May 2022 arXiv papers — page 36

Risako Kikuchi, Takumi Funato, Ai Yamakage

We theoretically study the quantum transport in a three-dimensional spin-1 chiral fermion system in the presence of impurity scattering. Within the self-consistent Born approximation, we find peak structure of the density of states and significant suppression of electrical conductivity around the zero energy. The zero-energy conductivity depends less on impu

S. Beolé, F. Carnesecchi, G. Contin, R. de Oliveira

We present a method of embedding a Monolithic Active Pixel Sensor (MAPS) into a flexible printed circuit board (FPC) and its interconnection by means of through-hole copper plating. The resulting assembly, baptised "MAPS foil", is a flexible, light, protected, and fully integrated detector module. By using widely available printed circuit board manufacturing

Faedi Loulidi, Ion Nechita

Measurement incompatibility and quantum non-locality are two key features of quantum theory. Violations of Bell inequalities require quantum entanglement and incompatibility of the measurements used by the two parties involved in the protocol. We analyze the converse question: for which Bell inequalities is the incompatibility of measurements enough to ensur

Qipeng Wang, Liang Liu, Shuowen Zhang, Francis C. M. Lau

The classic trilateration technique can localize each target based on its distances to three anchors with known coordinates. Usually, this technique requires all the anchors and targets, e.g., the satellites and the mobile phones in Global Navigation Satellite System (GNSS), to actively transmit/receive radio signals such that the delay of the one-way radio

Alexandru Chirvasitu

We prove a number of results involving categories enriched over \textsc{CMet}, the category of complete metric spaces with possibly infinite distances. The category \textsc{CPMet} of intrinsic complete metric spaces is locally $\aleph_1$-presentable, closed monoidal, and comonadic over \textsc{CMet}. We also prove that the category \textsc{CCMet} of convex c

Ahmed Farag Ali, Ibrahim Elmashad, Jonas Mureika

We present an argument reinterpreting the generalized uncertainty principle (GUP) and its associated minimal length as an effective variation of Planck constant ($\hbar$), complementing Dirac's large number hypothesis of varying $G$. We argue that the charge radii (i.e. the minimal length of a scattering process) of hadrons/nuclei along with their correspond

Samuel Joseph Amouyal, Tomer Wolfson, Ohad Rubin, Ori Yoran

Existing benchmarks for open-domain question answering (ODQA) typically focus on questions whose answers can be extracted from a single paragraph. By contrast, many natural questions, such as "What players were drafted by the Brooklyn Nets?" have a list of answers. Answering such questions requires retrieving and reading from many passages, in a large corpus

Pablo Caño, Manuel Hinojosa, Luis Cifuentes, Huy Nguyen

A tandem GaAsP/SiGe solar cell has been developed employing group-IV reverse buffer layers grown on silicon substrates with a subsurface porous layer. Reverse buffer layers facilitate a reduction in the threading dislocation density with limited thicknesses, but ease the appearance of cracks, as observed in previous designs grown on regular Si substrates. In

Abhishikta Das, T. Bag, S. Chatterjee

In this paper, definition of fuzzy strong $\phi$-b-normed linear space is given. Here the scalar function |c| is replaced by a general function $\phi$(c) where {\phi} satisfies some properties. Some basic results on finite dimensional fuzzy strong $\phi$-b-normed linear space are studied.

Sander Roet, Daniel T. Zhang, Titus S. van Erp

We developed a replica exchange method that is effectively parallelizable even if the computational cost of the Monte Carlo moves in the parallel replicas are considerably different, for instance, because the replicas run on different type of processor units or because of the algorithmic complexity. To prove detailed-balance, we make a paradigm shift from th

Zhi Chen, Jijia Bao, Lu Chen, Yuncong Liu

Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks. To achieve this goal, a large-scale well-annotated dialo

