October 2022 arXiv papers — page 25

Davide Bufalini, Sergio Iguri, Nicolas Kovensky, David Turton

We compute a large collection of string worldsheet correlators describing light probes interacting with heavy black hole microstates. The heavy states consist of NS5 branes carrying momentum and/or fundamental string charge. In the fivebrane decoupling limit, worldsheet string theory on a family of such backgrounds is given by exactly solvable null-gauged WZ

Hankyung Ko, Volodymyr Mazorchuk

In this paper, we investigate extensions between graded Verma modules in the BGG category $\mathcal{O}$. In particular, we determine exactly which information about extensions between graded Verma modules is given by the coefficients of the $R$-polynomials. We also give some upper bounds for the dimensions of graded extensions between Verma modules in terms

Jitender Singh

In this paper, we introduce round and sleek topological spaces and study their properties.

Ryosuke Sawata, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka

Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs. To tackle this problem, we introduce a DNN-based generative refiner, Diffiner, aiming to improve perceptual speech quality pre-processed by an SE method. We train a diffusion-base

Alessandro Ragano, Emmanouil Benetos, Andrew Hines

Learning music representations that are general-purpose offers the flexibility to finetune several downstream tasks using smaller datasets. The wav2vec 2.0 speech representation model showed promising results in many downstream speech tasks, but has been less effective when adapted to music. In this paper, we evaluate whether pre-training wav2vec 2.0 directl

Zhaopin Chen, Bin Zhang, Yiming Pan, Michael Krueger

We propose a novel spectral method for reconstructing quantum wavefunction of an electron pulse, free-electron spectral shearing interferometry (FESSI). We employ a Wien filter to generate two time-delayed replicas of the electron wavepacket and then shift one replica in energy using a light-electron modulator driven by a mid-infrared laser. As a direct demo

Floris Keizer

The Ring-Imaging Cherenkov (RICH) detectors at LHCb have an intrinsic time resolution of better than 10 ps owing to the prompt Cherenkov radiation and focusing mirrors optics. While only spatial information has been used in the experiment to date, the addition of photon time information is one of the cornerstones of the future RICH upgrade programme. The nov

Jens Chluba, Andrea Ravenni, Thomas Kite

In this paper, we formulate a generalised photon Boltzmann hierarchy that allows us to model the evolution and creation of spectral distortion anisotropies in the early Universe. We directly build on our first paper in this series, extending the thermalisation Green's function treatment to the anisotropic case. We show that the problem can be described with

T. Jalabert, E. F . C. Driessen, F. Gustavo, J. L. Thomassin

Besides its fundamental interest, understanding the dynamics of pair breaking in superconducting nanostructures is a central issue to optimize the performances of superconducting devices such as qubits or photon detectors. However, despite substantial research efforts, these dynamics are still not well understood as this requires experiments in which quasipa

Rodrigo Diaz, Ben Hayes, Charalampos Saitis, György Fazekas

Physical models of rigid bodies are used for sound synthesis in applications from virtual environments to music production. Traditional methods such as modal synthesis often rely on computationally expensive numerical solvers, while recent deep learning approaches are limited by post-processing of their results. In this work we present a novel end-to-end fra

William Ravenscroft, Stefan Goetze, Thomas Hain

Speech separation models are used for isolating individual speakers in many speech processing applications. Deep learning models have been shown to lead to state-of-the-art (SOTA) results on a number of speech separation benchmarks. One such class of models known as temporal convolutional networks (TCNs) has shown promising results for speech separation task

Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti

Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domai

Andrew Kyle Lampinen

How should we compare the capabilities of language models (LMs) and humans? I draw inspiration from comparative psychology to highlight some challenges. In particular, I consider a case study: processing of recursively nested grammatical structures. Prior work suggests that LMs cannot handle these structures as reliably as humans can. However, the humans wer

Samudra Sur, Diptiman Sen

The action of any local operator on a quantum system propagates through the system carrying the information of the operator. This is usually studied via the out-of-time-order correlator (OTOC). We numerically study the information propagation from one end of a periodically driven spin-1/2 $XY$ chain with open boundary conditions using the Floquet infinite-te

