October 2022 arXiv papers — page 110

Felix I. Stamm, Michael Scholkemper, Markus Strohmaier, Michael T. Schaub

We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades off the diversity in network samples against the depth of the neighborhood structure that is preserved. Our key innovation is to employ a colored Configuration Model with colors deri

Karl Friedrich Siburg, Christopher Strothmann

Given two multivariate copulas with corresponding tail dependence functions, we investigate the relation between a natural tail dependence ordering $\leq_{tdo}$ and the order $\leq_{loc}$ of local stochastic dominance. We show that, although the two orderings are not equivalent in general, they coincide for various important classes of copulas, among them al

Enrico Savi

In [4] Bierstone and Parusinski proved the existence of global smoothings for closed subanalytic sets, both in an embedded and a non-embedded sense. In particular, in the non-embedded desingularization procedure the authors construct smoothings of (generically) even degree, indeed it is well-known the existence of subanalytic sets which do not admit non-embe

Lukas Daubner, Martin Macak, Raimundas Matulevičius, Barbora Buhnova

Currently, numerous approaches exist supporting the implementation of forensic readiness and, indirectly, forensic-ready software systems. However, the terminology used in the approaches and their focus tends to vary. To facilitate the design of forensic-ready software systems, the clarity of the underlying concepts needs to be established so that their requ

Mihaela Chiappetta, Chiara Piazzola, Massimo Carraturo, Lorenzo Tamellini

The present paper aims at applying uncertainty quantification methodologies to process simulations of powder bed fusion of metal. In particular, for a part-scale thermomechanical model of an Inconel 625 super-alloy beam, we study the uncertainties of three process parameters, namely the activation temperature, the powder convection coefficient and the gas co

Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan

We introduce equi-tuning, a novel fine-tuning method that transforms (potentially non-equivariant) pretrained models into group equivariant models while incurring minimum $L_2$ loss between the feature representations of the pretrained and the equivariant models. Large pretrained models can be equi-tuned for different groups to satisfy the needs of various d

Stefano Di Noi, Luca Silvestrini

Renormalization group evolution above the electroweak scale is a crucial ingredient in the phenomenology of the Standard Model Effective Theory. The RGESolver open-source C++ library performs the evolution at leading order for dimension-six operators in the most general flavour scenario (assuming lepton and baryon number conservation). Given its efficiency,

Tanmay Kumar Poddar

The Standard Model of particle physics is a $SU(3)_c\times SU(2)_L\times U(1)_Y$ gauge theory that can explain the strong, weak, and electromagnetic interactions between the particles. The gravitational interaction is described by Einstein's General Relativity theory which is a classical theory of gravity. These theories can explain all the four fundamental

Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan

Collaborative Intelligence (CI) has emerged as a promising framework for deploying Artificial Intelligence (AI) models on resource-constrained edge devices. In CI, the AI model is partitioned between the edge device and the cloud, with intermediate features transmitted from the edge sub-model to the cloud sub-model to complete the inference task. However, re

Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li

Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (DAC), in which an agent learns dynamic configuration policies across instances by reinforcement learning (RL). However, in many complex algorithms, there may exist different types o

Sam Farrington, Katie Gittins

We investigate the heat flow in an open, bounded set $D$ in $\mathbb{R}^2$ with polygonal boundary $\partial D$. We suppose that $D$ contains an open, bounded set $\widetilde{D}$ with polygonal boundary $\partial \widetilde{D}$. The initial condition is the indicator function of $\widetilde{D}$ and we impose a Neumann boundary condition on the edges of $\par

Yi-Xuan Sun, Wei Wang

Out-of-Distribution (OOD) detection is essential in real-world applications, which has attracted increasing attention in recent years. However, most existing OOD detection methods require many labeled In-Distribution (ID) data, causing a heavy labeling cost. In this paper, we focus on the more realistic scenario, where limited labeled data and abundant unlab

Danyang Li, Quanling Deng

The study of quantum three-body problems has been centered on low-energy states that rely on accurate numerical approximation. Recently, isogeometric analysis (IGA) has been adopted to solve the problem as an alternative but more robust (with respect to atom mass ratios) method that outperforms the classical Born-Oppenheimer (BO) approximation. In this paper

