March 2023 arXiv papers — page 137

Victor Ferrari, Rafael Sousa, Marcio Pereira, João P. L. de Carvalho

Convolution is one of the most computationally intensive operations that must be performed for machine-learning model inference. A traditional approach to compute convolutions is known as the Im2Col + BLAS method. This paper proposes SConv: a direct-convolution algorithm based on a MLIR/LLVM code-generation toolchain that can be integrated into machine-learn

Parvez Mahbub, Ohiduzzaman Shuvo, Mohammad Masudur Rahman

Defect prediction has been a popular research topic where machine learning (ML) and deep learning (DL) have found numerous applications. However, these ML/DL-based defect prediction models are often limited by the quality and size of their datasets. In this paper, we present Defectors, a large dataset for just-in-time and line-level defect prediction. Defect

Yuqun Yang, Xu Tang, Xiangrong Zhang, Jingjing Ma

General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models is very expensive. Therefore, there is a

Ahmed Bou-Rabee, William Cooperman, Paul Dario

We use ideas from quantitative homogenization to show that nonconstant harmonic functions on the percolation cluster cannot satisfy certain structural constraints, for example, a Lipschitz bound. These unique-continuation-type results are false on the full lattice and hence the disorder is utilized in an essential way.

Veeti Ahvonen, Damian Heiman, Lauri Hella, Antti Kuusisto

We consider distributed algorithms in the realistic scenario where distributed message passing is operated via circuits. We show that within this setting, modal substitution calculus MSC captures the expressive power of circuits. The translations between circuits and MSC-programs are linear in both directions. Furthermore, we show that the colouring algorith

Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek, Lukas Sekanina

Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research has predominantly focused on ASICs, while not achieving similar gains when deployed for FPGA-based accelerator systems, due to the inherent architectural differences between the t

Elena S. Hafner, Karola Mészáros, Alexander Vidinas

The central question of knot theory is that of distinguishing links up to isotopy. The first polynomial invariant of links devised to help answer this question was the Alexander polynomial (1928). Almost a century after its introduction, it still presents us with tantalizing questions such as Fox's conjecture (1962) that the absolute values of the coefficien

Anand Kumar, Çağlar Samaner, Chanaprom Cholsuk, Tjorben Matthes

Quantum emitters in solid-state crystals have recently attracted a lot of attention due to their simple applicability in optical quantum technologies. The polarization of single photons generated by quantum emitters is one of the key parameters that play a crucial role in the applications, such as quantum computation that uses the indistinguishability of pho

Truong Thanh Hung Nguyen, Van Binh Truong, Vo Thanh Khang Nguyen, Quoc Hung Cao

The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods

Nicolò Zava

In this paper, we discuss the embeddability of subspaces of the Gromov-Hausdorff space, which consists of isometry classes of compact metric spaces endowed with the Gromov-Hausdorff distance, into Hilbert spaces. These embeddings are particularly valuable for applications to topological data analysis. We prove that its subspace consisting of metric spaces wi

Ali Naseh, Kalpesh Krishna, Mohit Iyyer, Amir Houmansadr

A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms. These algorithms determine how to generate text from the internal probability distribution generated by the LM. The process of choosing a decoding algorithm and tuning its hyperparameters takes significant time, manual effort, and computati

Zakhar Kabluchko, Joscha Prochno, Mathias Sonnleitner

We develop a probabilistic approach to study the volumetric and geometric properties of unit balls $\mathbb B_{q,1}^n$ of finite-dimensional Lorentz sequences spaces $\ell_{q,1}^n$. More precisely, we show that the empirical distribution of a random vector $X^{(n)}$ uniformly distributed on the volume normalized Lorentz ball in $\mathbb R^n$ converges weakly

B. S. Konrad, E. Alei, S. P. Quanz, P. Mollière

The Large Interferometer For Exoplanets (LIFE) initiative aims to develop a space based mid-infrared (MIR) nulling interferometer to measure the thermal emission spectra of temperate terrestrial exoplanets. We investigate how well LIFE could characterize a cloudy Venus-twin exoplanet to: (1) test our retrieval routine on a realistic non-Earth-like MIR spectr

