May 2022 arXiv papers — page 35

Alexandr Buryak

An algorithm to determine all the Gromov-Witten invariants of any smooth projective curve was obtained by Okounkov and Pandharipande in 2006. They identified stationary invariants with certain Hurwitz numbers and then presented Virasoro type constraints that allow to determine all the other Gromov-Witten invariants in terms of the stationary ones. In the cas

Nicolas Amiune, Zhiwei Fan, Vladislav V. Pankratov, Danila N. Puzyrev

The potential of frequency comb spectroscopy has aroused great interest in generating mid-infrared frequency combs in the integrated photonic setting. However, despite remarkable progress in microresonators and quantum cascade lasers, the availability of suitable mid-IR comb sources remains scarce. Here, we present a new approach for the generation of mid-IR

Abhishek Gupta, Sruthi Nair, Raunak Joshi, Vidya Chitre

Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering changes in very deep models with many regularization based techniques. In this paper we train a deep neural network that uses many regularization layers

Anthony Conway, Lisa Piccirillo, Mark Powell

We classify topological $4$-manifolds with boundary and fundamental group $\mathbb{Z}$, under some assumptions on the boundary. We apply this to classify surfaces in simply-connected $4$-manifolds with $S^3$ boundary, where the fundamental group of the surface complement is $\mathbb{Z}$. We then compare these homeomorphism classifications with the smooth set

Anton Karpishkov, Vladimir Saleev

We study a large-$p_T$ three-photon production in proton-proton collisions at the LHC. We use the leading order (LO) approximation of the parton Reggeization approach consistently merged with the next-to-leading order corrections originated from the emission of additional jet. For numerical calculations we use the parton-level generator KaTie and modified KM

Céline Moucer, Adrien Taylor, Francis Bach

First-order methods are often analyzed via their continuous-time models, where their worst-case convergence properties are usually approached via Lyapunov functions. In this work, we provide a systematic and principled approach to find and verify Lyapunov functions for classes of ordinary and stochastic differential equations. More precisely, we extend the p

Kathleen C. Fraser, Svetlana Kiritchenko, Esma Balkir

In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong. This is typically done in a bottom-up fashion, by exposing the model to different scenarios, annotated with human moral judgements. One qu

Shuyu Yin, Tao Luo, Peilin Liu, Zhi-Qin John Xu

Gradient descent or its variants are popular in training neural networks. However, in deep Q-learning with neural network approximation, a type of reinforcement learning, gradient descent (also known as Residual Gradient (RG)) is barely used to solve Bellman residual minimization problem. On the contrary, Temporal Difference (TD), an incomplete gradient desc

Joane S. Elleouet, Patrick Graham, Nikolai Kondratev, Abby K. Morgan

Many countries conduct a full census survey to report official population statistics. As no census survey ever achieves 100 per cent response rate, a post-enumeration survey (PES) is usually conducted and analysed to assess census coverage and produce official population estimates by geographic area and demographic attributes. Considering the usually small s

Trond Linjordet, Krisztian Balog

Knowledge graph question answering (KGQA) facilitates information access by leveraging structured data without requiring formal query language expertise from the user. Instead, users can express their information needs by simply asking their questions in natural language (NL). Datasets used to train KGQA models that would provide such a service are expensive

Xu-Dan Xie, Xingyu Guo, Hongxi Xing, Zheng-Yuan Xue

Confinement of quarks due to the strong interaction and the deconfinement at high temperatures and high densities are a basic paradigm for understanding the nuclear matter. Their simulation, however, is very challenging for classical computers due to the sign problem of solving equilibrium states of finite-temperature quantum chromodynamical systems at finit

Petr Stepanov, Dmitry L. Shcherbakov, Shi Che, Marc W. Bockrath

Long-distance spin transport through anti-ferromagnetic insulators (AFMIs) is a long-standing goal of spintronics research. Unlike conventional spintronics systems, monolayer graphene in quantum Hall regime (QH) offers an unprecedented tuneability of spin-polarization and charge carrier density in QH edge states. Here, using gate-controlled QH edges as spin-

Muhammad Yasir Muzayan Haq, Mattijs Jonker, Roland van Rijswijk-Deij, KC Claffy

We leverage large-scale DNS measurement data on authoritative name servers to study the reactions of domain owners affected by the 2016 DDoS attack on Dyn. We use industry sources of information about domain names to study the influence of factors such as industry sector and website popularity on the willingness of domain managers to invest in high availabil

Zdeněk Dvořák, Benjamin Moore, Abhiruk Lahiri

We prove that it is NP-hard to decide whether a graph is the square of a 6-apex graph. This shows that the square root problem is not tractable for squares of sparse graphs (or even graphs from proper minor-closed classes).