Andrey A. Samoylov, Anton I. Ivanov, Vladimir V. Echeistov, Elizaveta I. Malevannaya

Electromagnetic noise is one of the key external factors decreasing superconducting qubits coherence. Matched coaxial filters can prevent microwave and IR photons negative influence on superconducting quantum circuits. Here, we report on design and fabrication route of matched low-pass coaxial filters for noise-sensitive measurements at milliKelvin temperatu

Daniel Kvak

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challe

Sergiy V. Lukyanov

Using the methods of kinetic theory expressions for the diffusion and drift coefficients for a cold Fermi system are obtained. Their dependences on the momentum are calculated for the step distribution function as well as in the case of excitation of a particle-hole pair.

Cheng-Chen Li, Zheng-Quan Cui, Tao-Tao Sui, Yu-Xiao Liu

In extra dimensional theories, the four-dimensional field theory is reduced from a fundamental field theory in the bulk spacetime by integrating the extra dimensional part. In this paper we investigate the effective action of a self-interacting scalar field on a brane in the five-dimensional thick braneworld scenario. We consider two typical thick brane solu

Sadaf Khademi, Shahin Heidarian, Parnian Afshar, Farnoosh Naderkhani

The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading cause of mortality from cancer worldwide and has various histolo

Øyvind Lunde Rørtveit, Liv Bolstad Hysing, Andreas Størksen Stordal, Sara Pilskog

Objective: Organ deformation models have the potential to improve delivery and reduce toxicity of radiotherapy, but existing data-driven motion models are based on either patient-specific or population data. We propose to combine population and patient-specific data using a Bayesian framework. Our goal is to accurately predict individual motion patterns whil

M. M. Ferrari, A. Pasotti, T. Traetta

In this paper, we disprove a conjecture recently proposed in [L. Almodovar et al., arXiv:2108.00035] on the non-existence of biminimal pots realizing the cube, namely pots with the minimum number of tiles and the minimum number of bond-edge types. In particular, we present two biminimal pots realizing the cube and show that these two pots are unique up to is

Govind Waghmare, Ankur Debnath, Siddhartha Asthana, Aakarsh Malhotra

Temporal Point Processes (TPP) are probabilistic generative frameworks. They model discrete event sequences localized in continuous time. Generally, real-life events reveal descriptive information, known as marks. Marked TPPs model time and marks of the event together for practical relevance. Conditioned on past events, marked TPPs aim to learn the joint dis

A. A. Pishchimova, N. S. Smirnov, D. A. Ezenkova, E. A. Krivko

Josephson superconducting qubits and parametric amplifiers are prominent examples of superconducting quantum circuits that have shown rapid progress in recent years. With the growing complexity of such devices, the requirements for reproducibility of their electrical properties across a chip have become stricter. Thus, the critical current $I_c$ variation of

Flavia Remo, Gabriel Fuhrmann, Tobias Jäger

We study strange non-chaotic attractors in a class of quasiperiodically forced monotone interval maps known as pinched skew products. We prove that the probability of positive time-N Lyapunov exponents, with respect to the unique physical measure on the attractor, decays exponentially as N goes to infinity. The motivation for this work comes from the study o

Yibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao

3D deep learning models are shown to be as vulnerable to adversarial examples as 2D models. However, existing attack methods are still far from stealthy and suffer from severe performance degradation in the physical world. Although 3D data is highly structured, it is difficult to bound the perturbations with simple metrics in the Euclidean space. In this pap

Arshdeep Singh, Mark D. Plumbley

Convolution neural networks (CNNs) have shown great success in various applications. However, the computational complexity and memory storage of CNNs is a bottleneck for their deployment on resource-constrained devices. Recent efforts towards reducing the computation cost and the memory overhead of CNNs involve similarity-based passive filter pruning methods