Timothy Carleton, Seth H. Cohen, Brenda Frye, Alex Pigarelli

A full understanding of how unusually large "Ultra Diffuse Galaxies" (UDGs) fit into our conventional understanding of dwarf galaxies remains elusive, despite the large number of objects identified locally. A natural extension of UDG research is the study of similar galaxies at higher redshift to establish how their properties may evolve over time. However,

Johanna Bethge, Maik Pfefferkorn, Alexander Rose, Jan Peters

We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. A multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions

Morihiko Saito

We show that it is possible to utilize the Hirzebruch-Milnor classes of projective hypersurfaces in the classical sense to detect higher du Bois or rational singularities only in some special cases. We also give several remarks clarifying some points in my earlier papers.

Bo Tian, Jingyao Jiang, Ningsheng Xu, Zebo Zheng

Optical skyrmions, the optical analogue of topological configurations formed by three-dimensional vector fields covering the whole 4{\pi} solid angle but confined in a two-dimensional (2D) domain, have recently attracted growing interest due to their potential applications in high-density data transfer, storage, and processing. While the optical skyrmions ha

Roodabeh Safavi, Martin P. Seybold

Uniquely represented data structures represent each logical state with a unique storage state. We study the problem of maintaining a dynamic set of $n$ keys from a totally ordered universe in this context. We introduce a two-layer data structure called $(\alpha,\varepsilon)$-Randomized Block Search Tree (RBST) that is uniquely represented and suitable for ex

Malte J. Rasch, Fabio Carta, Omebayode Fagbohungbe, Tayfun Gokmen

In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory computing, it is desirable to accelerate the training as well as inference for large deep neural networks (DNNs). In the past, specialized in-memory training algorithms have been pr

A. Bruno, C. Caudai, G. R. Leone, M. Martinelli

Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management involves special care and high costs. However, this kind of waste contains a significant fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a comput

Nick Willemstein, Saivimal Sridar, Herman van der Kooij, Ali Sadeghi

Sensorized insoles provide a tool for gait studies and health monitoring during daily life. For users to accept such insoles they need to be comfortable and lightweight. Previous work has already demonstrated that estimation of ground reaction forces (GRFs) is possible with insoles. However, these are often assemblies of commercial components restricting des

Suman Dutta, Kirsten Martens, Pinaki Chaudhuri

Yield stress materials fail when the imposed stress crosses a critical threshold. A well-known dynamical response to the applied stress is the phenomenon of creep where the cumulative deformation grows sublinearly with time, prior to failure or arrest. Using extensive molecular dynamics simulations, we study such response for a model amorphous system, in the

S. Grafton-Waters, J. Mao, M. Mehdipour, G. Branduardi-Raymont

Obscuration events in type I active galactic nuclei (AGN) have been detected more frequently in recent years. The strong flux decrease in the soft X-ray band between observations has been caused by clouds with large column densities transiting our line-of-sight (LOS) and covering the central AGN. Another event has been captured in NGC 3227 at the end of 2019

Anik Mallik, Sanjoy Kundu, Md. Ashikur Rahman

Traffic Congestion is one of the severe problems in heavily populated countries like Bangladesh where Automated Traffic Control System needs to be implemented. An FPGA-based Semi-automated system is introduced in this paper including a completely new feature "Safe State" to avoid sudden unwanted collision. Here we used sequential encoding which has made the

Philipp Ennen, Po-Chun Hsu, Chan-Jan Hsu, Chang-Le Liu

In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese. BLOOM-zh has its origins in the open-source BLOOM models presented by BigScience in 2022. Starting from released models, we extended the pre-training of BLOOM by additional 7.4 billion tokens in Traditional Chinese and English covering a

Elie Abdo, Weinan Wang

We consider the inhomogeneous heat equation on the half-space $\mathbb R_{+}^{d}$ with Neumann boundary conditions. We prove a space-time Gevrey regularity of the solution, with a radius of analyticity uniform up to the boundary of the half-space. We also address the case of homogeneous Robin boundary conditions. Our results generalize the case of homogeneou

Jessica Christian, David Green, Peter Huston, David Penneys

Levin-Wen string-net models provide a construction of (2+1)D topologically ordered phases of matter with anyonic localized excitations described by the {Drinfeld} center of a unitary fusion category. Anyon condensation is a mechanism for phase transitions between (2+1)D topologically ordered phases. We construct an extension of Levin-Wen models in which tuni

