April 2023 arXiv papers — page 84

Thomas Neele, Jaco van de Pol

We study operations on fixpoint equation systems (FES) over arbitrary complete lattices. We investigate under which conditions these operations, such as substituting variables by their definition, and swapping the ordering of equations, preserve the solution of a FES. We provide rigorous, computer-checked proofs. Along the way, we list a number of known and

Mohammadali Mohammadi, Tung T. Vu, Hien Quoc Ngo, Michail Matthaiou

We consider network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems, where full-duplex (FD) transmission is virtually realized via half-duplex (HD) hardware devices. The HD access points (APs) operating in uplink (UL) mode and those operating in downlink (DL) mode simultaneously serve DL and UL user equipments

Timothy Sudijono

The restricted solid-on-solid (RSOS) model is a model of continuous-time surface growth characterized by the constraint that adjacent height differences are bounded by a fixed constant. Though the model is conjectured to belong to the KPZ universality class, mathematical progress on the model is very sparse. We study basic properties of the model and establi

Zitang Sun, Shin'ya Nishida, Zhengbo Luo

For visual estimation of optical flow, a crucial function for many vision tasks, unsupervised learning, using the supervision of view synthesis has emerged as a promising alternative to supervised methods, since ground-truth flow is not readily available in many cases. However, unsupervised learning is likely to be unstable when pixel tracking is lost due to

Saniya Khan, Andrea Miglio, Emma Willett, Benoît Mosser

Gaia EDR3 has provided unprecedented data that generate a lot of interest in the astrophysical community, despite the fact that systematics affect the reported parallaxes at the level of ~ 10 muas. Independent distance measurements are available from asteroseismology of red-giant stars with measurable parallaxes, whose magnitude and colour ranges more closel

Aleksandr D. Krotov, Denis S. Krotov

We discuss the problem of the existence of latin squares without a substructure consisting of six elements $(r_1,c_2,l_3)$, $(r_2,c_3,l_1)$, $(r_3,c_1,l_2)$, $(r_2,c_1,l_3)$, $(r_3,c_2,l_1)$, $(r_1,c_3,l_2)$. Equivalently, the corresponding latin square graph does not have an induced subgraph isomorphic to $K_{3,3}$. The exhaustive search [Brouwer, Wanless.

Johannes Keisers, Joachim Krug

The translation of messenger RNA transcripts to proteins is commonly modeled as a one-dimensional totally asymmetric exclusion process with extended particles. Here we focus on the effects of premature termination of translation through the irreversible detachment of ribosomes. We consider a model where the detachment is induced by the unsuccessful attempt t

Fabian Mentzer, Eirikur Agustsson, Michael Tschannen

We show how bidirectional transformers trained for masked token prediction can be applied to neural image compression to achieve state-of-the-art results. Such models were previously used for image generation by progressivly sampling groups of masked tokens according to uncertainty-adaptive schedules. Unlike these works, we demonstrate that predefined, deter

Lucas Hataishi

We prove cocontinuity of the $\max$-tensor product of C*-categories and develop a framework to perform factorization homology in a C*-setting. In such context, we specialize some results of D. Ben-Zvi, A. Brochier and D. Jordan. As a consequence of our constructions, we realize quantum Hamiltonian reduction in terms of bimodules over a factor $N$. We also pr

Priya Ghosh, Mahasweta Pandit, Chirag Srivastava, Ujjwal Sen

Coherences in mutually unbiased bases of states of an isolated quantum system follow a complementarity relation. The nonlocal advantage of quantum coherence (NAQC), defined in a bipartite scenario, is a situation in which the average quantum coherences of the ensembles of one subsystem, effected by a measurement performed on the other subsystem, violates the

Robert Fulsche, Lauritz van Luijk

We prove a new criterion for the essential self-adjointness of pseudodifferential operators that does not involve ellipticity-type assumptions. For example, we show that self-adjointness holds in case the symbol is $C^{2d+3}$ with derivatives of order two and higher being uniformly bounded. These results also apply to hermitian operator-valued symbols on inf

