November 2022 arXiv papers — page 169

C. C. Corrigan, L. Zhao

In this paper, we prove some zero density theorems for certain families of Dirichlet $L$-functions. More specifically, the subjects of our interest are the collections of Dirichlet $L$-functions associated with characters to moduli from certain sparse sets and of certain fixed orders.

Yulong Liu, Ming Zeng

Betatron oscillation is a commonly known phenomenon in laser or beam driven plasma wakefield accelerators. In the conventional model, the plasma wake provides a linear focusing force to a relativistic electron, and the electron oscillates in one transverse direction with the betatron frequency proportional to $1/\sqrt{\gamma}$, where $\gamma$ is the Lorentz

Jiashuo Wang, Yi Cheng, Wenjie Li

Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user's feelings and experiences. However, these approaches suffer from two critical issues. First, they only consider causalities between the user's emotion and the user's experiences, and ignore those between the user's experiences. Secon

Anubhav Sarkar, Swagata Chakraborty, Sohom Ghosh, Sudip Kumar Naskar

Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price pred

Karina Hudson, Ashwin Srinivasan, Dmitry Miserev, Qingwen Wang

Many modern spin-based devices rely on the spin-orbit interaction, which is highly sensitive to the host semiconductor heterostructure and varies substantially depending on crystal direction, crystal asymmetry (Dresselhaus), and quantum confinement asymmetry (Rashba). One-dimensional quantum point contacts are a powerful tool to probe both energy and directi

James Kotary, Vincenzo Di Vito, Ferdinando Fioretto

Model selection is a strategy aimed at creating accurate and robust models. A key challenge in designing these algorithms is identifying the optimal model for classifying any particular input sample. This paper addresses this challenge and proposes a novel framework for differentiable model selection integrating machine learning and combinatorial optimizatio

Wei Peng, Ziyuan Qin, Yue Hu, Yuqiang Xie

Emotional Support Conversation (ESConv) aims to reduce help-seekers'emotional distress with the supportive strategy and response. It is essential for the supporter to select an appropriate strategy with the feedback of the help-seeker (e.g., emotion change during dialog turns, etc) in ESConv. However, previous methods mainly focus on the dialog history to se

Maohao Shen, Bowen Jiang, Jacky Yibo Zhang, Oluwasanmi Koyejo

Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the

Xiamin Huang, Xiao Jiang, Bing Huang, Zhou Li

We investigate the nonlocal optical conductivity of Fermi surface nesting materials which support charge density waves or spin density waves. The nonlocal optical conductivity contains information of correlations in electron fluids which could not be accessed by standard optical probes. Half metal emerges from doping a charge density wave and similarly spin-

Martine Lokken, Weiguang Cui, J. Richard Bond, Renée Hložek

Upcoming advances in galaxy surveys and cosmic microwave background data will enable measurements of the anisotropic distribution of diffuse gas in filaments and superclusters at redshift $z=1$ and beyond, observed through the thermal Sunyaev-Zel'dovich (tSZ) effect. These measurements will help distinguish between different astrophysical feedback models, ac

Tony T. Wang, Adam Gleave, Tom Tseng, Kellin Pelrine

We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is compr

Shangning Xia, Suizhi Huang, Kexin Xu, Tao Li

The tracks recorded by a gaseous detector provide a possibility for charged particle identification. For searching the neutrinoless double beta decay events of 136Xe in the PandaX-III experiment, we optimized the convolutional neural network based on the Monte Carlo simulation data to improve the signal-background discrimination power. EfficientNet is chosen

John A. Snoap, James A. Latshaw, Dimitrie C. Popescu, Chad M. Spooner

The paper studies the problem of robust classification of digitally modulated signals using capsule networks and cyclic cumulant (CC) features extracted by cyclostationary signal processing (CSP). Two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently are used in the study, which reveals t

Yanqi Huang, Bo-Qiang Ma

Based on recent proposal to associate IceCube TeV and PeV neutrino events with gamma-ray bursts~(GRBs) by considering the Lorentz violation of neutrinos, we provide a new estimate on the GRB neutrino flux and such result is much bigger than previous results by the IceCube Collaboration. Among these 24 neutrino ``shower" events above 60~TeV, 12 events are ass

