October 2022 arXiv papers — page 33

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Extractive question answering (QA) models tend to exploit spurious correlations to make predictions when a training set has unintended biases. This tendency results in models not being generalizable to examples where the correlations do not hold. Determining the spurious correlations QA models can exploit is crucial in building generalizable QA models in rea

Yuxiang Liu

We classify rigid Schubert classes in orthogonal Grassmannians. More generally, given a representative $X$ of a Schubert class in an orthogonal Grassmannian, we give combinatorial conditions which guarantee that every linear space parametrized by $X$ meets a fixed linear space in the required dimension.

Qian Chen, Zhuan Ning, Yu Tian, Bin Wang

In this work, we study the real-time dynamics of the charged hairy black hole with the time-dependent source of scalar field in asymptotically anti-de Sitter (AdS) spacetime. The numerical results reveal a novel descalarization mechanism. In order to obtain the hairy black hole as the initial data for the quench process, we first analyze the quasi-normal mod

M. Köpf, S. H. Lee, Z. Q. Mao, C. A. Kuntscher

MnBi$_2$Te$_4$ is a promising representative of intrinsic antiferromagnetic topological insulators, which could enable rare quantum mechanical effects like the quantum anomalous Hall effect. Especially at low temperatures, numerous studies have been reported, demonstrating the great potential of this compound in the magnetically ordered state below $T_{\math

Jack Lidmar, Johan Spross, John Leander

We describe an adaptive Markov chain Monte Carlo method suitable for the estimation of rare failure probabilities in complex probabilistic models. This method, the Accelerated Weight Histogram (AWH) method, has its origin in statistical physics (Lidmar, 2012) and has successfully been applied to molecular dynamics simulations in biophysics. Here we introduce

David Diaz-Guerra, Archontis Politis, Tuomas Virtanen

Recent data- and learning-based sound source localization (SSL) methods have shown strong performance in challenging acoustic scenarios. However, little work has been done on adapting such methods to track consistently multiple sources appearing and disappearing, as would occur in reality. In this paper, we present a new training strategy for deep learning S

Binal D. Patel, Bhuwan Joshi, Kyung-Suk Cho, Rok-Soon Kim

We analyse the characteristics of interplanetary coronal mass ejections (ICMEs) during Solar Cycles 23 and 24. The present analysis is primarily based on the near-Earth ICME catalogue (Richardson and Cane, 2010). An important aspect of this study is to understand the near-Earth and geoeffective aspects of ICMEs in terms of their association (type II ICMEs) v

Zheyu Wu, Ya-Feng Liu, Bo Jiang, Yu-Hong Dai

Quantized constant envelope (QCE) precoding, a new transmission scheme that only discrete QCE transmit signals are allowed at each antenna, has gained growing research interests due to its ability of reducing the hardware cost and the energy consumption of massive multiple-input multiple-output (MIMO) systems. However, the discrete nature of QCE transmit sig

Olivier Coulaud, Luc Giraud, Martina Iannacito

We consider the solution of linear systems with tensor product structure using a GMRES algorithm. In order to cope with the computational complexity in large dimension both in terms of floating point operations and memory requirement, our algorithm is based on low-rank tensor representation, namely the Tensor Train format. In a backward error analysis framew

Julius Ott, Lorenzo Servadei, Gianfranco Mauro, Thomas Stadelmayer

Nowadays, Deep Learning (DL) methods often overcome the limitations of traditional signal processing approaches. Nevertheless, DL methods are barely applied in real-life applications. This is mainly due to limited robustness and distributional shift between training and test data. To this end, recent work has proposed uncertainty mechanisms to increase their

Joan Plepi, Béla Neuendorf, Lucie Flek, Charles Welch

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of

Gongyang Li, Yike Wang, Zhi Liu, Xinpeng Zhang

Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i.e. encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately,

Qinyuan Cheng, Linyang Li, Guofeng Quan, Feng Gao

Task-Oriented Dialogue (TOD) systems are drawing more and more attention in recent studies. Current methods focus on constructing pre-trained models or fine-tuning strategies while the evaluation of TOD is limited by a policy mismatch problem. That is, during evaluation, the user utterances are from the annotated dataset while these utterances should interac

