July 2022 arXiv papers — page 50

Miguel Correa, Mayukh R. Gangopadhyay, Nur Jaman, Grant J. Mathews

We report on a study of the natural warm inflationary paradigm (WNI). We show two important new results arise in this model. One is that the observational constraints on the primordial power spectrum from the cosmic microwave background (CMB) can be satisfied without going beyond the Planck scale of the effective field theory. The second is that WNI can inev

Mateusz Domanski, Zoran Mazej, Wojciech Grochala

Silver(II) compounds exhibit powerful oxidizing properties and strong magnetic superexchange. AgSO4 is a rare fluorine-free salt of Ag(II) which found some application in organic chemistry. Here, we report a discovery of a new AgSO4 polymorph (\b{eta}). The distinct nature of the two polytypes of AgSO4 is established using powder x-ray diffraction, vibration

Shuhe Zhang, Tao Peng, Zeyu Ke, Han Yang

The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous

Hao Lu, Wenze Liu, Hongtao Fu, Zhiguo Cao

We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FA

Jaeyeon Kang, Seoung Wug Oh, Seon Joo Kim

The key to video inpainting is to use correlation information from as many reference frames as possible. Existing flow-based propagation methods split the video synthesis process into multiple steps: flow completion -> pixel propagation -> synthesis. However, there is a significant drawback that the errors in each step continue to accumulate and amplify in t

Irene Vilà, Oriol Sallent, Jordi Pérez-Romero

The capacity sharing problem in Radio Access Network (RAN) slicing deals with the distribution of the capacity available in each RAN node among various RAN slices to satisfy their traffic demands and efficiently use the radio resources. While several capacity sharing algorithmic solutions have been proposed in the literature, their practical implementation s

Juanita Duque-Rosero, Sachi Hashimoto, Pim Spelier

Let $X$ be a curve of genus $g>1$ over $\mathbb{Q}$ whose Jacobian $J$ has Mordell--Weil rank $r$ and N\'eron--Severi rank $\rho$. When $r < g+ \rho - 1$, the geometric quadratic Chabauty method determines a finite set of $p$-adic points containing the rational points of $X$. We describe algorithms for geometric quadratic Chabauty that translate the geometri

Boyang Xia, Wenhao Wu, Haoran Wang, Rui Su

It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video recognition methods typically preview videos and focus on salient parts to reduce computation costs. Most existing works focus on complex networks learning with video classification

Lumin Xu, Sheng Jin, Wang Zeng, Wentao Liu

Existing works on 2D pose estimation mainly focus on a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting the poses/keypoints of the unseen class of objects. In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model cap

Taro Sekiyama, Hiroshi Unno

Type-and-effect systems are a widely-used approach to program verification, verifying the result of a computation using types, and the behavior using effects. This paper extends an effect system for verifying temporal, value-dependent properties on event sequences yielded by programs to the delimited control operators shift0/reset0. While these delimited con

Giovanni Geraci, David Lopez-Perez, Mohamed Benzaghta, Symeon Chatzinotas

Integrating terrestrial and non-terrestrial networks has the potential of connecting the unconnected and enhancing the user experience for the already-connected, with technological and societal implications of the greatest long-term significance. A convergence of ground, air, and space wireless communications also represents a formidable endeavor for the mob

Alexander Brown, Nenad Tomasev, Jan Freyberg, Yuan Liu

Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models - their tendency to perform differently across subgroups of the population - and to understand its underlying mechanisms. One potenti

Austin Dukes, Giacomo Micheli, Vincenzo Pallozzi Lavorante

In this paper we construct new optimal hierarchical locally recoverable codes. Our construction is based on a combination of the ideas of \cite{ballentine2019codes,sasidharan2015codes} with an algebraic number theoretical approach that allows to give a finer tuning of the minimum distance of the intermediate code (allowing larger dimension of the final code)

Alessandra Bertapelle, Nicola Mazzari, Arnab Saha

We study the $n$-th arithmetic jet space of the $p$-torsion subgroup attached to a smooth commutative formal group scheme. We show that the $n$-th jet space above fits in the middle of a canonical short exact sequence between a power of the formal scheme of Witt vectors of length $n$ and the $p$-torsion subgroup we started with. This result generalizes a res