Weida Wang

Shannon separation theorem lays the foundation for traditional image compression and transmission schemes, which consist of JPEG type image compression methods and the usual channel coding schemes such as Turbo and LDPC codes. One of the advantages of the separate design is that each of the two components, channel coding and source coding can be handled inde

Zhenyao He, Wei Xu, Hong Shen, Derrick Wing Kwan Ng

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design by considering the FD capability for both radar and communication. Specifically, we consider

Hee Jun Yang, Hyea Hyun Kim

To enhance solution accuracy and training efficiency in neural network approximation to partial differential equations, partitioned neural networks can be used as a solution surrogate instead of a single large and deep neural network defined on the whole problem domain. In such a partitioned neural network approach, suitable interface conditions or subdomain

Baixi Sun, Xiaodong Yu, Chengming Zhang, Jiannan Tian

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck wh

Yuchen Liang, Venugopal V. Veeravalli

The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is completely unknown. A window-limited leave-one-out (LOO) CuSum test is developed, which does not assume any knowledge of the post-change distribution, and does not require

Chen Zhang, Yi Ren, Kejun Zhang, Shuicheng Yan

While deep generative models have empowered music generation, it remains a challenging and under-explored problem to edit an existing musical piece at fine granularity. In this paper, we propose SDMuse, a unified Stochastic Differential Music editing and generation framework, which can not only compose a whole musical piece from scratch, but also modify exis

Lorenzo Luzi, Daniel LeJeune, Ali Siahkoohi, Sina Alemohammad

We study the interpolation capabilities of implicit neural representations (INRs) of images. In principle, INRs promise a number of advantages, such as continuous derivatives and arbitrary sampling, being freed from the restrictions of a raster grid. However, empirically, INRs have been observed to poorly interpolate between the pixels of the fit image; in o

Junqiang Huang, Zichao Guo

We present a simple but effective pixel-level self-supervised distillation framework friendly to dense prediction tasks. Our method, called Pixel-Wise Contrastive Distillation (PCD), distills knowledge by attracting the corresponding pixels from student's and teacher's output feature maps. PCD includes a novel design called SpatialAdaptor which ``reshapes''

F. Han, Y. Wei, P. Xie

Total least squares (TLS) is an effective method for solving linear equations with the situations, when noise is not just in observation matrices but also in mapping matrices. Moreover, the Tikhonov regularization is widely used in plenty of ill-posed problems. In this paper, we extend the regularized total least squares (RTLS) method from the matrix form du

Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is ab

Robert Bland

We obtain the following embedding theorem for symbolic dynamical systems. Let $G$ be a countable amenable group with the comparison property. Let $X$ be a strongly aperiodic subshift over $G$. Let $Y$ be a strongly irreducible shift of finite type over $G$ which has no global period, meaning that the shift action is faithful on $Y$. If the topological entrop

Uday Sood, Martin Kruczenski

The region near a critical point is studied using holographic models of second-order phase transitions. In a previous paper, we argued that the quantum circuit complexity of the vacuum ($C_0$) is the largest at the critical point. When deforming away from the critical point by a term $\int d^d x \, \tau \, O_\Delta$ the complexity $C(\tau)$ has a piece non-a

Patrick Jefferson, Manki Kim

We study Euclidean M5-branes wrapping vertical divisors in elliptic Calabi-Yau fourfold compactifications of M/F-theory that admit a Sen limit. We construct these Calabi-Yau fourfolds as elliptic fibrations over coordinate flip O3/O7 orientifolds of toric hypersurface Calabi-Yau threefolds. We devise a method to analyze the Hodge structure (and hence the dim

Luan Hoang

This paper establishes the precise asymptotic behavior, as time $t$ tends to infinity, for nontrivial, decaying solutions of genuinely nonlinear systems of ordinary differential equations. The lowest order term in these systems, when the spatial variables are small, is not linear, but rather positively homogeneous of a degree greater than one. We prove that

