April 2023 arXiv papers — page 61

Wei Pan, Xiaoqun Wang, Haiqing Lin, Shijie Hu

We investigate bipartite particle number fluctuations near the rank-$2$ exceptional points (EPs) of $\mathcal{PT}$-symmetric Su-Schrieffer-Heeger models. Beyond a conformal field theory of massless fermions, fluctuations or equivalently compressibility is negative definite and exhibits superinflation in leading order at EPs, due to the defectiveness in the b

Guodong Chen, Jiu Jimmy Jiao, Xiaoming Xue, Zhongzheng Wang

Surrogate-assisted evolutionary algorithms have been widely developed to solve complex and computationally expensive multi-objective optimization problems in recent years. However, when dealing with high-dimensional optimization problems, the performance of these surrogate-assisted multi-objective evolutionary algorithms deteriorate drastically. In this work

Yixuan Lin, Ji Liu

The push-sum algorithm is probably the most important distributed averaging approach over directed graphs, which has been applied to various problems including distributed optimization. This paper establishes the explicit absolute probability sequence for the push-sum algorithm, and based on which, constructs quadratic Lyapunov functions for push-sum based d

Hirotaka Ijima, Marko Jusup

Tuna and billfish are charismatic pelagic fishes attracting considerable scientific attention due to their ecophysiological and socioeconomic importance. However, the knowledge of their basin-wide spawning and larval habitats, especially in a warming ocean, is limited. This knowledge gap undermines effective fishery management by introducing recruitment unce

Yozo Tonaki, Yusuke Kaino, Masayuki Uchida

We deal with parameter estimation for a linear parabolic second-order stochastic partial differential equation in two space dimensions driven by two types of $Q$-Wiener processes based on high frequency data with respect to time and space. We propose minimum contrast estimators of the coefficient parameters based on temporal and spatial squared increments, a

A. Hossain, Md. N. Akhtar, M. A. Navascués

Formerly the geometry was based on shapes, but since the last centuries this founding mathematical science deals with transformations, projections and mappings. Projective geometry identifies a line with a single point, like the perspective on the horizon line and, due to this fact, it requires a restructuring of the real mathematical and numerical analysis.

Igor Molybog, Peter Albert, Moya Chen, Zachary DeVito

We present a theory for the previously unexplained divergent behavior noticed in the training of large language models. We argue that the phenomenon is an artifact of the dominant optimization algorithm used for training, called Adam. We observe that Adam can enter a state in which the parameter update vector has a relatively large norm and is essentially un

Dongwon Son, Beomjoon Kim

Our goal is to develop an efficient contact detection algorithm for large-scale GPU-based simulation of non-convex objects. Current GPU-based simulators such as IsaacGym and Brax must trade-off speed with fidelity, generality, or both when simulating non-convex objects. Their main issue lies in contact detection (CD): existing CD algorithms, such as Gilbert-

Shunpu Tang, Qianqian Yang, Lisheng Fan, Xianfu Lei

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving consi

Hiwot Tadese Kassa, Paul Johnson, Jason Akers, Mrinmoy Ghosh

Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires additional training data to avoid overfitting. This model growth demands a large number of resources in data centers. Hence, training efficiency is becoming considerably more impo

Hiroto Akaike

The delta invariant interprets the criterion for the K-(poly)stability of log terminal Fano varieties. In this paper, we determine the whole local delta invariant for all weak del Pezzo surfaces with the anti-canonical degree $\geq 5$.

