October 2020 arXiv papers — page 41

Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett

A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints the user wishes to impose on the generated text are dynamic, especially when authoring longer documents. We address this limitation with an interactive text generation setting in wh

Rochelle Choenni, Ekaterina Shutova

Multilingual sentence encoders are widely used to transfer NLP models across languages. The success of this transfer is, however, dependent on the model's ability to encode the patterns of cross-lingual similarity and variation. Yet, little is known as to how these models are able to do this. We propose a simple method to study how relationships between lang

Jiaqing Huang, Yijie Mo, Yao Yao

Injection from metallic electrodes serves as a main channel of charge generation in organic semiconducting devices and the quantum effect is normally regarded to be essential. We develop a dynamic approach based upon the surface hopping (SH) algorithm and classical device modeling, by which both quantum tunneling and thermionic emission of charge carrier inj

Huan Wang, Jing Wang

The search for large gap quantum spin Hall (QSH) and quantum anomalous Hall (QAH) insulators is important both for fundamental and practical interests. The degenerate multi-orbitals $p_x,p_y$ in honeycomb lattice provides a paradigm for QSH state with a boosted topological gap of the first order in atomic spin-orbit coupling. By using elementary band represe

Danilo Latini, Ian Marquette, Yao-Zhong Zhang

The rank-$1$ Racah algebra $R(3)$ plays a pivotal role in the theory of superintegrable systems. It appears as the symmetry algebra of the $3$-parameter system on the $2$-sphere from which all second-order conformally flat superintegrable models in $2$D can be obtained by means of suitable limits and contractions. A higher rank generalization of $R(3)$, the

Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson

We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By reallocating the in

Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining. These attacks are harmful because they propagate implicit biases and diminish a person's credibility. Since dialogue systems respond directly to user input, it is important to study ad hominems in dialogue responses. To this end, w

Siva Rajesh Kasa, Vaibhav Rajan

Copulas provide a modular parameterization of multivariate distributions that decouples the modeling of marginals from the dependencies between them. Gaussian Mixture Copula Model (GMCM) is a highly flexible copula that can model many kinds of multi-modal dependencies, as well as asymmetric and tail dependencies. They have been effectively used in clustering

Linnea Grans-Samuelsson, Jesper Lykke Jacobsen, Hubert Saleur

We investigate the action of discretized Virasoro generators, built out of generators of the lattice Temperley-Lieb algebra ("Koo-Saleur generators"[arXiv:hep-th/9312156]), in the critical XXZ quantum spin chain. We explore the structure of the continuum-limit Virasoro modules at generic central charge for the XXZ vertex model, paralleling [arXiv:2007.11539]

Enrico Pajer

The observation of primordial correlators by cosmological surveys is a very promising avenue to probe high energies and the perturbative regime of quantum gravity. Hence, it is imperative that we understand how these observables are shaped by the pillars of fundamental physics, namely unitarity, locality and symmetries. To this end, we study the three-point

Maria Gorelik

We consider several examples when the extension graph admits a bipartition compatible with the action of Duflo--Serganova functors.

Sebastian Perez-Salazar, Rachel Cummings

In this work we consider the problem of online submodular maximization under a cardinality constraint with differential privacy (DP). A stream of $T$ submodular functions over a common finite ground set $U$ arrives online, and at each time-step the decision maker must choose at most $k$ elements of $U$ before observing the function. The decision maker obtain

Mohamin B M Khan, Harekrushna Behera

The study examines the influence of a sloping porous bed on the efficiency of an oscillating water column (OWC) device facing oblique water waves. A vertical, surface piercing, thin plate near a rigid wall approximates the OWC. The system is simulated using a multi-domain boundary element method assuming the linear potential theory. The impact of varying slo

Manil T. Mohan

In this work, we carry out the asymptotic analysis of the two dimensional convective Brinkman-Forchheimer (CBF) equations, which characterize the motion of incompressible fluid flows in a saturated porous medium. We establish the existence of a global attractor in both bounded (using compact embedding) and Poincar\'e domains (using asymptotic compactness pro