Ashutosh Jindal, Debasish Chatterjee, Ravi Banavar

In this article, we present explicit estimates of the size of the domain on which the Implicit Function Theorem and the Inverse Function Theorem are valid. For maps that are twice continuously differentiable, these estimates depend upon the magnitude of the first-order derivatives evaluated at the point of interest, and a bound on the second-order derivative

Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee

Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient convolutional neural networks for embedded vision applications. DNN Latency is a hardware dependent metric and requires direct measurement or inference on target hardware. A recent

Ashwin Gopal, Massimiliano Esposito, Nahuel Freitas

The latest generation of transistors are nanoscale devices whose performance and reliability are limited by thermal noise in low-power applications. Therefore developing efficient methods to compute the voltage and current fluctuations in such non-linear electronic circuits is essential. Traditional approaches commonly rely on adding Gaussian white noise to

Howon Lee, Nicholas Simone, Yunxing Su, Yuanhang Zhu

The strength and trajectory of a leading edge vortex (LEV) formed by a pitching-heaving hydrofoil (chord $c$) is studied. The LEV is identified using the $Q$-criterion method, which is calculated from the 2D velocity field obtained from PIV measurements. The relative angle of attack at mid-stroke, ${\alpha_{T/4}} $, proves to be an effective method of combin

V. Scagnoli, D. D. Khalyavin, S. W. Lovesey

KV3Sb5 has recently attracted a considerable attention, due to its low temperature superconducting properties, which are heralded by a charge density wave. The apparent presence of a very weak magnetism does not result in long range ordering. We propose a model compatible with a detectable internal magnetic field with no evidence of magnetic long-range order

Edgar Arribas, Vicent Cholvi, Vincenzo Mancuso

The adoption of UAVs in communication networks is becoming reality thanks to the deployment of advanced solutions for connecting UAVs and using them as communication relays. However, the use of UAVs introduces novel energy constraints and scheduling challenges in the dynamic management of network devices, due to the need to call back and recharge, or substit

Nicola Vassena

Multistationarity is the property of a system to exhibit two distinct equilibria (steady-states) under otherwise identical conditions, and it is a phenomenon of recognized importance for biochemical systems. Multistationarity may appear in the parameter space as a consequence of saddle-node bifurcations, which necessarily require a simple eigenvalue zero of

Kevin Heffernan, Onur Çelebi, Holger Schwenk

Scaling multilingual representation learning beyond the hundred most frequent languages is challenging, in particular to cover the long tail of low-resource languages. A promising approach has been to train one-for-all multilingual models capable of cross-lingual transfer, but these models often suffer from insufficient capacity and interference between unre

Sławomir Kusiński

In this paper we present a few properties of $K$-partitions, which are partitions of Baire spaces such that all subfamilies of such a partition sum to a set with the Baire property. Among the result proven we have general existence result that state that the existence of any $K$-partition implies the existence of $K$-partition of a metrizable space as well a

Alexander J. Stewart, Nichola Raihani

Stereotypes are generalized beliefs about groups of people, which are used to make decisions and judgments about them. Although such heuristics can be useful when decisions must be made quickly, or when information is lacking, they can also serve as the basis for prejudice and discrimination. In this paper we study the evolution of stereotypes through group

P. Proust, Y. Lallouet, D. Davesne, J. Meyer

We generalize to N2LO Skyrme functionals the semi-classical approach of Grammaticos and Voros in order to calculate the Extended Thomas Fermi expressions of the new densities and currents appearing at the N2LO level. Within a one dimensional symmetric semi infinite nuclear matter model and using a simple Fermi-like density profile, we obtain an easy-to-use f

Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee

We study few-shot reranking for multi-hop QA with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on large language models prompting for multi-hop path reranking. PromptRank first constructs an instruction-based prompt that includes a candidate docu

Injy Hamed, Nizar Habash, Slim Abdennadher, Ngoc Thang Vu

Data sparsity is a main problem hindering the development of code-switching (CS) NLP systems. In this paper, we investigate data augmentation techniques for synthesizing dialectal Arabic-English CS text. We perform lexical replacements using word-aligned parallel corpora where CS points are either randomly chosen or learnt using a sequence-to-sequence model.