Nikhila Awasthi, Manoj Kumar, Monika Randhawa, Manmohan Gupta

Fritzsch-Xing matrices are a particular class of texture 4 zero hermitian quark mass matrices, known to be successful in accommodating the quark mixing data. In the present work, it is shown that these texture 4-zero matrices with only one phase parameter, unlike the usually considered two phase parameters, are not only consistent with the latest experimenta

Keiichiro Nomoto

Let $E_{-D}$ be the elliptic curve $y^2=x^3+Dx$ defined over $K=\mathbb{Q}(i)$ for $D\in K$ which is coprime to 2. In this paper, we give a lower bound for the 2-adic valuation of the algebraic part of the central value of Hecke $L$-function associated to $E_{-D}$.

Boyang Xia, Zhihao Wang, Wenhao Wu, Haoran Wang

Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient frames without awareness of the class-specific saliency scores, which neglect the implicit association between the saliency of frames and its belonging category. To alleviate this i

Shun'ichi Horigome, Kohei Hayashi, Shin'ichiro Ando

Dark matter halos of dwarf spheroidal galaxies (dSphs) play important roles in dark matter detection. Generally we estimate the halo profile using a kinematical equation of dSphs but the halo profile has a large uncertainty because we have only a limited number of kinematical dataset. In this paper, we utilize cosmological models of dark matter subhalos to o

Xinfu Li, Jianguang Bao, Wenguang Tang

In this paper, we study the existence and non-existence of normalized solutions to the lower critical Choquard equation with a local perturbation \begin{equation*} \begin{cases} -\Delta u+\lambda u=\gamma (I_{\alpha}\ast|u|^{\frac{N+\alpha}{N}})|u|^{\frac{N+\alpha}{N}-2}u+\mu |u|^{q-2}u,\quad \text{in}\ \mathbb{R}^N, \\ \int_{\mathbb{R}^N}|u|^2dx=c^2, \end{c

Yusuf Nasir, Louis J. Durlofsky

Closed-loop reservoir management (CLRM), in which history matching and production optimization are performed multiple times over the life of an asset, can provide significant improvement in the specified objective. These procedures are computationally expensive due to the large number of flow simulations required for data assimilation and optimization. Exist

Sudhir Pujahari, Neelam Saikia

Recently Ono, Saad and the second author \cite{KHN} initiated a study of value distribution of certain families of Gaussian hypergeometric functions over large finite fields. They investigated two families of Gaussian hypergeometric functions and showed that they satisfy semicircular and Batman distributions. Motivated by their results we aim to study distri

Nick Evans, Jack Mitchell

We study thermal transitions in a Domain Wall AdS/QCD model. The model is based on the D5/probe D7 system with a discontinuous mass profile which restricts chiral fermions to 3+1 dimensional domain walls. Fluctuations on the domain wall are dual to the quark mass and condensate and reveal the relation between domain wall separation and the quark mass. The ma

Griselda Deelstra, Lech A. Grzelak, Felix L. Wolf

The collateral choice option allows a collateral-posting party the opportunity to change the type of security in which the collateral is deposited. Due to non-zero collateral basis spreads, this optionality significantly impacts asset valuation. Because of the complexity of valuing the option, many practitioners resort to deterministic assumptions on the col

Marcella Bonazzoli, Houssem Haddar, Tuan Anh Vu

In this work we are interested in general linear inverse problems where the corresponding forward problem is solved iteratively using fixed point methods. Then one-shot methods, which iterate at the same time on the forward problem solution and on the inverse problem unknown, can be applied. We analyze two variants of the so-called multi-step one-shot method

Chien-Wei Fu, Meng-Lin Ku, Yu-Jia Chen, Tony Q. S. Quek

In this paper, we consider multiple solar-powered wireless nodes which utilize the harvested solar energy to transmit collected data to multiple unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly design UAV flight trajectories, UAV-node communication associations, and uplink power control to effectively utilize the harvested energy an