Shin'ichi Nojiri, Sergei D. Odintsov, Tanmoy Paul

Based on generalized holographic formalism, we establish a holographic realization of constant roll inflation during the early universe, where the corresponding cut-off depends on the Hubble parameter and its derivatives (up to the second order). The viability of this holographic constant roll inflation with respect to the Planck data in turn puts a certain

Jiancheng Wu, Qingwen Wu, Hanrui Xue, Weihua Lei

The hydrogen Balmer decrement (e.g., $\rm H\alpha/H\beta$) is widely adopted as an indicator of the internal reddening of active galactic nuclei (AGNs). This is challenged by some low-luminosity AGNs (LLAGNs) and changing-look AGNs (CLAGNs), which have steep Balmer decrement but without strong evidence for absorption. We compile a sample of normal AGNs and C

Donghyeon Kim, Glen Berseth, Mathew Schwartz, Jaeheung Park

In this paper, we review the question of which action space is best suited for controlling a real biped robot in combination with Sim2Real training. Position control has been popular as it has been shown to be more sample efficient and intuitive to combine with other planning algorithms. However, for position control gain tuning is required to achieve the be

Simran Arora, Brandon Yang, Sabri Eyuboglu, Avanika Narayan

A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effort or domain specific customization. Given the sheer variety of potential documents, state-of-the art systems make simplifying assumptions and use domain specific training. In this

Yang An, Peng Cheng

The recent developments related to the black hole information paradox have brought us a confusing object: the replica wormhole. We are trying to better understand the object from the viewpoint of the thermo-mixed double and spontaneous symmetry breaking. It was shown that the replica wormhole can be regarded as a transition between different degenerate vacua

Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong

A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussian processes. An ideal NP would learn everything from data without any inductive biases, but in practice, we often restrict the class of stochastic processes for the ease of estima

Chaitanya Ambi

We give a new proof of Cartan's fixed point theorem using topological fixed point theory. For an odd dimensional, simply connected and complete manifold having non-positive curvature, we further prove that every isometry with finite order not only fixes a point, but also maps a pencil of geodesics to itself. We apply similar techniques to the automorphism gr

Xi Tong, Yi Wang, Chen Zhang, Yuhang Zhu

We consider a Bardeen-Cooper-Schrieffer (BCS)-like model in the inflationary background. We show that with an axial chemical potential, the attractive quartic fermion self-interaction can lead to a BCS-like condensation. In the rigid-de Sitter (dS) limit of inflation where backreaction from the inflaton and graviton is neglected, we perform the first computa

Haruya Ishikawa, Yoshimitsu Aoki

In this paper, we present the Semantic Boundary Conditioned Backbone (SBCB) framework, a simple yet effective training framework that is model-agnostic and boosts segmentation performance, especially around the boundaries. Motivated by the recent development in improving semantic segmentation by incorporating boundaries as auxiliary tasks, we propose a multi

Giung Nam, Sunguk Jang, Juho Lee

Decoupling representation learning and classifier learning has been shown to be effective in classification with long-tailed data. There are two main ingredients in constructing a decoupled learning scheme; 1) how to train the feature extractor for representation learning so that it provides generalizable representations and 2) how to re-train the classifier

Ashraf Ayubinia, Yongquan Xue, Huynh Anh Nguyen Le, Fan Zou

We explore properties of stellar kinematics and ionized gas in a sample of 1106 local (U)LIRGs from the AKARI telescope. We combine data from $Wide-field\ Infrared\ Survey\ Explorer$ (WISE) and Sloan Digital Sky Survey (SDSS) Data Release 13 (DR13) to fit the spectral energy distribution (SED) of each source to constrain the contribution of active galactic n

Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai

Multicalibration is a notion of fairness for predictors that requires them to provide calibrated predictions across a large set of protected groups. Multicalibration is known to be a distinct goal than loss minimization, even for simple predictors such as linear functions. In this work, we consider the setting where the protected groups can be represented by

Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. However, many research endeavours heavily rely on parameter sharing among agents, which confines them to only homogeneous-agent setting and leads to training instability and lack of convergence guarantees. To achieve

Kai Yang, Hong Shang, Tianyang Shi, Xinghan Chen

The research fields of parametric face model and 3D face reconstruction have been extensively studied. However, a critical question remains unanswered: how to tailor the face model for specific reconstruction settings. We argue that reconstruction with multi-view uncalibrated images demands a new model with stronger capacity. Our study shifts attention from