The Tien Mai

In this paper we study the problem of bilinear regression and we further address the case when the response matrix contains missing data that referred as the problem of inductive matrix completion. We propose a quasi-Bayesian approach first to the problem of bilinear regression where a quasi-likelihood is employed. Then, we adapt this approach to the context

Nicholas Kluge Corrêa, Nythamar De Oliveira, Diogo Massmann

The 4th Industrial Revolution is the culmination of the digital age. Nowadays, technologies such as robotics, nanotechnology, genetics, and artificial intelligence promise to transform our world and the way we live. Artificial Intelligence Ethics and Safety is an emerging research field that has been gaining popularity in recent years. Several private, publi

Giovanni Cioffi, Leonard Bauersfeld, Elia Kaufmann, Davide Scaramuzza

Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements c

Clara Eminente, Oriol Artime, Manlio De Domenico

In recent years, statistical physics' methodologies have proven extremely successful in offering insights into the mechanisms that govern social interactions. However, the question of whether these models are able to capture trends observed in real-world datasets is hardly addressed in the current literature. With this work we aim at bridging the gap between

Zeping Min, Qian Ge, Guanhua Huang

In this paper, we propose a novel Siamese Adversarial Network (SAN) architecture for automatic speech recognition, which aims at solving the difficulty of fuzzy audio recognition. Specifically, SAN constructs two sub-networks to differentiate the audio feature input and then introduces a loss to unify the output distribution of these sub-networks. Adversaria

Laura A. Hayes, Peter T. Gallagher

We report the detection of a significant ionospheric disturbance in the D-region of Earth's ionosphere which was associated with the massive gamma-ray burst GRB 221009A that occurred on October 9 2022. We identified the disturbance over northern Europe - a result of the increased ionisation by X- and gamma-ray emission from the GRB - using very low frequency

Zaharah Bukhsh, Aaqib Saeed

Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of predictive models in a real-world environment, it is critical to avoid making confident predictions on OOD inputs as it can lead to potentially dangerous consequences. However, OOD

Steven Vander Eeckt, Hugo Van hamme

Adapting a trained Automatic Speech Recognition (ASR) model to new tasks results in catastrophic forgetting of old tasks, limiting the model's ability to learn continually and to be extended to new speakers, dialects, languages, etc. Focusing on End-to-End ASR, in this paper, we propose a simple yet effective method to overcome catastrophic forgetting: weigh

Harald Rose

A novel theory of the structure of elementary particles is outlined. The proposed relativistic covariant space-time approach supposes that all massive particles are composite particles formed by massless elementary particles with opposite four-dimensional (4D) helicity. The attraction between two basic particles originates from their mutual 4D density, which

Matthias Seibold, Bastian Sigrist, Tobias Götschi, Jonas Widmer

There is an unmet clinical need for developing novel methods to complement and replace the current radiation-emitting imaging-based methods for the detection of loose pedicle screws as a complication after spinal fusion surgery which fail to identify a substantial amount of loose implants. In this work, we propose a new methodology and paradigm for the radia

Yuang Zhang, Tiancai Wang, Weiyao Lin, Xiangyu Zhang

We present our 1st place solution to the Group Dance Multiple People Tracking Challenge. Based on MOTR: End-to-End Multiple-Object Tracking with Transformer, we explore: 1) detect queries as anchors, 2) tracking as query denoising, 3) joint training on pseudo video clips generated from CrowdHuman dataset, and 4) using the YOLOX detection proposals for the an

Daniel Drzisga, Andreas Wagner, Barbara Wohlmuth

Matrix-free techniques play an increasingly important role in large-scale simulations. Schur complement techniques and massively parallel multigrid solvers for second-order elliptic partial differential equations can significantly benefit from reduced memory traffic and consumption. The matrix-free approach often restricts solver components to purely local o

Gao Zhang, Jin-Hui Wu, Shao-Qun Zhang

Recent years have witnessed a hot wave of deep neural networks in various domains; however, it is not yet well understood theoretically. A theoretical characterization of deep neural networks should point out their approximation ability and complexity, i.e., showing which architecture and size are sufficient to handle the concerned tasks. This work takes one