Deepak Kumar, Nuoya Zhou, Fabian Brau, Narayanan Menon

We establish the existence of a cusp in the curvature of a solid sheet at its contact with a liquid subphase. We study two configurations in floating sheets where the solid-vapor-liquid contact line is a straight line and a circle, respectively. In the former case, a rectangular sheet is lifted at its edge, whereas in the latter a gas bubble is injected bene

Luca Iorio, Jacopo M. De Ponti, Alberto Corigliano, Raffaele Ardito

Elastic metamaterials made from locally resonant arrays have been developed as effective ways to create band gaps for elastic or acoustic travelling waves. They work by implementing stationary states in the structure that localise and partially reflect waves. A different, simpler, way of obtaining band gaps is using phononic crystals, where the generated ban

Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu

Dataset distillation reduces the network training cost by synthesizing small and informative datasets from large-scale ones. Despite the success of the recent dataset distillation algorithms, three drawbacks still limit their wider application: i). the synthetic images perform poorly on large architectures; ii). they need to be re-optimized when the distilla

Celia García-Pareja, Fabio Nobile

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright-Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of special interest for diffusion processes defined on a bounded domain, where numerical methods typically f

Daniel R. E. Hodgson

In quantum optics it is usual to describe the basic energy quanta of the electromagnetic (EM) field, photons, in terms of monochromatic waves which have a definite energy and momentum, and satisfy bosonic commutation relations. Taking this approach, however, leads to several no-go theorems regarding the localisability and superluminal propagation of single p

Johannes Pitz, Lennart Röstel, Leon Sievers, Berthold Bäuml

Dextrous in-hand manipulation with a multi-fingered robotic hand is a challenging task, esp. when performed with the hand oriented upside down, demanding permanent force-closure, and when no external sensors are used. For the task of reorienting an object to a given goal orientation (vs. infinitely spinning it around an axis), the lack of external sensors is

Abhay Singh

In this work, theoretical investigation of cubic Pm-3m phase RbGeO3 germanate perovskite using plain wave basis, density function theory (DFT) is conducted. DFT modelling calculations, clearly shows that this perovskite exhibits thermodynamic and dynamical stability in the cubic phase, identical to that of high pressure perovskite cubic SrGeO3. As from the c

Nastaran Naghshineh, Nathaniel S. Barlow, Mohamed A. Samaha, Steven J. Weinstein

The Sakiadis boundary layer induced by a moving wall in a semi-infinite fluid domain is a fundamental laminar flow field relevant to high speed coating processes. This work provides an analytical solution to the boundary layer problem for Ostwald-de Waele power law fluids via a power series expansion, and extends the approach taken for Newtonian fluids [Nagh

Mao Lin, Christopher Chamberland, Kyungjoo Noh

Quantum error correction (QEC) plays an essential role in fault-tolerantly realizing quantum algorithms of practical interest. Among different approaches to QEC, encoding logical quantum information in harmonic oscillator modes has been shown to be promising and hardware efficient. In this work, we study multimode Gottesman-Kitaev-Preskill (GKP) codes, encod

David K. Kolchmeyer

We quantize JT gravity with matter on the spatial interval with two asymptotically AdS boundaries. We consider the von Neumann algebra generated by the right Hamiltonian and the gravitationally dressed matter operators on the right boundary. We prove that the commutant of this algebra is the analogously defined left boundary algebra and that both algebras ar

Lukas Rustler, Jiri Matas, Matej Hoffmann

For robot manipulation, a complete and accurate object shape is desirable. Here, we present a method that combines visual and haptic reconstruction in a closed-loop pipeline. From an initial viewpoint, the object shape is reconstructed using an implicit surface deep neural network. The location with highest uncertainty is selected for haptic exploration, the

F. Theel, S. I. Mistakidis, P. Schmelcher

We study the impact of induced correlations and quasiparticle properties by immersing two distinguishable impurities in a harmonically trapped bosonic medium. It is found that when the impurities couple both either repulsively or attractively to their host, the latter mediates a two-body correlated behavior between them. In the reverse case, namely the impur