Marc Vinyals, Chunxiao Li, Noah Fleming, Antonina Kolokolova

In their seminal work, Atserias et al. and independently Pipatsrisawat and Darwiche in 2009 showed that CDCL solvers can simulate resolution proofs with polynomial overhead. However, previous work does not address the tightness of the simulation, i.e., the question of how large this overhead needs to be. In this paper, we address this question by focusing on

Quanjiang Guo, Zhao Kang, Ling Tian, Zhouguo Chen

Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: (1) ignore the inherent emotional inform

Bingxuan Xu, Rui Meng, Yue Chen, Xiaodong Xu

Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit the meaning of information rather than bit-by-bit transmission, thus enhancing data transmission efficiency and supporting future human-centric, data-, and resource-intensive intelli

Yasunori Okumura

We consider linear orders of finite alternatives constructed by aggregating individual preferences. Specifically, we focus on linear orders that respect modified collective preference relations derived from supermajority rules, where modifications are introduced through two procedures if cycles occur. One procedure utilizes the transitive closure, while the

Uditnarayan Kouskiya, Amit Acharya

A finite element based computational scheme is developed and employed to assess a duality based variational approach to the solution of the linear heat and transport PDE in one space dimension and time, and the nonlinear system of ODEs of Euler for the rotation of a rigid body about a fixed point. The formulation turns initial-(boundary) value problems into

Yuichi Shiozawa, Jian Wang

In this paper, we establish the Hausdorff dimensions of inverse images and collision time sets for a large class of symmetric Markov processes on metric measure spaces. We apply the approach in the works by Hawkes and Jain--Pruitt, and make full use of heat kernel estimates. In particular, the results efficiently apply to symmetric diffusion processes, symme

Chin-yuan Hu, Tsung-lin Cheng, Ie-bin Lian

This paper discuss a new class of functional equations by using both Poisson summation formula and Jacobi type theta a function. The class of Riemann type functional equations are derived from self-reciprocal probability density functions. Finally, the second order Zeta functional equations for Riemann type is also investigated.

M. Zeeshan Gul, M. Sharif

This paper uses the Noether symmetry approach to examine the viable and stable traversable wormhole solutions in the framework of $f(\mathcal{R,}\mathcal{T}^{2})$ theory, where $\mathcal{R}$ is the Ricci scalar and $\mathcal{T}^{2}=\mathcal{T}_{\mu\nu}\mathcal{T}^{\mu\nu}$ is the self-contraction of stress-energy tensor. For this purpose, we consider a speci

Shayan Monadjemi, Mengtian Guo, David Gotz, Roman Garnett

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspec

Rosaura G. VidalMata, Priscila Saboia, Daniel Moreira, Grant Jensen

Image manipulation detection algorithms designed to identify local anomalies often rely on the manipulated regions being ``sufficiently'' different from the rest of the non-tampered regions in the image. However, such anomalies might not be easily identifiable in high-quality manipulations, and their use is often based on the assumption that certain image ph

Snehesh Shrestha, Ge Gao, Cornelia Fermuller, Yiannis Aloimonos

Many of us researchers take extra measures to control for known-unknowns. However, unknown-unknowns can, at best, be negligible, but otherwise, they could produce unreliable data that might have dire consequences in real-life downstream applications. Human-Robot Interaction standards informed by empirical data could save us time and effort and provide us wit

Snehesh Shrestha, Ishan Tamrakar, Cornelia Fermuller, Yiannis Aloimonos

Haptic sensing can provide a new dimension to enhance people's musical and cinematic experiences. However, designing a haptic pattern is neither intuitive nor trivial. Imagined haptic patterns tend to be different from experienced ones. As a result, researchers use simple step-curve patterns to create haptic stimuli. To this end, we designed and developed an