Catherine Chen, Kevin Lin, Dan Klein

We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of p

Zexuan Zhong, Danqi Chen

End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipelined approach for entity and relation ext

Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec

Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. However, GNNs are prone to adversarial attacks. Here we introduce Graph Information Bottleneck (GIB), a

Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song

Autoregressive models use chain rule to define a joint probability distribution as a product of conditionals. These conditionals need to be normalized, imposing constraints on the functional families that can be used. To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of

Wangchunshu Zhou, Dong-Ho Lee, Ravi Kiran Selvam, Seyeon Lee

Pre-trained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the relational commonsense knowledge about eve

Yael Mathov, Tal Ben Senior, Asaf Shabtai, Yuval Elovici

Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy. These automated systems use learning models to analyze conversations, and calls that involve specific topics are routed to a human agent for further examination. In this study, we present an adversarial-learning-based framework for privacy protection for VoIP conve

Xiaodong Yu, Wenpeng Yin, Dan Roth

Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets. These tasks are typically formulated as (binary) classification problems over independently induced representations of the text snippets. In this work, we develop a Pairwise Representation Learning (PairwiseRL) scheme f

Weitong Hua, Zhongxiang Zhou, Jun Wu, Huang Huang

Object 6D pose estimation is a fundamental task in many applications. Conventional methods solve the task by detecting and matching the keypoints, then estimating the pose. Recent efforts bringing deep learning into the problem mainly overcome the vulnerability of conventional methods to environmental variation due to the hand-crafted feature design. However

Timo J. Doerries, Sarah A. M. Loos, Sabine H. L. Klapp

This paper is concerned with correlation functions of stochastic systems with memory, a prominent example being a molecule or colloid moving through a complex (e.g., viscoelastic) fluid environment. Analytical investigations of such systems based on non-Markovian stochastic equations are notoriously difficult. A common approximation is that of a single-expon

Martin Lee, Ali Mazaheri, Herre S. J. van der Zant, Riccardo Frisenda

Paper based thermoresistive sensors are fabricated by rubbing WS2 powder against a piece of standard copier paper, like the way a pencil is used to write on paper. The abrasion between the layered material and the rough paper surface erodes the material, breaking the weak van der Waals interlayer bonds, yielding a film of interconnected platelets. The resist

Pedro J. Chocano, Manuel A. Morón, Francisco R. Ruiz del Portal

We adapt the definition of the Vietoris map to the framework of finite topological spaces and we prove some coincidence theorems. From them, we deduce a Lefschetz fixed point theorem for multivalued maps that improves recent results in the literature. Finally, it is given an application to the approximation of discrete dynamical systems in polyhedra.

Zheda Mai, Ga Wu, Kai Luo, Scott Sanner

Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a user's preference as a latent vector by encoding their historical interactions. However, users often show diverse interests, which significantly increases the learning difficulty. In order to capture multifaceted user preferences, existing recommender systems either increase the en

Riccardo Frisenda, Yue Niu, Patricia Gant, Manuel Muñoz

The exfoliation of two naturally occurring van der Waals minerals, graphite and molybdenite, arouse an unprecedented level of interest by the scientific community and shaped a whole new field of research: 2D materials research. Several years later, the family of van der Waals materials that can be exfoliated to isolate 2D materials keeps growing, but most of

Pau Clusella, Antonio Politi

We analyse the collective behavior of a mean-field model of phase-oscillators of Kuramoto-Daido type coupled through pairwise interactions which depend on phase differences: the coupling function is composed of three harmonics. We provide convincing evidence of a transient but long-lasting chaotic collective chaos, which persists in the thermodynamic limit.