Elisa Alòs, Frido Rolloos, Kenichiro Shiraya

This paper shows the relationship between the forward start volatility swap price and the forward start zero vanna implied volatility of forward start options in rough volatility models. It is shown that in the short time-to-maturity limit the approximation error in the leading term of the correlated case with $H\in(0,\frac12)$ does not depend on the time to

Tadashi Fujioka

We introduce the notion of an extremal subset in a geodesically complete space with curvature bounded above, i.e., a GCBA space. This is an analogue of an extremal subset in an Alexandrov space with curvature bounded below introduced by Perelman and Petrunin. We prove that under an additional assumption the set of topological singularities in a GCBA space fo

Mehmet Cagri Aksoy, Beril Sirmacek, Cem Unsalan

Deep learning methods have been successfully applied to remote sensing problems for several years. Among these methods, CNN based models have high accuracy in solving the land classification problem using satellite or aerial images. Although these models have high accuracy, this generally comes with large memory size requirements. On the other hand, it is de

Moshe Sipper, Tomer Halperin, Itai Tzruia, Achiya Elyasaf

EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutio

Martin Dindoš, Steve Hofmann, Jill Pipher

In this paper, we continue the study of a class of second order elliptic operators of the form $\mathcal L=\mbox{div}(A\nabla\cdot)$ in a domain above a Lipschitz graph in $\mathbb R^n,$ where the coefficients of the matrix $A$ satisfy a Carleson measure condition, expressed as a condition on the oscillation on Whitney balls. For this class of operators, it

Christiane Alba-Simionesco, Gilles Tarjus

We discuss possible extraneous effects entering in the conventional measures of "fragility" at atmospheric pressure that may obscure a characterization of the genuine super-Arrhenius slowdown of relaxation. We first consider the role of density, which increases with decreasing temperature at constant pressure, and then the potential influence of the high-tem

K. Kreckel, O. Egorov, F. Belfiore, B. Groves

Aims: Temperature uncertainties plague our understanding of abundance variations within the ISM. Using the PHANGS-MUSE large program, we develop and apply a new technique to model the strong emission lines arising from HII regions in 19 nearby spiral galaxies at ~50 pc resolution and infer electron temperatures for the nebulae. Methods: Due to the charge-exc

Takahiro Matsushita, Shun Wakatsuki

We determine the homotopy types of the independence complexes of the $(n \times 6)$-square grid graphs. In fact, we show that these complexes are homotopy equivalent to wedges of spheres.

Meng Cao, Tianyu Yang, Junwu Weng, Can Zhang

Video-Text Pre-training (VTP) aims to learn transferable representations for various downstream tasks from large-scale web videos. To date, almost all existing VTP methods are limited to retrieval-based downstream tasks, e.g., video retrieval, whereas their transfer potentials on localization-based tasks, e.g., temporal grounding, are under-explored. In this

Giovanni Ferioli, Antoine Glicenstein, Igor Ferrier-Barbut, Antoine Browaeys

A class of systems exists in which dissipation, external drive and interactions compete and give rise to non equilibrium phases that would not exist without the drive. There, phase transitions could occur without the breaking of any symmetry, yet with a local order parameter, in contrast with the Landau theory of phase transitions at equilibrium. One of the

Susana Gomez-Carrasco, Daniel Felix-Gonzalez, Alfredo Aguado, Octavio Roncero

The cross section and rate constants for the title reaction are calculated for all the spin-orbit states of N$^+$($^3P_{J_A}$) using two statistical approaches, one purely adiabatic and the other one mixing quantum capture for the entrance channel and adiabatic treatment for the products channel. This is made by using a symmetry adapted basis set combining e

Samir F. Matar

Novel extended networks of C8, Si8 and silicon carbide Si4C4 are proposed based on crystal chemistry rationale and optimized structures to ground state energies and derived physical properties within the density functional theory (DFT). The two carbon and silicon allotropes and the silicon carbide belong to primitive tetragonal space group P-4m2 Number 115.