Alexander März

Current implementations of Gradient Boosting Machines are mostly designed for single-target regression tasks and commonly assume independence between responses when used in multivariate settings. As such, these models are not well suited if non-negligible dependencies exist between targets. To overcome this limitation, we present an extension of XGBoostLSS t

Nikolaos K. Efremidis, Demetrios N. Christodoulides

Optical forces are known to arise in a universal fashion in many and diverse physical settings. As such, they are successfully employed over a wide range of applications in areas like biophotonics, optomechanics and integrated optics. While inter-elemental optical forces in few-mode photonic networks have been so far systematically analyzed, little is known,

Pulah Dhandekar, Manu Joseph

Aspect Based Sentiment Analysis is the most granular form of sentiment analysis that can be performed on the documents / sentences. Besides delivering the most insights at a finer grain, it also poses equally daunting challenges. One of them being the shortage of labelled data. To bring in value right out of the box for the text data being generated at a ver

Haneul Yoo, Rifki Afina Putri, Changyoon Lee, Youngin Lee

Researchers have traditionally recruited native speakers to provide annotations for widely used benchmark datasets. However, there are languages for which recruiting native speakers can be difficult, and it would help to find learners of those languages to annotate the data. In this paper, we investigate whether language learners can contribute annotations t

Jianshen Liu, Carlos Maltzahn, Matthew L. Curry, Craig Ulmer

Many distributed applications implement complex data flows and need a flexible mechanism for routing data between producers and consumers. Recent advances in programmable network interface cards, or SmartNICs, represent an opportunity to offload data-flow tasks into the network fabric, thereby freeing the hosts to perform other work. System architects in thi

Ran Huang

A second order cross-linked network is applied onto the classical Husimi lattice, to investigate the role of a "phantom" non-neighboring interactions of mid- and long-range in Bethe-like lattices for the first time. Since antiferromagnetic Ising model on Husimi lattice has been exactly solved and successfully presented the melting and glass transition, the P

Ali Behrouz, Mathias Lecuyer, Cynthia Rudin, Margo Seltzer

Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for prediction, that work does not address policy design, because the algorithms cannot handle weighted data samples. Specifically, they rely on the discreteness of the loss function, which m

Weiwei Gu, Boyuan Zheng, Yunmo Chen, Tongfei Chen

We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future desi

Subin Kim, Sihyun Yu, Jaeho Lee, Jinwoo Shin

Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos. Here, the main challenge is how to (a) alleviate a compute-inefficiency in training CNRs to (b) achieve high-quality video encoding while (c) maintaining the parameter

Kelvin Onggadinata, Dagomir Kaszlikowski, Pawel Kurzynski

The Kochen-Specker theorem states that exclusive and complete deterministic outcome assignments are impossible for certain sets of measurements, called Kochen-Specker (KS) sets. A straightforward consequence is that KS sets do not have joint probability distributions because no set of joint outcomes over such a distribution can be constructed. However, we sh

Rémi Lacombe, Nikolce Murgovski, Sébastien Gros, Balázs Kulcsár

The rapid adoption of electric buses by transit agencies around the world is leading to new challenges in the planning and operation of bus networks. In particular, the limited driving range of electric vehicles imposes operational constraints such as the need to charge buses during service. Research on this topic has mostly focused on the strategic and tact

Doseok Jang, Larry Yan, Lucas Spangher, Costas J. Spanos

Multi-Agent Reinforcement Learning currently focuses on implementations where all data and training can be centralized to one machine. But what if local agents are split across multiple tasks, and need to keep data private between each? We develop the first application of Personalized Federated Hypernetworks (PFH) to Reinforcement Learning (RL). We then pres

Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli

The stochastic heavy ball method (SHB), also known as stochastic gradient descent (SGD) with Polyak's momentum, is widely used in training neural networks. However, despite the remarkable success of such algorithm in practice, its theoretical characterization remains limited. In this paper, we focus on neural networks with two and three layers and provide a