Xueru Wu, Yao Ma, Liangyun Chen

In this paper, we introduce the notion of a relative Rota-Baxter operator of weight $\lambda$ on a Lie triple system with respect to an action on another Lie triple system, which can be characterized by the graph of their semidirect product. We also establish a cohomology theory for a relative Rota-Baxter operator of weight $\lambda$ on Lie triple systems an

Ming-Xu Su, Tian-Xiang Zhu, Chao Liu, Zong-Quan Zhou

Quantum memory is a fundamental building block for large-scale quantum networks. On-demand optical storage with a large bandwidth, a high multimode capacity and an integrated structure simultaneously is crucial for practical application. However, this has not been demonstrated yet. Here, we fabricate an on-chip waveguide in a $\mathrm {^{151}Eu^{3+}:Y_2SiO_5

Weijia Yao, Bohuan Lin, Brian D. O. Anderson, Ming Cao

Accurately following a geometric desired path in a two-dimensional space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this paper, we develop a composite guiding vector field

Hongjun Gao, Zhaoyang Qiu, Huaqiao Wang

The existence of global martingale weak solution for the 2D and 3D stochastic Cahn-Hilliard-Navier-Stokes equations driven by multiplicative noise in a smooth bounded domain is established. In particular, the system is supplied with the dynamic boundary condition which accounts for the interaction between the fluid components and the rigid walls. The proof i

Alessandro Calamai, Maria Patrizia Pera, Marco Spadini

We study, by means of a topological approach, the forced oscillations of second order functional retarded differential equations subject to periodic perturbations. We consider a delay-type functional dependence involving a gamma probability distribution. By a linear chain trick we obtain a first order system of ODE's whose $T$-periodic solutions correspond t

Matej Kloska, Viera Rozinajova

This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it works rel

Tim Lackorzynski, Max Ostermann, Stefan Köpsell, Hermann Härtig

Future industrial networks will consist of a complex mixture of new and legacy components, while new use cases and applications envisioned by Industry 4.0 will demand increased flexibility and dynamics from these networks. Industrial security gateways will become an important building block to tackle new security requirements demanded by these changes. Their

Tianyi Ko, Koichi Nishiwaki, Koji Terada, Yusuke Tanaka

In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is det

Andrea Gesmundo, Jeff Dean

Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ML models rely on high customization for each task and leverage size and data scale rather than scaling the number of tasks. Also, continual learning, that a

Chengfeng Zhang, Hongji Wu, Baoyi Huang, Hao Yuan

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based

Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian

The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the robustness to distribution shift from the algorithmic perspective, i.e., designing better training algorithms to help the generalization in shifted test distributions. This paper st

Xiaonan Gao, Sen Wu, Wenjun Zhou

We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Designed specifically for categorical data,

Benjamin Dubois-Taine, Francis Bach, Quentin Berthet, Adrien Taylor

We consider the problem of minimizing the sum of two convex functions. One of those functions has Lipschitz-continuous gradients, and can be accessed via stochastic oracles, whereas the other is "simple". We provide a Bregman-type algorithm with accelerated convergence in function values to a ball containing the minimum. The radius of this ball depends on pr

S. Giarratana, L. Rhodes, B. Marcote, R. Fender

GRB 201015A is a long-duration Gamma-Ray Burst (GRB) which was detected at very high energies (> 100 GeV) using the MAGIC telescopes. If confirmed, this would be the fifth and least luminous GRB ever detected at this energies. We performed a radio follow-up of GRB 201015A over twelve different epochs, from 1.4 to 117 days post-burst, with the Karl G. Jansky

Sascha Saralajew, Ammar Shaker, Zhao Xu, Kiril Gashteovski

With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no agreed standard on how explainable AI systems should be assessed. Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accep