Qi Sun, Xuejun Xu, Haotian Yi

With recent advancements in computer hardware and software platforms, there has been a surge of interest in solving partial differential equations with deep learning-based methods, and the integration with domain decomposition strategies has attracted considerable attention owing to its enhanced representation and parallelization capacities of the network so

Shrisudhan Govindarajan, Prasan Shedligeri, Sarah, Kaushik Mitra

The hardware challenges associated with light-field(LF) imaging has made it difficult for consumers to access its benefits like applications in post-capture focus and aperture control. Learning-based techniques which solve the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have significantly reduced the requirement for complex hardware.

X. Li, J. K. Adkins, Y. Akiba, A. Albataineh

Exclusive heavy quarkonium photoproduction is one of the most popular processes in EIC, which has a large cross section and a simple final state. Due to the gluonic nature of the exchange Pomeron, this process can be related to the gluon distributions in the nucleus. The momentum transfer dependence of this process is sensitive to the interaction sites, whic

Mariya Hendriksen, Viggo Overes

The popularity of online fashion shopping continues to grow. The ability to offer an effective recommendation to customers is becoming increasingly important. In this work, we focus on Fashion Outfits Challenge, part of SIGIR 2022 Workshop on eCommerce. The challenge is centered around Fill in the Blank (FITB) task that implies predicting the missing outfit,

Jun Ho Lee, Jae Soon Baik, Tae Hwan Hwang, Jun Won Choi

There are inevitably many mislabeled data in real-world datasets. Because deep neural networks (DNNs) have an enormous capacity to memorize noisy labels, a robust training scheme is required to prevent labeling errors from degrading the generalization performance of DNNs. Current state-of-the-art methods present a co-training scheme that trains dual networks

Chintan Patel

The Internet of Things (IoT) is giving a boost to a plethora of new opportunities for the robust and sustainable deployment of cyber physical systems. The cornerstone of any IoT system is the sensing devices. These sensing devices have considerable resource constraints, including insufficient battery capacity, CPU capability, and physical security. Because o

S. S. Baturin, D. V. Grosman, G. K. Sizykh, D. V. Karlovets

We present a detailed analysis of the capture and acceleration of a non-relativistic charged vortex particle (electron, positron, proton, etc.) with an orbital angular momentum in a field of an axisymmetric electromagnetic lens, typical for a linear accelerator. We account for the acceleration as well as for the inhomogeneity of both electric and magnetic fi

Jianwei Zhang, Dong Li, Lituan Wang, Lei Zhang

Automated data augmentation, which aims at engineering augmentation policy automatically, recently draw a growing research interest. Many previous auto-augmentation methods utilized a Density Matching strategy by evaluating policies in terms of the test-time augmentation performance. In this paper, we theoretically and empirically demonstrated the inconsiste

Christian Brønnum-Hansen, Jérémie Quarroz, Chiara Signorile-Signorile, Chen-Yu Wang

Single top quark is mainly produced through the $t$-channel W boson exchange $q + b \rightarrow q' + t$ at LHC. This process probes Wtb vertex directly and can be used to measure the CKM matrix element $V_{tb}$ or to constrain the bottom quark PDF. The non-factorisable contributions are the last missing piece of the NNLO corrections. In these proceedings, we

Ryan W. Greening, Xin Fan

Measurements of spin-orbit torques in a ferromagnetic/nonmagnetic multilayer are typically based on an assumption that the entire ferromagnetic layer uniformly responds to the spin-orbit torque. This assumption breaks down when the thickness of the ferromagnetic layer is comparable to the dynamic exchange coupling length, which can be as short as a few nanom

M. S. Mrozowski, I. C. Chalmers, S. J. Ingleby, P. F. Griffin

We present the design process and implementation of fully open-source, ultra-low noise programmable current source systems in two configurations. Although originally designed as coil drivers for Optically Pumped Magnetometers (OPMs), the device specifications make them potentially useful in a range of applications. The devices feature a bi-directional curren