Yuxiang Zhang, Jingze Lu, Xingming Wang, Zhuo Li

This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion. The proposed system is a score-level fusion of several light convolutional neural network (LCNN) based models. Various front-ends are used as input features, including low-frequency short-ti

Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies, Alvin Wen-Yu Su

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not

Youngmin Oh, Donghyeon Baek, Bumsub Ham

We address the problem of incremental semantic segmentation (ISS) recognizing novel object/stuff categories continually without forgetting previous ones that have been learned. The catastrophic forgetting problem is particularly severe in ISS, since pixel-level ground-truth labels are available only for the novel categories at training time. To address the p

Seungjoo Baek

Recently, the out-of-time-ordered correlator(OTOC) and Krylov complexity have been studied actively as a measure of operator growth. OTOC is known to exhibit exponential growth in chaotic systems, which was confirmed in many previous works. However, in some non-chaotic systems, it was observed that OTOC shows chaotic behavior and cannot distinguish saddle-do

Rui Zhong, Enzhi Zhang, Masaharu Munetomo

In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitness landscape of original problems based on every generation of optimization. And simple but efficient $\epsilon

Sean P. Carney, Robert D. Moser

Wall-bounded turbulent shear flows are known to exhibit universal small-scale dynamics that are modulated by large-scale flow structures. Strong pressure gradients complicate this characterization, however; they can cause significant variation of the mean flow in the streamwise direction. For such situations, we perform asymptotic analysis of the Navier-Stok

Hui Wen Goh, Ulyana Tkachenko, Jonas Mueller

Real-world data for classification is often labeled by multiple annotators. For analyzing such data, we introduce CROWDLAB, a straightforward approach to utilize any trained classifier to estimate: (1) A consensus label for each example that aggregates the available annotations; (2) A confidence score for how likely each consensus label is correct; (3) A rat

Mariella Dreissig, Florian Piewak, Joschka Boedecker

The calibration of deep learning-based perception models plays a crucial role in their reliability. Our work focuses on a class-wise evaluation of several model's confidence performance for LiDAR-based semantic segmentation with the aim of providing insights into the calibration of underrepresented classes. Those classes often include VRUs and are thus of pa

The FADE Collaboration, Heliudson Bernardo, Benjamin Bose, Guilherme Franzmann

The cosmological constant and its phenomenology remain among the greatest puzzles in theoretical physics. We review how modifications of Einstein's general relativity could alleviate the different problems associated with it that result from the interplay of classical gravity and quantum field theory. We introduce a modern and concise language to describe th

Thibault Caillet

We give a proof of the "five gradients inequality" of Optimal Transportation Theory for general costs of the form $c(x,y)=h(x-y)$ where $h$ is a $C^1$ strictly convex radially symmetric function.

Yakun Huang, Boyuan Bai, Yuanwei Zhu, Xiuquan Qiao

The provisioning of immersive point cloud video (PCV) streaming on pervasive mobile devices is a cornerstone for enabling immersive communication and interactions in the future 6G metaverse era. However, most streaming techniques are dedicated to efficient PCV compression and codec extending from traditional 3-DoF video services. Some emerging AI-enabled app

Qixun Wang, Yifei Wang, Hong Zhu, Yisen Wang

Deep models often fail to generalize well in test domains when the data distribution differs from that in the training domain. Among numerous approaches to address this Out-of-Distribution (OOD) generalization problem, there has been a growing surge of interest in exploiting Adversarial Training (AT) to improve OOD performance. Recent works have revealed tha

Gunhee Nam, Taesoo Kim, Sanghyup Lee, Thijs Kooi

The accurate localization of inserted medical tubes and parts of human anatomy is a common problem when analyzing chest radiographs and something deep neural networks could potentially automate. However, many foreign objects like tubes and various anatomical structures are small in comparison to the entire chest X-ray, which leads to severely unbalanced data

Zhihao Duan, Kimyeong Lee, Sungjay Lee, Linfeng Li

We systematically study how the integrality of the conformal characters shapes the space of fermionic rational conformal field theories in two dimensions. The integrality suggests that conformal characters on torus with a given choice of spin structures should be invariant under a principal congruence subgroup of $\mathrm{PSL}(2,\mathbb{Z})$. The invariance

