October 2022 arXiv papers — page 90

Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. Traditional softmax-based confidence scores are susceptible to the overconfidence issue. In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples. F

Fatemeh Kavehmadavani, Van-Dinh Nguyen, Thang X. Vu, Symeon Chatzinotas

Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, \textit{namely} enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualize

Jihao Huang, Zhitao Liu, Xuemin Chi, Feng Hong

This paper proposed a novel method for autonomous parking. Autonomous parking has received a lot of attention because of its convenience, but due to the complex environment and the non-holonomic constraints of vehicle, it is difficult to get a collision-free and feasible path in a short time. To solve this problem, this paper introduced a novel algorithm cal

B. Belbaki, A. Bounames

We study particle creation phenomenon by the Coulomb potential of an external electric field in the presence of a gravitational field of a static cosmic string. For that, the generalized Klein-Gordon and Dirac equations are solved, and by using the Bogoliubov transformation we calculate the probability and the number density of created particles. It is shown

Brandon Smart, Gustavo Carneiro

Many state-of-the-art noisy-label learning methods rely on learning mechanisms that estimate the samples' clean labels during training and discard their original noisy labels. However, this approach prevents the learning of the relationship between images, noisy labels and clean labels, which has been shown to be useful when dealing with instance-dependent l

Theraa Tork

The self-normalised $\psi(2S)$ yields as a function of charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV and p--Pb collisions at $\sqrt{s_{\mathrm{NN}}}$= 8.16 TeV, using the ALICE detector at the CERN LHC, are reported in these proceedings. This measurement represents one of the main tools to understand the role of multiparton interactio

Charles Fromonteil, Dolev Bluvstein, Hannes Pichler

We introduce a novel family of protocols for entangling gates for neutral atom qubits based on the Rydberg blockade mechanism. These protocols realize controlled-phase gates through a series of global laser pulses that are on resonance with the Rydberg excitation frequency. We analyze these protocols with respect to their robustness against calibration error

Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a significant accuracy drop compared to the full fine-tuning. In this paper, we propose a new parameter-efficient fine-tuning method termed as SSF, representing that re

Chao Wang, Shi Peng Yin, Zhen Ming Xu, Bin Wu

The Ruppeiner geometry and the fluctuation for a four-dimensional charged AdS black hole in the framework of the extensive thermodynamics have been investigated. By fixing the AdS radius of Vissers construction, the extensive thermodynamics of the RN-AdS black hole is established, and the central charge plays the role of particle number. With the thermodynam

Yu Zhao, Xiangrui Cai, Yike Wu, Haiwei Zhang

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making

Shubhchintak, P. Descouvemont

Background: Nuclear transfer reactions are a useful tool to study the structure of a nucleus. For reactions involving weekly bound nuclei, breakup effects can play significant role and theoretical calculations can be computational expensive in such cases. Purpose: To utilize the Lagrange-mesh and R-matrix methods for nuclear transfer reactions. Methods: We u

Jong Hak Moon, Wonjae Kim, Edward Choi

Recently, dense contrastive learning has shown superior performance on dense prediction tasks compared to instance-level contrastive learning. Despite its supremacy, the properties of dense contrastive representations have not yet been carefully studied. Therefore, we analyze the theoretical ideas of dense contrastive learning using a standard CNN and straig

David Yu, Andy Xiao

Legacy AD/ADAS development from OEMs centers around developing functions on ECUs using services provided by AUTOSAR Classic Platform (CP) to meet automotive-grade and mass-production requirements. The AUTOSAR CP couples hardware and software components statically and encounters challenges to provide sufficient capacities for the processing of high-level inte

Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Qu

Karsten Ridder, Alexander Preuhs, Axel Mertins, Clemens Joerger

Research question: How can we establish an AI support for reading of chest X-rays in clinical routine and which benefits emerge for the clinicians and radiologists. Can it perform 24/7 support for practicing clinicians? 2. Findings: We installed an AI solution for Chest X-ray in a given structure (MVZ Uhlenbrock & Partner, Germany). We could demonstrate the

Alexander Gelfgat

A sequence of three steady - oscillatory transitions of buoyancy convection of air in a laterally heated cube with perfectly thermally insulated horizontal and spanwise boundaries is studied. The problem is treated by Newton and Arnoldi methods based on Krylov subspace iteration. The finite volume grid is gradually refined from 100^3 to 256^3 finite volumes.

Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall, Gilles Hénaff

Deep neural networks often fail to generalize outside of their training distribution, in particular when only a single data domain is available during training. While test-time adaptation has yielded encouraging results in this setting, we argue that, to reach further improvements, these approaches should be combined with training procedure modifications aim

Guofeng Mei, Fabio Poiesi, Cristiano Saltori, Jian Zhang

Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from mat

Lucas Etourneau, Thomas Burger

In their recent article, Madej et al. 1 proposed an original way to solve the recurrent issue of controlling for the false discovery rate (FDR) in peptide-spectrum-match (PSM) validation. Briefly, they proposed to derive a single precise distribution of decoy matches termed the Common Decoy Distribution (CDD) and to use it to control for FDR during a target-

Rukmini Dey, Kohinoor Ghosh

In this article we show that a Berezin-type quantization can be achieved on a compact even dimensional manifold $M^{2d}$ by removing a skeleton $M_0$ of lower dimension such that what remains is diffeomorphic to $R^{2d}$ (cell decomposition) which we identify with $C^d$ and embed in $ CP^d$. A local Poisson structure and Berezin-type quantization are induced

Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li

Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task. However, a performance gap between the training set and the test set has often been noticed. To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.

Sheng Shen, Huanjing Yue, Jingyu Yang, Kun Li

Nowadays, online screen sharing and remote cooperation are becoming ubiquitous. However, the screen content may be downsampled and compressed during transmission, while it may be displayed on large screens or the users would zoom in for detail observation at the receiver side. Therefore, developing a strong and effective screen content image (SCI) super-reso

Ali Behrouz, Farnoosh Hashemi

Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usually study networks with a single type of

Alfredo Rates, Joris Vrehen, Bert Mulder, Wilbert L. IJzerman

We study the secrecy of an optical communication system with two scattering layers, to hide both the sender and receiver, by measuring the correlation of the intermediate speckle generated between the two layers. The binary message is modulated as spatially shaped wavefronts, and the high number of transmission modes of the scattering layers allows for many

Anwesha Sengupta, Shashankaditya Upadhyay, Indranil Mukherjee, Prasanta K. Panigrahi

Market competition has a role which is directly or indirectly associated with influential effects of individual sectors on other sectors of the economy. The present work studies the relative position of a product in the market through the identification of influential spreaders and its corresponding effect on the other sectors of the market using complex net

Jingwei Yi, Fangzhao Wu, Chuhan Wu, Xiaolong Huang

Query-aware webpage snippet extraction is widely used in search engines to help users better understand the content of the returned webpages before clicking. Although important, it is very rarely studied. In this paper, we propose an effective query-aware webpage snippet extraction method named DeepQSE, aiming to select a few sentences which can best summari

Marc Martí-Sabaté, Bahram Djafari-Rouhani, Dani Torrent

In this work, we study the localization of flexural waves in highly symmetric clusters of scatterers. It is shown that when the scatterers are placed regularly in the perimeter of a circumference the quality factor of the resonances strongly increases with the number of scatterers in the cluster. It is also found that in the continuous limit, that is to say,

Hui Jiang, Ziyao Lu, Fandong Meng, Chulun Zhou

k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important research direction of NMT in recent years. Its main idea is to retrieve useful key-value pairs from an additional datastore to modify translations without updating the NMT model. However, the underlying retrieved noisy pairs will dramatically deteriorate the model performance. In this paper

Henri Mermoz Kouye, Gildas Mazo

Sobol' sensitivity index estimators for stochastic models are functions of nested Monte Carlo estimators, which are estimators built from two nested Monte Carlo loops. The outer loop explores the input space and, for each of the explorations, the inner loop repeats model runs to estimate conditional expectations. Although the optimal allocation between explo

Ruihan Zhang, Wei Wei, Xian-Ling Mao, Rui Fang

Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations. A commonly-adapted solution is to follow a identify-then-classify manner, which first identifies the triggers and then converts the classification task via a few-shot learning parad

Eugene Bilokopytov, Vladimir G. Troitsky

The paper investigates uniformly closed subspaces, sublattices, and ideals of finite codimension in Archimedean vector lattices. It is shown that every uniformly closed subspace (or sublattice) of finite codimension may be written as an intersection of uniformly closed subspaces (respectively, sublattices) of codimension one. Every uniformly closed sublattic

