nonequivariantly, can be interpreted as a calculation of the Euler class of a sum of line bundles over complex projective space, expressing it in terms of the rank of the bundle and its degree. We give here a generalization to the $C_2$-equivariant context, using the calculation of the cohomology of a $C_2$-complex projective space from a

Youjin Lee, Suhan Son, Chaebin Kim, Soonmin Kang

Van der Waals (vdW) magnets are an ideal platform for tailoring two-dimensional (2D) magnetism with immense potential for spintronics applications and are intensively investigated. However, little is known about the microscopic origin of magnetic order in these antiferromagnetic systems. We used X-ray photoemission electron microscopy to address the electron

Oluwaseyi M. Oderinde, Hassan Mostafavi, Daniel Simpson, James Murphy

Purpose: To assess the fiducial motion in abdominal stereotactic body radiotherapy (SBRT) using the cone-beam computed tomography (CBCT) projections acquired for pre-treatment patient set-up. Materials and Methods: Pre-treatment CBCT projections and anterior-posterior (AP) and lateral (LAT) pair of fluoroscopic sequences of 7 pancreatic and 6 liver SBRT pati

Mitia Duerinckx, Antoine Gloria

In this note, we provide a short and robust proof of the Clausius-Mossotti formula for the effective conductivity in the dilute regime, together with an optimal error estimate. The proof makes no assumption on the underlying point process besides stationarity and ergodicity, and can be applied to dilute systems in many other contexts.

Michael Gr. Voskoglou

The present paper comes across the main steps that laid from Zadeh's fuzziness ana Atanassov's intuitionistic fuzzy sets to Smarandache's indeterminacy and to Molodstov's soft sets. Two hybrid methods for assessment and decision making respectively under fuzzy conditions are also presented through suitable examples that use soft sets and real intervals as to

Chao Zhang, Hang Zou, Samson Lasaulce, Walid Saad

Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications. However, new challenges arise from the widespread popularity of IoT devices, including the need for pro

L. Gelo, C. J. A. P. Martins, N. Quevedo, A. M. M. Vieira

The cosmic microwave background temperature is a cornerstone astrophysical observable. Its present value is tightly constrained, but its redshift dependence, which can now be determined until redshift $z\sim6.34$, is also an important probe of fundamental cosmology. We show that its constraining power is now comparable to that of other background cosmology p

Baekjune Kang, Miju Park, Sehwan Song, Seunghyun Noh

Kitaev quantum spin liquid, massively quantum entangled states, is so scarce in nature that searching for new candidate systems remains a great challenge. Honeycomb heterostructure could be a promising route to realize and utilize such an exotic quantum phase by providing additional controllability of Hamiltonian and device compatibility, respectively. Here,

Ahad M. Rauf, Jack S. Bernardo, Sean Follmer

Shape displays are a class of haptic devices that enable whole-hand haptic exploration of 3D surfaces. However, their scalability is limited by the mechanical complexity and high cost of traditional actuator arrays. In this paper, we propose using electroadhesive auxetic skins as a strain-limiting layer to create programmable shape change in a continuous ("f

Lukas Tiefenthaler, Siegfried Kollotzek, Andrew M. Ellis, Paul Scheier

We demonstrate a novel method to ionize molecules or molecular clusters by proton transfer at temperatures below 1 K. The method yields nascent ions and largely eliminates secondary reactions, even for notoriously "delicate" molecules. Protonation is achieved inside liquid helium nanodroplets (HNDs) and begins with the formation of (H$_{2}$)$_{m}$H$^{+}$ ion

Xiaoni Wang, Lijie Wang, Yixin Liu, Wanpeng Gao

Superconductivity and the normal-state properties of NbN films can be tuned in a wide range, supplying a suitable platform to investigate the systematical evolution of the superconducting performances. Herein, we report the upper critical field of NbN films in both the vertical ($B\perp$ film) and parallel ($B\parallel$ film) orientations over a wide tempera