H. Rasiwala, K. Murray, Y. Lan, C. Chambers

nEXO is a proposed experiment that will search for neutrinoless double-beta decay (0$\nu\beta\beta$) in 5-tonnes of liquid xenon (LXe), isotopically enriched in $^{136}$Xe. A technique called Ba-tagging is being developed as a potential future upgrade for nEXO to detect the $^{136}$Xe double-beta decay daughter isotope, $^{136}$Ba. An efficient Ba-tagging te

Tomasz Stefaniuka, Jan Suffczyński, Małgorzata Wierzbowsk, Jarosław Z. Domagała

Magnesium aluminate scandium oxide (ScAlMgO4) is a promising lattice-matched substrate material for GaN- and ZnO-based optoelectronic devices. Yet, despite its clear advantages over substrates commonly used in heteroepitaxial growth, several fundamental properties of ScAlMgO4 remain unsettled. Here, we provide a comprehensive picture of its optical, electron

Ramin Nakhli, Allen Zhang, Hossein Farahani, Amirali Darbandsari

In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells. In this paper, we propose a self-supervised framework (VOLTA) for cell representation learning in histopatho

Hugo Merbouche, Boris Divinskiy, Diane Gouéré, Romain Lebrun

Magnonic nano-devices exploit magnons -- quanta of spin waves -- to transmit and process information within a single integrated platform that has the potential to outperform traditional semiconductor-based electronics for low power applications. The main missing cornerstone of this information nanotechnology is an efficient scheme for the direct amplificatio

Nayel Bettache, Cristina Butucea

The two-sided matrix regression model $Y = A^*X B^* +E$ aims at predicting $Y$ by taking into account both linear links between column features of $X$, via the unknown matrix $B^*$, and also among the row features of $X$, via the matrix $A^*$. We propose low-rank predictors in this high-dimensional matrix regression model via rank-penalized and nuclear norm-

Luca Patelli, Michela Cameletti, Natalia Golini, Rosaria Ignaccolo

Random Forest (RF) is a well-known data-driven algorithm applied in several fields thanks to its flexibility in modeling the relationship between the response variable and the predictors, also in case of strong non-linearities. In environmental applications, it often occurs that the phenomenon of interest may present spatial and/or temporal dependence that i

Shi Chen, Kenji Fukushima, Zebin Qiu

We discuss the baryon properties under a strong magnetic field. We adopt the Skyrme model and calculate the magnetic field dependence of the mass and the pressure distribution in the soliton. We elucidate a magnetically induced contribution to the pressure sum rule and interpret it as an extra confining force. We also quantize the soliton to estimate the dif

Juan Guerrero Montero, Richard A. Blythe

We construct a reliable estimation of evolutionary parameters within the Wright-Fisher model, which describes changes in allele frequencies due to selection and genetic drift, from time-series data. Such data exists for biological populations, for example via artificial evolution experiments, and for the cultural evolution of behavior, such as linguistic cor

João C. Getelina, Zekun Zhuang, Premala Chandra, Piers Coleman

We investigate quantum order by disorder in a frustrated spin nanotube formed by wrapping a $J_1$-$J_2$ Heisenberg model at 45$^\circ$ around a cylinder. Using Schwinger boson theory and Density Matrix Renormalization Group (DMRG), we have computed the ground-state phase diagram to reveal a $\mathbb{Z}_2$ phase in which collinear spin stripes form a right or

Alexis Arnaudon, Juni Schindler, Robert L. Peach, Adam Gosztolai

We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different levels of resolution by maximizing the generalized Markov Stability quality function with the Louvain or Leiden

V. V. Vien

We propose a $U(1)_L$ model with $A_4$ symmetry in light of the linear seesaw for majorana neutrino that capable of generating the current lepton and quark mass and mixing patterns. The smallness of Majorana neutrino mass is reproduced through the linear seesaw mechanism. The model can accommodate the current observed patterns of lepton and quark mixing in w

Teun D. H. van Nuland, Fedor Sukochev, Dmitriy Zanin

We explicitly compute the local invariants (heat kernel coefficients) of a conformally deformed non-commutative $d$-torus using multiple operator integrals. We derive a recursive formula that easily produces an explicit expression for the local invariants of any order $k$ and in any dimension $d$. Our recursive formula can conveniently produce all formulas r