Yingjie Xue, Di Jin, Maurice Herlihy

Cross-chain swaps enable exchange of different assets that reside on different blockchains. Several protocols have been proposed for atomic cross-chain swaps. However, those protocols are not fault-tolerant, in the sense that if any party deviates, no asset transfer can happen. In this paper, we propose two alternative protocols for structuring composable an

Zhenghua Xu, Changhong Deng, Qiuling Yang

Variable-speed pumped-storage (VSPS) has great potential in helping solve the frequency control problem caused by low inertia, owing to its remarkable flexibility beyond conventional fixed-speed one, to make better use of which, a primary frequency control strategy based on adaptive model predictive control (AMPC) is proposed in this paper for VSPS plant in

Aravind P. Ravi, Jeonghee Rho, Sangwook Park, Seong Hyun Park

We present near-infrared (NIR) and optical observations of the Type Ic supernova (SN Ic) SN 2021krf obtained between days 13 and 259 at several ground-based telescopes. The NIR spectrum at day 68 exhibits a rising $K$-band continuum flux density longward of $\sim$ 2.0 $\mu$m, and a late-time optical spectrum at day 259 shows strong [O I] 6300 and 6364 \r{A}

Kyungduk Kim, Stefan Bittner, Yuhao Jin, Yongquan Zeng

Lima\c{c}on-shaped microdisk lasers are promising on-chip light sources with low lasing threshold and unidirectional output. We conduct an experimental study on the lasing dynamics of Lima\c{c}on-shaped semiconductor microcavities. The edge emission exhibits intensity fluctuations over a wide range of spatial and temporal scales. They result from multiple dy

John Ashmead

If we use the path integral approach, we can write quantum electrodynamics (QED) in a way that is manifestly relativistic. However the path integrals are confined to paths that are on mass-shell. What happens if we extend QED by computing the path integrals over all paths in energy momentum space, not only those on mass-shell? We use the requirement of covar

Mikio Tada, Ursula E. Lang, Iwei Yeh, Elizabeth S. Keiser

Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reliability. As melanoma is a cancer of the melanocyte, there is a clear need to develop a melanocytic cell segmentation tool that is agnostic to pathologist variability and automates

Irfan Al-Hussaini, Davi Nakajima An, Albert J. Lee, Sarah Bi

Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more tha

Brett Levac, Sidharth Kumar, Ajil Jalal, Jonathan I. Tamir

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the imag

Bernd Pfrommer

Due to their high temporal resolution and large dynamic range, event cameras are uniquely suited for the analysis of time-periodic signals in an image. In this work we present an efficient and fully asynchronous event camera algorithm for detecting the fundamental frequency at which image pixels flicker. The algorithm employs a second-order digital infinite

Vanessa Lopez, Gabriele Picco, Inge Vejsbjerg, Thanh Lam Hoang

Regulations govern many aspects of citizens' daily lives. Governments and businesses routinely automate these in the form of coded rules (e.g., to check a citizen's eligibility for specific benefits). However, the path to automation is long and challenging. To address this, recent global initiatives for digital government, proposing to simultaneously

Leonardo Kanashiro Felizardo, Francisco Caio Lima Paiva, Anna Helena Reali Costa, Emilio Del-Moral-Hernandez

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitab

Mette Gamst, David Pisinger

The technician routing and scheduling problem (TRSP) consists of technicians serving tasks subject to qualifications, time constraints and routing costs. In the literature, the TRSP is solved either to provide actual technician plans or for performing what-if analyses on different TRSP scenarios. We present a method for building optimal TRSP scenarios, e.g.,

Remco Coppens, Robbert Reijnen, Yingqian Zhang, Laurens Bliek

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning could be very computationally expensive in solving non-trial (combinatorial) optimization problems. This paper proposes a framework that in

Kyeong Min Kim

The one dimensional probabilistic toy model of particle scattering theory is proposed. The toy model version of scattering probability is proved to be equal to the hypervolume of a n-dimensional figure. The solution for any n-particle toy model is presented as a contour integral, through Mellin trasnformation. The method of solving the contour integral is di