Yi Zheng, Aasheesh Kolli, Shaizeen Aga

While non-volatile memories (NVMs) provide several desirable characteristics like better density and comparable energy efficiency than DRAM, DRAM-like performance, and disk-like durability, the limited endurance NVMs manifest remains a challenge with these memories. Indeed, the endurance constraints of NVMs can prevent solutions that are commonly employed fo

Tobias Serwatka, Pierre-Nicholas Roy

In this contribution, we employ a density matrix based optimization procedure to obtain customized basis functions to describe chains of rotating water molecules in interaction regimes associated with different intermolecular distances. This procedure is shown to yield a very compact basis with a clear truncation criterion based on the population of the sing

Chengpeng Hu, Jiyuan Pei, Jialin Liu, Xin Yao

Evolutionary algorithms have been used to evolve a population of actors to generate diverse experiences for training reinforcement learning agents, which helps to tackle the temporal credit assignment problem and improves the exploration efficiency. However, when adapting this approach to address constrained problems, balancing the trade-off between the rewa

Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version

Xian-Kai Chen, Christopher C. S. Chan, Sudhi Mahadevan, Yu Guo

State-of-the-art Y6-type molecular acceptors exhibit nanosecond excited-state lifetimes despite their low optical gaps (~1.4 eV), thus allowing organic solar cells (OSCs) to achieve highly efficient charge generation with extended near-infrared (NIR) absorption range (up to ~1000 nm). However, the precise molecular-level mechanism that enables low-energy exc

Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He

Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology. Recently, Deep Reinforcement Learning (DRL) has been increasingly employed to solve TSP due to its high inference efficiency. Neverthele

Minh Hua, Rita Raley

The premise of this article is that a basic understanding of the composition and functioning of large language models is critically urgent. To that end, we extract a representational map of OpenAI's GPT-2 with what we articulate as two classes of deep learning code, that which pertains to the model and that which underwrites applications built around the mod

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

Based on 4.5 fb$^{-1}$ data taken at seven center-of-mass energies ranging from 4.600 to 4.699 GeV with the BESIII detector at the BEPCII collider, we measure the branching fractions of $\Lambda_{c}^{+}\rightarrow\Sigma^{+}+hadrons$ relative to $\Lambda_{c}^{+}\rightarrow \Sigma^+ \pi^+ \pi^-$. Combining with the world average branching fraction of $\Lambda_

Ruan Lu, Zhou HangCheng, Ran Meng, Zhao Jin

The challenges faced by text classification with large tag systems in natural language processing tasks include multiple tag systems, uneven data distribution, and high noise. To address these problems, the ESimCSE unsupervised comparative learning and UDA semi-supervised comparative learning models are combined through the use of joint training techniques i

Tobias Serwatka, Pierre-Nicholas Roy

Fullerene cages allow the confinement of single molecules and the construction of molecular assemblies whose properties strongly deviate from those of free species. In this work, we employ the density-matrix renormalization group method to show that chains of fullerenes filled with polar molecules (LiF, HF, and H2O) can form dipole-ordered quantum phases. In

Jingtong Su, Julia Kempe

Research on improving the robustness of neural networks to adversarial noise - imperceptible malicious perturbations of the data - has received significant attention. The currently uncontested state-of-the-art defense to obtain robust deep neural networks is Adversarial Training (AT), but it consumes significantly more resources compared to standard training

Bohan Li, Longxu Dou, Yutai Hou, Yunlong Feng

Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template. This approach demonstrates its effectiveness, especially in few-shot learning scenarios, where the model is trained on a scarce amount of data. Despite its successes, the limited templates

Shlok Nahar, Twesh Upadhyaya, Norbert Lütkenhaus

Decoy-state methods [1-3] are essential to perform quantum key distribution (QKD) at large distances in the absence of single photon sources. However, the standard techniques apply only if laser pulses are used that are independent and identically distributed (iid). Moreover, they require that the laser pulses are fully phase-randomised. However, realistic h