Orcel Thys-dingou, Atanda Raji, Zinhle Buthelezi, Siegfried Fortsch

A Large Ion Collider Experiment (ALICE) at the Large Hadron Collider (LHC) at CERN went through a major upgrade in which some of its subdetectors were replaced with new ones, while others are equipped with new electronics. The aim of the upgrade is to withstand higher collision rates during the third running period (Run 3), which started in 2022. As part of

Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy

Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inner products. Here, we show that such graphs can be reproduced using an infinite-dimensional inner product model, where the node representations lie on a low-dimensional manifold. Re

Valery V. Ryzhikov

We show that typical extensions of ergodic systems inherit the triviality of pairwise independent self-joinings. This property (introduced by A. del Junco and D. Rudolph) is related with Rokhlin's famous multiple mixing problem and several questions from joining theory.

Naoyuki Sakumichi, Takashi Yasuda, Takamasa Sakai

Polymer gels such as jellies and soft contact lenses are soft solids consisting of three-dimensional polymer networks swollen with a large amount of solvent. For approximately 80 years, the swelling of polymer gels has been described using the Flory--Huggins mean-field theory. However, this theory is problematic when applied to polymer gels with large solven

Yudong Chen, Sen Wang, Jiajun Liu, Xuwei Xu

In knowledge distillation, previous feature distillation methods mainly focus on the design of loss functions and the selection of the distilled layers, while the effect of the feature projector between the student and the teacher remains under-explored. In this paper, we first discuss a plausible mechanism of the projector with empirical evidence and then p

Qi Yan, Xian'an Jin

Gross, Mansour and Tucker introduced the partial-twuality polynomial of a ribbon graph. Chumutov and Vignes-Tourneret posed a problem: it would be interesting to know whether the partial duality polynomial and the related conjectures would make sense for general delta-matroids. In this paper we consider analogues of partial-twuality polynomials for delta-mat

Yisi Liu, Peter Wu, Alan W Black, Gopala K. Anumanchipalli

Estimation of fundamental frequency (F0) in voiced segments of speech signals, also known as pitch tracking, plays a crucial role in pitch synchronous speech analysis, speech synthesis, and speech manipulation. In this paper, we capitalize on the high time and frequency resolution of the pseudo Wigner-Ville distribution (PWVD) and propose a new PWVD-based pi

Yetao Wu, Han Liu, Jie Yan, Xiaolin Hu

Deep Learning and DRUG-seq (Digital RNA with perturbation of genes) have attracted attention in drug discovery. However, the public DRUG-seq dataset is too small to be used for directly training a deep learning neural network from scratch. Inspired by the transfer learning technique, we pretrain a drug efficacy prediction neural network model with the Librar

Jianping Wang, Alexander Yarovoy

A generalized matrix-pencil approach is proposed for the estimation of complex exponential components with segmented signal samples, which is very efficient and provides super-resolution estimations. It is applicable to the signals sampled segmentally with the same sampling frequency and direction of arrival (DOA) estimation with distributed arrays within wh

Theodor Griepe, Unai Atxitia

Despite intense experimental effort, theoretical proposals and modeling approaches, a lack of consensus exists about the intrinsic mechanisms driving ultrafast magnetization dynamics in 3$d$ ferromagnets. In this work, we find evidence of electron-phonon mediated spin-flip as the driving mechanism for the ultrafast magnetization dynamics in all three 3$d$ fe

Jozef Genzor

The critical behavior of the Ising model on a fractal lattice, which has the Hausdorff dimension $\log_{4} 12 \approx 1.792$, is investigated using a modified higher-order tensor renormalization group algorithm supplemented with automatic differentiation to compute relevant derivatives efficiently and accurately. The complete set of critical exponents charac

Davide Lonigro

We study and discuss the extension of the rotating-wave spin$\unicode{x2013}$boson model, together with more general models describing a system$\unicode{x2013}$field coupling with a similar rotating-wave structure, to interactions mediated by possibly singular (non-normalizable) form factors satisfying a weaker growth constraint. To this purpose, a construct