Jiashu Wu, Hao Dai, Yang Wang, Zhiying Tu

In this paper, we allocate IoT devices as resources for smart services with time-constrained resource requirements. The allocation method named as BRAD can work under multiple resource scenarios with diverse resource richnesses, availabilities and costs, such as the intelligent healthcare system deployed by Harbin Institute of Technology (HIT-IHC). The alloc

Jaechul Roh, Minhao Cheng, Yajun Fang

Pre-trained language models allowed us to process downstream tasks with the help of fine-tuning, which aids the model to achieve fairly high accuracy in various Natural Language Processing (NLP) tasks. Such easily-downloaded language models from various websites empowered the public users as well as some major institutions to give a momentum to their real-li

Yunhang Guo, Zongliang Du, Lubin Wang, Wen Meng

Developing appropriate analytic-function-based constitutive models for new materials with nonlinear mechanical behavior is demanding. For such kinds of materials, it is more challenging to realize the integrated design from the collection of the material experiment under the classical topology optimization framework based on constitutive models. The present

Sakshi Goel, Haripriya Dharmala, Yuchen Zhang, Keith Burghardt

Fear is a universal concept; people crave it in urban legends, scary movies, and modern stories. Open questions remain, however, about why these stories are scary and more generally what scares people. In this study, we explore these questions by analyzing tens of thousands of scary stories on forums (known as subreddits) in a social media website, Reddit. W

Haiyang Lin, Mingyu Yan, Xiaochun Ye, Dongrui Fan

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At

Wahid Faidi

We study a robust utility maximization problem in the case of an incomplete market and logarithmic utility with general stochastic constraints, not necessarily convex. Our problem is equivalent to maximizing of nonlinear expected logarithmic utility. We characterize the optimal solution using quadratic BSDE.

Xiaohang Chen, Joe Giacalone, Fan Guo

We present a 2D kinematic model to study the acceleration of solar energetic particles (SEPs) at a shock driven by a coronal mass ejection. The shock is assumed to be spherical about an origin that is offset from the center of the Sun. This leads to a spatial and temporal evolution of the angle between the magnetic field and shock normal direction ($\theta_{

Mohammad Khairul Bashar, Nikhil Shukla

The Ising model provides a natural mapping for many computationally hard combinatorial optimization problems (COPs). Consequently, dynamical system-inspired computing models and hardware platforms that minimize the Ising Hamiltonian, have recently been proposed as a potential candidate for solving COPs, with the promise of significant performance benefit. Ho

Chao Hu, Liqiang Zhu

Due to the problem of performance constraints of unsupervised video object detection, its large-scale application is limited. In response to this pain point, we propose another excellent method to solve this problematic point. By incorporating motion characterization in unsupervised video object detection, detection accuracy is improved while reducing the co

Yan Zhao, Jiangyan Yi, Jianhua Tao, Chenglong Wang

Many datasets have been designed to further the development of fake audio detection, such as datasets of the ASVspoof and ADD challenges. However, these datasets do not consider a situation that the emotion of the audio has been changed from one to another, while other information (e.g. speaker identity and content) remains the same. Changing the emotion of

Kyle Broder

We exhibit a curious link between the Quadratic Orthogonal Bisectional Curvature, combinatorics, and distance geometry. The Weitzenb\"ock curvature operator, acting on real (1,1)--forms, is realized as the Dirichlet energy of a finite graph, weighted by a matrix of the curvature. These results also illuminate the difference in the nature of the Quadratic Ort

Zeyu Feng, Bowen Zhang, Jianxin Bi, Harold Soh

In this work, we focus on the problem of safe policy transfer in reinforcement learning: we seek to leverage existing policies when learning a new task with specified constraints. This problem is important for safety-critical applications where interactions are costly and unconstrained policies can lead to undesirable or dangerous outcomes, e.g., with physic

Jiaxin Xiao, Zihan Li, Berkin Bilgic, Jonathan R. Polimeni

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation. We propose to train NNs for Super-Resolution using Noisy Reference data (SRNR), leveraging the mechanism of the classic NN-based den

Emon Dey, Mikolaj Walczak, Mohammad Saeid Anwar, Nirmalya Roy

Search and rescue, wildfire monitoring, and flood/hurricane impact assessment are mission-critical services for recent IoT networks. Communication synchronization, dependability, and minimal communication jitter are major simulation and system issues for the time-based physics-based ROS simulator, event-based network-based wireless simulator, and complex dyn