Yang Cheng, Junyu Tang, Justin J. Michel, Su Kong Chong

Unidirectional spin Hall magnetoresistance (USMR) has been widely reported in the heavy metal / ferromagnet (HM/FM) bilayer systems. We observe the USMR in the Pt/{\alpha}-Fe2O3 bilayers where the {\alpha}-Fe2O3 is an antiferromagnetic (AFM) insulator. Systematic field and temperature dependent measurements confirm the magnonic origin of the USMR. The appear

Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao

Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversarial examples with slight perturbations on different levels (sentence / word / character), adversarial attacks can fool PrLMs to generate incorrect predictions, which questions the r

Cheeun Hong, Sungyong Baik, Heewon Kim, Seungjun Nah

Despite breakthrough advances in image super-resolution (SR) with convolutional neural networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational complexity of SR networks. Quantization is one of the promising approaches to solve this problem. However, existing methods fail to quantize SR models with a bit-width lower than 8 b

Giuseppe Floridia, Hiroshi Takase

In this paper, we consider a Cauchy problem for a first-order hyperbolic equation with time-dependent coefficients. Cauchy data are given on a lateral subboundary and we obtain local H\"older stabilities for inverse source and coefficient problems via a Carleman estimate.

Laurent Bourgeois, Jérémi Dardé

This paper is focused on the Morozov's principle applied to an abstract data assimilation framework, with particular attention to three simple examples: the data assimilation problem for the Laplace equation, the Cauchy problem for the Laplace equation and the data assimilation problem for the heat equation. Those ill-posed problems are regularized with the

David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin

Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities. These compositions are probabilistic models, and may be expressed in the language of graphical models with random variables whose values are complex dat

Teng Xi, Yifan Sun, Deli Yu, Bi Li

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single task with a large-scale pretraining on all tasks. Compared with the well known foundation model, UFO has two different

BaBar Collaboration

We study the processes $e^+e^-\to K^+K^-\pi^0\pi^0\pi^0\gamma$, $K^{0}_{S}K^{\pm}\pi^{\mp}\pi^0\pi^0\gamma$, and $K^{0}_{S}K^{\pm}\pi^{\mp}\pi^+\pi^-\gamma$ in which an energetic photon is radiated from the initial state. The data were collected with the \babar~ detector at the SLAC National Accelerator Laboratory. About 1200, 2600, and 6000 events, respecti

Riasat Ali, Rimsha Babar, Muhammad Asgher, Xia Tie-Cheng

We consider the regular black holes solution with cosmic strings(RBHCS) in the rotation parameter by assuming the Newman-Janis method. After this, we study thermodynamical property (i.e., Hawking temperature $T_H$) for the RBHCS in the presence of spin parameter. Moreover, we study the graphical interpretation of Hawking temperature with event horizon to che

Kosuke Yamato

We consider quasi-stationary distributions for one-dimensional diffusions via the renewal dynamical approach. We show that convergence of the iterative renewal transform to quasi-stationary distributions is equivalent to a condition on the moment growth rate of the lifetime, which is at the same time a necessary condition for the existence of Yaglom limits.

Ming-Chun Jiang, Guang-Yu Guo, Motoaki Hirayama, Tonghua Yu

Materials carrying topological surface states (TSS) provide a fascinating platform for the hydrogen evolution reaction (HER). Based on systematic first-principles calculations for $A_3 B$ ($A$ = Ni, Pd, Pt; $B$ = Si, Ge, Sn), we propose that topological electric polarization characterized by the Zak phase can be crucial to designing efficient catalysts for t

Antonio Ortiz

The similarity between small- and large-collision systems is explored using the charged-particle multiplicity in the transverse region, $N^{\rm T}_{\rm ch}$, which is sensitive to the underlying event. Measurements of charged-particle production as a function of $N^{\rm T}_{\rm ch}$ in pp, p-Pb and Pb-Pb collisions at $\sqrt{s_{\rm NN}}=5.02$ TeV in the towa