Boris Adamczewski, Colin Faverjon

In a recent work [3], the authors established new results about general linear Mahler systems in several variables from the perspective of transcendental number theory, such as a multivariate extension of Nishioka's theorem. Working with functions of several variables and with different Mahler transformations leads to a number of complications, including the

L. Benussi, S. Bianco, R. Campagnola, M. Caponero

Driven by the recent improvement in additive manufacturing technologies, we designed a detector that can be fully printed with a standard and commercial 3D printer. The main goals of this research concern the marginal design and construction costs, the reproducibility/modularity of the products, and the reduced assembly time. During the first phase of this r

Sandeep Kumar Kataria, Juntai Shen

Cosmological simulations show that dark matter halos surrounding baryonic disks have a wide range of angular momenta, measured by the spin parameter ($\lambda$). In this study, we bring out the importance of inner angular momentum($<$30 kpc), measured in terms of the halo spin parameter, on the secular evolution of the bar using N-body simulations. We have v

Zhuonan Lin, Vitaliy Lomakin

We present a general finite element linearized Landau-Lifshitz-Gilbert equation (LLGE) solver for magnetic systems under weak time-harmonic excitation field. The linearized LLGE is obtained by assuming a small deviation around the equilibrium state of the magnetic system. Inserting such expansion into LLGE and keeping only first order terms gives the lineari

Can Li, Lei Bai, Lina Yao, S. Travis Waller

Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g., weather, accidents), and the interactions among multiple travel m

Jie Cao, Yin Zhang

Extreme multi-label text classification (XMTC) is the task of finding the most relevant subset labels from an extremely large-scale label collection. Recently, some deep learning models have achieved state-of-the-art results in XMTC tasks. These models commonly predict scores for all labels by a fully connected layer as the last layer of the model. However,

Zhihao Cao, Hongchun Qu

The cultivated strawberry Fragaria ananassa Duch. is widely planted in greenhouses in China. Its production heavily depends on pollination services. Compared with artificial pollination, bee pollination can significantly improve fruit quality and save considerable labor requirement. Multiple factors such as bee foraging behavior, planting pattern and the spa

Yong-Ju Hai, Junning Li, Junkai Zeng, Xiu-Hao Deng

Quantum information processing faces a significant hurdle: noise. Different noise sources induce varying errors in quantum operations depending on the underlying dynamics. To gain a deeper understanding of these error mechanisms, we introduce the concept of Quantum Error Evolution Diagrams (QEED). These QEEDs establish a dual correspondence between driven no

Frédéric de Gournay, Alban Gossard

In the context of the optimization of Deep Neural Networks, we propose to rescale the learning rate using a new technique of automatic differentiation. This technique relies on the computation of the {\em curvature}, a second order information whose computational complexity is in between the computation of the gradient and the one of the Hessian-vector produ

Hyundo Jung, Hyunjin Kim, Woojin Lee, Jinwoo Jeon

Probabilistic computing has been introduced to operate functional networks using a probabilistic bit (p-bit), generating 0 or 1 probabilistically from its electrical input. In contrast to quantum computers, probabilistic computing enables the operation of adiabatic algorithms even at room temperature, and is expected to broaden computational abilities in non

Zhi-Xin Meng, Pei-Qiang Yan, Sheng-Zhe Wang, Xiao-Jie Li

We demonstrate a closed-loop light-pulse atom interferometer inertial sensor that can realize continuous decoupled measurements of acceleration and rotation rate. The sensor operates with double-loop atom interferometers, which share the same Raman light pulses in a spatially separated Mach-Zehnder configuration and use continuous cold atomic beams propagati

Max Berre

While startup valuations are influenced by revenues, risks, age, and macroeconomic conditions, specific causality is traditionally a black box. Because valuations are not disclosed, roles played by other factors (industry, geography, and intellectual property) can often only be guessed at. VC valuation research indicates the importance of establishing a fact

Clara N. Breiø, Brian M. Andersen

At a certain "magic" relative twist angle of two graphene sheets it remains a challenge to obtain a detailed description of the proliferation of correlated topological electronic phases and their filling-dependence. We perform a self-consistent real-space Hartree-Fock study of an effective moir{\'e} lattice model to map out the preferred ordered phases as a