Tim Lackorzynski, Sebastian Rehms, Tao Li, Stefan Köpsell

Trends like Industry 4.0 will pose new challenges for future industrial networks. Greater interconnectedness, higher data volumes as well as new requirements for speeds as well as security will make new approaches necessary. Performanceoptimized networking techniques will be demanded to implement new use cases, like network separation and isolation, in a sec

Andrew J. Ramsay, Reza Hekmati, Charlie J. Patrickson, Simon Baber

Spin defects in foils of hexagonal boron nitride are an attractive platform for magnetic field imaging, since the probe can be placed in close proximity to the target. However, as a III-V material the electron spin coherence is limited by the nuclear spin environment, with spin echo coherence time of $\sim100~\mathrm{ns}$ at room temperature accessible magne

Raphael P. B. Piovezan, Pedro Paulo de Andrade Junior

This article aims to propose and apply a machine learning method to analyze the direction of returns from Exchange Traded Funds (ETFs) using the historical return data of its components, helping to make investment strategy decisions through a trading algorithm. In methodological terms, regression and classification models were applied, using standard dataset

Marie S Rider, Rakesh Arul, Jeremy J Baumberg, William L Barnes

The strong coupling of molecules with surface plasmons results in hybrid states which are part molecule, part surface-bound light. Since molecular resonances may acquire the spatial coherence of plasmons, which have mm-scale propagation lengths, strong-coupling with molecular resonances potentially enables long-range molecular energy transfer. Gratings are o

Roberto Fontana, Patrizia Semeraro

The main contribution of this paper is to find a representation of the class $\mathcal{F}_d(p)$ of multivariate Bernoulli distributions with the same mean $p$ that allows us to find its generators analytically in any dimension. We map $\mathcal{F}_d(p)$ to an ideal of points and we prove that the class $\mathcal{F}_d(p)$ can be generated from a finite set of

Igor Krylov, Takuzo Okada, Erik Paemurru, Jihun Park

The $4 n^2$-inequality for smooth points plays an important role in the proofs of birational (super)rigidity. The main aim of this paper is to generalize such an inequality to terminal singular points of type $cA_1$, and obtain a $2 n^2$-inequality for $cA_1$ points. As applications, we prove birational (super)rigidity of sextic double solids, many other pri

Moritz Schneider, Ramya Jayaram Masti, Shweta Shinde, Srdjan Capkun

The growing complexity of modern computing platforms and the need for strong isolation protections among their software components has led to the increased adoption of Trusted Execution Environments (TEEs). While several commercial and academic TEE architectures have emerged in recent times, they remain hard to compare and contrast. More generally, existing

Mehmet Günay, Priyam Das, Emre Yuce, Emre Ozan Polat

Integration of devices generating nonclassical states~(such as entanglement) into photonic circuits is one of the major goals in achieving integrated quantum circuits~(IQCs). This is demonstrated successfully in recent decades. Controlling the nonclassicality generation in these micron-scale devices is also crucial for the robust operation of the IQCs. Here,

Zhora Gevorgyan

The effectiveness of Object Detection, one of the central problems in computer vision tasks, highly depends on the definition of the loss function - a measure of how accurately your ML model can predict the expected outcome. Conventional object detection loss functions depend on aggregation of metrics of bounding box regression such as the distance, overlap

Chemseddine Benkalfate, Mohammed Feham, Achour Ouslimani, Abed-Elhak Kasbari

The proposed UWB antenna covers mobile communications (GSM, EDG, UMTS(3G), LTE(4G)) and wireless networks (WIFI, WiMAX), within a theoretical bandwidth defined from 780MHz to 4.22GHz. The UWB antenna is designed and realized on a FR-4 substrate with an electrical permittivity of 4.4. It presents a 98.75% average analytical efficiency and an omnidirectional r

Lidan Zhang, Shengyuan Chang, Xi Chen, Yimin Ding

Metalenses, artificially engineered subwavelength nanostructures to focus light within ultrathin thickness, promise potential for a paradigm shift of conventional optical devices. However, the aperture sizes of metalenses are usually bound within hundreds of micrometers by the commonly-used scanning-based fabrication methods, limiting their usage on practica