Petri J. Käpylä, Nishant K. Singh

Turbulent motions enhance the diffusion of large-scale flows and temperature gradients. Such diffusion is often parameterized by coefficients of turbulent viscosity ($\nu_{\rm t}$) and turbulent thermal diffusivity ($\chi_{\rm t}$) that are analogous to their microscopic counterparts. We compute the turbulent diffusion coefficients by imposing large-scale ve

Yuhei Noda, Shota Saito, Shinichi Shirakawa

Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both performance and complexity and can find well-performing architectures with low computational resources. Although ordinary NAS methods result in tremendous computational costs owing to th

Bagus Tris Atmaja, Zanjabila, Akira Sasou

In this paper, we demonstrated the benefit of using pre-trained model to extract acoustic embedding to jointly predict (multitask learning) three tasks: emotion, age, and native country. The pre-trained model was trained with wav2vec 2.0 large robust model on the speech emotion corpus. The emotion and age tasks were regression problems, while country predict

Chuyuan Li, Chloé Braud, Maxime Amblard

Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to expl

Ash Arsenault, Kivanc Y. Cingiloglu, Mariana Frank

The discovery of Standard-Model like Higgs at 125 GeV may raise more questions than the answers it provides. In particular, the hierarchy problem remains unsolved, and the Standard Model Higgs quartic self-coupling becomes negative below the Planck scale, necessitating new physics beyond the Standard Model. In this work we investigate a popular scenario, ext

Viktor Skorniakov, Ugnė Čižikovienė

Originally suggested for the blood testing problem by Dorfman in 1943, an idea of Group Testing (GT) has found many applications in other fields as well. Among many (binomial) GT procedures introduced since then, in 1990, Yao and Hwang proposed the Pairwise Testing Algorithm (PTA) and demonstrated that PTA is the \emph{unique} optimal nested GT procedure pro

Gaëtan Serré, Eva Boguslawski, Benjamin Donnot, Adrien Pavão

Current rapid changes in climate increase the urgency to change energy production and consumption management, to reduce carbon and other green-house gas production. In this context, the French electricity network management company RTE (R{\'e}seau de Transport d'{\'E}lectricit{\'e}) has recently published the results of an extensive study outlining various s

Islay O. Robertson, Cheng Tan, Sam C. Scholten, Alexander J. Healey

Van der Waals (vdW) magnets are appealing candidates for realising spintronic devices that exploit current control of magnetization (e.g. switching or domain wall motion), but so far experimental demonstrations have been sparse, in part because of challenges associated with imaging the magnetization in these systems. Widefield nitrogen-vacancy (NV) microscop

Augustin Caillas, Stéphan Suffit, Pascal Filloux, Emmanuel Lhuillier

Periodic arrays of noble metal nanoparticles are emblematic nanostructures in photonics. Their ability to sustain localized surface plasmon resonances has been used throughout the years to demonstrate a variety of passive and active functionalities such as enhanced luminescence in dipolar media and LEDs as well as higher responsivities in photoconductive det

Shuixin Xiao, Yuanlong Wang, Jun Zhang, Daoyi Dong

Quantum detector tomography (QDT) is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we utilize regularization to improve the QDT accuracy whenever the probe states are informationally complete or informationally incomplete. In the informationally complete scenario, without regularization, we o

Thierry Paul

We study the propagation of quantum T{\"o}plitz observables through quantized complex linear canonical transformation of one degree of freedom systems. We associate to such a propagated observable a non local "T{\"o}plitz'' expression involving off diagonal terms. We study the link of this constrauction with the usual Weyl symbolic paradigm.

Kyungsu Kim, Seong Je Oh, Chae Yeon Lim, Ju Hwan Lee

Image translation based on a generative adversarial network (GAN-IT) is a promising method for the precise localization of abnormal regions in chest X-ray images (AL-CXR) even without the pixel-level annotation. However, heterogeneous unpaired datasets undermine existing methods to extract key features and distinguish normal from abnormal cases, resulting in

Frédéric de Gournay, Alban Gossard, Pierre Weiss

A recent trend in the signal/image processing literature is the optimization of Fourier sampling schemes for specific datasets of signals. In this paper, we explain why choosing optimal non Cartesian Fourier sampling patterns is a difficult nonconvex problem by bringing to light two optimization issues. The first one is the existence of a combinatorial numbe