Ryo Araki, Wouter J. T. Bos

We investigate how inhomogeneity influences the $k^{-5/3}$ inertial range scaling of turbulent kinetic energy spectra (with $k$ the wavenumber). For weak statistical inhomogeneity, the energy spectrum can be described as an equilibrium spectrum plus a perturbation. Theoretical arguments suggest that this latter contribution scales as $k^{-7/3}$. This predict

Md Khurshid Alam, S. Surendra Singh, L. Anjana Devi

In this work, the Renyi holographic dark energy (RHDE)and its behaviour has been explored with the anisotropic and spatially homogeneous Bianchi type-I Universe in the framework of $f(G)$ gravity. We use IR cutoff as the Hubble and Granda-Oliveros (GO) horizons. To find a consistent solutions of the field equations of the models, it is assumed that the decel

Li Fu, Siqi Li, Qingtao Li, Liping Deng

In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows for online and offline modes into one process, and 2) improves the Word Error Rate (WER) performance with limited utterance annotating. Specifically, we extend the conventional of

Xuan-Phi Nguyen, Sravya Popuri, Changhan Wang, Yun Tang

Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been made to increase the data size from unlabeled speech by cascading pretrained speech recognition (ASR), machine translation (MT) and text-to-speech (TTS) models; unlabeled text has re

Na Xu, Shiwang Ma

We consider the nonlinear Choquard equation $$\begin{cases} & - \Delta u = (I_\alpha \ast F(u))F'(u) -\mu u \ \text{in}\ \mathbb{R}^N, & u \in \ H^1(\mathbb{R}^N), \ \int_{\mathbb{R}^N} |u|^2 dx=m, \end{cases} $$ where $\alpha\in(0,N)$, $m>0$ is prescribed, $\mu \in \mathbb{R}$ is a Lagarange multiplier, and $I_\alpha$ is the Riesz potential. Under general a

Huda Alamri, Anthony Bilic, Michael Hu, Apoorva Beedu

Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance records. This progress is largely powered by the adaptation of the more powerful transformer-based language encoders. Despite this progress, existing approaches do not effectively uti

Shane R. Carberry Mogan, Orenthal J. Tucker, Robert E. Johnson, Lorenz Roth

We explore the parameter space for the contribution to Callisto's H corona observed by the Hubble Space Telescope (Roth et al. 2017a) from sublimated H2O and radiolytically produced H2 using the Direct Simulation Monte Carlo (DSMC) method. The spatial morphology of this corona produced via photo- and magnetospheric electron impact-induced dissociation is des

Anastasios Foliadis, Mario H. Castañeda Garcia, Richard A. Stirling-Gallacher, Reiner S. Thomä

Radio based positioning of a user equipment (UE) based on deep learning (DL) methods using channel state information (CSI) fingerprints have shown promising results. DL models are able to capture complex properties embedded in the CSI about a particular environment and map UE's CSI to the UE's position. However, the CSI fingerprints and the DL models trained

Jianqiao Cui, Stefan Bleeck

Deep learning algorithm are increasingly used for speech enhancement (SE). In supervised methods, global and local information is required for accurate spectral mapping. A key restriction is often poor capture of key contextual information. To leverage long-term for target speakers and compensate distortions of cleaned speech, this paper adopts a sequence-to

Jumpei F. Yamagishi, Tetsuhiro S. Hatakeyama

Predicting cellular metabolic states is a central problem in biophysics. Conventional approaches, however, sensitively depend on the microscopic details of individual metabolic systems. In this Letter, we derived a universal linear relationship between the metabolic responses against nutrient conditions and metabolic inhibition, with the aid of a microeconom

Zhi Lv, Bo Lin, Siyuan Liang, Lihua Wang

We present a simple domain generalization baseline, which wins second place in both the common context generalization track and the hybrid context generalization track respectively in NICO CHALLENGE 2022. We verify the founding in recent literature, domainbed, that ERM is a strong baseline compared to recent state-of-the-art domain generalization methods and