Xinliang Frederick Zhang, Heming Sun, Xiang Yue, Simon Lin

We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1

Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low

This paper presents the private-outsourced-Gaussian process-upper confidence bound (PO-GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization (BO) in the outsourced setting with a provable performance guarantee. We consider the outsourced setting where the entity holding the dataset and the entity performing BO are repre

Xavier Thomas

Recommender systems are a class of machine learning algorithms that provide relevant recommendations to a user based on the user's interaction with similar items or based on the content of the item. In settings where the content of the item is to be preserved, a content-based approach would be beneficial. This paper aims to highlight the advantages of the co

Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low

Collaborative machine learning (ML) is an appealing paradigm to build high-quality ML models by training on the aggregated data from many parties. However, these parties are only willing to share their data when given enough incentives, such as a guaranteed fair reward based on their contributions. This motivates the need for measuring a party's contribution

Xiaqing Ding, Yue Wang, Li Tang, Yanmei Jiao

Visual localization occupies an important position in many areas such as Augmented Reality, robotics and 3D reconstruction. The state-of-the-art visual localization methods perform pose estimation using geometry based solver within the RANSAC framework. However, these methods require accurate pixel-level matching at high image resolution, which is hard to sa

Navita Goyal, Roodram Paneri, Ayush Agarwal, Udit Kalani

Content is created for a well-defined purpose, often described by a metric or signal represented in the form of structured information. The relationship between the goal (metrics) of target content and the content itself is non-trivial. While large-scale language models show promising text generation capabilities, guiding the generated text with external met

Zihan Wang, Dheeraj Mekala, Jingbo Shang

In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names. This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class. We opt to attack this problem from a representation learning perspective -- ideal document representations shou

F. H. Garcia, C. Andreoiu, G. C. Ball, A. Bell

The $^{80}$Ge structure was investigated in a high-statistics $\beta$-decay experiment of $^{80}$Ga using the GRIFFIN spectrometer at TRIUMF-ISAC through $\gamma$, $\beta$-$e$, $e$-$\gamma$ and $\gamma$-$\gamma$ spectroscopy. No evidence was found for the recently reported 0$_2^{+}$ 639-keV level suggested as evidence for low-energy shape coexistence in $^{8

Xiaolong Li, Kui Wang

We establish lower bound for the first nonzero eigenvalue of the Laplacian on a closed K\"ahler manifold in terms of dimension, diameter, and lower bounds of holomorphic sectional curvature and orthogonal Ricci curvature. On compact K\"ahler manifolds with boundary, we prove lower bounds for the first nonzero Neumann or Dirichlet eigenvalue in terms of geome

Amirreza Silani, Michele Cucuzzella, Jacquelien M. A. Scherpen, Mohammad Javad Yazdanpanah

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and

Morgan B. Fox, Andrew S. Voyles, Jonathan T. Morrell, Lee A. Bernstein

A number of accelerator-based isotope production facilities utilize 100- to 200-MeV proton beams due to the high production rates enabled by high-intensity beam capabilities and the greater diversity of isotope production brought on by the long range of high-energy protons. However, nuclear reaction modeling at these energies can be challenging because of th

Limin Zhang

We must recognize that natural language is a way of information encoding, and it encodes not only the information but also the procedures for how information is processed. To understand natural language, the same as we conceive and design computer languages, the first step is to separate information (or data) and the processing procedures of information (or

Zining Zhang, Bingsheng He, Zhenjie Zhang

Non-parallel many-to-many voice conversion is recently attract-ing huge research efforts in the speech processing community. A voice conversion system transforms an utterance of a source speaker to another utterance of a target speaker by keeping the content in the original utterance and replacing by the vocal features from the target speaker. Existing solut

Kung-Hsiang Huang, Nanyun Peng

Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usua

Huda Khayrallah, João Sedoc

We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the `I don't know' problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnosti

Pablo Ducru, Vladimir Sobes, Gerald Hale, Mark Paris

In this follow-up article to [Shadow poles in the alternative parametrization of R-matrix theory, Ducru (2020)], we establish new results on scattering matrix pole expansions for complex wavenumbers in R-matrix theory. In the past, two branches of theoretical formalisms emerged to describe the scattering matrix in nuclear physics: R-matrix theory, and pole e

Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li

Multiplication (e.g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs). However, intensive multiplications cause expensive resource costs that challenge DNNs' deployment on resource-constrained edge devices, driving several attempts for multiplication-less deep networks. This paper presented ShiftAddNet, whose main inspiration is