Belal Abuelnasr, Adam R. Stinchcombe

We present a detailed physiological model of the retina that includes the biochemistry and electrophysiology of phototransduction, neuronal electrical coupling, and the spherical geometry of the eye. The model is a parabolic-elliptic system of partial differential equations based on the mathematical framework of the bi-domain equations, which we have general

Alan Sunny, Semin Xavier, S. Shankaranarayanan

This work tests the no-hair conjecture in $f(R)$ gravity models. No-hair conjecture asserts that all black holes in General Relativity coupled to any matter must be Kerr-Newman type. However, the conjecture fails in some cases with non-linear matter sources. Here, we address this by explicitly constructing multiple slow-rotating black hole solutions, up to s

Jun Zhao, Xinyu Zhou, Yang Li, Liangxin Qian

The Metaverse, an emerging digital space, is expected to offer various services mirroring the real world. Wireless communications for mobile Metaverse users should be tailored to meet the following user characteristics: 1) emphasizing application-specific perceptual utility instead of simply the transmission rate, 2) concerned with energy efficiency due to t

R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla

Atmospheric muon neutrinos are produced by meson decays in cosmic-ray-induced air showers. The flux depends on meteorological quantities such as the air temperature, which affects the density of air. Competition between decay and re-interaction of those mesons in the first particle production generations gives rise to a higher neutrino flux when the air dens

Sungho Shin, Yeonguk Yu, Kyoobin Lee

In this study, we introduce a feature knowledge distillation framework to improve low-resolution (LR) face recognition performance using knowledge obtained from high-resolution (HR) images. The proposed framework transfers informative features from an HR-trained network to an LR-trained network by reducing the distance between them. A cosine similarity measu

Laurent Loosveldt

We define multifractional Hermite processes which generalize and extend both multifractional Brownian motion and Hermite processes. It is done by substituting the Hurst parameter in the definition of Hermite processes as a multiple Wiener-It\^o integral by a Hurst function. Then, we study the pointwise regularity of these processes, their local asymptotic se

Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos

We present a potent computational method for the solution of inverse problems in fluid mechanics. We consider inverse problems formulated in terms of a deterministic loss function that can accommodate data and regularization terms. We introduce a multigrid decomposition technique that accelerates the convergence of gradient-based methods for optimization pro

Eva Graversen, Andrew K. Hirsch, Fabrizio Montesi

We present PolyChor$\lambda$, a language for higher-order functional \emph{choreographic programming} -- an emerging paradigm by which programmers write the desired cooperative behaviour of a system of communicating processes and then compile it into distributed implementations for each process, a translation called \emph{endpoint projection}. Unlike its pre

H. M. Doeleman, T. Schatteburg, R. Benevides, S. Vollenweider

Bulk acoustic wave (BAW) resonators are attractive as intermediaries in a microwave-to-optical transducer, due to their long coherence times and controllable coupling to optical photons and superconducting qubits. However, for an optomechanical transducer to operate without detrimental added noise, the mechanical modes must be in the quantum ground state. Th

Marten van Dijk, Phuong Ha Nguyen

In federated learning collaborative learning takes place by a set of clients who each want to remain in control of how their local training data is used, in particular, how can each client's local training data remain private? Differential privacy is one method to limit privacy leakage. We provide a general overview of its framework and provable properties,

Nicola Cavallini, Riccardo Ferretti, Gunnar Bostrom, Stephen Croft

Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency as a way to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and so determine potential diversion. Constructing the analysis and interpretation of PGET measurements rely on the availability of comprehensive dat

Hailing Wu, Shenlin Wu, Longwen Zhou

One-dimensional Floquet topological superconductors possess two types of degenerate Majorana edge modes at zero and $\pi$ quasieneriges, leaving more room for the design of boundary time crystals and quantum computing schemes than their static counterparts. In this work, we discover Floquet superconducting phases with large topological invariants and arbitra

Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah

Large Language Models (LLMs) have sparked significant interest in their generative capabilities, leading to the development of various commercial applications. The high cost of using the models drives application builders to maximize the value of generation under a limited inference budget. This paper presents a study of optimizing inference hyperparameters