Leonardo Trujillo, Paul Banse, Guillaume Beslon

Molecular evolution is often conceptualised as adaptive walks on rugged fitness landscapes, driven by mutations and constrained by incremental fitness selection. It is well known that epistasis shapes the ruggedness of the landscape's surface, outlining their topography (with high-fitness peaks separated by valleys of lower fitness genotypes). However, withi

Ioannis Dadiotis, Arturo Laurenzi, Nikos Tsagarakis

This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. The proposed payload-aware formulation simultaneously plans locomotion, payload manipulation and considers both robot and payload model dynamics while remaining computationally effi

Yakun Huang, Xiuquan Qiao, Haowen Wang, Xiang Su

The metaverse has awakened users' expectations of an immersive interaction that fuses the virtual digital world and the physical world across space and time. However, the metaverse is still in its infancy, typically expanding multi-player applications (e.g., multi-player games) to implement a prototype with the help of 5G/Beyond 5G, Artificial Intelligence,

Jing Xie, Fabio Bonassi, Marcello Farina, Riccardo Scattolini

This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models. The NNARX model learns the dynamics of the plant from input-output data, and during the training the Incremental Input-to-State Stability (${\delta}$ISS) property is

Ji Li, Qingze Lin, Liang Song

We investigate the Dirichlet problem associated to the Schr\"odinger operator $\mathcal L=-\Delta_{\mathbb{H}^n}+V$ on Heisenberg group $\mathbb H^n$: \begin{align*} \begin{cases} \partial_{ss}u(g,s)-\mathcal L u(g,s)=0\,,\quad &{\rm in \,\ } \mathbb{H}^n\times\mathbb{R}^+,\\ u(g,0)=f \,,\quad &{\rm on \,\ } \mathbb{H}^n \end{cases} \end{align*} with $f$ in

Ameya Godbole, Robin Jia

In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances. We propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets. We create 'Likelihood Splits' where examples that are assigned lower likelihood by a pre-trained langu

Itai Lashover, Liav Weiss, Amichai Kafka, Shoshana Levin

The executive branch (the government) is usually not directly elected by the people, but is created by another elected body or person such as the parliament or the president. As a result, its members are not directly accountable to the people, individually or as a group. We propose a scenario where government members are directly elected by the people, and s

Serhii Nazarovets

In Ukraine, Scopus data are used to evaluate academics. Existing shortcomings in the Ukrainian evaluation system allow them to publish in titles that have been delisted from Scopus, and continue to use those papers as credible research output for evaluation. The purpose of this study was to analyse the publishing activity of Ukrainian institutions in Scopus-

Joan Mateu, Maria Giovanna Mora, Luca Rondi, Lucia Scardia

In this paper we consider a general class of anisotropic energies in three dimensions and give a complete characterisation of their minimisers. We show that, depending on the Fourier transform of the interaction potential, the minimiser is either the normalised characteristic function of an ellipsoid or a measure supported on a two-dimensional ellipse. In pa

Arkin Tikku, Isaac H. Kim

We prove a nontrivial circuit-depth lower bound for preparing a low-energy state of a locally interacting quantum many-body system in two dimensions, assuming the circuit is geometrically local. For preparing any state which has an energy density of at most $\epsilon$ with respect to Kitaev's toric code Hamiltonian on a two dimensional lattice $\Lambda$, we

Fu Lele, Zhang Lei, Yang Jinghua, Chen Chuan

Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information hidden in different views. To tackle these issues, we propose a novel Subspace-Contrastive Multi-View Clustering (SCMC) approach. Specifically, SCMC utilizes view-specific auto-en

Yakun Huang, Yuanwei Zhu, Xiuquan Qiao, Xiang Su

Real-time holographic video communications enable immersive experiences for next-generation video services in the future metaverse era. However, high-fidelity holographic videos require high bandwidth and significant computation resources, which exceed the transferring and computing capacity of 5G networks. This article reviews state-of-the-art holographic p