Thierry Paul

We study the behaviour of Husimi, Wigner and T{\"o}plitz symbols of quantum density matrices when quantum statistics are tested on them, that is when on exchange two coordinates in one of the two variables of their integral kernel. We show that to each of these actions is associated a canonical transform on the cotangent bundle of the underlying classical ph

El-Mehdi El Arar, Devan Sohier, Pablo de Oliveira Castro, Eric Petit

Stochastic rounding (SR) offers an alternative to the deterministic IEEE-754 floating-point rounding modes. In some applications such as PDEs, ODEs and neural networks, SR empirically improves the numerical behavior and convergence to accurate solutions while no sound theoretical background has been provided. Recent works by Ipsen, Zhou, Higham, and Mary hav

Sanghoon Lee, Youngmin Oh, Donghyeon Baek, Junghyup Lee

We address the task of person search, that is, localizing and re-identifying query persons from a set of raw scene images. Recent approaches are typically built upon OIMNet, a pioneer work on person search, that learns joint person representations for performing both detection and person re-identification (reID) tasks. To obtain the representations, they ext

Andreas Hartmann, Xavier Massaneda

We investigate different geometrical properties of the inhomogeneous Poisson point process $\Lambda_{\mu}$ associated to a positive, locally finite, $\sigma$-finite measure $\mu$ on the unit disk. In particular, we characterize the processes $\Lambda_{\mu}$ such that almost surely: 1) $\Lambda_{\mu}$ is a Carleson-Newman sequence; 2) $\Lambda_{\mu}$ is the u

Liangqi Zhang, Haibo Shen, Yihao Luo, Xiang Cao

Modern efficient Convolutional Neural Networks(CNNs) always use Depthwise Separable Convolutions(DSCs) and Neural Architecture Search(NAS) to reduce the number of parameters and the computational complexity. But some inherent characteristics of networks are overlooked. Inspired by visualizing feature maps and N$\times$N(N$>$1) convolution kernels, several gu

Fatemeh Nasiri, Wassim Hamidouche, Luce Morin, Nicolas Dholland

Changing the encoding parameters, in particular the video resolution, is a common practice before transcoding. To this end, streaming and broadcast platforms benefit from so-called bitrate ladders to determine the optimal resolution for given bitrates. However, the task of determining the bitrate ladder can usually be challenging as, on one hand, so-called f

Zehui Chen, Zhenyu Li, Shiquan Zhang, Liangji Fang

Point clouds and RGB images are two general perceptional sources in autonomous driving. The former can provide accurate localization of objects, and the latter is denser and richer in semantic information. Recently, AutoAlign presents a learnable paradigm in combining these two modalities for 3D object detection. However, it suffers from high computational c

Haoran Zhou, Yun Cao, Wenqing Chu, Junwei Zhu

Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation. In this paper, we propose a novel SeedFormer to improve the ability of detail preservation and recovery in point cloud completion. Unlike

Woobin Im, Sebin Lee, Sung-Eui Yoon

A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset. However, obtaining ground truth flows from a target video requires a tremendous effort. This paper proposes a practical fine tuning method to adapt a pretrained model to a target dataset without ground truth fl

Faezeh Khodabandehlou, Christian Maes, Karel Netočný

We discuss via general arguments and examples when and why the steady nonequilibrium heat capacity vanishes with temperature. The framework is the one of Markov jump processes on finite connected graphs where the condition of local detailed balance allows to identify the heat fluxes, and where the discreteness more easily enables sufficient nondegeneracy of

Andreas Kurz, Thomas Neff, Zhaoyang Lv, Michael Zollhöfer

Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray. Previous work has mainly focused on speeding up the n

Gao-Lei Hou, Olga V. Lushchikova, Joost M. Bakker, Peter Lievens

Infrared emission bands with wavelengths between 3-20 {\mu}m are observed in a variety of astrophysical environments [1,2]. They were discovered in the 1970s and are generally attributed to organic compounds [3,4]. However, over 40 years of research efforts still leave the source of these emission bands largely unidentified [5-7]. Here, we report the first l