Qianqian Zhang, Hu Zhou, Ying-Chang Liang, Sumei Sun

In this paper, we are interested in a reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS assists a primary transmission by passive beamforming and simultaneously acts as an information transmitter by periodically adjusting its reflection coefficients. The above RIS functions innately enable a new multiplicative multip

Zhixin Li, Ying Xu

Advances in artificial intelligence have facilitated the use of large language models (LLMs) and AI-generated synthetic media in education, which may inspire HCI researchers to develop technologies, in particular, embodied conversational agents (ECAs) to simulate the kind of scaffolding children might receive from a human partner. In this paper, we will prop

Subhodh Kotekal, Chao Gao

Sparse additive models are an attractive choice in circumstances calling for modelling flexibility in the face of high dimensionality. We study the signal detection problem and establish the minimax separation rate for the detection of a sparse additive signal. Our result is nonasymptotic and applicable to the general case where the univariate component func

Yisheng Lei, Faezeh Kimiaee Asadi, Tian Zhong, Alexander Kuzmich

Optical photons are powerful carriers of quantum information, which can be delivered in free space by satellites or in fibers on the ground over long distances. Entanglement of quantum states over long distances can empower quantum computing, quantum communications, and quantum sensing. Quantum optical memories can effectively store and manipulate quantum st

İsmail Özbakır, Kadri Yakut

We present a comprehensive parameter-space study of binary black hole (BBH) mergers using the SEOBNRv4\_opt waveform model. Our analysis spans $\sim 10^6$ simulated waveforms across a broad range of mass ratios \( q = \frac{m_1}{m_2} \in [1.0, 2.0] \) and aligned spin configurations. We investigate the influence of these parameters on remnant properties, inc

Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng

We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP instance starting from the scratch relying on two components: the upper-level policy chooses a small subset of nodes (up to 200 in our exper

Dinesh Kumar Nanduri, Elizabeth M. Bonsignore

According to UNESCO, there are nearly 7,000 languages spoken worldwide, of which around 3,000 languages are in danger of disappearing before the end of the century. With roughly 230 languages having already become extinct between the years 1950-2010, collectively this represents a significant loss of linguistic and cultural diversity. This position paper aim

Emily Bain

In Bain [J. Math. Phys. 64, 023301 (2023)], we found asymptotics of one-point correlation functions of the two-periodic weighted Aztec diamond in the mesoscopic limit, where the linear size of the ordered region is of the same order as the correlation length. In this paper, we follow up with a numerical study of two-point correlation functions of dimers sepa

Abigail I. Hartley, Erica J. Nelson, Katherine A. Suess, Alex M. Garcia

We identify the first quiescent galaxies in TNG300, the largest volume of the IllustrisTNG cosmological simulation suite, and explore their quenching processes and time evolution to z=0. We find that the first quiescent galaxies with stellar masses M_* > 3 x 10^{10} M_sun and specific star formation rates sSFR < 10^{-11} yr^{-1} emerge at z~4.2 in TNG300. Su

Zhiwei Wei, Yi Xiao, Wenjia Xu, Mi Shu

Effective building pattern recognition is critical for understanding urban form, automating map generalization, and visualizing 3D city models. Most existing studies use object-independent methods based on visual perception rules and proximity graph models to extract patterns. However, because human vision is a part-based system, pattern recognition may requ

F. Michael Bartram, Meng Li, Liangyang Liu, Zhiming Xu

Atomically thin van der Waals magnetic materials have not only provided a fertile playground to explore basic physics in the two-dimensional (2D) limit but also created vast opportunities for novel ultrafast functional devices. Here we systematically investigate ultrafast magnetization dynamics and spin wave dynamics in few-layer topological antiferromagneti

Marcelo Orenes-Vera, Esin Tureci, David Wentzlaff, Margaret Martonosi

Recent works have introduced task-based parallelization schemes to accelerate graph search and sparse data-structure traversal, where some solutions scale up to thousands of processing units (PUs) on a single chip. However parallelizing these memory-intensive workloads across millions of cores requires a scalable communication scheme as well as designing a c