Áron Márton, János K. Asbóth

We consider the combined effect of readout errors and coherent errors, i.e., deterministic phase rotations, on the surface code. We use a recently developed numerical approach, via a mapping of the physical qubits to Majorana fermions. We show how to use this approach in the presence of readout errors, treated on the phenomenological level: perfect projectiv

Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang

ChatGPT is attracting a cross-field interest as it provides a language interface with remarkable conversational competency and reasoning capabilities across many domains. However, since ChatGPT is trained with languages, it is currently not capable of processing or generating images from the visual world. At the same time, Visual Foundation Models, such as V

Sankeerth Durvasula, Yushi Guan, Nandita Vijaykumar

Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., microseconds) in rapidly changing environments. This makes event cameras highly useful for high-speed robotics tasks involving rapid motion, such as high-speed perception, object tracki

Sebastian Bordt, Ulrike von Luxburg

We asked ChatGPT to participate in an undergraduate computer science exam on ''Algorithms and Data Structures''. The program was evaluated on the entire exam as posed to the students. We hand-copied its answers onto an exam sheet, which was subsequently graded in a blind setup alongside those of 200 participating students. We find that ChatGPT narrowly passe

Nicoletta D'Angelo, Giada Adelfio

In this paper, we harness a result in point process theory, specifically the expectation of the weighted $K$-function, where the weighting is done by the true first-order intensity function. This theoretical result can be employed as an estimation method to derive parameter estimates for a particular model assumed for the data. The underlying motivation is t

Sinead Lyle

Let $\mathcal{H}$ denote an Ariki-Koike algebra over a field of characteristic $p\geq 0$. For each $r$-multipartition ${\bf \lambda}$ of $n$, we define a $\mathcal{H}$-module $S^{{\bf \lambda}}$ and for each Kleshchev $r$-multipartition ${\bf \mu}$ of $n$, we define an irreducible $\mathcal{H}$-module $D^{{\bf \mu}}$. Given a multipartition ${\bf \lambda}$ a

Rui Hu, Jianan Cui, Chengjin Yu, Yunmei Chen

Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatial-temporal convolutional primal dual network (STPDnet) for dynamic PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The phy

Andrea Droghetti, Ilya V. Tokatly

We present the results of first-principles calculations based on density functional theory estimating the magnitude of the current-induced spin polarization (CISP) at the surfaces of the $5d$ transition metals with fcc and bcc crystal structures. We predict that the largest surface CISP occurs for W and Ta, whereas CISP is considerably weaker for Pt and Au s

Valentina Beorchia, Rosa M. Miro'-Roig

The Jacobian scheme of a reduced, singular projective plane curve is the zero-dimensional scheme, whose homogeneous ideal is generated by the partials of its defining polynomial. The degree of such a scheme is called the global Tjurina number and, if the curve is not a set of concurrent lines, some upper and lower bounds depending on the degree of the curve

Xin Yan, Zuchao Li, Lefei Zhang

Masked Image Modeling (MIM) is a new self-supervised vision pre-training paradigm using a Vision Transformer (ViT). Previous works can be pixel-based or token-based, using original pixels or discrete visual tokens from parametric tokenizer models, respectively. Our proposed centroid-based approach, CCViT, leverages k-means clustering to obtain centroids for

Amr Osman, Jorge Fernàndez-Pendàs, Christopher Warren, Sandoko Kosen

The reproducibility of qubit parameters is a challenge for scaling up superconducting quantum processors. Signal crosstalk imposes constraints on the frequency separation between neighboring qubits. The frequency uncertainty of transmon qubits arising from the fabrication process is attributed to deviations in the Josephson junction area, tunnel barrier thic

M. Köksal, A. Senol, H. Denizli

The $\nu\bar{\nu}\gamma \gamma$ couplings parametrized with the non-standard dimension-seven operators defined by the Effective Field Theory framework are investigated through the process $pp\to \nu\bar{\nu}\gamma$ at the High Luminosity-LHC and the Future Circular proton-proton Collider. The effective Lagrangian of $\nu\bar{\nu}\gamma \gamma$ couplings is i