Zhongwei Tang, Ning Zhou

In this paper, we study the higher order Brezis-Nirenberg problem under the Navier boundary condition \be\label{eq} \begin{cases} (-\Delta)^m u=\varepsilon u+u^{p} & \text { in }\, \Omega, \\ u>0 & \text { in }\, \Omega, \\ u=-\Delta u=\cdots=(-\Delta)^{m-1} u=0 & \text { on }\, \partial \Omega, \end{cases} \ee where $\Omega$ is a strictly convex smooth boun

Kubilay Ahmet Küçük, Steve Moyle, Andrew Martin, Alexandru Mereacre

Besides Intel's SGX technology, there are long-running discussions on how trusted computing technologies can be used to cloak malware. Past research showed example methods of malicious activities utilising Flicker, Trusted Platform Module, and recently integrating with enclaves. We observe two ambiguous methodologies of malware development being associated w

Dongmin Park, Yooju Shin, Jihwan Bang, Youngjun Lee

Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection by filtering out the noisy examples. However, because focusing on the purity of examples in a query set leads to overlooking the informativeness of the examples, the best balancing

Yehong Jiang

Despite tremendous progress in natural language processing using deep learning techniques in recent years, sign language production and comprehension has advanced very little. One critical barrier is the lack of largescale datasets available to the public due to the unbearable cost of labeled data generation. Efforts to provide public data for American Sign

Hitoshi Teshima, Naoki Wake, Diego Thomas, Yuta Nakashima

Body language such as conversational gesture is a powerful way to ease communication. Conversational gestures do not only make a speech more lively but also contain semantic meaning that helps to stress important information in the discussion. In the field of robotics, giving conversational agents (humanoid robots or virtual avatars) the ability to properly

Andres Palechor, Annesha Bhoumik, Manuel Günther

Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while rejecting previously unseen unknown samples. Only recently, research started to investigate on algorithms that are able to handle these unknown samples correctly. Some of these appro

Seong Min Kye, Kwanghee Choi, Hyeongmin Byun, Buru Chang

Active learning (AL) aims to select the most useful data samples from an unlabeled data pool and annotate them to expand the labeled dataset under a limited budget. Especially, uncertainty-based methods choose the most uncertain samples, which are known to be effective in improving model performance. However, AL literature often overlooks training dynamics (

Eugene Lim, Harold Soh

In this work, we point out the problem of observed adversaries for deep policies. Specifically, recent work has shown that deep reinforcement learning is susceptible to adversarial attacks where an observed adversary acts under environmental constraints to invoke natural but adversarial observations. This setting is particularly relevant for HRI since HRI-re

Jules Bourcier, Gohar Dashyan, Jocelyn Chanussot, Karteek Alahari

The application of deep neural networks to remote sensing imagery is often constrained by the lack of ground-truth annotations. Adressing this issue requires models that generalize efficiently from limited amounts of labeled data, allowing us to tackle a wider range of Earth observation tasks. Another challenge in this domain is developing algorithms that op

Xiao-Ying Zhang, Ming-Ming Wang

Quantum machine learning has shown advantages in many ways compared to classical machine learning. In machine learning, a difficult problem is how to learn a model with high robustness and strong generalization ability from a limited feature space. Combining multiple models as base learners, ensemble learning (EL) can effectively improve the accuracy, genera

Thomas Mullor, David Vigouroux, Louis Bethune

Quantum walks on binary trees are used in many quantum algorithms to achieve important speedup over classical algorithms. The formulation of this kind of algorithms as quantum circuit presents the advantage of being easily readable, executable on circuit based quantum computers and simulators and optimal on the usage of resources. We propose a strategy to co

Xiao-Yun Wang, Gang Li, Chun-Sheng An, Ju-Jun Xie

We study the radiative decays $Z_c(3900)/Z_c(4020) \to \gamma \chi_{cJ}(\gamma\chi_{cJ}^\prime)$ ($J=0, 1, 2$), with the assumption that the $Z_c(3900)$ and $Z_c(4020)$ couple strongly to $D\bar D^* +c.c$ and $D^*{\bar D}^*$ channel, respectively. By considering the contributions of intermediate charmed mesons triangle loops within an effective Lagrangian ap