Andrei Sukhanovskii, Elena Popova

A new shallow layer laboratory model of global atmosphere circulation is realized. The shallow rotating cylindrical layer of fluid with the localized heater at the bottom periphery and localized cooler in the central part of the upper boundary is considered. The rim heater imitates the equator heating and disc cooler -- the north pole cooling. The rim heater

Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, huge amount of human annotated d

Cornelia Dunger-Baldauf, Rob Hemmings, Frank Bretz, Byron Jones

The past few years have seen an increasing number of initiatives aimed at integrating information generated outside of confirmatory randomised clinical trials (RCTs) into drug development. However, data generated non-concurrently and through observational studies can provide results that are difficult to compare with randomised trial data. Moreover, the scie

Minghao Miao, Gang Tian

In this note, we show that the solution of K\"ahler-Ricci flow on every Fano threefold from the family No.2.23 in the Mori-Mukai's list develops type II singularity. In fact, we show that no Fano threefold from the family No.2.23 admits K\"ahler-Ricci soliton and the Gromov-Hausdorff limit of the K\"ahler-Ricci flow must be a singular $\mathbb{Q}$-Fano varie

Vladimir A. Petrov

We propose a generalisation of the Pomeranchuk theorem which argues that elastic cross-sections should show a universal energy dependence: difference of integrated elastic cross-sections of any pair of initial channels has to disappear at high enough energy

Kexin Feng, Theodora Chaspari

We propose a novel explainable machine learning (ML) model that identifies depression from speech, by modeling the temporal dependencies across utterances and utilizing the spectrotemporal information at the vowel level. Our method first models the variable-length utterances at the local-level into a fixed-size vowel-based embedding using a convolutional neu

Vutha Vichhea Chea, Luc Vinet, Meri Zaimi, Alexei Zhedanov

The properties of the Pastro polynomials on the real line are studied with the help of a triplet of $q$-difference operators. The $q$-difference equation and recurrence relation these polynomials obey are shown to arise as generalized eigenvalue problems involving the triplet of operators, with the Pastro polynomials as solutions. Moreover, a discrete biorth

Dominik Semmler, Michael Joham, Wolfgang Utschick

We analyze the influence of a reconfigurable intelligent surface (RIS) on the Gram channel eigenvalues in a high signal-to-noise ratio (SNR) scenario. This allows to connect specific channel properties with the rank improvement capabilities of the RIS. In particular, fundamental limits due to a possible line of sight (LOS) setup between the base station (BS)

Jelmer van der Hoeven, Alberto Natali, Geert Leus

Forecasting time series on graphs is a fundamental problem in graph signal processing. When each entity of the network carries a vector of values for each time stamp instead of a scalar one, existing approaches resort to the use of product graphs to combine this multidimensional information, at the expense of creating a larger graph. In this paper, we show t

Zhida Feng, Zhenyu Zhang, Xintong Yu, Yewei Fang

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose

Antonio Bucchiarone, Tommaso Martorella, Diego Colombo

The digital age is changing the role of educators and pushing for a paradigm shift in the education system as a whole. Growing demand for general and specialized education inside and outside classrooms is at the heart of this rising trend. In modern, heterogeneous learning environments, the one-size-fits-all approach is proven to be fundamentally flawed. Ind

Yilong Zhao, Li Jiang, Mingyu Gao, Naifeng Jing

The second-order training methods can converge much faster than first-order optimizers in DNN training. This is because the second-order training utilizes the inversion of the second-order information (SOI) matrix to find a more accurate descent direction and step size. However, the huge SOI matrices bring significant computational and memory overheads in th

Hao Xu, Qiang Zeng

We study the energy landscape near the ground state of a model of a single particle in a random potential with trivial topology. More precisely, we find the large dimensional limit of the Hessian spectrum at the global minimum of the Hamiltonian $X_N(x) +\frac\mu2 \|x\|^2, x\in\mathbb{R}^N,$ when $\mu$ is above the phase transition threshold so that the syst