R. A. Konoplya

The fundamental quasinormal modes of black holes in higher-derivative gravity given by the Einstein-Weyl action are known to be moderately corrected by the Weyl term. Here we will show that the first several overtones are highly sensitive to even a relatively small Weyl correction, which might be important when representing the earlier stage of the black hol

Xiujing Zheng, Liqi Wang, Shixin Zhu

By generalizing the stabilizer quantum error-correcting codes, entanglement-assisted quantum error-correcting (EAQEC) codes were introduced, which could be derived from any classical linear codes via the relaxation of self-orthogonality conditions with the aid of pre-shared entanglement between the sender and the receiver. In this paper, three classes of ent

Jacques Bair, Alexandre Borovik, Vladimir Kanovei, Mikhail G. Katz

In the history of infinitesimal calculus, we trace innovation from Leibniz to Cauchy and reaction from Berkeley to Mansion and beyond. We explore 19th century infinitesimal lores, including the approaches of Simeon-Denis Poisson, Gaspard-Gustave de Coriolis, and Jean-Nicolas Noel. We examine contrasting historiographic approaches to such lores, in the work o

Sitong Chen, Xianhua Tang

In this paper, we prove the existence of normalized solutions for the following Schr\"odinger equation \begin{equation*} \left\{ \begin{array}{ll} -\Delta u-\lambda u=f(u), & x\in \R^N, \int_{\R^N}u^2\mathrm{d}x=c \end{array} \right. \end{equation*} with $N\ge3$, $c>0$, $\lambda\in \R$ and $f\in \mathcal{C}(\R,\R)$ in the Sobolev subcritical case with weaker

Chenhui Shen, Liying Cheng, Lidong Bing, Yang You

A wide range of control perspectives have been explored in controllable text generation. Structure-controlled summarization is recently proposed as a useful and interesting research direction. However, current structure-controlling methods have limited effectiveness in enforcing the desired structure. To address this limitation, we propose a sentence-level b

Bagus Tris Atmaja, Akira Sasou

Traditional speech emotion recognition (SER) evaluations have been performed merely on a speaker-independent condition; some of them even did not evaluate their result on this condition. This paper highlights the importance of splitting training and test data for SER by script, known as sentence-open or text-independent criteria. The results show that employ

Yan Wang, Quansheng Guan, Fei Ji, Weiqi Chen

The propagation delay is non-negligible in underwater acoustic networks (UANs) since the propagation speed is five orders of magnitude smaller than the speed of light. In this case, space and time factors are strongly coupled to determine the collisions of packet transmissions. To this end, this paper analyzes the impact of spatial-time coupling on slotted m

S. S. Agaev, K. Azizi, H. Sundu

The spectroscopic parameters and widths of the axial-vector $T_{cc:s}^{ \mathrm{AV}}$ and scalar $T_{cc:s}^{\mathrm{S}}$, $\widetilde{T}_{cc:s}^{ \mathrm{S}}$ strange partners of the doubly charmed exotic meson $T_{cc}^{+}$ with the content $cc\overline{u}\overline{s}$, are calculated in the framework of the QCD sum rule method. We model $T_{cc:s}^{\mathrm{A

Salvatore Spina

The Paterno' Castello Principi di Biscari Archive, preserved at the State Archives of Catania, amongst one of the most crucial family archives, is, in the light of a digital historical methodology, the best computable historical heritage for demonstrating the applicability of applying an HTR tool, such as Transkribus, to digitised historical documents.

Y. Yin

We investigate the temperature-driven quantum-to-classical crossover in a single-electron emitter. The emitter is composed of a quantum conductor and an electrode, which is coupled via an Ohmic contact. At zero temperature, it has been shown that a single electron can be injected coherently by applying an unit-charge Lorentzian pulse on the electrode. As the

Kaicheng Fu, Changde Du, Shengpei Wang, Huiguang He

Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expres

Fong Yuan Lim, Yu Wei Tan, Anand Bhojan

Procedural terrain generation is the process of generating a digital representation of terrain using a computer program or procedure, with little to no human guidance. This paper proposes a procedural terrain generation algorithm based on a graph representation of fluvial erosion that offers several novel improvements over existing algorithms. Namely, the us