Sebastian Bitzer, Julian Renner, Antonia Wachter-Zeh, Violetta Weger

In this paper, we study the hardness of decoding a random code endowed with the cover metric. As the cover metric lies in between the Hamming and rank metric, it presents itself as a promising candidate for code-based cryptography. We give a polynomial-time reduction from the classical Hamming-metric decoding problem, which proves the NP-hardness of the deco

Niklas Gögge, Elias Rohrer, Florian Tschorsch

Nodes in the Lightning Network synchronise routing information through a gossip protocol that makes use of a staggered broadcast mechanism. In this work, we show that the convergence delay in the network is larger than what would be expected from the protocol's specification and that payment attempt failures caused by the delay are more frequent, the larger

Philip Thomas, Leonardo Ruscio, Olivier Morin, Gerhard Rempe

Entanglement is a powerful concept with an enormous potential for scientific and technological advances. A central focus in modern research is to extend the generation and control of entangled states from few to many qubits, and protect them against decoherence. Optical photons play a prominent role as these qubit carriers are naturally robust and easy to ma

Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo

One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data. However, most current approaches require manually engineered symbol

Robert A. Wilson

I propose the group SL(4,R) as a generalisation of the Dirac group SL(2,C) used in quantum mechanics, as a possible basis on which to build a more general theory from which the standard model of particle physics might be derived as an approximation in an appropriate limit.

Enguerrand Bon-Lavigne, Loïc Le Treust, Nicolas Raymond, Julien Royer

We consider the Dirichlet Laplacian with uniform magnetic field on a curved strip in two dimensions. We give a sufficient condition ensuring the existence of the discrete spectrum in the strong magnetic field limit.

Lucas Kook, Andrea Götschi, Philipp FM Baumann, Torsten Hothorn

Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently, partially interpretable semi-structured deep transformation models. However, interpretability of the ensemble members is generally lost upo

Tianxiao Han, Qianqian Yang, Zhiguo Shi, Shibo He

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wireless communication methods that focus on the transmission of abstract symbols, semantic communication approaches attempt to achieve better transmission efficiency by only sending

Irati Alonso Calafell, Lee A. Rozema, David Alcaraz Iranzo, Alessandro Trenti

Nonlinear nanophotonics leverages engineered nanostructures to funnel light into small volumes and intensify nonlinear optical processes with spectral and spatial control. Due to its intrinsically large and electrically tunable nonlinear optical response, graphene is an especially promising nanomaterial for nonlinear optoelectronic applications. Here we repo

Fatemeh Zarei, Jan Ryckebusch, Koen Schoors, Luis E C Rocha

Innovation is fundamental for development and provides a competitive advantage for societies. It is the process of creating more complex technologies, ideas, or protocols from existing ones. While innovation may be created by single agents (i.e. individuals or organisations), it is often a result of social interactions between agents exchanging and combining

T. V. Anoop, Mrityunjoy Ghosh

Let $\Omega$ be a multiply-connected domain in $\mathbb{R}^n$ ($n\geq 2$) of the form $\Omega=\Omega_{\text{out}}\setminus \bar{\Omega_{\text{in}}}.$ Set $\Omega_D$ to be either $\Omega_{\text{out}}$ or $\Omega_{\text{in}}$. For $p\in (1,\infty),$ and $q\in [1,p],$ let $\tau_{1,q}(\Omega)$ be the first eigenvalue of \begin{equation*} -\Delta_p u =\tau \left(

M. Hübner, D. Huppenkothen, P. D. Lasky, A. R. Inglis

Analyses of quasi-periodic oscillations (QPOs) are important to understanding the dynamic behaviour in many astrophysical objects during transient events like gamma-ray bursts, solar flares, magnetar flares and fast radio bursts. Astrophysicists often search for QPOs with frequency-domain methods such as (Lomb-Scargle) periodograms, which generally assume po

A. C. Williams

Divertor plasma detachment is likely needed for the function of magnetically confined nuclear fusion. It greatly reduces the particle and heat flux incident on a target, and thus reduces the sputtering and heat loading on the target. It is therefore advantageous to have accurate simulations of plasma behaviour in the divertor to design future Tokamak diverto

Usman Sanusi, Sona John, Johannes Mueller, Aurélien Tellier

Mathematical modelling of epidemiological and coevolutionary dynamics is widely being used to improve disease management strategies of infectious diseases. Many diseases present some form of intra-host quiescent stage, also known as covert infection, while others exhibit dormant stages in the environment. As quiescent/dormant stages can be resistant to drug,