Martin Stoffers, Alexander Weinert

Monitoring often requires insight into the monitored system as well as concrete specifications of expected behavior. More and more systems, however, provide information about their inner procedures by emitting provenance information in a W3C-standardized graph format. In this work, we present an approach to monitor such provenance data for anomalous behavior

Ryosuke T. Tominaga, Hiroshi Kobayashi, Shu-ichiro Inutsuka

Our previous linear analysis presents a new instability driven by dust coagulation in protoplanetary disks. The coagulation instability has the potential to concentrate dust grains into rings and assist dust coagulation and planetesimal formation. In this series of papers, we perform numerical simulations and investigate nonlinear outcome of coagulation inst

Ming Liu, Yuxiang Wei, Xiaohe Wu, Wangmeng Zuo

Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality. With the ability to generate photo-realistic high-resolution (e.g., $1024\times1024$) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore,

Xiaoyuan Liu, Tianneng Shi, Chulin Xie, Qinbin Li

Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use cases makes it challenging to use in practice. In addition, selecting an appropriate FL framework for a specific use case can be a daunting task. In this work, we present UniFed,

Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items. Due to

Yubo Wan, An Liu, Rui Du, Tony Xiao Han

Multiband sensing is a promising technology that utilizes multiple non-contiguous frequency bands to achieve high-resolution target sensing. In this paper, we investigate the fundamental limits and optimization of multiband sensing, focusing on the fundamental limits associated with time delay. We first derive a Fisher information matrix (FIM) with a compact

Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang

Recent advances have shown the success of using reinforcement learning and search to solve NP-hard graph-related tasks, such as Traveling Salesman Optimization, Graph Edit Distance computation, etc. However, it remains unclear how one can efficiently and accurately detect the occurrences of a small query graph in a large target graph, which is a core operati

Chia-Wei Hsu, Matthew Alexander Ho, Ali Mostafavi

Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of i

Shalanika Dayarathna, Rajitha Senanayake, Peter Smith, Jamie Evans

This paper focuses on waveform design for joint radar and communication systems and presents a new subset selection process to improve the communication error rate performance and global accuracy of radar sensing of the random stepped frequency permutation waveform. An optimal communication receiver based on integer programming is proposed to handle any subs

Hirak Doshi, N. Uday Kiran

It is well known that classical formulations resembling the Horn and Schunck model are still largely competitive due to the modern implementation practices. In most cases, these models outperform many modern flow estimation methods. In view of this, we propose an effective implementation design for an edge-preserving $L^1$ regularization approach to optical

Dapeng Yao, Fangzheng Xie, Yanxun Xu

We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size. A minimax lower bound for parameter estimation is established, and we show that a constrained maximum likelihood estimator achieves the minimax lower bound. However, this optimization-based estimator is computationally intractable

Fukeng Huang, Weizhu Bao, Tiezheng Qian

The pinch-off dynamics of a liquid thread has been studied through numerical simulations and theoretical analysis. Occurring at small length scales, the pinch-off dynamics admits similarity solutions that can be classified into the Stokes regime and the diffusion-dominated regime, with the latter being recently experimentally observed in aqueous two-phase sy

Yuhang Zhang, Chengrui Wang, Xu Ling, Weihong Deng

Noisy label Facial Expression Recognition (FER) is more challenging than traditional noisy label classification tasks due to the inter-class similarity and the annotation ambiguity. Recent works mainly tackle this problem by filtering out large-loss samples. In this paper, we explore dealing with noisy labels from a new feature-learning perspective. We find

Xian Tao, Xinyi Gong, Xin Zhang, Shaohua Yan

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localizati

Junho Jang, Ji Young Woo, Huy Kang Kim

Multiplayer Online Battle Arena (MOBA) is one of the most successful game genres. MOBA games such as League of Legends have competitive environments where players race for their rank. In most MOBA games, a player's rank is determined by the match result (win or lose). It seems natural because of the nature of team play, but in some sense, it is unfair becaus