Sungryull Sohn, Hyunjae Woo, Jongwook Choi, lyubing qiang

We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph that defines a set of subtasks and their dependencies that are unknown to the agent. Different from the previous meta-rl methods trying to directly infer

Tu Vu, Aditya Barua, Brian Lester, Daniel Cer

In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study. We assume a strict setting with no access to parallel data or machine translation and find that common transfer learning approaches struggle in this setting, as a generativ

Yimin Ou, Rui Yang, Lufan Ma, Yong Liu

Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions. Thus, mainstream methods usually rely on a hand-designed non-maximum suppression (NMS) pos

Stefano Marcantoni, Federico Carollo, Filippo M. Gambetta, Igor Lesanovsky

We study the effect of spatially correlated classical noise on both Anderson and many-body localization of a disordered fermionic chain. By analyzing the evolution of the particle density imbalance following a quench from an initial charge density wave state, we find prominent signatures of localization also in the presence of the time-dependent noise, even

Shon Otmazgin, Arie Cattan, Yoav Goldberg

While coreference resolution typically involves various linguistic challenges, recent models are based on a single pairwise scorer for all types of pairs. We present LingMess, a new coreference model that defines different categories of coreference cases and optimize multiple pairwise scorers, where each scorer learns a specific set of linguistic challenges.

Nils Holzenberger, Yunmo Chen, Benjamin Van Durme

Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency. Especially in template extraction (TE), mapping an ontology to a set of questions can be more time-efficient than collecting labeled examples. We ask whether end users of TE systems can de

Vincent Szolnoky, Viktor Andersson, Balazs Kulcsar, Rebecka Jörnsten

Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite having a level of implicit regularisation when trained with gradient descent, often require the aid of explicit regularisers. We introduce a new framewo

Marco A. Arroyo-Ureña, Amit Chakraborty, J. Lorenzo Díaz-Cruz, Dilip Kumar Ghosh

The detection of a single Higgs boson at the Large Hadron Collider (LHC) has allowed one to probe some properties of it, including the Yukawa and gauge couplings. However, in order to probe the Higgs potential, one has to rely on new production mechanisms, such as Higgs pair production. In this paper, we show that such a channel is also sensitive to the prod

Nan Xu, Fei Wang, Bangzheng Li, Mingtao Dong

Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations. To comprehensively investigate the faithfulness and reliability of entity ty

Jędrzej Kozal, Michał Leś, Paweł Zyblewski, Paweł Ksieniewicz

The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever before. Consequently, there is a need to develop novel fake news detection approaches capable of adapting to changing factual contexts and generalizin

Marco Cotogni, Claudio Cusano

In this paper we present TreEnhance, an automatic method for low-light image enhancement capable of improving the quality of digital images. The method combines tree search theory, and in particular the Monte Carlo Tree Search (MCTS) algorithm, with deep reinforcement learning. Given as input a low-light image, TreEnhance produces as output its enhanced vers

Yukihiro Kanda, Nobuhiro Maekawa

We construct a non-topological string solution for a supersymmetric gauge theory with $SU(2)\times U(1)$ gauge symmetry which is spontaneously broken to $U(1)$ by developing the vacuum expectation value of two doublet Higgses. It is a supersymmetric extension of the electroweak string while supersymmetry is unbroken. We discuss the classical stability of the

Kristian Holm

We use a method developed by Bj\"orklund and Gorodnik to show a central limit theorem (as $T$ tends to $\infty$) for the counting functions $\# \left( \Lambda \cap \Omega_T \right)$ where $\Lambda$ ranges over the space $Y_{2d}$ of symplectic lattices in $\mathbb{R}^{2d}$ ($d \geqslant 4$). Here $\lbrace \Omega_T \rbrace_T$ is a certain family of bounded dom