Bagus Tris Atmaja, Masato Akagi

Automatic speech emotion recognition (SER) by a computer is a critical component for more natural human-machine interaction. As in human-human interaction, the capability to perceive emotion correctly is essential to take further steps in a particular situation. One issue in SER is whether it is necessary to combine acoustic features with other data such as

Changyoon Lee, Yeon Seonwoo, Alice Oh

We introduce CS1QA, a dataset for code-based question answering in the programming education domain. CS1QA consists of 9,237 question-answer pairs gathered from chat logs in an introductory programming class using Python, and 17,698 unannotated chat data with code. Each question is accompanied with the student's code, and the portion of the code relevant to

Masato Hagiwara

The lack of annotated training data in bioacoustics hinders the use of large-scale neural network models trained in a supervised way. In order to leverage a large amount of unannotated audio data, we propose AVES (Animal Vocalization Encoder based on Self-Supervision), a self-supervised, transformer-based audio representation model for encoding animal vocali

Andrew Bennett, Dipendra Misra, Nathan Kallus

Safety is a crucial necessity in many applications of reinforcement learning (RL), whether robotic, automotive, or medical. Many existing approaches to safe RL rely on receiving numeric safety feedback, but in many cases this feedback can only take binary values; that is, whether an action in a given state is safe or unsafe. This is particularly true when fe

Hana Hebishima, Mina Arakaki, Chikako Dozono, Hanna Frolova

To propose a mathematical model of consciousness and will, we first simulated the inverted qualia with a toy model of a neural network. As a result, we confirmed that there can be an inverted qualia on the neural network. In other words, the qualia were individual-dependent and considered difficult as an indicator of consciousness and will. To solve that dif

Chenyu Tang, Yanting Wang

Ionic liquids (ILs), also known as room-temperature molten salts, are composed of pure ions with melting points usually below 100 degrees centigrade. Because of their low volatility and vast amounts of species, ILs can serve as "green solvents" and "designer solvents" to meet the requirements of various applications by fine tuning their molecular structures.

Matthew Tsao, Karthik Gopalakrishnan, Kaidi Yang, Marco Pavone

Network routing problems are common across many engineering applications. Computing optimal routing policies requires knowledge about network demand, i.e., the origin and destination (OD) of all requests in the network. However, privacy considerations make it challenging to share individual OD data that would be required for computing optimal policies. Priva

Mohamed Aziz Bhouri, Pierre Gentine

Physical parameterizations are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions are too coarse to resolve small-scale processes. These parameterizations are typically grounded on physically-based, yet empirical, representations of the underlying small-scale pr

Masanori Takano

In this study, we consider users' online communication rhythms (online social rhythms) as coupled oscillators in a complex social network. Users' rhythms may be entrained onto those of their friends, and macro-scale pattern of such rhythms can emerge. We investigated the entrainment in online social rhythms and long-range correlations of the rhythms using an

Md Mosharaf Hossain, Eduardo Blanco

Negation poses a challenge in many natural language understanding tasks. Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we show that doing so benefits models for three natural language understanding tasks. We present an automated procedure to collect pairs of sentences wit

Tongkun Liu, Bing Li, Zhuo Zhao, Xiao Du

Reconstruction-based methods are widely explored in industrial visual anomaly detection. Such methods commonly require the model to well reconstruct the normal patterns but fail in the anomalies, and thus the anomalies can be detected by evaluating the reconstruction errors. However, in practice, it's usually difficult to control the generalization boundary

Wouter van Loon, Marjolein Fokkema, Frank de Vos, Marisa Koini

Data for which a set of objects is described by multiple distinct feature sets (called views) is known as multi-view data. When missing values occur in multi-view data, all features in a view are likely to be missing simultaneously. This may lead to very large quantities of missing data which, especially when combined with high-dimensionality, can make the a

Rong Zhu, Branislav Kveton

Model misspecification is a major consideration in applications of statistical methods and machine learning. However, it is often neglected in contextual bandits. This paper studies a common form of misspecification, an inter-arm heterogeneity that is not captured by context. To address this issue, we assume that the heterogeneity arises due to arm-specific