Indro Spinelli, Michele Guerra, Filippo Maria Bianchi, Simone Scardapane

Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework. This model family represents each graph as a collection of subgraphs, generally extracted by random sampling or with hand-crafted heuristics. Our key observation is that by selecting "meaningful" subgraphs, besides improving the express

Rushil Vohra, Ali Rajaei, Jochen L. Cremer

With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines featur

Jingyu Gong, Yi Nie, Jonas van Ouwerkerk, Felix Wege

Decarbonizing the energy sector is one of the main challenges to combat the climate crisis. Cities play an important role to reach climate neutrality as more than 70% of global CO2 emissions originate from urban areas. Decarbonization of energy supply systems can be achieved through various means, including the use of renewable energy sources, improving the

Richard Aron, Verónica Dimant, Luis C. García-Lirola, Manuel Maestre

Let $X$ and $Y$ be complex Banach spaces with $B_X$ denoting the open unit ball of $X.$ This paper studies various aspects of the {\em holomorphic Lipschitz space} $\mathcal HL_0(B_X,Y)$, endowed with the Lipschitz norm. This space is the intersection of the spaces, $\operatorname{Lip}_0(B_X,Y)$ of Lipschitz mappings and $\mathcal H^\infty(B_X,Y)$ of bounded

You-An Lee, Detlef Lohse, Sander G. Huisman

We have experimentally investigated mixing in highly confined turbulent fountains, namely quasi-two-dimensional fountains. Fountains are formed when the momentum of the jet fluid is in the opposite direction to its buoyancy force. This work consists of two parts. First, we injected an ethanol/oil mixture (ouzo mixture) downward into quiescent water, forming

Geoffery Agorku, Divine Agbobli, Vuban Chowdhury, Kwadwo Amankwah-Nkyi

The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-time YOLOv5 Deep Learning (DL) model for de

Ashay Patel, Petru-Danial Tudiosu, Walter H. L. Pinaya, Gary Cook

Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. 18F-fluorodeoxyglucose is an imaging modality commonly used to detect cancer due to its high sensitivity and clear visualisation of the pattern of metabolic activity. Nonetheless, as cancer is highly heterogeneous, it is challenging to train general-purpose discrimin

Stefano Pasquali

We study analytically the dynamics of a $d$-dimensional Klein-Gordon lattice with periodic boundary conditions, for $d \leq 3$. We consider initial data supported on one low-frequency Fourier mode. We show that, in the continuous approximation, the resonant normal form of the system is given by a small-dispersion nonlinear Schr\"odinger (NLS) equation. By ex

Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo

EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities

Wlodzimierz Bryc, Jacek Wesolowski

We discuss Pitman's representation of a Markov process, which serves as a discrete analog to the Bessel 3D process starting at time 0 from an arbitrary initial law. This representation involves maxima of lazy simple random walks and an auxiliary independent random variable. The law of the auxiliary random variable is explicitly related to the initial law of

Tianya Zhang, Ph. D., Peter J. Jin, Ph. D.

Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS). Insights from the model of car-following behavior help us understand the causes of various macro phenomena that arise from interactions between pairs of vehicles. Car-foll

William Ravenscroft, Stefan Goetze, Thomas Hain

Speech separation remains an important area of multi-speaker signal processing. Deep neural network (DNN) models have attained the best performance on many speech separation benchmarks. Some of these models can take significant time to train and have high memory requirements. Previous work has proposed shortening training examples to address these issues but

Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song

Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. However, three pivotal questions remain unanswered: (1) How effective are current LLMs in utilizing tools? (2) How can we enhance LLMs' ability to utilize tools? (3) What obstacles need to be overcome to leverage tools? To address th

Arthur Lipstein, Silvia Nagy

We show that self-dual gravity in Euclidean four-dimensional Anti-de Sitter space (AdS$_4$) can be described by a minimally coupled scalar field with a cubic interaction written in terms of a deformed Poisson bracket, providing a remarkably simple generalisation of the Plebanski action for self-dual gravity in flat space. This implies a novel symmetry algebr