Dan Berrebbi, Brian Yan, Shinji Watanabe

Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result in slow inference and network overthinking, where predictions made from the last layer of large models is worse than those made from intermediate layers. Early exit (EE) strategies

Amir Nassereldine, Patrick Chen, Jinjun Xiong

Spreadsheets are widely used in various fields to do large numerical analysis. While several companies have relied on spreadsheets for decades, data scientists are going in the direction of using scientific programming languages such as python to do their data analysis due to the support, community, and vast amount of libraries. While using python to analyze

Callie Chappell, Henry A. -A., Elvia B. O., Emily B.

BioJam is a political, artistic, and educational project in which Bay Area artists, scientists, and educators collaborate with youth and communities of color to address historical exclusion of their communities in STEM fields and reframe what science can be. As an intergenerational collective, we co-learn on topics of culture (social and biological), communi

Neil P. Oxtoby

Intense debate in the Neurology community before 2010 culminated in hypothetical models of Alzheimer's disease progression: a pathophysiological cascade of biomarkers, each dynamic for only a segment of the full disease timeline. Inspired by this, data-driven disease progression modelling emerged from the computer science community with the aim to recons

Nuriel Shalom Mor

We presented a method for improving computer vision tasks on images affected by adverse weather conditions, including distortions caused by adherent raindrops. Overcoming the challenge of applying computer vision to images affected by adverse weather conditions is essential for autonomous vehicles utilizing RGB cameras. For this purpose, we trained an approp

Savannah Shively

FASER (ForwArd Search ExpeRiment) fills the axial blindspot of other, radially arranged LHC experiments. It is installed 480 meters from the ATLAS interaction point, along the collision axis. FASER will search for new, long-lived particles that may be hidden in the collimated reaction products exiting ATLAS. The tracking detector is an essential component fo

Daniel Friedan

The cosmological gauge field (CGF) is a classical solution of SU(2)-weak gauge theory oscillating rapidly in time. It is the dark matter driving the CGF cosmology. A general, local, mathematically natural construction of the CGF is given here. The macroscopic properties are derived. The CGF is an irrotational perfect fluid. It provides a synchronized global

Aya Mutaz Zeidan, Paula Ramirez Gilliland, Ashay Patel, Zhanchong Ou

Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal org

Alfred Potier, Edward Kuester

This is an English translation of a paper by the French physicist Alfred Potier (1840-1905) that originally appeared 150 years ago [A. Potier, ``Recherches sur l'intégration d'un système d'équations aux différentielles partielles à coefficients périodiques,'' Comptes Rendus de l'Association Française pour l'Avancement des Sciences

Kevin Jose, Neil Ferguson, Atul Bhaskar

Propagation of elastic waves along the axis of cylindrical shells is of great current interest due to their ubiquitous presence and technological importance. Geometric imperfections and spatial variations of properties are inevitable in such structures. Here we report the existence of branched flows of flexural waves in such waveguides. The location of high

Max van Haren, Lennart Blanken, Tom Oomen

Feedforward for motion systems is getting increasingly more important to achieve performance requirements. This leads to a situation where position-dependent effects cannot be neglected anymore.

Yifei Wang, Tavor Baharav, Yanjun Han, Jiantao Jiao

In the infinite-armed bandit problem, each arm's average reward is sampled from an unknown distribution, and each arm can be sampled further to obtain noisy estimates of the average reward of that arm. Prior work focuses on identifying the best arm, i.e., estimating the maximum of the average reward distribution. We consider a general class of distributi

Bo You, Fang Li

This paper is concerned with the existence of insensitizing controls for a fourth order semilinear parabolic equation. Here, the initial data is partially unknown, we would like to find controls such that a specific functional is insensitive for small perturbations of the initial data. In general, this kind of problems can be recast as a null controllability

Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data.