Chuan-Zhi Xie, Tie-Qiao Tang, Bo-Tao Zhang, Alexandre Nicolas

Pedestrian egress from training schools in the after-class period (especially in China, as children walk down stairs together with their parents) raises practical concerns related to degraded flow conditions and possible safety hazards, but also represents a challenge to mainstream modeling approaches for several reasons: they involve heterogeneous groups (a

Xiangjue Dong, Jiaying Lu, Jianling Wang, James Caverlee

Question Generation (QG) is a fundamental NLP task for many downstream applications. Recent studies on open-book QG, where supportive answer-context pairs are provided to models, have achieved promising progress. However, generating natural questions under a more practical closed-book setting that lacks these supporting documents still remains a challenge. I

Yuanwei Liu, Nian Liu, Xiwen Yao, Junwei Han

Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective category information from the support to match with the corresponding objects in query. However, they all ignored the category information gap between query and support images. If

Weichen Yu, Hongyuan Yu, Yan Huang, Liang Wang

Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. To this end, we propose a generaliz

Shubhankar Borse, Marvin Klingner, Varun Ravi Kumar, Hong Cai

Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras only to perform segmentation in BEV space, which is fundamentally constrained by the absence of reliable depth information. Latest works leverage both camera and LiDAR modalities, bu

Yan-Li Qin, Binzhou Xia, Sanming Zhou

A pair of graphs $(\Gamma,\Sigma)$ is said to be stable if the full automorphism group of $\Gamma\times\Sigma$ is isomorphic to the product of the full automorphism groups of $\Gamma$ and $\Sigma$ and unstable otherwise, where $\Gamma\times\Sigma$ is the direct product of $\Gamma$ and $\Sigma$. In this paper, we reduce the study of the stability of any pair

Haoxuan Qu, Yanchao Li, Lin Geng Foo, Jason Kuen

Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the a

Qirui Cui, Jinghua Liang, Yingmei Zhu, Xiong Yao

We report the interplay between two different topological phases in condensed matter physics, the magnetic chiral domain wall (DW), and the quantum anomalous Hall (QAH) effect. We show that the chiral DW driven by Dzyaloshinskii-Moriya interaction (DMI) can divide the uniform domain into several zones where the neighboring zone possesses opposite quantized H

Kevin Yang, Yuandong Tian, Nanyun Peng, Dan Klein

We consider the problem of automatically generating longer stories of over two thousand words. Compared to prior work on shorter stories, long-range plot coherence and relevance are more central challenges here. We propose the Recursive Reprompting and Revision framework (Re3) to address these challenges by (a) prompting a general-purpose language model to c

Tommi Kärkkäinen, Jan Hänninen

An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Computational experiments confirm that an autoencoder of this form, with only a shallow network to encapsulate the nonlinear behavior, is able to identify an intrinsi

Zhe Liu, Xuedong Zhang, Fuchun Peng

Recent research has shown that language models have a tendency to memorize rare or unique sequences in the training corpora which can thus leak sensitive attributes of user data. We employ a teacher-student framework and propose a novel approach called alternating teaching to mitigate unintended memorization in sequential modeling. In our method, multiple te

Peng Ye, Zhifeng Jiang, Wei Wang, Bo Li

Federated learning (FL) has increasingly been deployed, in its vertical form, among organizations to facilitate secure collaborative training over siloed data. In vertical FL (VFL), participants hold disjoint features of the same set of sample instances. Among them, only one has labels. This participant, known as the active party, initiates the training and

Sachin Gautam, Matthew Rupert, Curtis Wendlandt

Let $U_\hbar\mathfrak{g}$ denote the Drinfeld-Jimbo quantum group associated to a complex semisimple Lie algebra $\mathfrak{g}$. We apply a modification of the $R$-matrix construction for quantum groups to the evaluation of the universal $R$-matrix of $U_\hbar\mathfrak{g}$ on the tensor square of any of its finite-dimensional representations. This produces a

Zheng Sun, Hui Tian, P. F. Chen, Shuo Yao

Solar extreme-ultraviolet (EUV) waves generally refer to large-scale disturbances propagating outward from sites of solar eruptions in EUV imaging observations. Using the recent observations from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), we report a quasi-periodic wave train propagating outward at an average speed