Olaf Wysocki, Ludwig Hoegner, Uwe Stilla

Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for fa\c{c}ade segmentation. Robust fa\c{c}ade segmentation is becoming a key fac

Yannick Schnider, Stanislaw Wozniak, Mathias Gehrig, Jules Lecomte

Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their complexity is often prohibitive for application at the edge or in robots, where efficiency and latency play crucial role. To address this challenge, we build on the latest develop

Haomin Rao, Dehao Zhao

The parity violating model based on teleparallel gravity is a competitive scheme for parity violating gravity, which has been preliminary studied in the literature. To further investigate the parity violating model in teleparallel gravity, in this paper, we construct all independent parity-odd terms that are quadratic in torsion tensor and coupled to a scala

David J. Jordan, Eric A. Miska

How do the same mechanisms that faithfully regenerate complex developmental programs in spite of environmental and genetic perturbations also permit responsiveness to environmental signals, adaptation, and genetic evolution? Using the nematode Caenorhabditis elegans as a model, we explore the phenotypic space of growth and development in various genetic and

Felix Friedrich, David Steinmann, Kristian Kersting

Current machine learning models produce outstanding results in many areas but, at the same time, suffer from shortcut learning and spurious correlations. To address such flaws, the explanatory interactive machine learning (XIL) framework has been proposed to revise a model by employing user feedback on a model's explanation. This work sheds light on the expl

A. V. Mikhailov, A. S. Kurdyubov, E. S. Khramtsov, I. V. Ignatiev

Exciton energy structure and population dynamics in a wide CdTe/CdZnTe quantum well are studied by spectrally-resolved pump-probe spectroscopy. Multiple excitonic resonances in reflectance spectra are observed and identified by solving numerically three-dimensional Schr\"odinger equation. The pump-probe reflectivity signal is shown to be dominated by the pho

Giacomo Ceoldo, Tom A. B. Snijders, Ernst C. Wit

The stochastic actor oriented model (SAOM) is a method for modelling social interactions and social behaviour over time. It can be used to model drivers of dynamic interactions using both exogenous covariates and endogenous network configurations, but also the co-evolution of behaviour and social interactions. In its standard implementations, it assumes that

Andrea Gadotti, Florimond Houssiau, Meenatchi Sundaram Muthu Selva Annamalai, Yves-Alexandre de Montjoye

Behavioral data generated by users' devices, ranging from emoji use to pages visited, are collected at scale to improve apps and services. These data, however, contain fine-grained records and can reveal sensitive information about individual users. Local differential privacy has been used by companies as a solution to collect data from users while preservin

Julian Haas, Ragnar Mogk, Elena Yanakieva, Annette Bieniusa

Local-first software manages and processes private data locally while still enabling collaboration between multiple parties connected via partially unreliable networks. Such software typically involves interactions with users and the execution environment (the outside world). The unpredictability of such interactions paired with their decentralized nature ma

Hengtong Zhang, Tingyang Xu

By formulating data samples' formation as a Markov denoising process, diffusion models achieve state-of-the-art performances in a collection of tasks. Recently, many variants of diffusion models have been proposed to enable controlled sample generation. Most of these existing methods either formulate the controlling information as an input (i.e.,: conditiona

Nick Wulbusch, Reinhild Roden, Alexey Chernov, Matthias Blau

In many applications, knowledge of the sound pressure transfer to the eardrum is important. The transfer is highly influenced by the shape of the ear canal and its acoustic properties, such as the acoustic impedance at the eardrum. Invasive procedures to measure the sound pressure at the eardrum are usually elaborate or costly. In this work, we propose a num

Sławomir Dadas

This paper describes our submission to the SemEval 2023 multilingual tweet intimacy analysis shared task. The goal of the task was to assess the level of intimacy of Twitter posts in ten languages. The proposed approach consists of several steps. First, we perform in-domain pre-training to create a language model adapted to Twitter data. In the next step, we