Zhenhua Wang, Ming Ren, Dong Gao, Zhuang Li

Entity extraction is critical in the intelligent advancement across diverse domains. Nevertheless, a challenge to its effectiveness arises from the data imbalance. This paper proposes a novel approach by viewing the issue through the quantitative information, recognizing that entities exhibit certain levels of commonality while others are scarce, which can b

Hailong Ma, Xin Xia, Xing Wang, Xuefeng Xiao

Recently, Transformer networks have achieved impressive results on a variety of vision tasks. However, most of them are computationally expensive and not suitable for real-world mobile applications. In this work, we present Mobile Convolutional Vision Transformer (MoCoViT), which improves in performance and efficiency by introducing transformer into mobile c

Hyeongseok Son, Junyong Lee, Sunghyun Cho, Seungyong Lee

While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real-time video deblurring framework consisting of a lightweight multi-task unit that supports both video deblurring and motion compensation in an efficient way. The mul

Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from mult

Dennis Gramlich, Carsten W. Scherer, Christian Ebenbauer

Differential Dynamic Programming is an optimal control technique often used for trajectory generation. Many variations of this algorithm have been developed in the literature, including algorithms for stochastic dynamics or state and input constraints. In this contribution, we develop a robust version of Differential Dynamic Programming that uses generalized

C. Zalinescu

The aim of this paper is to revisit some duality results in conic linear programming and to answer an open problem related to the duality gap function for Gale's example.

Youngjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel

Large language models readily adapt to novel settings, even without task-specific training data. Can their zero-shot capacity be extended to multimodal inputs? In this work, we propose ESPER which extends language-only zero-shot models to unseen multimodal tasks, like image and audio captioning. Our key novelty is to use reinforcement learning to align multi

Ermanda Dekaj, Erida Gjini

Modern molecular technologies have revolutionized our understanding of bacterial epidemiology, but reported data across different settings remain under-integrated in common theoretical frameworks. Pneumococcus serotype co-colonization, caused by the polymorphic bacteria Streptococcus pneumoniae, has been increasingly investigated in recent years. While the g

Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang

Are Large Pre-Trained Language Models Leaking Your Personal Information? In this paper, we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name. We find that PLMs do leak personal information due t

Xinke Li, Henghui Ding, Zekun Tong, Yuwei Wu

Numerous advancements in deep learning can be attributed to the access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collection cost. To alleviate this issue, we propose a cost-effective method for automatically generating a large amount of 3D objects with annotatio

Holger Boche, Volker Pohl

It is known that there exist mathematical problems of practical relevance which cannot be computed on a Turing machine. An important example is the calculation of the first derivative of continuously differentiable functions. This paper precisely classifies the non-computability of the first derivative, and of the maximum-norm of the first derivative in the

Murat Kaynak, Amit Dolev, Mahmut Selman Sakar

The concept of creating all-mechanical soft microrobotic systems has great potential to address outstanding challenges in biomedical applications, and introduce more sustainable and multifunctional products. To this end, magnetic fields and light have been extensively studied as potential energy sources. On the other hand, coupling the response of materials

Yingxiu. Ma, Jianjun. Zhou, Jarken. Esimbek, Willem. Baan

We studied the cloud-cloud collision candidate G323.18+0.15 based on signatures of induced filaments, clumps, and star formation. We used archival molecular spectrum line data from the SEDIGISM $^{13}$CO($J$\,=\,2--1) survey, from the Mopra southern Galactic plane CO survey, and infrared to radio data from the GLIMPSE, MIPS, Hi-GAL, and SGPS surveys. Our new

Yiwei Liao, Zhuorui Li, Kun Huang, Shi Pu

Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over a multi-agent network using only local computation and peer-to-peer communication. In this paper, we propose a novel compressed gradien