Vikram Gupta, Haoyue Shi, Kevin Gimpel, Mrinmaya Sachan

We explore deep clustering of text representations for unsupervised model interpretation and induction of syntax. As these representations are high-dimensional, out-of-the-box methods like KMeans do not work well. Thus, our approach jointly transforms the representations into a lower-dimensional cluster-friendly space and clusters them. We consider two notio

Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

Thanks to graph neural networks (GNNs), semi-supervised node classification has shown the state-of-the-art performance in graph data. However, GNNs have not considered different types of uncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source un

Annalisa Conversano

We present a diagram surveying equivalence or strict implication for properties of different nature (algebraic, model theoretic, topological, etc.) about groups definable in o-minimal structures. All results are well-known and an extensive bibliography is provided.

Yan Zeng, Jian-Yun Nie

We investigate the problem of multi-domain Dialogue State Tracking (DST) with open vocabulary. Existing approaches exploit BERT encoder and copy-based RNN decoder, where the encoder predicts the state operation, and the decoder generates new slot values. However, in such a stacked encoder-decoder structure, the operation prediction objective only affects the

Hongping Wang, Zhaobin Li, Xinlei Zhang, Lixing Zhu

Coronavirus disease 2019 (COVID-19) has become a global pandemic infectious respiratory disease with high mortality and infectiousness. This paper investigates respiratory droplet transmission, which is critical to understanding, modeling and controlling epidemics. In the present work, we implemented flow visualization, particle image velocimetry (PIV) and p

Yan Zeng, Jian-Yun Nie

Pre-trained language models have been successfully used in response generation for open-domain dialogue. Four main frameworks have been proposed: (1) Transformer-ED using Transformer encoder and decoder separately for source and target sentences; (2) Transformer-Dec using Transformer decoder for both source and target sentences; (3) Transformer-MLM using Tra

Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari

Approaches for mitigating bias in supervised models are designed to reduce models' dependence on specific sensitive features of the input data, e.g., mentioned social groups. However, in the case of hate speech detection, it is not always desirable to equalize the effects of social groups because of their essential role in distinguishing outgroup-derogatory

Mehran Rahmani, Asif Al Zubayer Swapnil

In this work, a new SMS is proposed to achieve high tracking and suitable robustness. However, the chattering phenomenon should be regarded as the main drawback of the SMC. Therefore, a new compound control algorithm is used for reducing the chattering phenomenon. The applied compound control law constantly evaluates the error and send the correct value to t

Hyung Won Chung, Dan Garrette, Kiat Chuan Tan, Jason Riesa

State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications. In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the sepa

Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan

We propose a distributionally robust model for the influence maximization problem. Unlike the classic independent cascade model \citep{kempe2003maximizing}, this model's diffusion process is adversarially adapted to the choice of seed set. Hence, instead of optimizing under the assumption that all influence relationships in the network are independent, we se

Yanda Chen, Md Arafat Sultan, Vittorio Castelli

Automatically generated synthetic training examples have been shown to improve performance in machine reading comprehension (MRC). Compared to human annotated gold standard data, synthetic training data has unique properties, such as high availability at the possible expense of quality. In view of such differences, in this paper, we explore novel application

Ce Huang, Enze Zhang, Yong Zhang, Jinglei Zhang

The interplay between quenched disorder and critical behavior in quantum phase transitions is conceptually fascinating and of fundamental importance for understanding phase transitions. However, it is still unclear whether or not the quenched disorder influences the universality class of quantum phase transitions. More crucially, the absence of superconducti

Mehran Rahmani

In this research we propose a new compound Fractional Order Sliding Mode Controller (FOSMC) and SuperTwisting Controller (FOSMC+STC) to control of a MEMS gyroscope. A new sliding mode surface has been defined to design the proposed new sliding mode controller. The main advantages of a FOSMC is its high tracking performance and robustness against external per

Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (StruG) for text-to-SQL that can effectively lear