Yingcan Wei, Matthias Langer, Fan Yu, Minseok Lee

Recommendation systems are of crucial importance for a variety of modern apps and web services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak prediction accuracy, modern recommendation models combine deep learning with terabyte-scale embedding tables to obtain a fine-grained representation of the underlying data. Traditional i

Joey Wang, Yingcan Wei, Minseok Lee, Matthias Langer

In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source, GPU-accelerated integration framework for click-through rate estimation. It optimizes both training and inference, whilst enabling model training at scale with model-parallel embeddings and data-parallel neural networks. In particular, Merlin HugeCTR combines a high-performance GPU

Shubo Lv, Yihui Fu, Yukai Jv, Lei Xie

Recently, multi-channel speech enhancement has drawn much interest due to the use of spatial information to distinguish target speech from interfering signal. To make full use of spatial information and neural network based masking estimation, we propose a multi-channel denoising neural network -- Spatial DCCRN. Firstly, we extend S-DCCRN to multi-channel sc

Xiaofei Wen, Wei Wei, Xian-Ling Mao

Recently, topic-grounded dialogue system has attracted significant attention due to its effectiveness in predicting the next topic to yield better responses via the historical context and given topic sequence. However, almost all existing topic prediction solutions focus on only the current conversation and corresponding topic sequence to predict the next co

Shogo Nakakita

We propose an online parametric estimation method of stochastic differential equations with discrete observations and misspecified modelling based on online gradient descent. Our study provides uniform upper bounds for the risks of the estimators over a family of stochastic differential equations. The derivation of the bounds involves three underlying theore

Janosch Kunczik, Kerstin Hubbermann, Lucas Mösch, Andreas Follmann

Breathing is one of the most important body functions because it provides it with oxygen, which is vital for energy production. In addition, the removal of carbon dioxide actively regulates the acid-base level, which is essential for the physiological function of the body. Due to its close connection with many other body functions, respiration can also be us

Qi-Min Feng, Zhong-Wen Feng, Xia Zhou, Qing-Quan Jiang

In this work we investigate the stochastic gravitational wave background generated during the f\/irst-order cosmological QCD phase transition of the early universe in the framework of the Barrow entropy. We f\/irst derived the Barrow corrections to the expression of stochastic gravitational wave background spectrum in presence of trace anomaly. Then, by taki

Connor Lawless, Oktay Gunluk

Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features. Traditional clustering algorithms provide limited insight into the groups they find as their main focus is accuracy and not the interpretability of the group assignments. This has spurred a recent line of work on explainable machine

Jungtaek Oh, Chongjin Park, Sungsu Kim

Let $V(k)$ denote the waiting time, the number of trials needed to get a consecutive $k$ ones. We propose recurrence algorithms for the probability distribution function (pdf) and the probability generating function (pgf) of $V(k)$ in sequences of independent and Markov dependent Bernoulli trials using generalized Fibonacci sequences of order $k$. Maximum li

Jules Dichamp, Geraldine Cellière, Ahmed Ghallab, Reham Hassan

In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical homogeneous compartment pharmaco-dynamic (PD) models and a multiscale digital twin model resolving liver microarchitecture at cellular resolution. The mode

Karl Winsor

We give a new proof of the classification of ${\rm GL}^+(2,\mathbb{R})$-orbit closures that are saturated for the absolute period foliation of the Hodge bundle. As a consequence, we obtain a short proof of the classification of closures of leaves of the absolute period foliation of the Hodge bundle. Our approach is based on a method for classifying ${\rm GL}

Zihao Chen, Wenyong Wang, Sai Zou

In the absence of artificial labels, the independent and dependent features in the data are cluttered. How to construct the inductive biases of the model to flexibly divide and effectively contain features with different complexity is the main focal point of unsupervised disentangled representation learning. This paper proposes a new iterative decomposition

Zikai Wei, Xinge Zhu, Bo Dai, Dahua Lin

To accurately predict trajectories in multi-agent settings, e.g. team games, it is important to effectively model the interactions among agents. Whereas a number of methods have been developed for this purpose, existing methods implicitly model these interactions as part of the deep net architecture. However, in the real world, interactions often exist at mu

Yizhen Zheng, Yu Zheng, Xiaofei Zhou, Chen Gong

Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to alleviate the reliance on labelling information for graph representation learning. The core of GCL is to maximise the mutual information between the representation of a node and its contextual representation (i.e., the corresponding instance with similar semantic infor