Liyun Zhao, Zhenyou Wang, Zhirong Su

Let $\mu$ be a positive Borel measure on the interval $[0,1)$. The Hankel matrix $\mathcal{H}_{\mu}=(\mu_{n,k})_{n,k\geq0}$ with entries $\mu_{n,k}=\mu_{n+k}$, where $\mu_n=\int_{[0,1)}t^{n}d\mu(t)$. For $f(z)=\sum_{n=0}^{\infty}a_nz^n$ is an analytic function in $\mathbb{D}$, the Hilbert operator is defined by $$\mathcal{H}_{\mu}(f)(z)=\sum_{n=0}^{\infty}\B

Miloš Stanojević

Most computational models of dependency syntax consist of distributions over spanning trees. However, the majority of dependency treebanks require that every valid dependency tree has a single edge coming out of the ROOT node, a constraint that is not part of the definition of spanning trees. For this reason all standard inference algorithms for spanning tre

Julius Fergy T. Rabago

We expose here a novel application of the so-called coupled complex boundary method -- first put forward by Cheng et al. (2014) to deal with inverse source problems -- in the framework of shape optimization for solving the exterior Bernoulli problem, a prototypical model of stationary free boundary problems. The idea of the method is to transform the overdet

Xixia Xu, Yingguo Gao, Ke Yan, Xue Lin

Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained annotations for human body. To alleviate this issue, we revisit HPE and propose a location-free framework without supervision of keypoint locations. We reformulate the regression-based

Fernando Martin-Maroto, Gonzalo G. de Polavieja

To represent anything from mathematical concepts to real-world objects, we have to resort to an encoding. Encodings, such as written language, usually assume a decoder that understands a rich shared code. A semantic embedding is a form of encoding that assumes a decoder with no knowledge, or little knowledge, beyond the basic rules of a mathematical formalis

Jingnong Qu, Liunian Harold Li, Jieyu Zhao, Sunipa Dev

Disinformation has become a serious problem on social media. In particular, given their short format, visual attraction, and humorous nature, memes have a significant advantage in dissemination among online communities, making them an effective vehicle for the spread of disinformation. We present DisinfoMeme to help detect disinformation memes. The dataset c

Thao Minh Le, Vuong Le, Sunil Gupta, Svetha Venkatesh

The current success of modern visual reasoning systems is arguably attributed to cross-modality attention mechanisms. However, in deliberative reasoning such as in VQA, attention is unconstrained at each step, and thus may serve as a statistical pooling mechanism rather than a semantic operation intended to select information relevant to inference. This is b

Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe

Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed elusive for a long time, we show large

Md Hamid, M A H Ahsan

We present exact diagonalization study on a system of $10 \leq N \leq 24$ spinless bosons interacting via repulsive Gaussian potential, harmonically confined in $xy$-plane with an externally impressed rotation about the $z$-axis. The two-body interaction strength in the Gaussian potential is taken in the strongly interacting regime with values of interaction

H. Backe, W. Lauth, P. Drexler, P. Heil

A design study has been performed for a positron beam with an energy of 500 MeV to be realized at the applied physics area of the Mainz Microtron MAMI. Positrons will be created after pair conversion of bremsstrahlung, produced by the 855 MeV electron beam af MAMI in a tungsten converter target. From the two conceivable geometries (i) pair conversion in the

Pedro Borlido, Friedhelm Bechstedt, Silvana Botti, Claudia Rödl

We present a comprehensive first-principles investigation of optical, transport, and thermoelectric properties of pure and doped hexagonal Si$_x$Ge$_{1-x}$ alloys based on density-functional theory calculations, the Boltzmann transport equation, and the generalized quasi-chemical approximation to obtain alloy averages of electronic properties. At low tempera

Wilson Silva, Maria Carvalho, Carlos Mavioso, Maria J. Cardoso

Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80\% of patients having a 10-year survival period. Given the serious impact that breast cancer treatments can have on a patient's body image, consequently affecting her self-confidence and sexual and int