N. I. Aleksandrova

The block medium is modeled by a discrete-periodic spatial lattice of masses connected by elastic springs and viscous dampers. To describe the viscoelastic behavior of the interblock layers, a rheological model of internal friction with two Maxwell elements and one Voigt element with the quality factor of the material as the determining parameter is proposed

Steven G. Xu, Reetam Majumder, Brian J. Reich

We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021). The method begins by fitting a flexible density regression model using monotonic splines whose weights are modeled as data-dependent functions using artificial neural networks. Subsequently, estimates of conditional density and quantile

Junhao Zhang, Zheyuan Yi, Yujiao Zhao, Linfang Xiao

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity inform

Jiwoong Park, Jisu Jeong, Kyungmin Kim, Jin Young Choi

Existing message passing neural networks for heterogeneous graphs rely on the concepts of meta-paths or meta-graphs due to the intrinsic nature of heterogeneous graphs. However, the meta-paths and meta-graphs need to be pre-configured before learning and are highly dependent on expert knowledge to construct them. To tackle this challenge, we propose a novel

Zhi Dou, Liu Hong, Zhengwei Li, Fan Kiat Chan

Small - but finite - fluid inertia can be leveraged to generate steady flows out of liquid vibrations around an immersed interface. In engineering, external high-frequency drivers (10$^2$-10$^5$ Hz) allow this inertial rectification phenomenon, known as viscous streaming, to be employed in micron-scale devices for precise flow control, particle manipulation

Jie Zhang, B. T. Chow, P. C. Haljan

An array of ions in a linear radio-frequency (RF) Paul trap is a good candidate for investigating structural phase transitions, such as the linear-to-zigzag (LZ) transition, due to the convenient control provided by modification of the trap confinement strength. In such studies, the trap secular frequencies are a key factor that limits the stability of the c

Georgios Tsikalas

We characterize interpolating sequences for pairs of reproducing kernels $(s, \ell)$, where $s$ is a complete Pick factor of $\ell.$ This answers a question of Aleman, Hartz, McCarthy and Richter.

Ben Hayes, Charalampos Saitis, György Fazekas

Sinusoidal parameter estimation is a fundamental task in applications from spectral analysis to time-series forecasting. Estimating the sinusoidal frequency parameter by gradient descent is, however, often impossible as the error function is non-convex and densely populated with local minima. The growing family of differentiable signal processing methods has

JingChun Wang, Yu Su, Haoyang Cheng, Yao Wang

Electronic correlation is a fundamental topic in many-electron systems. To characterize this correlation, one may introduce the concept of exchange-correlation hole. In this paper, we first briefly revisit its definition and relation to electron and geminal densities, followed by their intimate relations to copula functions in probability theory and statisti

Vasily Zadorozhnyy, Qiang Ye, Kazuhito Koishida

In recent years, Generative Adversarial Networks (GANs) have produced significantly improved results in speech enhancement (SE) tasks. They are difficult to train, however. In this work, we introduce several improvements to the GAN training schemes, which can be applied to most GAN-based SE models. We propose using consistency loss functions, which target th

Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen

Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence without explicitly modeling its structure. Some prior works show that pre-trained language models can capture the syntactic rules of natural languages without finetuning on syntax un

Gihan Weeraprameshwara, Vihanga Jayawickrama, Nisansa de Silva, Yudhanjaya Wijeratne

In the process of numerically modeling natural languages, developing language embeddings is a vital step. However, it is challenging to develop functional embeddings for resource-poor languages such as Sinhala, for which sufficiently large corpora, effective language parsers, and any other required resources are difficult to find. In such conditions, the exp

Fu-Jiun Jiang

The numerical value of a universal quantity associated with the quantum critical regime, namely $\chi_u c^2/T$, for a two-dimensional (2D) dimerized spin-1/2 antiferromagnet is calculated using the quantum Monte Carlo simulations (QMC). Here $\chi_u$, $c$, and $T$ are the uniform susceptibility, the spin-wave velocity, and the temperature, respectively. By s