Koki Inaba, Yoshihiro Ueda, Satoshi Yamada, Shoji Ogawa

We present the result of a broadband (0.5-70 keV) X-ray spectral analysis of the late-merger galaxy Mrk 739, which contains a dual active galactic nucleus (AGN), Mrk 739E and Mrk 739W, with a separation of $\sim$3.4 kpc. The spectra obtained with NuSTAR, Chandra, XMM-Newton and Swift/BAT are simultaneously analyzed by separating the contributions from the tw

E. S. Andriyakhina, S. Apostoloff, I. S. Burmistrov

In this paper we study repulsion of a N\'eel-type skyrmion in a chiral ferromagnetic film from a superconducting Pearl vortex due to the stray fields. Taking into account an effect of the vortex magnetic field on the skyrmion non-perturbatively, we find that the repulsion between them is suppressed with increase of the dimensionless strength of the vortex ma

Emma Yu Jin

We present (bi-)symmetric generating functions for the joint distributions of Euler-Stirling statistics on permutations, including the number of descents ($\mathsf{des}$), inverse descents ($\mathsf{ides}$), the number of left-to-right maxima ($\mathsf{lmax}$), the number of right-to-left maxima ($\mathsf{rmax}$) and the number of left-to-right minima ($\mat

Yuying Hao, Yi Liu, Yizhou Chen, Lin Han

In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the full potential of such models, large numbers of high-quality annotated images are necessary for model training. Currently, many widely used open-source image segmentation software rel

Benny Avelin, Vesa Julin

In this paper we further develop the ideas from Geometric Function Theory initially introduced in [arXiv:2206.13206], to derive capacity estimate in metastability for arbitrary configurations. The novelty of this paper is twofold. First, the graph theoretical connection enables us to exactly compute the pre-factor in the capacity. Second, we complete the met

Zhizhong Huang, Junping Zhang, Hongming Shan

To minimize the impact of age variation on face recognition, age-invariant face recognition (AIFR) extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features while face age synthesis (FAS) eliminates age variation by converting the faces in different age groups to the same group. However, AIFR l

Fatima Ezzeddine, Luca Luceri, Omran Ayoub, Ihab Sbeity

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their

Wahab Ibrahim, Ola Hall

The present study uses domain experts to estimate welfare levels and indicators from high-resolution satellite imagery. We use the wealth quintiles from the 2015 Tanzania DHS dataset as ground truth data. We analyse the performance of the visual estimation of relative wealth at the cluster level and compare these with wealth rankings from the DHS survey of 2

Ranran Huang, Yu Wang, Huazhong Yang

Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based works, our work does not explicitly define or localize the part regions of interest; instead, we leverage the complementary properties of different stages of the network, and build a

Yi-Yu Lin, Jie-Chen Jin

Starting from an interesting coincidence between the bit threads and SS (surface/state) correspondence, both of which are closely related to the holographic RT formula, we introduce a property of bit threads that has not been explicitly proposed before, which can be referred to as thread/state correspondence (see~\cite{Lin:2022agc} for a brief pre-release ve

Andrew Lowy, Devansh Gupta, Meisam Razaviyayn

Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learn

Anuj Pal, Tianyi He, Wenpeng Wei

This paper presents a sample-efficient data-driven method to design model predictive control (MPC) for cable-actuated soft robotics using Bayesian optimization. Instead of modeling the complex dynamics of the soft robots, the proposed approach uses Bayesian optimization to search the best-guessed low-dimensional prediction model and its associated controller

Mathieu Ravaut, Shafiq Joty, Nancy F. Chen

Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap between the top beam search output and the oracle beam. Recently, re-ranking methods have been proposed, to learn to s

V. V. Flambaum, A. J. Mansour, I. B. Samsonov, C. Weitenberg

Fundamental constants such as masses and coupling constants of elementary particles can have small temporal and spatial variations in the scalar field dark matter model. These variations entail time oscillations of other constants, such as the Bohr and nuclear magnetons, Bohr radius and the hyperfine structure constant. In the presence of an external magneti

Kazushi Aoyama

We investigate spin and thermal transport near the N\'{e}el transition temperature $T_N$ in three dimensions, by numerically analyzing the classical antiferromagnetic $XXZ$ model on the cubic lattice, where in the model, the anisotropy of the exchange interaction $\Delta=J_z/J_x$ plays a role to control the universality class of the transition. It is found b