Touqeer Ahmad, Carlo Gaetan, Philippe Naveau

The statistical modeling of discrete extremes has received less attention than their continuous counterparts in the Extreme Value Theory (EVT) literature. One approach to the transition from continuous to discrete extremes is the modeling of threshold exceedances of integer random variables by the discrete version of the generalized Pareto distribution. Howe

Zhanglu Yan, Jun Zhou, Weng-Fai Wong

Spiking neural networks (SNNs) are well suited for resource-constrained applications as they do not need expensive multipliers. In a typical rate-encoded SNN, a series of binary spikes within a globally fixed time window is used to fire the neurons. The maximum number of spikes in this time window is also the latency of the network in performing a single inf

Subrata Golui, Chandan Pal, Manikandan R., Abhay Sobhanan

In this article, we investigate a dynamic control problem of a production-inventory system. Here, demands arrive at the production unit according to a Poisson process and are processed in an FCFS manner. The processing time of the customers' demand is the exponential distribution. The production manufacturers produce the items on a make-to-order basis to mee

Alejandro Gomez-Alanis, Lukas Drude, Andreas Schwarz, Rupak Vignesh Swaminathan

Recent studies of streaming automatic speech recognition (ASR) recurrent neural network transducer (RNN-T)-based systems have fed the encoder with past contextual information in order to improve its word error rate (WER) performance. In this paper, we first propose a contextual-utterance training technique which makes use of the previous and future contextua

Tsuyoshi Itoh

Let $K/ \mathbb{Q}$ be an imaginary $S_3$-extension, and $p$ a prime number which splits into exactly three primes in $K$. We give a sufficient condition for the validity of Greenberg's generalized conjecture for $K$ and $p$.

Ademir Hujdurović, Đorđe Mitrović

A graph $X$ is said to be unstable if the direct product $X\times K_2$ (also called the canonical double cover of $X$) has automorphisms that do not come from automorphisms of its factors $X$ and $K_2$. It is non-trivially unstable if it is unstable, connected, non-bipartite, and distinct vertices have distinct sets of neighbours. In this paper, we prove two

Chenglin Wang, Yucheng Zhou, Guodong Long, Xiaodong Wang

With the advance of natural language inference (NLI), a rising demand for NLI is to handle scientific texts. Existing methods depend on pre-trained models (PTM) which lack domain-specific knowledge. To tackle this drawback, we introduce a scientific knowledge graph to generalize PTM to scientific domain. However, existing knowledge graph construction approac

Kexin Feng, Jacqueline B. Duong, Kayla E. Carta, Sierra Walters

We design a metric learning approach that aims to address computational challenges that yield from modeling human outcomes from ambulatory real-life data. The proposed metric learning is based on a Siamese neural network (SNN) that learns the relative difference between pairs of samples from a target user and non-target users, thus being able to address the

Ayon Tarafdar, Srijit Bhattacharjee

We study event horizon candidates for slowly evolving dynamical black holes in General Relativity and Einstein-Gauss-Bonnet (EGB) gravity. Such a type of horizon candidate has been termed as slowly evolving null surface (SENS). It signifies a near-equilibrium state of a dynamic black hole. We demonstrate the time evolution of such surfaces for three differen

Stanisław Jaworski, Wojciech Zieliński

The problem is in the estimation of the fraction of population with a stigmatizing characteristic. In the paper the nonrandomized response model proposed by Tian, Yu, Tang, and Geng (2007) is considered. The exact confidence interval for this fraction is constructed. Also the optimal sample size for obtaining the confidence interval of a given length is deri

Weitao Wang, Matteo Saveriano, Fares J. Abu-Dakka

In this paper, we propose RiemannianFlow, a deep generative model that allows robots to learn complex and stable skills evolving on Riemannian manifolds. Examples of Riemannian data in robotics include stiffness (symmetric and positive definite matrix (SPD)) and orientation (unit quaternion (UQ)) trajectories. For Riemannian data, unlike Euclidean ones, diff