Nicola Abatangelo, Sven Jarohs

We collect some peculiarities of higher-order fractional Laplacians $(-\Delta)^s$, $s>1$, with special attention to the range $s\in(1,2)$, which show their oscillatory nature. These include the failure of the polarization and P\'olya-Szeg\"o inequalities and the explicit example of a domain with sign-changing first eigenfunction. In spite of these fluctuatin

Jin-Hwa Kim, Yunji Kim, Jiyoung Lee, Kang Min Yoo

Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by their sampling techniques in the generation, making evaluation complicated and intractable to get marginal distributions. Based on a recent trend that multimodal generative evaluatio

Gangwoo Kim, Sungdong Kim, Kang Min Yoo, Jaewoo Kang

In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants. In our baseline SIMSEEK-SYM, a questioner generates follow-up questions upon the predetermined answer by an answerer. On the contrary, SIMSEEK-ASYM first generates the question and then finds its corres

Pierre Auclair, Christophe Ringeval

The next generation of cosmological observations will be sensitive to small deviations from a pure power law in the primordial power spectrum of the curvature perturbations. In the context of slow-roll inflation, these deviations are expected and correspond to the so-called running of the spectral index. Their measurement would bring as much information as t

Oliver Butterley, Giovanni Canestrari, Sakshi Jain

We study transfer operators associated to piecewise monotone interval transformations and show that the essential spectrum is large whenever the Banach space bounds $L^\infty$ and the transformation fails to be Markov. Constructing a family of Banach spaces we show that the lower bound on the essential spectral radius is optimal. Indeed, these Banach spaces

Chenyang Wang, Junjun Jiang, Xiong Zhou, Xianming Liu

Data augmentation (DA) is a widely used technique for enhancing the training of deep neural networks. Recent DA techniques which achieve state-of-the-art performance always meet the need for diversity in augmented training samples. However, an augmentation strategy that has a high diversity usually introduces out-of-distribution (OOD) augmented samples and t

A. Alonso-Izquierdo, A. J. Balseyro Sebastian, J. Mateos Guilarte, M. A. Gonzalez Leon

In this paper the whole kink varieties arising in several massive non-linear Sigma models whose target space is the torus ${\mathbb S}^1\times{\mathbb S}^1$ are analytically calculated. This possibility underlies the construction of first-order differential equations by adapting the Bogomolny procedure to non-Euclidean spaces. Among the families of solutions

Dheeraj Mekala, Tu Vu, Timo Schick, Jingbo Shang

The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation. In this work, we propose CONDA, an approach to further improve GLMs' ability to generate synthetic data by reformulating data generation as context generation for a given question-answer (QA)

Gianmarco Lazzeri, Cristian Micheletti, Samuela Pasquali, Pietro Faccioli

Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. To tackle both these issues, we extend to RNA an enhanced path sampling method previously successfully applied to proteins. In this

Yuxing Chen, Renshu Gu, Ouhan Huang, Gangyong Jia

This paper presents Volumetric Transformer Pose estimator (VTP), the first 3D volumetric transformer framework for multi-view multi-person 3D human pose estimation. VTP aggregates features from 2D keypoints in all camera views and directly learns the spatial relationships in the 3D voxel space in an end-to-end fashion. The aggregated 3D features are passed t

Yunfei Yang

We study how well generative adversarial networks (GAN) learn probability distributions from finite samples by analyzing the convergence rates of these models. Our analysis is based on a new oracle inequality that decomposes the estimation error of GAN into the discriminator and generator approximation errors, generalization error and optimization error. To

Xiaochuang Han, Yulia Tsvetkov

Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting. However, it remains unclear from where the model learns the task-specific knowledge, especially in a zero-shot setup. In this work, we want to find evidence of the model's task-specific competence from pretraining and are specifically inter

Cuneyd Ozturk, Musa Furkan Keskin, Henk Wymeersch, Sinan Gezici

We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal

Soumya Sanyal, Zeyi Liao, Xiang Ren

Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language. While the progress is promising, it is currently unclear if these models indeed perform logical reasoning by understanding the underlying logical semantics in the language. To this end, we propose R

Helmut Prodinger

Using the Lagrange inversion formula, $t$-ary trees are enumerated with respect to edge type (left, middle, right for ternary trees).