Mohammad Ashraf, Mohammad Afajal Ansari, Md Shamim Akhter

Let $\mathfrak{A}$ be a unital ring with a nontrivial idempotent. In this paper, it is shown that under certain conditions every multiplicative generalized Jordan $n$-derivation $\Delta:\mathfrak{A}\rightarrow\mathfrak{A}$ is additive. More precisely, it is proved that $\Delta$ is of the form $\Delta(t)=\mu t+\delta(t),$ where $\mu\in\mathcal{Z}(\mathfrak{A}

Zhihai Wang, Hongwei Yu, Jin Wang

We investigate a quantum battery system under both external driving and dissipation. The system consists of a coupled two-level charger and battery immersed in nonequilibrium fermionic reservoirs. By considering the changes in the energy spectrum induced by external driving and charger-battery coupling in a non-perturbative manner, we go beyond the secular a

Amit Kumar

In this paper, we describe the Grothendieck groups $K_1(V)$ and $K(V)$ of an absolute matrix order unit space $V$ for unitary and partial unitary elements respectively. For this purpose, we study some basic properties of unitary and partial unitary elements, and define their path homotopy equivalence. The construction of $K(V)$ follows in a almost similar ma

Anthony Meng Huat Tiong, Junnan Li, Boyang Li, Silvio Savarese

Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular framework for zero-shot VQA. In contrast to most existing works, which require substantial adaptation of pretrained language models (PLMs) for the vision modality, PNP-VQA require

Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan

Implicit Neural Representations (INRs) encoding continuous multi-media data via multi-layer perceptrons has shown undebatable promise in various computer vision tasks. Despite many successful applications, editing and processing an INR remains intractable as signals are represented by latent parameters of a neural network. Existing works manipulate such cont

Yunxia Chen, Naichung Conan Leung

This is a review paper about ADE bundles over surfaces. Based on the deep connections between the geometry of surfaces and ADE Lie theory, we construct the corresponding ADE bundles over surfaces and study some related problems.

Hwanjin Kim, Junil Choi, David J. Love

Accurate channel knowledge is critical in massive multiple-input multiple-output (MIMO), which motivates the use of channel prediction. Machine learning techniques for channel prediction hold much promise, but current schemes are limited in their ability to adapt to changes in the environment because they require large training overheads. To accurately predi

Junjie Cao, Xinglong Jia, Lei Meng, Yuanfang Yue

With the rapid progress of dark matter direct detection experiments, the attractiveness of the popular bino-dominated dark matter in economical supersymmetric theories is fading. As an alternative, the singlino-dominated dark matter in general Next-to-Minimal Supersymmetric Standard Model (NMSSM) is paying due attention. This scenario has the following disti

JunKyu Jang, Eugene Hwang, Sung-Hyuk Park

Identifying defects in the images of industrial products has been an important task to enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly detection models were developed using pre-trained networks to learn nominal representations. To employ the relative positional information of each pixel, we present \textit{\textbf{

Adrian Langer

We prove Bogomolov's inequality on a normal projective variety in positive characteristic and we use it to show some new restriction theorems and a new boundedness result. Then we redefine Higgs sheaves on normal varieties and we prove restriction theorems and Bogomolov type inequalities for semistable logarithmic Higgs sheaves on some normal varieties in an

Adrian Langer

We study intersection theory and Chern classes of reflexive sheaves on normal varieties. In particular, we define generalization of Mumford's intersection theory on normal surfaces to higher dimensions. We also define and study the second Chern class for reflexive sheaves on normal varieties. We use these results to prove some Bogomolov type inequalities on

Meng Qin, Dit-Yan Yeung

Dynamic graphs serve as a generic abstraction and description of the evolutionary behaviors of various complex systems (e.g., social networks and communication networks). Temporal link prediction (TLP) is a classic yet challenging inference task on dynamic graphs, which predicts possible future linkage based on historical topology. The predicted future topol

V. V. Pipin, A. G. Kosovichev, V. E. Tomin

We model the physical parameters of Solar Cycles 23 and 24 using a nonlinear dynamical mean-field dynamo model that includes the formation and evolution of bipolar magnetic regions (BMR). The Parker-type dynamo model consists of a complete MHD system in the mean-field formulation: the 3D magnetic induction equation, and 2D momentum and energy equations in th

Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu

The ability of reasoning over evidence has received increasing attention in question answering (QA). Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence. However, existin

Noriaki Kitazawa

In the realistic model of cosmic inflation the inflaton potential should be flat and stable under quantum corrections. It is natural to imagine that there is some symmetry behind and an idea of the inflaton as a Nambu-Goldstone boson of spontaneous breaking of some symmetry has been examined. We give a general formulation of this idea using the non-linear re

Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong

Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and desired functions has been a long-standing challenge in the field of bioengineering. Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models, both of which

Taoufik Hmidi, Liutang Xue, Zhilong Xue

In this paper we address the existence of time periodic solutions for the generalized inviscid SQG equation in the unit disc with homogeneous Dirichlet boundary condition when $\alpha\in (0,1)$. We show the existence of a countable family of bifurcating curves from the radial patches. In contrast with the preceding studies in active scalar equations, the Gre

Tongtong Wu, Guitao Wang, Jinming Zhao, Zhaoran Liu

Relation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is the spoken language, such as interviews and dialogues. However, the error propagation introduced in automatic speech recognition (ASR) has been ignored in relation extraction, and

Xudong Han, Aili Shen, Trevor Cohn, Timothy Baldwin

Mitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, considering very narrow data conditions. For instance, the effect of target class imbalance and stereotyping is under-studied. To address this gap, we examine the performance of various deb

Abhishek, Nifeeya Singh, Pooja Siwach, P. Arumugam

We present a quantum algorithm to obtain the response of the atomic nucleus to a small external electromagnetic perturbation. The Hamiltonian of the system is presented by a harmonic oscillator, and the linear combination of unitaries (LCU) based method is utilized to simulate the Hamiltonian on the quantum computer. The output of the Hamiltonian simulation

Tomoo Yokoyama

Gradient vector fields are fundamental objects from both theoretical and practical perspectives, since various phenomena can be modeled within this framework. The ``moduli space'' of such vector fields provides the foundation for describing these phenomena. However, little is known about the topology of the space of gradient vector fields. For instance, it r

Soumya Sengupta, Sujan Sengupta

Hot Jupiters are the most studied and easily detectable exoplanets for transit observations.However, the correlation between the atmospheric flow and the emission spectra of such planets is still not understood. Due to huge day-night temperature contrast in hot Jupiter, the thermal redistribution through atmospheric circulation has a significant impact on th

Emanuele Sobacchi, Yuri Lyubarsky, Andrei M. Beloborodov, Lorenzo Sironi

Nonlinear effects are crucial for the propagation of Fast Radio Bursts (FRBs) near the source. We study the filamentation of FRBs in the relativistic winds of magnetars, which are commonly invoked as the most natural FRB progenitors. As a result of filamentation, the particle number density and the radiation intensity develop strong gradients along the direc

Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Zhengyi Ma

Personalized chatbots focus on endowing the chatbots with a consistent personality to behave like real users and further act as personal assistants. Previous studies have explored generating implicit user profiles from the user's dialogue history for building personalized chatbots. However, these studies only use the response generation loss to train the ent

Sreedev Manikoth

The Bj\"orling problem and its solution is a well known result for minimal surfaces in Euclidean three-space. The minimal surface equation is similar to the Born-Infeld equation, which is naturally studied in physics. In this semi-expository article, we ask the question of the Bj\"orling problem for Born-Infeld solitons. This begins with the case of locally

Sanjoy Kumar Pal, Soumen Sarkar, Surajit Chakrabarti

In this work we have determined the focal length of a concave lens by photographing the virtual image of an object by a smartphone camera. We have similarly determined the focal length of a convex lens by forming a virtual image of an object keeping it within the focal distance from the lens. When a photograph is taken by a smartphone, the transverse width o

Sanghwan Bae, Donghyun Kwak, Soyoung Kang, Min Young Lee

Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations. However, previous literature does not deal with cases where the memorized information is outdated, which may cause confusion in later conversations. To address this issue, we present a novel task and a corresponding dataset o

Wenlu Wang, Ye Wang, Honggang Zhao, Simone Sciabola

In the scope of drug discovery, the molecular design aims to identify novel compounds from the chemical space where the potential drug-like molecules are estimated to be in the order of 10^60 - 10^100. Since this search task is computationally intractable due to the unbounded search space, deep learning draws a lot of attention as a new way of generating uns