Charles Hovine, Alexander Bertrand

Computing the optimal solution to a spatial filtering problems in a Wireless Sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. The so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of

C. R. Phillips, B. Willenberg, A. Nussbaum-Lapping, F. Callegari

We present a new type of dual optical frequency comb source capable of scaling applications to high measurement speeds while combining high average power, ultra-low noise operation, and a compact setup. Our approach is based on a diode-pumped solid-state laser cavity which includes an intracavity biprism operated at Brewster angle to generate two spatially-s

Ryosuke Masuya, Yuichi Ike, Hiroshi Kera

Vanishing component analysis (VCA) computes approximate generators of vanishing ideals of samples, which are further used for extracting nonlinear features of the samples. Recent studies have shown that normalization of approximate generators plays an important role and different normalization leads to generators of different properties. In this paper, inspi

Jiguang He, Aymen Fakhreddine, Henk Wymeersch, George C. Alexandropoulos

In this paper, the programmable signal propagation paradigm, enabled by Reconfigurable Intelligent Surfaces (RISs), is exploited for high accuracy $3$-Dimensional (3D) user localization with a single multi-antenna base station. Capitalizing on the tunable reflection capability of passive RISs, we present a two-stage user localization method leveraging the mu

Hui Tong, Chencan Wang, Sibo Wang

The momentum and isospin dependence of the single-particle potential for the in-medium nucleon are the key quantities in the Relativistic Brueckner-Hartree-Fock (RBHF) theory. It depends on how to extract the scalar and the vector components of the single-particle potential inside nuclear matter. In contrast to the RBHF calculations in the Dirac space with t

Jan Boschheidgen

Let $G$ be a residually finite group. We give an explicit example in the discrete Heisenberg group that the Brown measure of multiplication operators $A \in \mathbb{Z}[G] \subseteq \mathcal{B}(\ell^2(G))$ in general can not be approximated using finite quotients $G/N$ of $G$. We show that in finitely generated abelian groups the Brown measure can be approxim

Xavier Guidetti, Marino Kühne, Yannick Nagel, Efe C. Balta

The tuning of fused filament fabrication parameters is notoriously challenging. We propose an autonomous data-driven method to select parameters based on in situ measurements. We use a laser sensor to evaluate the surface roughness of a printed part. We then correlate the roughness to the mechanical properties of the part, and show how print quality affects

Jiguang He, Aymen Fakhreddine, George C. Alexandropoulos

Hybrid Reconfigurable Intelligent Surfaces (HRISs), which are capable of simultaneous programmable reflections and sensing, are expected to play a significant role in future wireless networks, enabling various Integrated Sensing and Communication (ISAC) applications. In this paper, we focus on HRIS-enabled Unmanned Aerial Vehicle (UAV) networks and design th

Ju-Hyung Lee, Dong-Ho Lee, Eunsoo Sheen, Thomas Choi

In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre-trained language model. The goal is to achieve unprecedented communication efficiency by focusing on the meaning of messages in semantic communication. We employ a performance metric c

Federico Cabitza, Matteo Cameli, Andrea Campagner, Chiara Natali

The shift from symbolic AI systems to black-box, sub-symbolic, and statistical ones has motivated a rapid increase in the interest toward explainable AI (XAI), i.e. approaches to make black-box AI systems explainable to human decision makers with the aim of making these systems more acceptable and more usable tools and supports. However, we make the point th

Zhaorui Tan, Xi Yang, Zihan Ye, Qiufeng Wang

Generating consistent and high-quality images from given texts is essential for visual-language understanding. Although impressive results have been achieved in generating high-quality images, text-image consistency is still a major concern in existing GAN-based methods. Particularly, the most popular metric $R$-precision may not accurately reflect the text-

Maksud Sharipov, Jamolbek Mattiev, Jasur Sobirov, Rustam Baltayev

Nowadays, creation of the tagged corpora is becoming one of the most important tasks of Natural Language Processing (NLP). There are not enough tagged corpora to build machine learning models for the low-resource Uzbek language. In this paper, we tried to fill that gap by developing a novel Part Of Speech (POS) and syntactic tagset for creating the syntactic