Mehran Rahmani, Asif Al Zubayer Swapnil, Ivan Rulik

Robot arms have been using in different systems, which the control of designed in desired trajectory is the main task. Also, it is anticipated that while in operation the developed 2DoF robot arm will be constantly encountered with noises such as friction forces. A new integral sliding mode control (NISMC) is therefore being introduced to suppress noise due

Yixin Liu, Graham Neubig, John Wieting

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originall

Suman Saha, Arindam Mishra, Syamal K. Dana, Chittaranjan Hens

We investigate spreading and recovery of disease in a square lattice, and in particular, emphasize the role of the initial distribution of infected patches in the network, on the progression of an endemic and initiation of a recovery process, if any, due to migration of both the susceptible and infected hosts. The disease starts in the lattice with three pos

Jianpeng Cheng, Devang Agrawal, Hector Martinez Alonso, Shruti Bhargava

We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations an

Pradyumna Chari, Krish Kabra, Doruk Karinca, Soumyarup Lahiri

Heart rate (HR) is an essential clinical measure for the assessment of cardiorespiratory instability. Since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease, there is a pressing need to deploy contactless HR sensing solutions for high-quality telemedicine evaluations. Existing computer vision methods that estim

Yosry A. Azzam, Emad A. -B. Abdel-Salam, Mohamed I. Nouh

The isothermal gas sphere is a particular type of Lane-Emden equation and is used widely to model many problems in astrophysics like stars, star clusters, and the formation of galaxies. In this paper, we present a computational scheme to simulate the conformable fractional isothermal gas sphere using an artificial neural network (ANN) technique and compare t

Jia-Zheng Sun

We study the fate of an impurity in a two-component, non-interacting Fermi gas under a non- Hermitian spin-orbit coupling (SOC) which is generated by dissipative Raman lasers. While SOC mixes the two spin species in the Fermi gas thus modifies the single-particle dispersions, we consider the case where the impurity only interacts with one of the spin species

Zining Zhang, Bingsheng He, Zhenjie Zhang

Extracting the speech of a target speaker from mixed audios, based on a reference speech from the target speaker, is a challenging yet powerful technology in speech processing. Recent studies of speaker-independent speech separation, such as TasNet, have shown promising results by applying deep neural networks over the time-domain waveform. Such separation n

Jianchao Bai, William W. Hager, Hongchao Zhang

An inexact accelerated stochastic Alternating Direction Method of Multipliers (AS-ADMM) scheme is developed for solving structured separable convex optimization problems with linear constraints. The objective function is the sum of a possibly nonsmooth convex function and a smooth function which is an average of many component convex functions. Problems havi

Hung Viet Chu

Zeckendorf proved that every positive integer has a unique partition as a sum of non-consecutive Fibonacci numbers. We study the difference between the number of summands in the partition of two consecutive integers. In particular, let $L(n)$ be the number of summands in the partition of $n$. We characterize all positive integers such that $L(n) > L(n+1)$, $

Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein

Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural

Zhaowei Zhu, Jingxuan Zhu, Ji Liu, Yang Liu

In this paper, we study \emph{Federated Bandit}, a decentralized Multi-Armed Bandit problem with a set of $N$ agents, who can only communicate their local data with neighbors described by a connected graph $G$. Each agent makes a sequence of decisions on selecting an arm from $M$ candidates, yet they only have access to local and potentially biased feedback/

Sarah Wiegreffe, Ana Marasović, Noah A. Smith

In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance. While prior work focuses on extractive rationales (a subset of the input words), we investigate their less-studied counterpart: free-text natural language rationales. We demonstrate that pipelines, existing models for faithful extr

Wenhan Dai, Zhi Zeng, Daowei Dou, Hao Ma

The monitoring of Cs-137 in seawater using scintillation detector relies on the spectrum analysis method to extract the Cs-137 concentration. And when in poor statistic situation, the calculation result of the traditional net peak area (NPA) method has a large uncertainty. We present a machine learning based method to better analyze the gamma-ray spectrum wi