Longhui Yu, Yifan Zhang, Lanqing Hong, Fei Chen

Object detection for autonomous vehicles has received increasing attention in recent years, where labeled data are often expensive while unlabeled data can be collected readily, calling for research on semi-supervised learning for this area. Existing semi-supervised object detection (SSOD) methods usually assume that the labeled and unlabeled data come from

Ryo Ikehata

In this paper we consider the local energy decay result for wave equations with a short-range potential. It is important to note that one never uses a finite speed of propagation property unlike the historical previous papers. The essential parts of analysis are in getting L^2-bound of the solution itself, and deriving the weighted energy estimates. In this

Zhuan Ge, Teng Man, Herbert E. Huppert, Kimberly Hill

In this communication, we present a new lengthscale-based rheology for dense sheared particle suspensions as they transition from inertial- to viscous-dominated. We derive a lengthscale ratio using straightforward physics-based considerations for a particle subjected to pressure and drag forces. In doing so, we demonstrate that an appropriately chosen length

N. S. Kavya, V. Venkatesha, Sanjay Mandal, P. K. Sahoo

The observational evidence regarding the present cosmological aspects tells us about the presence of very little anisotropy in the universe on a large scale. Here, in this paper, we attempt to study locally rotationally symmetric (LRS) homogeneous Bianchi-I spacetime with the isotropic matter distribution. This is done within the framework of $f(\mathcal{R},

Dong-Hee Paek, Kevin Tirta Wijaya, Seung-Hyun Kong

Lane detection is one of the most important functions for autonomous driving. In recent years, deep learning-based lane detection networks with RGB camera images have shown promising performance. However, camera-based methods are inherently vulnerable to adverse lighting conditions such as poor or dazzling lighting. Unlike camera, LiDAR sensor is robust to t

Sudipto Chowdhury, Divay Garg, Ravina Shokeen

We revisit the L2 norm error estimate for the C0 interior penalty analysis of fourth order Dirichlet boundary control problem. The L2 norm estimate for the optimal control is derived under reduced regularity assumption and this analysis can be carried out on any convex polygonal domains. Residual based a-posteriori error bounds are derived for optimal contro

Mikihiro Fujii

In this paper, we consider the compressible Navier--Stokes system around the constant equilibrium states and prove the unique existence of a global solution for arbitrarily large initial data in the scaling critical Besov space provided that the Mach number is sufficiently small and the incompressible part of the initial velocity generates the global solutio

Zhiwei He, Xing Wang, Zhaopeng Tu, Shuming Shi

This paper describes Tencent AI Lab - Shanghai Jiao Tong University (TAL-SJTU) Low-Resource Translation systems for the WMT22 shared task. We participate in the general translation task on English$\Leftrightarrow$Livonian. Our system is based on M2M100 with novel techniques that adapt it to the target language pair. (1) Cross-model word embedding alignment:

Md Faiyaz Kabir, Md Borhan Mia, Ishtiaque Ahmed, Nafiz Jaidye

Electromagnetic coupling via either exponentially decaying evanescent field or radiative wave is a primary characteristic of light, allowing optical signal/power transfer but limiting integration density in a photonic circuit. A leaky mode combines both evanescent field and radiative wave, causing stronger crosstalk and thus not ideal for dense integration.

Li Xia, Peter W. Glynn

CVaR (Conditional Value at Risk) is a risk metric widely used in finance. However, dynamically optimizing CVaR is difficult since it is not a standard Markov decision process (MDP) and the principle of dynamic programming fails. In this paper, we study the infinite-horizon discrete-time MDP with a long-run CVaR criterion, from the view of sensitivity-based o

En-Ze Shao, Kai Liu, Hao Xie, Kaiqi Geng

Metal chalcogenide is a promising material for studying novel underlying physical phenomena and nanoelectronics applications. Here, we systematically investigate the crystal structure and electronic properties of the AlSe surface alloy on Al (111) using scanning tunneling microscopy, angle-resolved photoelectron spectrometer, and first-principle calculations

Chenqi Li, Yuan Ren, Bingbing Liu

Data is a fundamental building block for LiDAR perception systems. Unfortunately, real-world data collection and annotation is extremely costly & laborious. Recently, real data based LiDAR simulators have shown tremendous potential to complement real data, due to their scalability and high-fidelity compared to graphics engine based methods. Before simulation