Anusha Prakash, Arun Kumar, Ashish Seth, Bhagyashree Mukherjee

Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video. This task becomes challeng

Shashwat Sourav

Author names often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library, and expert disc

G. A. Grigorian

In this paper convolution type integral equations in the conservative case are studied. The conservative case of convolution type of equations relates to the case of non normal type of equations and is that of the corresponding symbols degenerate at some points of the real line, and the classical Furier transformation method meets difficulties with its appli

Suk Hyun Sung, Robert Hovden

Infinite series of Bessel function of the first kind, $\sum_ν^{\pm\infty} J_{Nν+p}(x)$, $\sum_ν^{\pm\infty} (-1)^νJ_{Nν+p}(x)$, are summed in closed form. These expressions are evaluated by engineering a Dirac comb that selects specific sequences within the Bessel series.

Sen Lin, Li Yang, Deliang Fan, Junshan Zhang

By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. However, most existing CL methods focus on addressing catastrophic forgetting in neural networks by minimiz

Xiaoyang Shi, Gahyun Annie Lee, Shuohan Liu, Dongjae Kim

The extra CO2 that has already been released into the atmosphere has to be removed in order to create a world that is carbon neutral. Technologies have been created to remove carbon dioxide from wet flue gas or even directly from ambient air, however these technologies are not widely deployed yet. New generations of creative CO2 capture sorbents have been pr

Keisuke Hanada, Junji Moriya, Masahiro Kojima

The restricted mean survival time is a clinically easy-to-interpret measure that does not require any assumption of proportional hazards. We focus on two ways to directly model the survival time and adjust the covariates. One is to calculate the pseudo-survival time for each subject using leave-one-out, and then perform a model analysis using all pseudo-valu

Haris Mansoor, Sarwan Ali, Shafiq Alam, Muhammad Asad Khan

Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further har

Carlos Pinzón, Santiago Quintero, Sergio Ramírez, Camilo Rueda

Structures involving a lattice and join-endomorphisms on it are ubiquitous in computer science. We study the cardinality of the set $\mathcal{E}(L)$ of all join-endomorphisms of a given finite lattice $L$. In particular, we show for $\mathbf{M}_n$, the discrete order of $n$ elements extended with top and bottom, $| \mathcal{E}(\mathbf{M}_n) | =n!\mathcal{L}_

Herkulaas Combrink, Vukosi Marivate, Benjamin Rosman

Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment. Reinforcement leaning solves unsupervised problems where agents move through a state-action-reward loop to maximize the overall reward for the agent, which in turn optimizes the sol

Yaoguang Zhai, Sicun Gao

The key to Black-Box Optimization is to efficiently search through input regions with potentially widely-varying numerical properties, to achieve low-regret descent and fast progress toward the optima. Monte Carlo Tree Search (MCTS) methods have recently been introduced to improve Bayesian optimization by computing better partitioning of the search space tha

Petr A. Mishchenko, Keita Xagawa

Secure multi-party quantum computation (MPQC) protocol is a versatile tool that enables error-free distributed quantum computation to a group of $n$ mutually distrustful quantum nodes even when some of the quantum nodes do not follow the instructions of the protocol honestly. However, in case of the MPQC protocols built on top of the quantum error correction

Jean-Baptiste Ruffio, Markus Kasper

The primary goal of this thesis was the correction of Non-Common-Path-Aberrations in the SPHERE instrument for helping it meeting its contrast requirements. SPHERE's purpose is the search and characterization of giant exo-planets around nearby stars. The author implemented a method called Electric Field Conjugation that he tested in simulation as well as

E. López-Navas, P. Arévalo, S. Bernal, Matthew J. Graham

The scarce optical variability studies in spectrally classified Type 2 active galactic nuclei (AGNs) have led to the discovery of anomalous objects that are incompatible with the simplest unified models (UM). This paper focuses on the exploration of different variability features that allows to separate between obscured, Type 2 AGNs, and the variable, unobsc

Levent Tunçel, Lieven Vandenberghe

A convex cone is homogeneous if its automorphism group acts transitively on the interior of the cone, i.e., for every pair of points in the interior of the cone, there exists a cone automorphism that maps one point to the other. Cones that are homogeneous and self-dual are called symmetric. The symmetric cones include the positive semidefinite matrix cone an

Georgiana Caltais, Can Olmezoglu

In this abstract we propose a framework for explaining violations of safety properties in Software Defined Networks, using counterfactual causal reasoning.