Alexander Dawid Bincalar, M. C. Schraefel, Christopher Freeman

While vibration is a well-used output signal in HCI as part of haptic interaction, vibration outside HCI is used in many other modes to support human performance, from rehabilitation to cognition. In this late breaking work, we present preliminary positive results of a novel protocol that informs how vibration might be used to enrich HCI interventions for as

Faris B. Mismar, Aliye Ozge Kaya

This paper proposes an algorithm that uses geospatial analytics and the muting of physical resources in next-generation base stations (BSs) to avoid interference between cellular (or terrestrial) and satellite communication (non-terrestrial) systems. The information exchange between satellite and terrestrial stations is minimal, but a hybrid edge cloud node

N Mont-Geli, A Tarifeño-Saldivia, L M Fraile, S Viñals

miniBELEN-10A is a modular and transportable moderated neutron counter with a nearly flat detection efficiency up to 8 MeV. The detector was designed to carry out measurements of (alpha, n) reactions in the context of the Measurement of Alpha Neutron Yields (MANY) project. In this work we present the results of the commissioning of miniBELEN-10A using the re

Tiziano Granucci

In this paper we study the regularity and the boundedness of the minima of two classes of functionals of the calculus of variations

Sławomir Dadas

The goal of visual word sense disambiguation is to find the image that best matches the provided description of the word's meaning. It is a challenging problem, requiring approaches that combine language and image understanding. In this paper, we present our submission to SemEval 2023 visual word sense disambiguation shared task. The proposed system integrat

Robert F. Bailey, Keenan B. Nicholson

We consider twisted permutation codes, a class of frequency permutation arrays obtained from finite groups with multiple permutation representations of the same degree, introduced by Gillespie, Praeger and Spiga (and later studied by Akbari, Gillespie and Praeger), and develop a decoding algorithm for such codes based on earlier work of the first author for

Munazza Zaib, Quan Z. Sheng, Wei Emma Zhang, Adnan Mahmood

Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given question. However, these sequential que

Shridhar Velhal, Srikrishna B R, Mukunda Bharatheesha, Suresh Sundaram

This paper presents a non-iterative approach for finding the assignment of heterogeneous robots to efficiently execute online Pickup and Just-In-Time Delivery (PJITD) tasks with optimal resource utilization. The PJITD assignments problem is formulated as a spatio-temporal multi-task assignment (STMTA) problem. The physical constraints on the map and vehicle

Jiahua Dong, Guohua Cheng, Yue Zhang, Chengtao Peng

Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks. However, due to expensive labor costs and expertise, the availability of multi-organ annotations is usually

Henning Schlüter, Frank Allgöwer

We present a Stochastic Model Predictive Control (SMPC) framework for linear systems subject to Gaussian disturbances. In order to avoid feasibility issues, we employ a recent initialization strategy, optimizing over an interpolation of the initial state between the current measurement and previous prediction. By also considering the variance in the interpol

Martin M. Roth

Image sensors, most notably the Charge Coupled Device (CCD), have revolutionized observational astronomy as perhaps the most important innovation after photography. Since the 50th anniversary of the invention of the CCD has passed in 2019, it is time to review the development of detectors for the visible wavelength range, starting with the discovery of the p

Yangyi Zhang, Xinrong Guan, Qingqing Wu, Zhi Ji

In this correspondence, we investigate an intelligent reflective surface (IRS) assisted downlink ultra-reliable and low-latency communication (URLLC) system, where an access point (AP) sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access (FDMA) and time division

Vaishali Sharma, Mayank Goswami

Electrical Impedance Tomography can be cost-effective, portable, non-invasive imaging technique. It has preclinical and a few of them already proven industrial applications. This technique can only recover images of low spatial and contrast resolution, partially due to existing physical models. The capability of discriminating between Impedance profiles in r