Varun Gumma, Raj Dabre, Pratyush Kumar

Knowledge distillation (KD) is a well-known method for compressing neural models. However, works focusing on distilling knowledge from large multilingual neural machine translation (MNMT) models into smaller ones are practically nonexistent, despite the popularity and superiority of MNMT. This paper bridges this gap by presenting an empirical investigation o

Hao Chen, Peng Zheng, Xin Wang, Shu Hu

As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information. Although a plenty of studies have applied the supervised machine learning approaches to detect such content, the lack of gold standard training data has hindered the

Masanari Kimura, Hideitsu Hino

Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is very often violated. In particular, the phenomenon that the marginal distribution of the data changes is called covariate shift, one of the most important research topics in machine learning. We show that the well-

Sungmin Kang, Shin Yoo

While Genetic Improvement (GI) is a useful paradigm to improve functional and nonfunctional aspects of software, existing techniques tended to use the same set of mutation operators for differing objectives, due to the difficulty of writing custom mutation operators. In this work, we suggest that Large Language Models (LLMs) can be used to generate objective

Jicheng Jin, Li He, Jian Lu, Lin Chang

The field of topological photonics studies unique and robust photonic systems that are immune to defects and disorders due to the protection of their underlying topological phases. Mostly implemented in static systems, the studied topological phases are often defined in linear photonic band structures. In this study, we experimentally demonstrate Floquet Che

Chrystian, Wahyono

Following the contention of AI arts, our research focuses on bringing AI for all, particularly for artists, to create AI arts with limited data and settings. We are interested in geometrically symmetric pattern generation, which appears on many artworks such as Portuguese, Moroccan tiles, and Batik, a cultural heritage in Southeast Asia. Symmetric pattern ge

Pham Ngoc Huy, Tran Minh Quan

In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentation

Lewis Tseng, Neo Zhou, Cole Dumas, Tigran Bantikyan

A distributed multi-writer multi-reader (MWMR) atomic register is an important primitive that enables a wide range of distributed algorithms. Hence, improving its performance can have large-scale consequences. Since the seminal work of ABD emulation in the message-passing networks [JACM '95], many researchers study fast implementations of atomic registers un

Anton Kolotilin, Alexander Wolitzky

We study the problem of a partisan gerrymanderer who assigns voters to equipopulous districts so as to maximize his party's expected seat share. The designer faces both aggregate uncertainty (how many votes his party will receive) and idiosyncratic, voter-level uncertainty (which voters will vote for his party). We argue that pack-and-pair districting, where

David S. Lipham

We prove that the classes of weakly $1$-dimensional and almost $0$-dimensional spaces are disjoint. The result has applications to hereditarily locally connected spaces, $\mathbb R$-trees, and endpoints of smooth fans.

Xiang-Jie Li, Dong Pan, Gui-Lu Long, Lajos Hanzo

Quantum secure direct communication (QSDC) has attracted a lot of attention, which exploits deep-rooted quantum physical principles to guarantee unconditional security of communication in the face of eavesdropping. We first briefly review the fundamentals of QSDC, and then present its evolution, including its security proof, its performance improvement techn

Zixiao Ma, Zhaoyu Wang

The microgrid (MG) plays a crucial role in the energy transition, but its nonlinearity presents a significant challenge for large-signal power systems studies in the electromagnetic transient (EMT) time scale. In this paper, we develop a large-signal linear MG model that considers the detailed dynamics of the primary and zero-control levels based on the Koop

Stavros Garoufalidis, Don Zagier

We discuss a matrix of periodic holomorphic functions in the upper and lower half-plane which can be obtained from a factorization of an Andersen-Kashaev state integral of a knot complement with remarkable analytic and asymptotic properties that defines a ${\rm PSL}_2({\mathbb Z})$-cocycle on the space of matrix-valued piecewise analytic functions on the rea