Dan Alistarh, Joel Rybicki, Sasha Voitovych

In the stochastic population protocol model, we are given a connected graph with $n$ nodes, and in every time step, a scheduler samples an edge of the graph uniformly at random and the nodes connected by this edge interact. A fundamental task in this model is stable leader election, in which all nodes start in an identical state and the aim is to reach a con

Marcello Cinque, Luigi De Simone, Andrea Marchetta

Nowadays, a feature-rich automotive vehicle offers several technologies to assist the driver during his trip and guarantee an amusing infotainment system to the other passengers, too. Consolidating worlds at different criticalities is a welcomed challenge for car manufacturers that have recently tried to leverage virtualization technologies due to reduced ma

Milad Ramezani, Kasra Khosoussi, Gavin Catt, Peyman Moghadam

We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odometry module, utilising a continuous-time trajectory representation, with an efficient pose-graph optimisation module that seamlessly supports both the single- and multi-agent setting

Zohreh Ansari, Farzin Pourhoseini, Fatemeh Hadaeghi

Over the last decade, deep-learning methods have been gradually incorporated into conventional automatic speech recognition (ASR) frameworks to create acoustic, pronunciation, and language models. Although it led to significant improvements in ASRs' recognition accuracy, due to their hard constraints related to hardware requirements (e.g., computing power an

Yi Zheng, Mu Yang, Zheng-Hao Liu, Jin-Shi Xu

The weak measurement wavefront sensor detects the phase gradient of light like the Shack-Hartmann sensor does. However, the use of one thin birefringent crystal to displace light beams results in a wavelength-dependent phase difference between the two polarization components, which limits the practical application. Using a Savart plate which consists of two

Yanrui Du, Jing Yan, Yan Chen, Jing Liu

Recent research has revealed that deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks, leading to failures in real-world applications. In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution of training data. In particular,

Xianting Ding, Zach Njus, Taejoon Kong, Wenqiong Su

Infections from parasitic nematodes (or roundworms) contribute to a significant disease burden and productivity losses for humans and livestock. The limited number of anthelmintics (or antinematode drugs) available today to treat these infections are rapidly losing their efficacy as multidrug resistance in parasites becomes a global health challenge. We prop

Carlo Sinigaglia, Andrea Manzoni, Francesco Braghin, Spring Berman

In this paper, we propose a computationally efficient, robust density control strategy for the mean-field model of a robotic swarm. We formulate a static optimal control problem (OCP) that computes a robot velocity field which drives the swarm to a target equilibrium density, and we prove the stability of the controlled system in the presence of transient pe

Andrei T. Patrascu

Using a higher categorial interpretation of entanglement involving gauge theories and $\sigma$-models instead of qubits, one recovers T-duality as a form of ancilla aided entanglement generation. This opens the way towards new dualities in gauge theories and $\sigma$-models produced by means of analogies with quantum circuits of various types.

Hongjiang Lei, Xusheng She, Ki-Hong Park, Imran Shafique Ansari

This paper proposes a new scheme to enhance the secrecy performance of a NOMA-based coordinated direct relay transmission system (NOMA-CDRT) with an untrusted relay. The physical-layer network coding and the non-orthogonal multiple access scheme are combined to improve the spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals

Rilwan A. Adewoyin, Ritabrata Dutta, Yulan He

In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models. To this end, we propose RSTGen, a framework that utilises Rhetorical Structure Theory (RST), a classical language theory, to control the discourse structure, semantics and topics of generated text. Firstly, we demonstrate our model's abili