Sergii Kutnii

Bosonization in a theory with four-fermion interaction of Nambu-Jona-Lasinio type with additional U(N) symmetry is studied. It is demonstrated that bosonization is not uniquely determined by the interaction terms due to Fierz identities. Effective action including both fermions and composite fields is constructed. R-operation renormalization scheme is develo

Giulia Venditti, Ilaria Maccari, Alexis Jouan, Gyanendra Singh

Different classes of low-dimensional superconducting systems exhibit an inhomogeneous filamentary superconducting condensate whose macroscopic coherence still needs to be fully investigated and understood. Here we present a thorough analysis of the superfluid response of a prototypical filamentary superconductor embedded in a {two-dimensional} metallic matri

Shouvik Datta Choudhury

A Riemannian metric bundle G(M) is a fiber bundle over a smooth manifold M, whose fibers are the spaces of symmetric, positive-definite bilinear forms on the tangent spaces of M, which represent the Rieman?nian metrics. In this work, we aim to study the category of Riemannian metric bundles and explore their connections with K-theory and other areas of mathe

V. P. Grinin, L. V. Tambovtseva, O. Yu. Barsunova, D. N. Shakhovskoy

The star CQ Tau belongs to the family of UX Ori type stars. It has very complex photometric behavior and complex structure of the circumstellar environment. In our paper we constructed the historical 125 years light curve of this star basing on the published photometric observations. It follows that besides a random component characteristic of UX Ori type st

Yichun Li, Yi Li, Rajesh Nair, Syed Mohsen Naqvi

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action characteristics of ADHD are desired. Skele

Alexei S. Ilin, Alexander B. Balakin

Based on the formalism of nonlocal extension of the Israel-Stewart causal thermodynamics on the one hand, and on the formalism of the extended thermostatics on the other hand, we propose the new model of nonlocal relativistic non-equilibrium thermostatics for description of the static spherically symmetric stellar systems. This nonlocal formalism operates wi

Kreske Ecker, Xavier de Luna, Lina Schelin

This paper presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect and develop an outcome regression estimator. We show how to obtain valid inference on the FATE using simultaneous confidence bands, which cover the FATE with a given probability over the

Florian Huber, Hannes Engler, Anna Kicherer, Katja Herzog

Explainability in yield prediction helps us fully explore the potential of machine learning models that are already able to achieve high accuracy for a variety of yield prediction scenarios. The data included for the prediction of yields are intricate and the models are often difficult to understand. However, understanding the models can be simplified by usi

Piotr Szańkowski, Łukasz Cywiński

We investigate what can be concluded about a quantum system when sequential quantum measurements of its observable -- a prominent example of the so-called quantum stochastic process -- fulfill the Kolmogorov consistency condition and thus appear to an observer as a sampling of a classical trajectory. We identify a set of physical conditions imposed on the sy

Peter C. Dillinger, Martín Farach-Colton, Guido Tagliavini, Stefan Walzer

In the Uncoordinated Unique Identifiers Problem (UUIDP) there are $n$ independent instances of an algorithm $\mathcal{A}$ that generates IDs from a universe $\{1, \dots, m\}$, and there is an adversary that requests IDs from these instances. The goal is to design $\mathcal{A}$ such that it minimizes the probability that the same ID is ever generated twice ac

Masaaki Fujii, Masashi Sekine

In this paper, using the mean-field game theory, we study a problem of equilibrium price formation among many investors with exponential utility in the presence of liabilities unspanned by the security prices. The investors are heterogeneous in their initial wealth, risk-averseness parameter, as well as stochastic liability at the terminal time. We character

Philipp Schneider

Shortest paths problems are subject to extensive studies in classic distributed models such as the CONGEST or Congested Clique. These models dictate how nodes may communicate in order to determine shortest paths in a distributed input graph. This article focuses on shortest paths problems in the HYBRID model, which combines local communication along edges of