Yuxin Meng, Feng Gao, Eric Rigall, Ran Dong

Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface

Thang Pham

In this paper, we study the distribution of parallelograms and rhombi in a given set in the plane over arbitrary finite fields $\mathbb{F}_q^2$. As an application, we improve a recent result due to Fitzpatrick, Iosevich, McDonald, and Wyman (2021) on the Vapnik-Chervonenkis dimension of the induced distance graph. Our proofs are based on the discrete Fourier

Joonseok Lee, Seongho Joe, Kyoungwon Park, Bogun Kim

We propose a self-supervised learning method for long text documents based on contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple text augmentation algorithm that sets up a pretext task required for contrastive updates to BERT-based document embedding. SaD splits a document into two sub-documents containing randomly shuffled words

Haodong Pan, Feng Gao, Junyu Dong, Qian Du

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral struc

Moses Y-H. Chan, Matthew Plumlee, Stefan M. Wild

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This a

Yangming Li

3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. 3D reconstruction is an important and extensively studied problem, but it is far from being solved. This work systematically introduces classical methods of 3D dense reconstruction based on geometric and optical models, as w

Ted Tyler, Vaibhav Malhotra, Adam Montague, Zhigen Zhao

The ability of bipedal robots to adapt to diverse and unstructured terrain conditions is crucial for their deployment in real-world environments. To this end, we present a novel, bio-inspired robot foot design with stabilizing tarsal segments and a multifarious sensor suite involving acoustic, capacitive, tactile, temperature, and acceleration sensors. A rea

Seongho Joe, Byoungjip Kim, Hoyoung Kang, Kyoungwon Park

The recent advances in representation learning inspire us to take on the challenging problem of unsupervised image classification tasks in a principled way. We propose ContraCluster, an unsupervised image classification method that combines clustering with the power of contrastive self-supervised learning. ContraCluster consists of three stages: (1) contrast

Morgan MacLeod, Jonathan Grindlay

Stellar-mass black holes (BHs), with masses comparable to stars, are a major constituent of our Milky Way galaxy. This chapter describes the landscape of challenging, and long-sought efforts to identify these objects in the Galaxy. The first stellar-mass BHs were identified as persistent, but highly variable cosmic X-ray sources. Later, transient BH candidat

Yuxuan Song, Yongyu Wang

This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key cluster properties of the original data sets based on a novel spectral data compression approach. Then, the resultant spectrally-compressed data sets are leveraged for the development

Katie Buchhorn, Edgar Santos-Fernandez, Kerrie Mengersen, Robert Salomone

Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexit

Chunjie Yan, Lina Chen, Kaiyuan Zhou, Liupeng Yang

We systematically investigated the Ni and Co thickness-dependent perpendicular magnetic anisotropy (PMA) coefficient, magnetic domain structures, and magnetization dynamics of Pt(5 nm)/[Co(t_Co nm)/Ni(t_Ni nm)]5/Pt(1 nm) multilayers by combining the four standard magnetic characterization techniques. The magnetic-related hysteresis loops obtained from the fi

Xiaoliang Ju, Yiyang Sun, Yiming Hao, Yikang Li

We propose a perception imitation method to simulate results of a certain perception model, and discuss a new heuristic route of autonomous driving simulator without data synthesis. The motivation is that original sensor data is not always necessary for tasks such as planning and control when semantic perception results are ready, so that simulating percepti

Bumseop Kim, Dongbin Shin, Seon Namgung, Noejung Park

Chiral materials have garnered significant attention in the field of condensed matter physics. Nevertheless, the magnetic moment induced by the chiral spatial motion of electrons in helical materials, such as elemental Te and Se, remains inadequately understood. In this work, we investigate the development of quantum angular momentum enforced by chirality us