Y R Kartik, Sujit Sarkar

Long-range effects induce some interesting behavior and considered as a gateway to understand the non-local behavior in the quantum systems. Especially, the long-range topological models became a platform for the realization of new quasi-particles, which are believed to be potential candidates for the topological qubits. In this work, we consider non-Hermiti

Joshua J Bon, David J Warne, David J Nott, Christopher Drovandi

Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging since the corresponding likelihood function is often intractable and model simulation may be computationally burdensome. Fortunately, in many of these situations it is possible to ad

Taylan Kabbani

In this paper, a heuristic method based on TabuSearch and TokenRing Search is being used in order to solve the Portfolio Optimization Problem. The seminal mean-variance model of Markowitz is being considered with the addition of cardinality and quantity constraints to better capture the dynamics of the trading procedure, the model becomes an NP-hard problem

Paul Görlach, Thomas Reichelt, Christian Sevenheck, Avi Steiner

Many hypergeometric differential systems that arise from a geometric setting can be endowed with the structure of mixed Hodge modules. We generalize this fundamental result to the tautological systems associated to homogeneous spaces by giving a functorial construction for them. As an application, we solve the holonomic rank problem for such tautological sys

Takenori Fujii, Yu Uezono, Takumi Otsuka, Shotaro Hagisawa

Knowledge of the doping (x)-temperature (T) phase diagram of Fe-based superconductors is crucial in understanding the mechanism of high transition temperature superconductivity. Here, we measured the Se doping dependence of the magnetic susceptibility for Te-annealed FeTe$_{1-x}$Se$_x$. Two characteristic temperatures $T^*_{\chi}$ and $T^{**}_{\chi}$ were ob

Katsunori Kubo

We investigate the correlation effects on spin-orbit coupling (SOC) in a two-orbital Hubbard model on a square lattice by applying the variational Monte Carlo method. We consider an effective SOC constant $\lambda_{\text{eff}}$ in the one-body part of the variational wave function and mainly discuss the cases of the electron number per site $n=1$, that is, q

Chunmei Zhang, Ping Guo, Jian Zhou

The bulk photovoltaic effect that is intimately associated with crystalline symmetry has been extensively studied in various nonmagnetic materials, especially ferroelectrics with a switchable electric polarization. In order to further engineer the symmetry, one could resort to spin-polarized systems possessing an extra magnetic degree of freedom. Here, we in

Rui Deng, Qian Wu, Yuke Li

In this paper, we introduce 3D-CSL, a compact pipeline for Near-Duplicate Video Retrieval (NDVR), and explore a novel self-supervised learning strategy for video similarity learning. Most previous methods only extract video spatial features from frames separately and then design kinds of complex mechanisms to learn the temporal correlations among frame featu

Dattaraj J. Rao, Shraddha S. Mane, Mukta A. Paliwal

Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and unified in a single resource. The goal of biomedical multi-hop question-answering over knowledge graph (KGQA) is to help biologist and scient

Bridget Smart, Matthew Roughan, Lewis Mitchell

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive model able to generate sequences with a given autocorrelation structure. While a first-order Markov Chain model generates new values by conditioning on the current state, the LAMP model takes the transition state from the sequence's history accordi

Yoko Yamamoto, Ryoichi Saito, Takashi Mukaiyama

We developed an injection-locked Ti:sapphire laser at a wavelength of 671 nm where the fluorescence gain of the Ti:sapphire crystal is quite low. We obtained an output of more than 500 mW at a pump power of 10 W. The injection-locked lasing operates at a single-frequency and unidirectionally lasing with no intracavity optical components. The spectral propert

Xingchi Mu, Qianqian Xue, Yan Sun, Jian Zhou

Bulk photovoltaic (BPV) effect, a second order nonlinear process that generates static current under light irradiation, requires centrosymmetric broken systems as its application platform. In order to realize measurable BPV photocurrent in spatially centrosymmetric materials, various schemes such as chemical doping, structural deformation, or electric bias h