Baosong Wu

Recently the rapid-scan technique is reviving in NMR or EPR, because of its benefits of zero-dead time and low RF power. While signal baseline is still a big problem in such experiments, time-share method has been used to indirectly avoid it. However, it is obviously not a truly zero-dead time method. Other data-processing methods were also adopted to deal w

Deepak Pahwa, Umut Dur, Binil Starly

Two-sided manufacturing-as-a-service (MaaS) marketplaces connect clients requesting manufacturing services to suppliers providing those services. Matching mechanisms i.e. allocation of clients' orders to suppliers is a key design parameter of the marketplace platform. The platform might perform an allocation to maximize its revenue or optimize for social wel

David Alvarez-Melis, Nicolò Fusi

Various machine learning tasks, from generative modeling to domain adaptation, revolve around the concept of dataset transformation and manipulation. While various methods exist for transforming unlabeled datasets, principled methods to do so for labeled (e.g., classification) datasets are missing. In this work, we propose a novel framework for dataset trans

Elham Izadi, Herbert Lange

We study symmetric correspondences with completely decomposable minimal equation on smooth projective curves $C$. The Jacobian of $C$ then decomposes correspondingly. For all positive integers $g$ and $\ell$, we give series of examples of smooth curves $C$ of genus $n^\ell (g-1) +1$ with correspondences satisfying minimal equations of degree $\ell+1$ such th

Zhiyu Chen, Honglei Liu, Hu Xu, Seungwhan Moon

Existing conversational systems are mostly agent-centric, which assumes the user utterances would closely follow the system ontology (for NLU or dialogue state tracking). However, in real-world scenarios, it is highly desirable that the users can speak freely in their own way. It is extremely hard, if not impossible, for the users to adapt to the unknown sys

Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller

Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations. In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (

Kexin Yang, Xiaofei Zhou, Iulian Radu

Recently, the HCI community has seen an increased interest in applying Virtual Reality (VR), AugmentedReality (AR) and Mixed Reality (MR) into educational settings. Despite many literature reviews, there stilllacks a clear framework that reveals the different design dimensions in educational Extended Reality (XR)systems. Addressing this gap, we synthesize a

Hamid Reza Moradi, Mohammad Sababheh

In this article, we present new inequalities for the numerical radius of the sum of two Hilbert space operators. These new inequalities will enable us to obtain many generalizations and refinements of some well known inequalities, including multiplicative behavior of the numerical radius and norm bounds. Among many other applications, it is shown that if $T$

Xinyu Zhao, Shih-ting Lin, Greg Durrett

A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more. We present a method of automatically collecting distantly-supervised examples of temporal relations. We scrape and automatically label event pairs where the temporal relations are made explicit

Justin Bui, Robert J Marks

Neural networks have often been described as black boxes. A generic neural network trained to differentiate between kittens and puppies will classify a picture of a kumquat as a kitten or a puppy. An autoencoder watch dog screens trained classifier/regression machine input candidates before processing, e.g. to first test whether the neural network input is a

Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers

Yanping Liu, Yang Jiao, Guoqiang Li, Gao Wang

Cell migration is an indispensable physiological and pathological process for normal tissue development and cancer metastasis, which is greatly regulated by intracellular signal pathways and extracellular microenvironment (ECM). However, there is a lack of adequate tools to analyze the time-varying cell migration characteristics because of the effects of som

Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan

Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client. Unfortunately, prior works focus on the models trained over the Euclidean space, e.g., images and texts, while how to extract a GNN model that contains a g

Pintu Bhunia, Kallol Paul

Several refinements of norm and numerical radius inequalities of bounded linear operators on a complex Hilbert space are given. In particular, we show that if $A$ is a bounded linear operator on a complex Hilbert space, then $$ \frac{1}{4}\|A^*A+AA^*\| \leq \frac{1}{8}\bigg( \|A+A^*\|^2+\|A-A^*\|^2 +c^2(A+A^*)+c^2(A-A^*)\bigg) \leq w^2(A)$$ and \begin{eqnarr