Fan Bai, Yingjie Lu, Yujie Zhi, Yueye Huang

In recent years, cold atmospheric plasma (CAP) using inert gas has been successfully applied for biomedicine, such as sterilization, wound healing, skin diseases, and tumor treatment. Here, we reported air cold atmospheric plasma with three different patterns (I. Non: basic square grid structure; II. Square: basic square grid structure + square node; III. Ci

Chao Li, Yiwen Zhang, Xinda Yan, Yuzhe Wang

High-speed optical wireless communication can address the exponential growth in data traffic. Adaptive beamforming customized for the target location is crucial, but existing solutions such as liquidcrystal spatial light modulators and microelectromechanical systems require costly micro/nano manufacturing, delicate alignment, and a high degree of mechanical

Qin Tan, Wen-Di Guo, Yu-Peng Zhang, Yu-Xiao Liu

In this work, we investigate the gravitational quasinormal modes (QNMs) and the gravitational resonances of a thick brane model. We use the asymptotic iteration and shooting methods to obtain the quasinormal frequencies (QNFs) of the brane. On the other hand, we investigate the resonances and their evolution numerically. The results show that the oscillation

Tongxin Yin, Reilly Raab, Mingyan Liu, Yang Liu

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness in the context of online reinforcement learning. This formulation can accommodate dynamical control objectives, such as driving equity inherent in the state of a population, that canno

TingFang Lee, Ashley L. Buchanan, Natallia Katenka, Laura Forastiere

Estimating causal effects in the presence of spillover among individuals embedded within a social network is often challenging with missing information. The spillover effect is the effect of an intervention if a participant is not exposed to the intervention themselves but is connected to intervention recipients in the network. In network-based studies, outc

Md Asaduzzaman, Ryan M. L. McFadden, Anne-Marie Valente-Feliciano, David R. Beverstock

Superconducting radio frequency (SRF) cavities, which are critical components in many particle accelerators, need to be operated in the Meissner state to avoid strong dissipation from magnetic vortices. For a defect-free superconductor, the maximum attainable magnetic field for operation is set by the superheating field, $B_{\mathrm{sh}}$, which directly dep

Susanna Raj, Sudha Jamthe, Yashaswini Viswanath, Suresh Lokiah

Construct theory in social psychology, developed by George Kelly are mental constructs to predict and anticipate events. Constructs are how humans interpret, curate, predict and validate data; information. AI today is biased because it is trained with a narrow construct as defined by the training data labels. Machine Learning algorithms for facial recognitio

Ke Ji, Zongshuo Wu, Xiaofan Shen, Jianli Wang

The Jahn-Teller distortion and the resulting orbital order usually cause some fascinating correlated electronic behaviors, and generally lead to antiferromagnetism in perovskite bulks. Here we demonstrate that the Jahn-Teller distortion present in the perovskite fluoride KCrF$_3$ bulk can be retained to the two-dimensional limit, resulting in a staggered orb

Shoaib Ahmed Siddiqui, David Krueger, Thomas Breuel

Visual object recognition systems need to generalize from a set of 2D training views to novel views. The question of how the human visual system can generalize to novel views has been studied and modeled in psychology, computer vision, and neuroscience. Modern deep learning architectures for object recognition generalize well to novel views, but the mechanis

Sitaram Ramakrishnan, Shidaling Matteppanavar, Andreas Schonleber, Bikash Patra

Temperature-dependent electronic and magnetic properties are reported for a Z2 topological metal single-crystalline nickel-deficient NiV$_2$Se$_4$. It is found to crystallize in the monoclinic Cr3S4 structure type with space group I2=m. From single-crystal x-ray diffraction, we find that there are vacancies on the Ni site, resulting in the composition Ni0:85

R. Vogt

Background: Intrinsic charm, nonperturbative charm in the hadron wavefunction, has long been speculated but has never been satisfactorily proven. Open charm and $J/\psi$ measurements in a fixed-taget configuration at the LHCb searched for this contribution but reported no evidence. Purpose: $\overline D$ meson and $J/\psi$ production is calculated for the SM