Wenkai Ouyang, Xiangying Zuo, Bolin Liao

Understanding the transport of photogenerated charge carriers in semiconductors is crucial for applications in photovoltaics, optoelectronics and photo-detectors. While recent experimental studies using scanning ultrafast electron microscopy (SUEM) have demonstrated that the local change in the secondary electron emission induced by photoexcitation enables d

Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu

Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training only a small portion of parameters. In this paper, we propose to understand and further develop prefix-tuning through the

Alexander Volberg, Haonan Zhang

Bohnenblust--Hille inequalities for Boolean cubes have been proven with dimension-free constants that grow subexponentially in the degree \cite{defant2019fourier}. Such inequalities have found great applications in learning low-degree Boolean functions \cite{eskenazis2022learning}. Motivated by learning quantum observables, a qubit analogue of Bohnenblust--H

Haoran Zhan, Mingke Zhang, Yingcun Xia

The projection pursuit regression (PPR) has played an important role in the development of statistics and machine learning. However, when compared to other established methods like random forests (RF) and support vector machines (SVM), PPR has yet to showcase a similar level of accuracy as a statistical learning technique. In this paper, we revisit the estim

Nathaniel J. Wei, John O. Dabiri

Energy-harvesting systems in complex flow environments, such as floating offshore wind turbines, tidal turbines, and ground-fixed turbines in axial gusts, encounter unsteady streamwise flow conditions that affect their power generation and structural loads. In some cases, enhancements in time-averaged power generation above the steady-flow operating point ar

Saliha Muradoglu, Mans Hulden

Data scarcity is a widespread problem in numerous natural language processing (NLP) tasks for low-resource languages. Within morphology, the labour-intensive work of tagging/glossing data is a serious bottleneck for both NLP and language documentation. Active learning (AL) aims to reduce the cost of data annotation by selecting data that is most informative

Cristian Lenart, Satoshi Naito, Fumihiko Nomoto, Daisuke Sagaki

In this paper we derive a counterpart of the well-known Ram-Yip formula for symmetric and nonsymmetric Macdonald polynomials of arbitrary type. Our new formula is in terms of a generalization of the Lakshmibai-Seshadri paths (originating in standard monomial theory), which we call pseudo-quantum Lakshmibai-Seshadri (LS) paths. This model carries less informa

Bohua Peng, Shihao Liang, Mobarakol Islam

Inductive knowledge graph completion requires models to comprehend the underlying semantics and logic patterns of relations. With the advance of pretrained language models, recent research have designed transformers for link prediction tasks. However, empirical studies show that linearizing triples affects the learning of relational patterns, such as inversi

Philip Jordan D. Blancas, Eric A. Galapon

The one-sided and full Hilbert transforms are evaluated exactly by means of the method of finite-part integration [E.A. Galapon, \textit{Proc. Roy. Soc. A} \textbf{473}, 20160567 (2017)]. In general, the result consists of two terms -- the first is an infinite series of finite-part of divergent integrals, and the second is a contribution arising from the sin

Onkar Susladkar, Dhruv Makwana, Gayatri Deshmukh, Sparsh Mittal

Text erasure from an image is helpful for various tasks such as image editing and privacy preservation. In this paper, we present TPFNet, a novel one-stage (end-toend) network for text removal from images. Our network has two parts: feature synthesis and image generation. Since noise can be more effectively removed from low-resolution images, part 1 operates

Liuchun Yuan, Zehao Huang, Naiyan Wang

In this paper, we present a general and effective framework for Neural Architecture Search (NAS), named PredNAS. The motivation is that given a differentiable performance estimation function, we can directly optimize the architecture towards higher performance by simple gradient ascent. Specifically, we adopt a neural predictor as the performance predictor.