Anton Alekseev, Olga Chekeres, Donald R. Youmans

We show that Schwarzian theories associated to certain hyperbolic and parabolic Virasoro coadjoint orbits admit bosonization, i.e. a global $S^1$-equivariant Darboux chart in which the corresponding path integral becomes Gaussian. In this chart, correlation functions of bilocals, time-ordered and out-of-time ordered, can be computed explicitly. We conjecture

Chamberlain Fong

The squircle is an intermediate shape between the square and the circle. In this paper, we examine and discuss equations for different types of squircles. We then build upon these 2D shapes to come-up with various 3D surfaces based on squircles.

Peijie Jiang, Dingkun Long, Yanzhao Zhang, Pengjun Xie

Boundary information is critical for various Chinese language processing tasks, such as word segmentation, part-of-speech tagging, and named entity recognition. Previous studies usually resorted to the use of a high-quality external lexicon, where lexicon items can offer explicit boundary information. However, to ensure the quality of the lexicon, great huma

Lei Kou, Chuang Liu, Guowei Cai, Zhe Zhang

A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression is proposed in this paper. The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier. Firstly, the correlation analysis of the voltage or current data running in various fault states

Hritik Bansal, Da Yin, Masoud Monajatipoor, Kai-Wei Chang

Text-to-image generative models have achieved unprecedented success in generating high-quality images based on natural language descriptions. However, it is shown that these models tend to favor specific social groups when prompted with neutral text descriptions (e.g., 'a photo of a lawyer'). Following Zhao et al. (2021), we study the effect on the diversity

Ana María Botero

We give an explicit combinatorial presentation of the Chow groups of a toric scheme over a DVR. As an application, we compute the Chow groups of several toric schemes over a DVR and of their special fibers.

Eloi Moliner, Jaakko Lehtinen, Vesa Välimäki

This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting. CQT-Diff is a neural diffusion model with an architecture that is carefully constructed to exploit pitch-equivariant symmetries in music. This is achieved by preconditioning the

Priyanka Choudhary, Praveen C. Srivastava

We implement the ab initio no-core shell model approach to study neutron-rich $^{18}$C, $^{19}$C and $^{20}$C isotopes. For this purpose, we employ charge-dependent Bonn 2000 (CDB2K), inside non-local outside Yukawa (INOY) and chiral next-to-next-to-next-to-leading order (N$^{3}$LO) nucleon-nucleon interactions. Low-lying energy spectra, electromagnetic prop

Fernando López, Jordi Luque

High-quality data labeling from specific domains is costly and human time-consuming. In this work, we propose a self-supervised domain adaptation method, based upon an iterative pseudo-forced alignment algorithm. The produced alignments are employed to customize an end-to-end Automatic Speech Recognition (ASR) and iteratively refined. The algorithm is fed wi

Haihao Shen, Ofir Zafrir, Bo Dong, Hengyu Meng

Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput to comply with certain latency constraints that prevents Transformer models from being used in production. To address this gap, model compression techniques such as quantization a

Ziwen Liu, Josep Grau-Bove, Scott Allan Orr

Multi-label Text Classification (MLTC) is the task of categorizing documents into one or more topics. Considering the large volumes of data and varying domains of such tasks, fully supervised learning requires manually fully annotated datasets which is costly and time-consuming. In this paper, we propose BERT-Flow-VAE (BFV), a Weakly-Supervised Multi-Label T

Tadesse Destaw Belay, Atnafu Lambebo Tonja, Olga Kolesnikova, Seid Muhie Yimam

Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT tasks has received great attention. These networks require lots of data to learn abstract representations of the input and store it in continuous

Or Raz

We are interested in expanding our understanding of symplectic matroids by exploring the properties of a class of symplectic matroids with a "lattice of flats". Taking a well-behaved family of subdivisions of the cross polytope we obtain a construction of lattices, resembling a known definition for the geometric lattice corresponding to ordinary matroid. We