Vincent Deledicque

At a global scale, the universe is generally fitted by an idealized manifold described by the FLRW metric. This is in particular the case when probing the universe to determine its dynamics. The process that fits the idealized manifold to the real universe is however not uniquely defined. This process may depend on the cosmic probe that has been used for the

Holger Boche, Yannik N. Böck, Christian Deppe, Frank H. P. Fitzek

We consider the problem of remotely tracking the state of and unstable linear time-invariant plant by means of data transmitted through a noisy communication channel from an algorithmic point of view. Assuming the dynamics of the plant are known, does there exist an algorithm that accepts a description of the channel's characteristics as input, and retur

Marcelo Lerendegui, Kai Riemer, Bingxue Wang, Christopher Dunsby

Ultrasound contrast enhanced imaging has seen widespread uptake in research and clinical diagnostic imaging. This includes applications such as vector flow imaging, functional ultrasound and super-resolution Ultrasound Localization Microscopy (ULM). All of these require testing and validation during development of new algorithms with ground truth data. In th

W. B. Elsharkawy, Abeer Mera, M. Kotb, A. M. Khalaf

The positive-parity states of even-even Xe nuclei are investigated within the framework of modified O(6) limit of the interacting boson model (IBM1). The effective three-body interaction [QQQ] where Q is the IBM O(6) quadrupole operator is introduced to exhibit the triaxiality nature. The shape of nuclear surface is described by the deformation parameters be

Artem Lykov, Aleksey Fedoseev, Dzmitry Tsetserukou

This paper presents a novel haptic device DeltaFinger designed to deliver the force of interaction with virtual objects by guiding user's finger with wearable delta mechanism. The developed interface is capable to deliver 3D force vector to the fingertip of the index finger of the user, allowing complex rendering of virtual reality (VR) environment. The

Rinaldo B. Schinazi

We introduce the following discrete time model. Each natural number represents an ecological niche and is assigned a fitness in $(0,1)$. All the sites are updated simultaneously at every discrete time. At any given time the environment may be normal with probability $p$ or a catastrophe may occur with probability $1-p$. If the environment is normal the fitne

Jingli Shi

Recent advances in brain organoid technology are exciting new ways, which have the potential to change the way how doctors and researchers understand and treat cerebral diseases. Despite the remarkable use of brain organoids derived from human stem cells in new drug testing, disease modeling, and scientific research, it is still heavily time-consuming work t

Amira Alotaibi, Tarik Alafif, Faris Alkhilaiwi, Yasser Alatawi

Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This

Javier Maroto, Gérôme Bovet, Pascal Frossard

Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular. However, they are very susceptible to small adversarial perturbations that are carefully crafted to change the network decision. In this work, we build on knowledge distillation ideas and adversarial training in order to build

Jyoti Kini, Ajmal Mian, Mubarak Shah

We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followed by data association. Our method embeds both steps into a single end-to-end trainable network eliminating the dependency on external object detectors. Our model exploits temporal

Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov

Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging methods. Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images. Ultimately, these issues could affect the p

Kirill A. Bronnikov

The paper is a brief review on the existence and basic properties of static, spherically symmetric regular black hole solutions of general relativity, where the source of gravity is represented by nonlinear electromagnetic fields with the Lagrangian function $L$ depending on the single invariant $f = F_{μν}F^{μν}$ or on two variables: either $L(f, h)$, where

Sangeeta Rajpurohit, Jacopo Simoni, Liang Z. Tan

Photo-induced phase-transitions (PIPTs) driven by highly cooperative interactions are of fundamental interest as they offer a way to tune and control material properties on ultrafast timescales. Due to strong correlations and interactions, complex quantum materials host several fascinating PIPTs such as light-induced charge density waves and ferroelectricity