Hao Wang, Wenjie Qu, Gilad Katz, Wenyu Zhu

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Trans

Antoine Aerts, Alex Brown, Fabien Gatti

The intramolecular vibrational relaxation dynamics of formic acid and its deuterated isotopologues is simulated on the full-dimensional potential energy surface of Richter and Carbonniere [F. Richter and P. Carbonniere, J. Chem. Phys., 148, 064303 (2018)] using the Heidelberg MCTDH package. Mode couplings with the torsion coordinate capturing most of the tra

Aymen Hamrouni, Hakim Ghazzai, Yehia Massoud

Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering IoT devices existing in the network and requesting corresponding services from them. As the highly dynamic nature of the IoT environme

Kajal Kumari, Snehlata Shakya, Mayank Goswami

Computed tomography (CT) has become a vital tool in a variety of fields as a result of technological developments and continual improvement. High-quality CT images are desirable for image interpretation and obtaining information from CT images. A variety of things influence the CT image quality. Various research groups have investigated and attempted to impr

Xiangshan Gao, Xingjun Ma, Jingyi Wang, Youcheng Sun

Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning i

Rubén Medina

In this paper, two main results concerning uniformly continuous retractions are proved. First, an $\alpha$-H\"older retraction from any separable Banach space onto a compact convex subset whose closed linear span is the whole space is constructed for every positive $\alpha<1$. This constitutes a positive solution to a H\"older version of a question raised by

F. Cuteri, J. Goswami, F. Karsch, Anirban Lahiri

We discuss the interplay between chiral and center sector phase transitions that occur in QCD with an imaginary quark chemical potential $\mu=i(2n+1) \pi T/3$. Based on a finite size scaling analysis in (2+1)-flavor QCD using HISQ fermions with a physical strange quark mass and a range of light quark masses, we show that the endpoint of the line of first-ord

Florian Kalinke, Marco Heyden, Georg Gntuni, Edouard Fouché

Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled approach to detect changes is to compare the distributions of observations within the stream to each other via hypothesis testing. Maximum mean discrepancy (MMD), a (semi-)metric on

Aditi Sagar, Aman Swaraj, Karan Verma

Biomedical imaging analysis combined with artificial intelligence (AI) methods has proven to be quite valuable in order to diagnose COVID-19. So far, various classification models have been used for diagnosing COVID-19. However, classification of patients based on their severity level is not yet analyzed. In this work, we classify covid images based on the s

Ruben Lier, Charlie Duclut, Stefano Bo, Jay Armas

We compute the response matrix for a tracer particle in a compressible fluid with odd viscosity living on a two-dimensional surface. Unlike the incompressible case, we find that an odd compressible fluid can produce an odd lift force on a tracer particle. Using a "shell localization" formalism, we provide analytic expressions for the drag and odd lift forces

Thomas Place, Marc Zeitoun

We study a standard operator on classes of languages: unambiguous polynomial closure. We prove that for every class C of regular languages satisfying mild properties, the membership problem for its unambiguous polynomial closure UPol(C) reduces to the same problem for C. We also show that unambiguous polynomial closure coincides with alternating left and rig

Derek Chong, Jenny Hong, Christopher D. Manning

We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly outperforms more complex error-detection mechanisms proposed in previous work. To this end, we contribute a novel method for

Qinyuan Ye, Juan Zha, Xiang Ren

Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapting to new tasks efficiently. However, the potential of these multi-task models may be limited as they use the same set of parameters for all tasks. In contrast, humans tackle tasks in a more flexible way, by making proper presumptions on what skills and k

Jun Yan, Vansh Gupta, Xiang Ren

Backdoor attacks have become an emerging threat to NLP systems. By providing poisoned training data, the adversary can embed a "backdoor" into the victim model, which allows input instances satisfying certain textual patterns (e.g., containing a keyword) to be predicted as a target label of the adversary's choice. In this paper, we demonstrate that it is pos

İbrahim Halil Efendioğlu, Adnan Talha Mutlu, Yakup Durmaz

The aim of the study is to determine the effect of the brand on purchasing decision on generation Y. For this purpose, a face-to-face survey was conducted with 231 people in the Y age range who have purchased mobile phones in the last year. The study was conducted with young academicians and university students working in Harran University Vocational High Sc