Dongyub Lee, Byeongil Ko, Myeong Cheol Shin, Taesun Whang

Detecting disfluencies in spontaneous speech is an important preprocessing step in natural language processing and speech recognition applications. Existing works for disfluency detection have focused on designing a single objective only for disfluency detection, while auxiliary objectives utilizing linguistic information of a word such as named entity or pa

K. Zhang, Z. B. Zhang, Y. F. Huang, L. M. Song

We systematically analyze three GRB samples named as radio-loud, radio-quiet and radio-none afterglows, respectively. It is shown that dichotomy of the radio-loud afterglows is not necessary. Interestingly, we find that the intrinsic durations ($T_{int}$), isotropic energies of prompt gamma-rays ($E_{\gamma, iso}$) and redshifts ($z$) of their host galaxies

Pengfei Li, Boris A. Malomed, Dumitru Mihalache

We demonstrate that fractional cubic-quintic nonlinear Schr\"odinger equation,characterized by its L\'evy index, maintains ring-shaped soliton clusters ("necklaces") carrying orbital angular momentum. They can be built, in the respective optical setting, as circular chains of fundamental solitons linked by a vortical phase field. We predict semi-analytically

Deniz Mengu, Yair Rivenson, Aydogan Ozcan

Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications. Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces, design

Baodi Shan, Aabid Shamji, Jiannan Tian, Guanpeng Li

Error-bounded lossy compression is becoming more and more important to today's extreme-scale HPC applications because of the ever-increasing volume of data generated because it has been widely used in in-situ visualization, data stream intensity reduction, storage reduction, I/O performance improvement, checkpoint/restart acceleration, memory footprint reduc

Ian J. M. Crossfield, Diana Dragomir, Nicolas B. Cowan, Tansu Daylan

Phase curve measurements provide a global view of the composition, thermal structure, and dynamics of exoplanet atmospheres. Although most of the dozens of phase curve measurements made to date are of large, massive hot Jupiters, there is considerable interest in probing the atmospheres of the smaller planets that are the more typical end product of planet f

Diana Dragomir, Ian J. M. Crossfield, Bjorn Benneke, Ian Wong

Non-rocky sub-jovian exoplanets in high irradiation environments are rare. LTT 9979b, also known as TESS Object of Interest (TOI) 193.01, is one of the few such planets discovered to date, and the first example of an ultra-hot Neptune. The planet's bulk density indicates that it has a substantial atmosphere, so to investigate its atmospheric composition and

Conor A. Nixon, Alexander E. Thelen, Martin A. Cordiner, Zbigniew Kisiel

We report the first detection on Titan of the small cyclic molecule cyclopropenylidene (c-C3H2) from high sensitivity spectroscopic observations made with the Atacama Large Millimeter/sub-millimeter Array (ALMA). Multiple lines of cyclopropenylidene were detected in two separate datasets: ~251 GHz in 2016 (Band 6) and ~352 GHz in 2017 (Band 7). Modeling of t

Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang

Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not o

Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal, Aston Zhang

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed,

Seolhwa Lee, Heuiseok Lim, João Sedoc

There is a multitude of novel generative models for open-domain conversational systems; however, there is no systematic evaluation of different systems. Systematic comparisons require consistency in experimental design, evaluation sets, conversational systems and their outputs, and statistical analysis. We lay out a protocol for the evaluation of conversatio

Brandon R. Ferrer, Juan Ruben Gomez-Solano, Alejandro V. Arzola

Thermally activated transitions are ubiquitous in nature, occurring in complex environments which are typically conceived as ideal viscous fluids. We report the first direct observations of a Brownian bead transiting between the wells of a bistable optical potential in a viscoelastic fluid with a single long relaxation time. We precisely characterize both th

Sumit Dahal, Michael K. Brewer, John W. Appel, Aamir Ali

Using the Cosmology Large Angular Scale Surveyor, we measure the disk-averaged absolute Venus brightness temperature to be 432.3 $\pm$ 2.8 K and 355.6 $\pm$ 1.3 K in the Q and W frequency bands centered at 38.8 and 93.7 GHz, respectively. At both frequency bands, these are the most precise measurements to date. Furthermore, we observe no phase dependence of