Sheng-Feng Yu, Wei-Chen Chiu

Online continual learning (OCL) aims to enable model learning from a non-stationary data stream to continuously acquire new knowledge as well as retain the learnt one, under the constraints of having limited system size and computational cost, in which the main challenge comes from the "catastrophic forgetting" issue -- the inability to well remember the lea

Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi

The exponential growth in demand for digital services drives massive datacenter energy consumption and negative environmental impacts. Promoting sustainable solutions to pressing energy and digital infrastructure challenges is crucial. Several hyperscale cloud providers have announced plans to power their datacenters using renewable energy. However, integrat

John Reuben Gilbert

Federated learning is a collaborative method that aims to preserve data privacy while creating AI models. Current approaches to federated learning tend to rely heavily on secure aggregation protocols to preserve data privacy. However, to some degree, such protocols assume that the entity orchestrating the federated learning process (i.e., the server) is not

Timothy M. Chan

We consider problems related to finding short cycles, small cliques, small independent sets, and small subgraphs in geometric intersection graphs. We obtain a plethora of new results. For example: * For the intersection graph of $n$ line segments in the plane, we give algorithms to find a 3-cycle in $O(n^{1.408})$ time, a size-3 independent set in $O(n^{1.65

Lixiang Zhang, Jia Li

For computational efficiency, surrogate models have been used to emulate mathematical simulators for physical or biological processes. High-speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation is repeated over many randomly sampled input points (aka, the Monte Carlo method). In some cases, UQ is only feasible with a

Yiming Cui, Wanxiang Che, Shijin Wang, Ting Liu

Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training tasks on the surface form of the text, such as the masked language model (MLM). To further empower the PLMs with richer linguistic features, in this paper, we aim to propose a simp

Zhichao Duan, Xiuxing Li, Zhenyu Li, Zhuo Wang

Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document. One major challenge of DocRE is to dig decisive details regarding a specific entity pair from long text. However, in many cases, only a fraction of text carries required information, even in the manually labeled supporting evidence. To better

Ryan McGovern, Nikolaos Athanasopolous, Seán McLoone

We develop a new framework for trajectory planning on predefined paths, for general N-link manipulators. Different from previous approaches generating open-loop minimum time controllers or pre-tuned motion profiles by time-scaling, we establish analytic algorithms that recover all initial conditions that can be driven to the desirable target set while adheri

Yun-Hao Shi, Yu Liu, Yu-Ran Zhang, Zhongcheng Xiang

Quantum simulation of different exotic topological phases of quantum matter on a noisy intermediate-scale quantum (NISQ) processor is attracting growing interest. Here, we develop a one-dimensional 43-qubit superconducting quantum processor, named as Chuang-tzu, to simulate and characterize emergent topological states. By engineering diagonal Aubry-Andr$\acu

Vijaya Yajnanarayana, Henk Wymeersch

Sensing using cellular infrastructure may be one of the defining feature of sixth generation (6G) wireless systems. Wideband 6G communication channels operating at higher frequency bands (upper mmWave bands) are better modeled using clustered geometric channel models. In this paper, we propose methods for detection of passive targets and estimating their pos

Dipesh Gyawali

Big Data processing systems handle huge unstructured and structured data to store, process, and analyze through cluster analysis which helps in identifying unseen patterns to find the relationships between them. Clustering analysis over the shared machines in big data technologies helps in deriving the relations and making decisions using data in context. It

Vanamala Venkataswamy

This paper studies a model for online job scheduling in green datacenters. In green datacenters, resource availability depends on the power supply from the renewables. Intermittent power supply from renewables leads to intermittent resource availability, inducing job delays (and associated costs). Green datacenter operators must intelligently manage their wo

Olga Dorabiala, Devavrat Vivek Dabke, Jennifer Webster, Nathan Kutz

Spatiotemporal data is increasingly available due to emerging sensor and data acquisition technologies that track moving objects. Spatiotemporal clustering addresses the need to efficiently discover patterns and trends in moving object behavior without human supervision. One application of interest is the discovery of moving clusters, where clusters have a s