Naoki Murabayashi, Hayato Yoshida

In \cite{d4}, we gave a method to construct a continued fraction of the function $F(x):=e^{x}E_{1}(x)$. More precisely we define $F_{1}(x)$ as the reciprocal of $F(x)$ and we inductively define $F_{m}(x)$ as the reciprocal of ``$F_{m-1}(x)$ minus the main term of $F_{m-1}(x)$ at infinity''. We calculated the main term of $F_{m}(x)$ at infinity by using \cite

Shuang-Yen Lee

Let $\mathfrak{X}$ and $\mathfrak{X}'$ be two smooth projective varieties over the ring of integers of a $p$-adic field $\textbf{K}$ with generic fibers being $X$ and $X'$. We introduce a (family of) good $s$-norms on the pluricanonical spaces of $X$ and $X'$, which are called global Igusa zeta functions in $s$, and show that if the $r$-canonical maps send $

Edoardo Cetin, Oya Celiktutan

We introduce a new framework that performs decision-making in reinforcement learning (RL) as an iterative reasoning process. We model agent behavior as the steady-state distribution of a parameterized reasoning Markov chain (RMC), optimized with a new tractable estimate of the policy gradient. We perform action selection by simulating the RMC for enough reas

Jing Yang, Juan Wang, Ranfeng Wu, Yiming Chen

The detection of biomolecules at the single molecular level have important applications in the fields of biosensing and biomedical diagnosis. Solid state nanopore (SS-nanopore) is an effective tool to perform the single molecular detection, due to its unique properties of label-free and less sample consumption. The current SS-nanopore translocations of small

Siying Wu, Binbin Yin, Xiaoxue Ran, Qi-Fang Li

By developing a cluster sampling of stochastic series expansion quantum Monte Carlo method, we investigate a spin-$1/2$ model on a bilayer square lattice with intra-layer ferromagnetic (FM) Ising coupling and inter-layer antiferromagnetic Heisenberg interaction. The continuous quantum phase transition which occurs at $g_c=3.045(2)$ between the FM Ising phase

Florian Kirchschlager, Franziska D. Schmidt, M. J. Barlow, Ilse De Looze

Dust grains form in the clumpy ejecta of core-collapse supernovae where they are subject to the reverse shock, which is able to disrupt the clumps and destroy the grains. Important dust destruction processes include thermal and kinetic sputtering as well as fragmentation and grain vaporization. In the present study, we focus on the effect of magnetic fields

Jun Nian, Xiaoquan Yu, Jinwu Ye

Fluid turbulence is a far-from-equilibrium phenomenon and remains one of the most challenging problems in physics. Two-dimensional, fully developed turbulence may possess the largest possible symmetry, the conformal symmetry. We focus on the steady-state solution of two-dimensional bounded turbulent flow and propose a $c=0$ boundary logarithmic conformal fie

Tianyu Zhang, Xiangkun Liu, Chengliang Wei, Guoliang Li

The galaxy intrinsic alignment (IA) is a dominant source of systematics in weak lensing (WL) studies. In this paper, by employing large simulations with semi-analytical galaxy formation, we investigate the IA effects on WL peak statistics. Different simulated source galaxy samples of different redshift distributions are constructed, where both WL shear and I

Bartłomiej Dyda, Michał Kijaczko

We investigate the weighted fractional order Hardy inequality $$ \int_{\Omega}\int_{\Omega}\frac{|f(x)-f(y)|^{p}}{|x-y|^{d+sp}}\text{dist}(x,\partial\Omega)^{-\alpha}\text{dist}(y,\partial\Omega)^{-\beta}\,dy\,dx\geq C\int_{\Omega}\frac{|f(x)|^{p}}{\text{dist}(x,\partial\Omega)^{sp+\alpha+\beta}}\,dx, $$ for $\Omega=\mathbb{R}^{d-1}\times(0,\infty)$, $\Omega

Yuchen Zeng, Kristjan Greenewald, Kangwook Lee, Justin Solomon

Traditional machine learning models focus on achieving good performance on the overall training distribution, but they often underperform on minority groups. Existing methods can improve the worst-group performance, but they can have several limitations: (i) they require group annotations, which are often expensive and sometimes infeasible to obtain, and/or