Qiulei Wang, Lei Yan, Gang Hu, Chao Li

We propose an open-source python platform for applications of Deep Reinforcement Learning (DRL) in fluid mechanics. DRL has been widely used in optimizing decision-making in nonlinear and high-dimensional problems. Here, an agent maximizes a cumulative reward with learning a feedback policy by acting in an environment. In control theory terms, the cumulative

Damilola Omitaomu, Shabnam Tafreshi, Tingting Liu, Sven Buechel

Empathy is a cognitive and emotional reaction to an observed situation of others. Empathy has recently attracted interest because it has numerous applications in psychology and AI, but it is unclear how different forms of empathy (e.g., self-report vs counterpart other-report, concern vs. distress) interact with other affective phenomena or demographics like

Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han

Logical table-to-text generation is a task that involves generating logically faithful sentences from tables, which requires models to derive logical level facts from table records via logical inference. It raises a new challenge on the logical-level content planning of table-to-text models. However, directly learning the logical inference knowledge from tab

Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng

The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE). It adopts a recommend-revise annotation scheme so as to have a large-scale annotated dataset. However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent. We analyze the causes and effects of the o

Antônio H. Ribeiro, Dave Zachariah, Thomas B. Schön

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against such examples. It is formulated as a min-max problem, searching for the best solution when the training data was corrupted by the worst-case attacks. For linear regres

Clara Na, Sanket Vaibhav Mehta, Emma Strubell

Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP. Inspired by prior works suggesting a connection between simpler, more generalizable models and those that lie within wider loss basins, we hypothes

Zhihan Zhou, Jiangchao Yao, Yanfeng Wang, Bo Han

Self-supervised learning has achieved a great success in the representation learning of visual and textual data. However, the current methods are mainly validated on the well-curated datasets, which do not exhibit the real-world long-tailed distribution. Recent attempts to consider self-supervised long-tailed learning are made by rebalancing in the loss pers

Suhail Dhawan, Antonin Borderies, Hayley J. Macpherson, Asta Heinesen

The cosmological principle asserts that the Universe looks spatially homogeneous and isotropic on sufficiently large scales. Given the fundamental implications of the cosmological principle, it is important to empirically test its validity on various scales. In this paper, we use the Type Ia supernova (SN~Ia) magnitude-redshift relation, from both the Panthe

Avichai Levy, Erez Karpas

Recent years have seen an increasing number of applications that have a natural language interface, either in the form of chatbots or via personal assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana (Microsoft). To use these applications, a basic dialog between the robot and the human is required. While this kind of dialog exists t

Fabio Podestà, Alberto Raffero

Starting from compact symmetric spaces of inner type, we provide infinite families of compact homogeneous spaces carrying invariant non-flat Bismut connections with vanishing Ricci tensor. These examples turn out to be generalized symmetric spaces of order $4$ and (up to coverings) can be realized as minimal submanifolds of the Bismut flat model spaces, name

Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim

A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT, perform well at zero- and few-shot informati

Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse uneth

Aarish Chhabra, Nandini Bansal, Venktesh V, Mukesh Mohania

Exams are conducted to test the learner's understanding of the subject. To prevent the learners from guessing or exchanging solutions, the mode of tests administered must have sufficient subjective questions that can gauge whether the learner has understood the concept by mandating a detailed answer. Hence, in this paper, we propose a novel hybrid unsupervis

Kirill Mitsen, Olga Ivanenko

We have previously shown that most of the anomalies in the superconducting characteristics of cuprates and ferropnictides observed at dopant concentrations within the superconducting dome, as well as the position of the domes in the phase diagrams, do not require knowledge of the details of their electronic structure for explanation, but can be understood an

Jiao Sun, Swabha Swayamdipta, Jonathan May, Xuezhe Ma

Free-form rationales aim to aid model interpretability by supplying the background knowledge that can help understand model decisions. Crowdsourced rationales are provided for commonsense QA instances in popular datasets such as CoS-E and ECQA, but their utility remains under-investigated. We present human studies which show that ECQA rationales indeed provi