Shimin Wang, Martin Guay, Denis Dochain

This paper proposes a non-adaptive control solution framework to the practical output regulation problem (PORP) for a class of nonlinear systems with uncertain parameters, unknown control directions and uncertain exosystem dynamics. The concurrence of the unknown control directions and uncertainties in both the system dynamics and the exosystem pose a signif

Paul G. Severino, Randall D. Kamien

Volterra's definition of dislocations in crystals distinguishes edge and screw defects geometrically, according to whether the Burgers vector is perpendicular or parallel to the defect. Here, we demonstrate a distinction between screw and edge dislocations that enables a unified, purely topological means of classification. Our construction relies on the cons

Alexey Slunyaev, Anna Kokorina, Efim Pelinovsky

Effects of nonlinear dynamics of solitary waves and wave modulations within the modular (also known as quadratically cubic) Korteweg - de Vries equation are studied analytically and numerically. Large wave events can occur in the course of interaction between solitons of different signs. Stable and unstable (finite-time-lived) breathers can be generated in i

Miloslav Znojil

Quantum mechanics of closed, unitary quantum systems can be formulated in non-Hermitian interaction picture (NIP) in which both the states and the observables vary with time. Then, in general, not only the Schr\"{o}dinger-equation generators $G(t)$ but also the Heisenberg-equation generators $\Sigma(t)$ are phenomenologically irrelevant, with spectra which a

You-An Lee, Sander G. Huisman, Detlef Lohse

We inject with jet mixtures of ethanol and dissolved anise oil upward into quiescent water with jet Reynolds numbers, 500<Re_0<810. Nucleation of oil droplets, also known as the ouzo effect, follows from the entrainment and mixing with ambient water, where the oil has much lower oil solubility than the initial jet fluid. We experimentally investigate the loc

David Thulke, Nico Daheim, Christian Dugast, Hermann Ney

This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10). In both iterations the task consists of three subtasks: first detect whether the current turn is knowledge seeking, second select a relevant knowledge document, and third generate a response grounded on the

Pietro M. Bonetti, Debmalya Chakraborty, Xianxin Wu, Andreas P. Schnyder

We investigate topological superconductivity in the Rashba-Hubbard model, describing heavy-atom superlattice and van der Waals materials with broken inversion. We focus in particular on fillings close to the van Hove singularities, where a large density of states enhances the superconducting transition temperature. To determine the topology of the supercondu

Amit Shomer, Shai Avidan

We propose SampleDepth, a Convolutional Neural Network (CNN), that is suited for an adaptive LiDAR. Typically,LiDAR sampling strategy is pre-defined, constant and independent of the observed scene. Instead of letting a LiDAR sample the scene in this agnostic fashion, SampleDepth determines, adaptively, where it is best to sample the current frame. To do that

Amir F. Shakirzyanov, Alexander B. Balakin

In the framework of axionically extended Einstein-Maxwell-aether theory we study the structure of the electromagnetic field allowed by the anisotropic cosmological spacetime platforms associated with the Bianchi models. These models guarantee that the aether velocity possesses the shear, and we focus on its role in the evolution of the axion-photon systems.

Misgina Tsighe Hagos, Niamh Belton, Ronan P. Killeen, Kathleen M. Curran

Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on classifying brain images into one of three major categories: healthy, MCI, and AD; or categorizing MCI patients into (1)

Nicolas Bianco, Mauro Bernardi, Daniele Bianchi

We develop a variational Bayes approach for dynamic variable selection in high-dimensional regression models with time-varying parameters and predictors that exhibit a predefined group structure. Through comprehensive simulation studies, we demonstrate that our method yields more accurate parameter estimates than existing Bayesian static and dynamic variable

Giovanni M. Prosepri, Massimiliano Baldicchi

We reconsider the problem of the interpretation of the Quantum Theory (QT) in the perspective of the entire universe and of Bphr idea that the classical language is the language of our experience and QT acquires a meaning only with a reference to it. We distinguish a classical or macroscopic level, and a quantum or microscopic one that is perceived only thro