Alexander Slesarev, Mikhail Mikhailov, George Chernishev

Modern high load applications store data using multiple database instances. Such an architecture requires data consistency, and it is important to ensure even distribution of data among nodes. Load balancing is used to achieve these goals. Hashing is the backbone of virtually all load balancing systems. Since the introduction of classic Consistent Hashing, m

Teo Susnjak, Paula Maddigan

Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic

Hirohito Ogasawara, Han Wang, Jörgen Gladh, Alessandro Gallo

We report optical pumping and X-ray absorption spectroscopy experiments at the PAL free electron laser that directly probe the electron dynamics of a graphene monolayer adsorbed on copper in the femtosecond regime. By analyzing the results with ab-initio theory we infer that the excitation of graphene is dominated by indirect excitation from hot electron-hol

Jimmy Lin

While differential privacy and gradient compression are separately well-researched topics in machine learning, the study of interaction between these two topics is still relatively new. We perform a detailed empirical study on how the Gaussian mechanism for differential privacy and gradient compression jointly impact test accuracy in deep learning. The exist

Syed Ali John Naqvi, Syed Bazil Ali

We present a list of datasets and their best models with the goal of advancing the state-of-the-art in object detection by placing the question of object recognition in the context of the two types of state-of-the-art methods: one-stage methods and two stage-methods. We provided an in-depth statistical analysis of the five top datasets in the light of recent

Moseli Mots'oehli, Anna Sergeevna Bosman, Johan Pieter De Villiers

Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a la

Qiaoqiao Ren, Tony Belpaeme

Tactile interaction plays a crucial role in interactions between people. Touch can, for example, help people calm down and lower physiological stress responses. Consequently, it is believed that tactile and haptic interaction matter also in human-robot interaction. We study if the intensity of the tactile interaction has an impact on people, and do so by stu

Amin Karamlou, Marcel Pfaffhauser, James Wootton

The emergence of noisy medium-scale quantum devices has led to proof-of-concept applications for quantum computing in various domains. Examples include Natural Language Processing (NLP) where sentence classification experiments have been carried out, as well as procedural generation, where tasks such as geopolitical map creation, and image manipulation have

Jinwei Zhang, Pascal Spincemaille, Hang Zhang, Thanh D. Nguyen

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accel

ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Jiamin Hou

We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new ${\rm S{\scriptsize IM}BIG}$ forward modeling framework. ${\rm S{\scriptsize IM}BIG}$ leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on

Brandon Yushan Feng, Susmija Jabbireddy, Amitabh Varshney

We present VIINTER, a method for view interpolation by interpolating the implicit neural representation (INR) of the captured images. We leverage the learned code vector associated with each image and interpolate between these codes to achieve viewpoint transitions. We propose several techniques that significantly enhance the interpolation quality. VIINTER s

O. Mousis, A. Bouquet, Y. Langevin, N. André

Enceladus, an icy moon of Saturn, possesses an internal water ocean and jets expelling ocean material into space. Cassini investigations indicated that the subsurface ocean could be a habitable environment having a complex interaction with the rocky core. Further investigation of the composition of the plume formed by the jets is necessary to fully understan

Connor Kenyon, Collin Capano

With the release of the Apple Silicon System-on-a-Chip processors, and the impressive performance shown in general use by both the M1 and M1 Ultra, the potential use for Apple Silicon processors in scientific computing is explored. Both the M1 and M1 Ultra are compared to current state-of-the-art data-center GPUs, including an NVIDIA V100 with PCIe, an NVIDI

Jomin Jose, Andrew J, Kumudha Raimond, Shweta Vincent

Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conj

Bartłomiej Przybylski, Maciej Pawlik, Paweł Żuk, Bartłomiej Łagosz

Modern HPC workload managers and their careful tuning contribute to the high utilization of HPC clusters. However, due to inevitable uncertainty it is impossible to completely avoid node idleness. Although such idle slots are usually too short for any HPC job, they are too long to ignore them. Function-as-a-Service (FaaS) paradigm promisingly fills this gap,