Shreyas Pai, Sriram V. Pemmaraju

In this paper we present a deterministic $O(\log\log n)$-round algorithm for the 2-ruling set problem in the Massively Parallel Computation model with $\tilde{O}(n)$ memory; this algorithm also runs in $O(\log\log n)$ rounds in the Congested Clique model. This is exponentially faster than the fastest known deterministic 2-ruling set algorithm for these model

Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho

Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive. Intuitively, ground-truth labels should have as much impact in in-context learning (ICL) as supervised learning, but recent work reported that the input-label correspondence is significantly less impor

Muzammil Hussain Rammay, Sergey Alyaev, Ahmed H Elsheikh

The advent of fast sensing technologies allows for real-time model updates in many applications where the model parameters are uncertain. Bayesian algorithms, such as ensemble smoothers, offer a real-time probabilistic inversion accounting for uncertainties. However, they rely on the repeated evaluation of the computational models, and deep neural network (D

Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki

We propose a fundamental theory on ensemble learning that answers the central question: what factors make an ensemble system good or bad? Previous studies used a variant of Fano's inequality of information theory and derived a lower bound of the classification error rate on the basis of the $\textit{accuracy}$ and $\textit{diversity}$ of models. We revisit t

Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng

Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks. However, auto-regressive PLMs are challenged by recent emerging numerical reasoning datasets, such as TAT-QA, due to the error

Benjamin Brubaker, Gabriel Frieden, Pavlo Pylyavskyy, Travis Scrimshaw

The geometric crystal operators and geometric $R$-matrices (or geometric Weyl group actions) give commuting actions on the field of rational functions in $mn$ variables. We study the invariants of various combinations of these actions, which we view as "crystal analogues" of the invariants of $S_m$, ${\rm SL}_m$, $S_n \times S_m$, ${\rm SL}_n \times \, S_m$,

Mujeen Sung, Jungsoo Park, Jaewoo Kang, Danqi Chen

Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. In this paper, we introduce TOUR (Test-Time Optimization of Query Representations), which further optimizes instance-level query representations guided by signals from test-time retrieval results. We leverage a cross-enc

Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu

There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generation-based zero-shot learning has achieved impressive success. In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used to train a task-specific model with or

Shaozhi Li, Steven Johnston

We study a multi-orbital Hubbard--Su-Schrieffer-Heeger model for the one-dimensional (1D) corner-shared cuprates in the adiabatic and nonadiabatic limits using the exact diagonalization and determinant quantum Monte Carlo methods. Our results demonstrate that lattice dimerization can be achieved only over a narrow range of couplings slightly below a critical

Yang Xu, Yutai Hou, Wanxiang Che, Min Zhang

Multilingual pre-trained language models can learn task-specific abilities or memorize facts across multiple languages but inevitably make undesired predictions with specific inputs. Under similar observation, model editing aims to post-hoc calibrate a model targeted to specific inputs with keeping the model's raw behavior. However, existing work only studie

Simran Khanuja, Sebastian Ruder, Partha Talukdar

In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world's languages, be equitable, i.e., not unduly biased towards any particular language, and be inclusive of all users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluati

S. Ta Ho, V. Nam Do

The mechanisms of optical activity and quantum transport of twisted bilayer graphene are studied. The formation of unique electron states in the bilayer systems is studied using an effective continuum model. Such states are shown to support the correlation of transverse motions of electrons in two graphene layers. Because of the chiral structure of the atomi

Zexuan Zhong, Tao Lei, Danqi Chen

Recent work has improved language models (LMs) remarkably by equipping them with a non-parametric memory component. However, most existing approaches only introduce mem-ories at testing time or represent them using a separately trained encoder, resulting in suboptimal training of the language model. In this work, we present TRIME, a novel yet simple training

Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri

Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruct

Negar Foroutan, Mohammadreza Banaei, Remi Lebret, Antoine Bosselut

Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks. However, the extent to which they learn language-neutral representations (i.e., shared representations that encode similar phenomena across languages), and the effect of such representations on cross-lingual transfer performance, remain open questions. In thi

Tsunenori Inakura

We evaluate uncertainty of peak energy of giant dipole resonance (GDR), propagated from uncertainty of parameters of Skyrme interaction. The Monte Carlo calculation of the random phase approximation using randomized Skyrme parameters is performed. Under the condition that the correlations between each of the Skyrme parameters is considered, the GDR peak ener