Rutger Goekoop, Roy de Kleijn

Living systems are hierarchical control systems that display a small world network structure, in which many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a power-law cluster size distribution (a mereology). Apart from their structure, the dynamics of living systems also shows fractal-like qualities: the timeser

Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review exa

Saswata Naha, Sayantan Roy, Arkaprava Sanki, Diptanil Santra

Data obfuscation deals with the problem of masking a data-set in such a way that the utility of the data is maximized while minimizing the risk of the disclosure of sensitive information. To protect data we address some ways that may as well retain its statistical uses to some extent. One such way is to mask a data with additive noise and revert to certain d

Amir Hertz, Kfir Aberman, Daniel Cohen-Or

We introduce Delta Denoising Score (DDS), a novel scoring function for text-based image editing that guides minimal modifications of an input image towards the content described in a target prompt. DDS leverages the rich generative prior of text-to-image diffusion models and can be used as a loss term in an optimization problem to steer an image towards a de

Ryan Boldi, Ashley Bao, Martin Briesch, Thomas Helmuth

Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection, which is often computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. More specifically, recent studies have shown that both random

Alessandro Camasta, Genni Fragnelli

We consider a beam equation in presence of a leading degenerate operator which is not in divergence form. We impose clamped conditions where the degeneracy occurs and dissipative conditions at the other endpoint. We provide some conditions for the uniform exponential decay of solutions for the associated problem.

Shinei Arakawa, Hideki Tsunashima, Daichi Horita, Keitaro Tanaka

Diffusion probabilistic models have been successful in generating high-quality and diverse images. However, traditional models, whose input and output are high-resolution images, suffer from excessive memory requirements, making them less practical for edge devices. Previous approaches for generative adversarial networks proposed a patch-based method that us

Nandini Iyer, Ronaldo Menezes, Hugo Barbosa

Segregation is a highly nuanced concept that researchers have worked to define and measure over the past several decades. Conventional approaches tend to estimate segregation based on residential patterns in a static manner. In this work, we analyse socioeconomic inequalities, assessing segregation in various dimensions of the urban experience. Moreover, we

Avery Meiksin

The scattering of Ly$\alpha$ photons from the first radiating sources in the Universe plays a pivotal role in 21-cm radio detections of Cosmic Dawn and the Epoch of Reionization through the Wouthuysen-Field effect. New data from JWST show the Ly$\alpha$ photon scattering rate exceeds that required to decouple the intergalactic hydrogen spin temperature from

Leon Ruf, Tosson Elalaily, Claudio Puglia, Yurii P. Ivanov

The control of a superconducting current via the application of a gate voltage has been recently demonstrated in a variety of superconducting devices. Although the mechanism underlying this gate-controlled supercurrent (GCS) effect remains under debate, the GCS effect has raised great interest for the development of the superconducting equivalent of conventi

Peter Beelen, Vincent Neiger

In this article, we present a fast algorithm performing an instance of the Guruswami-Sudan list decoder for algebraic geometry codes. We show that any such code can be decoded in $\tilde{O}(s^2\ell^{\omega-1}\mu^{\omega-1}(n+g) + \ell^\omega \mu^\omega)$ operations in the underlying finite field, where $n$ is the code length, $g$ is the genus of the function

Zongheng Tang, Yifan Sun, Si Liu, Yi Yang

This paper presents a DETR-based method for cross-domain weakly supervised object detection (CDWSOD), aiming at adapting the detector from source to target domain through weak supervision. We think DETR has strong potential for CDWSOD due to an insight: the encoder and the decoder in DETR are both based on the attention mechanism and are thus capable of aggr

Martina Camaioni, Rachid Guerraoui, Matteo Monti, Pierre-Louis Roman

At the heart of state machine replication, the celebrated technique enabling decentralized and secure universal computation, lies Atomic Broadcast, a fundamental communication primitive that orders, authenticates, and deduplicates messages. This paper presents Chop Chop, a Byzantine Atomic Broadcast system that uses a novel authenticated memory pool to amort