Mathieu Granzotto, Olivier Lindamulage De Silva, Romain Postoyan, Dragan Nesic

This paper investigates recursive feasibility, recursive robust stability and near-optimality properties of policy iteration (PI). For this purpose, we consider deterministic nonlinear discrete-time systems whose inputs are generated by PI for undiscounted cost functions. We first assume that PI is recursively feasible, in the sense that the optimization pro

Zahra Esmaeilbeig, Arian Eamaz, Kumar Vijay Mishra, Mojtaba Soltanalian

Intelligent reflecting surface (IRS) technology has recently attracted a significant interest in non-light-of-sight radar remote sensing. Prior works have largely focused on designing single IRS beamformers for this problem. For the first time in the literature, this paper considers multi-IRS-aided multiple-input multiple-output (MIMO) radar and jointly desi

Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang

In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various

Jiangnan Li, Mo Yu, Fandong Meng, Zheng Lin

In this work, we focus on dialogue reading comprehension (DRC), a task extracting answer spans for questions from dialogues. Dialogue context modeling in DRC is tricky due to complex speaker information and noisy dialogue context. To solve the two problems, previous research proposes two self-supervised tasks respectively: guessing who a randomly masked spea

Soumik Purkayastha, Peter X. K. Song

Causal investigations in observational studies pose a great challenge in research where randomized trials or intervention-based studies are not feasible. We develop an information geometric causal discovery and inference framework of "predictive asymmetry". For $(X, Y)$, predictive asymmetry enables assessment of whether $X$ is more likely to cause $Y$ or vi

Chen Jia, Da-quan Jiang, Bingjie Wu

Here we propose the Donsker-Varadhan-type compactness conditions and prove the joint large deviation principle for the empirical measure and empirical flow of Markov renewal processes (semi-Markov processes) with a countable state space, generalizing the relevant results for continuous-time Markov chains with a countable state space obtained in [Ann. Inst. H

Zhenwei Liu, Ali Saberi, Anton A. Stoorvogel

This paper studies global regulated state synchronization of discrete-time double-integrator multi-agent systems subject to actuator saturation by utilizing localized information exchange. We propose a scale-free linear protocol that achieves global regulated state synchronization for any network with arbitrary number of agents and arbitrarily directed commu

Chidera Biringa, Gaspard Baye, Gökhan Kul

Spectre intrusions exploit speculative execution design vulnerabilities in modern processors. The attacks violate the principles of isolation in programs to gain unauthorized private user information. Current state-of-the-art detection techniques utilize micro-architectural features or vulnerable speculative code to detect these threats. However, these techn

H. P. Hong

Two popular spectral-based approaches for estimating the evolutionary power spectral density (EPSD) function from the samples of the evolutionary process are based on the short-time Fourier transform (STFT) and the continuous wavelet transform. Both rely on the concept of slowly varying modulation or EPSD function, although the quantification of the effect o

Yuezhi Yang, Hao Pan

Sketch design concepts are recurring patterns found in parametric CAD sketches. Though rarely explicitly formalized by the CAD designers, these concepts are implicitly used in design for modularity and regularity. In this paper, we propose a learning based approach that discovers the modular concepts by induction over raw sketches. We propose the dual implic

Jia-Yi Lin, Xin-Yu Li, Yu-Hao Shao, Wei Wang

The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operations. Here we propose an alternative approach; we use a simple discrete-time quantum walk (DTQW) on a cycle graph to model an arbitrary unitary operation $U(N)$ without the need to

Kunal Bhagat, Shiva Rudraraju

Dendrites are one of the most widely observed patterns in nature and occur across a wide spectrum of physical phenomena. In solidification and growth patterns in metals and crystals, the multi-level branching structures of dendrites pose a modeling challenge, and a full resolution of these structures is computationally demanding. In the literature, theoretic

Yuhao Liang, Peikun Chen, Fan Yu, Xinfa Zhu

This paper describes our NPU-ASLP system submitted to the ISCSLP 2022 Magichub Code-Switching ASR Challenge. In this challenge, we first explore several popular end-to-end ASR architectures and training strategies, including bi-encoder, language-aware encoder (LAE) and mixture of experts (MoE). To improve our system's language modeling ability, we further at

Tinggui Zhang, Qiming Luo, Xiaofen Huang

Quantum Bell nonlocality is an important quantum phenomenon. Recently, the shareability of Bell nonlocality under unilateral measurements has been widely studied. In this study, we consider the shareability of quantum Bell nonlocality under bilateral measurements. Under a specific class of projection operators, we find that quantum Bell nonlocality cannot be