Pooya Elahi, Elizabeth Winterholler, Taylor D. Sparks

The current work studies the effect of sintering conditions on the densification behavior, microstructure, and electrochemical performance of 6 mol% Sc2O3, 1 mol% CeO2 co-doped ZrO2 (6Sc1CeZr) for solid-state electrolyte applications. The specimen's physical properties and electrochemical performance sintered at different temperatures ranging from 1000 {\deg

Salvatore De Vincenzo

We reanalyze the problem of a 1D Dirac single particle colliding with the electrostatic potential step of height $V_{0}$ with a positive incoming energy that tends to the limit point of the so-called Klein energy zone, i.e., $E\rightarrow V_{0}-\mathrm{m}c^{2}$, for a given $V_{0}$. In such a case, the particle is actually colliding with an impenetrable barr

Yong Xu, Zhihua Xia, Zichi Wang, Xinpeng Zhang

Steganography conceals the secret message into the cover media, generating a stego media which can be transmitted on public channels without drawing suspicion. As its countermeasure, steganalysis mainly aims to detect whether the secret message is hidden in a given media. Although the steganography techniques are improving constantly, the sophisticated stega

Rebecca Qian, Candace Ross, Jude Fernandes, Eric Smith

Unwanted and often harmful social biases are becoming ever more salient in NLP research, affecting both models and datasets. In this work, we ask whether training on demographically perturbed data leads to fairer language models. We collect a large dataset of human annotated text perturbations and train a neural perturbation model, which we show outperforms

I-Hung Hsu, Kuan-Hao Huang, Shuning Zhang, Wenxin Cheng

Many tasks in natural language processing require the extraction of relationship information for a given condition, such as event argument extraction, relation extraction, and task-oriented semantic parsing. Recent works usually propose sophisticated models for each task independently and pay less attention to the commonality of these tasks and to have a uni

Hongjing Yang, Weicheng Zang, Andrew Gould, Jennifer C. Yee

Follow-up observations of high-magnification gravitational microlensing events can fully exploit their intrinsic sensitivity to detect extrasolar planets, especially those with small mass ratios. To make followup more uniform and efficient, we develop a system, HighMagFinder, based on the real-time data from the Korean Microlensing Telescope Network (KMTNet)

Chenyan Wu, Yandong Li, Xianfeng Tang, James Wang

Reconstructing multi-human body mesh from a single monocular image is an important but challenging computer vision problem. In addition to the individual body mesh models, we need to estimate relative 3D positions among subjects to generate a coherent representation. In this work, through a single graph neural network, named MUG (Multi-hUman Graph network),

Jingshi Cui, Peibiao Zhao

Brendle [6] successfully establishes the sharp Michael-Simon inequality for mean curvature on Riemannian manifolds with nonnegative sectional curvature ($\mathcal{K} \geq 0$), and the proof relies on the Alexandrov-Bakelman-Pucci method. Nevertheless, this result cannot be extended to hyperbolic space $\mathbb{H}^{n+1}$ ($\mathcal{K} = -1$), as demonstrated

Elena Bachini, Philip Brandner, Thomas Jankuhn, Michael Nestler

We study the diffusion of tangential tensor-valued data on curved surfaces. For this purpose, several finite-element-based numerical methods are collected and used to solve a tangential surface n-tensor heat flow problem. These methods differ with respect to the surface representation used, the geometric information required, and the treatment of the tangent

Michael Rogenmoser, Nils Wistoff, Pirmin Vogel, Frank Gürkaynak

With the shrinking of technology nodes and the use of parallel processor clusters in hostile and critical environments, such as space, run-time faults caused by radiation are a serious cross-cutting concern, also impacting architectural design. This paper introduces an architectural approach to run-time configurable soft-error tolerance at the core level, au

Ross Greer, Mohan Trivedi

In this work, we contribute an EM algorithm for estimation of corner points and linear crossing segments for both marked and unmarked pedestrian crosswalks using the detections of pedestrians from processed LiDAR point clouds or camera images. We demonstrate the algorithmic performance by analyzing three real-world datasets containing multiple periods of dat