Kristjan Kannike

We consider the classically scale invariant Higgs-dilaton model of dynamical symmetry breaking extended with an extra scalar field that plays the role of dark matter. The Higgs boson is light near a critical boundary between different symmetry breaking phases, where quantum corrections beyond the usual Gildener-Weinberg approximation become relevant. The onl

Kunlun Wang, Dusit Niyato, Wen Chen, Arumugam Nallanathan

Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for

Yu Min, Yupeng Wang

Let $\frakX$ be a smooth $p$-adic formal scheme over $\calO_C$ with rigid generic fiber $X$. In this paper, we construct a new period sheaf $\calO\widehat \bC_{\pd}^+$ on $X_{\proet}$ and use it to establish an integral $p$-adic Simspon correspondence for small $\OXp$-representations on $X_{\proet}$ and small Higgs bundles on $\frakX_{\et}$ which is compatib

Alexander B. Balakin, Gleb B. Kiselev

Based on the concept of the dynamic aether emergence as a result of spontaneous polarization of the color aether, we consider the SU(2) symmetric theory of interaction of the gauge and axion fields in the framework of anisotropic cosmological model of the Bianchi-I type. We focus on the analysis of the non-Abelian analog of the U(1) symmetric model of axioni

Wenhao Liao

With the rapid development of big data and edge computing, many researchers focus on improving the accuracy of bearing fault classification using deep learning models, and implementing the deep learning classification model on limited resource platforms such as STM32. To this end, this paper realizes the identification of bearing fault vibration signal based

Cheng Liao, Han Hu, Xuekun Yuan, Haifeng Li

Automatic and periodic recompiling of building databases with up-to-date high-resolution images has become a critical requirement for rapidly developing urban environments. However, the architecture of most existing approaches for change extraction attempts to learn features related to changes but ignores objectives related to buildings. This inevitably lead

Lukas Tarra, Andreas Deutschmann-Olek, Andreas Kugi

Actively Q-switched lasers are widely used tools which are required to produce stable output pulse energies for many applications. In this paper, a model-based control concept for actively Q-switched lasers is presented which stabilises their nonlinear pulse-to-pulse dynamics and rejects stochastic disturbances arising from amplified spontaneous emission. Th

Bartłomiej Zawalski

We will prove that an origin-symmetric star-convex body $K$ with sufficiently smooth boundary and such that every hyperplane section of $K$ passing through the origin is a body of affine revolution, is itself a body of affine revolution. This will give a positive answer to the recent question asked by G. Bor, L. Hern\'andez-Lamoneda, V. Jim\'enez de Santiago

Jihed Khiari, Cristina Olaverri-Monreal

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reducing the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g. liters of fuel per kilometer. Leading factors that impact the energy efficien

Hongbo Tian, Yulong Li, Linzhi Huang, Xu Ling

Structured reconstruction is a non-trivial dense prediction problem, which extracts structural information (\eg, building corners and edges) from a raster image, then reconstructs it to a 2D planar graph accordingly. Compared with common segmentation or detection problems, it significantly relays on the capability that leveraging holistic geometric informati

Fuhu Che, Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen

Non-Line-of-Sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the Ultra-Wideband (UWB) Indoor Positioning System (IPS). Numerous supervised Machine Learning (ML) approaches have been applied for NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to ma

Anna O. Efremova, Alexander B. Balakin

We consider dynamics of the quartet of interacting cosmic substrata, which includes the dynamic aether, presented by the unit timelike vector field, the axionic dark matter, described by the pseudoscalar field, the spinor field associated with fermion particles, and the gravity field. The extended set of master equations is derived based on the idea that the

Julian Burghoff, Marc Heinrich Monells, Hanno Gottschalk

The highly structured energy landscape of the loss as a function of parameters for deep neural networks makes it necessary to use sophisticated optimization strategies in order to discover (local) minima that guarantee reasonable performance. Overcoming less suitable local minima is an important prerequisite and often momentum methods are employed to achieve