Shoaib Azam, Farzeen Munir, Ville Kyrki, Moongu Jeon

Learning contextual and spatial environmental representations enhances autonomous vehicle's hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such a

Igor G. Vladimirov, Ian R. Petersen

This paper considers a class of open quantum systems with an algebraic structure of dynamic variables, including the Pauli matrices for finite-level systems as a particular case. The Hamiltonian and the operators of coupling of the system to the external bosonic fields depend linearly on the system variables. The fields are represented by quantum Wiener proc

Changde Du, Kaicheng Fu, Jinpeng Li, Huiguang He

Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligent machines. Most existing methods are difficult to generalize to novel categories that have no corresponding neural data for training. The two main reasons are 1) the under-exploit

Keigo Takeuchi

This paper addresses the reconstruction of sparse signals from spatially coupled, linear, and noisy measurements. A unified framework of rigorous state evolution is established for developing long-memory message-passing (LM-MP) in spatially coupled systems. LM-MP utilizes all previous messages to compute the current message while conventional MP only uses th

E. Sierda, X. Huang, D. I. Badrtdinov, B. Kiraly

Bottom-up quantum simulators have been developed to quantify the role of various interactions, dimensionality, and structure in creating electronic states of matter. Here, we demonstrated a solid-state quantum simulator emulating molecular orbitals, based solely on positioning individual cesium atoms on an indium antimonide surface. Using scanning tunneling

Simon Ji, Shouzhuo Yang, Wilber Dominguez, Cacey Bester

This article demonstrates a new approach to finding ideal bowling targeting strategies through computer simulation. To model bowling ball behaviour, a system of five coupled differential equations is derived using Euler equations for rigid body rotations. We used a computer program to demonstrate the phases of ball motion and output a plot that displays the

Xiaolong Hans Han, Yuxin He, Han Hong

In this paper, we first construct a sequence of hyperbolic surfaces with connected geodesic boundary such that the first normalized Steklov eigenvalue $\tilde{\sigma}_1$ tends to infinity. We then prove that as $g\rightarrow \infty$, a generic $\Sigma\in \mathcal{M}_{g,n}(L_g)$ satisfies $\tilde{\sigma}_1(\Sigma)>C\cdot \|L_g\|_1$ where $C$ is a positive uni

Marat V. Burnashev

We consider the transmission of nonexponentially many messages through a binary symmetric channel with noiseless feedback. We obtain an upper bound for the best decoding error exponent. Combined with the corresponding known lower bound, this allows to find the reliability function for this channel at zero rate.

Yuxia Guo, Yichen Hu, Shaolong Peng

In this paper, we consider the following elliptic system \begin{equation*} \begin{cases} -\Delta u = |v|^{p-1}v +\epsilon(\alpha u + \beta_1 v), &\hbox{ in }\Omega, \\-\Delta v = |u|^{q-1}u+\epsilon(\beta_2 u +\alpha v), &\hbox{ in }\Omega, \\u=v=0,&\hbox{ on }\partial\Omega, \end{cases} \end{equation*} where $\Omega$ is a smooth bounded domain in $\mathbb{R

Sara M. Kandil, Dia'aaldin J. Bisharat, Daniel F. Sievenpiper

Metasurfaces provide unique capability in guiding surface waves and controlling their polarization and dispersion properties. One way to do that is by analyzing their equifrequency contours. Equifrequency contours are the 2D projection of the 3D dispersion diagram. Since they are a k-space map representation of the surface, many of the wave properties can be

Sharat Agarwal, Saket Anand, Chetan Arora

In Active Domain Adaptation (ADA), one uses Active Learning (AL) to select a subset of images from the target domain, which are then annotated and used for supervised domain adaptation (DA). Given the large performance gap between supervised and unsupervised DA techniques, ADA allows for an excellent trade-off between annotation cost and performance. Prior a

Ryo Kamoi, Tanya Goyal, Greg Durrett

Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (QA)-based factuality metrics. These have been shown to work well at predicting summary-level factuality and have potential to localize error