Rui Hu, Yunmei Chen, Kyungsang Kim, Marcio Aloisio Bezerra Cavalcanti Rockenbach

Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining this la

Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra

Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribution data. Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learni

Fabian Hoppe, Hannes Meinlschmidt, Ira Neitzel

We prove existence and uniqueness of global-in-time solutions in the $W^{-1,p}_D$-$W^{1,p}_D$-setting for abstract quasilinear parabolic PDEs with nonsmooth data and mixed boundary conditions, including a nonlinear source term with at most linear growth. Subsequently, we use a bootstrapping argument to achieve improved regularity of these global-in-time solu

Jacqueline Ankenbauer, Parker C. Lusk, Annika Thomas, Jonathan P. How

We present a novel framework for global localization and guided relocalization of a vehicle in an unstructured environment. Compared to existing methods, our pipeline does not rely on cues from urban fixtures (e.g., lane markings, buildings), nor does it make assumptions that require the vehicle to be navigating on a road network. Instead, we achieve localiz

Yingli Kang, Ligang Jin, Xuding Zhu

This paper proves that every planar graph without cycles of length 4, 7, or 9 is DP-3-colorable.

Sergey V. Grebnev, Maxim A. Gavreev, Evgeniy O. Kiktenko, Anton P. Guglya

Quantum computing devices are believed to be powerful in solving the prime factorization problem, which is at the heart of widely deployed public-key cryptographic tools. However, the implementation of Shor's quantum factorization algorithm requires significant resources scaling linearly with the number size; taking into account an overhead that is required

Raghavendra Srikanth Hundi

We have analyzed the vacuum structure of the Dirac scotogenic model, whose scalar sector consists of two complex Higgs doublets and a real singlet field. In this model, the standard model like Higgs doublet acquires non-zero vacuum expectation value (VEV), whereas, the other two fields acquire zero VEVs. This pattern of VEVs constitute a minimum, which is th

Xinge Yang, Qiang Fu, Mohammed Elhoseiny, Wolfgang Heidrich

Computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated data, especially for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate the domain gap caused by off-axis aberrations th

Suman Pal, Soumen Podder, Debashree Sen, Gargi Chaudhuri

We compute the hybrid star (HS) properties with the help of Maxwell construction. For the purpose we choose a fixed hadronic model and four different forms of MIT bag model for the quark phase. We investigate thoroughly the effects of the different parameters of the bag model on the speed of sound in HS matter and the structural properties of HSs in the ligh

P. Griveaud, A. Crida, E. Lega

When considering the migration of Jupiter and Saturn, a classical result is to find the planets migrating outwards and locked in the 3:2 mean motion resonance (MMR). These results were obtained in the framework of viscously accreting discs, in which the observed stellar accretion rates constrained the viscosity values. However, it has recently been shown obs

Guoliang He, Zak Singh, Eiko Yoneki

Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one. As such, optimal results can be achieved by avoiding the phase-ordering problem. However, we observe that when the

Minoru Hirose, Hideki Murahara, Tomokazu Onozuka

In this paper, we investigate an asymptotic behavior of the double zeta function of Euler-Zagier type for indices with large negative real parts.

Akhilesh Kumar Patel, Y. Venkateswara, S. Shanmukharao Samatham, Archana Lakhani

Spin semimetals are amongst the most recently discovered new class of spintronic materials, which exhibit a band gap in one spin channel and semimetallic feature in the other, thus facilitating tunable spin transport. Here, we report Mn$_2$IrGa to be a candidate material for spin semimetal along with giant coercivity and spin-valve characteristics using a co

Yingli Kang, Hongkai Lu, Ligang Jin

A graph $G$ is $(I,F)$-partitionable if its vertex set can be partitioned into two parts such that one part is an independent set, and the other induces a forest. In this paper, we prove that every planar graph without cycles of length $4, 6, 9$ is $(I,F)$-partitionable.

Renata Della Picca, Juan Martín Randazzo, Sebastián David López, Marcelo Fabián Ciappina

We present a theoretical study of atomic laser-assisted photoionization emission (LAPE) beyond the dipole approximation. By considering the non-relativistic non-dipole strong-field approximation (non-dipole Gordon-Volkov wave function), we analyze the different contributions to the photoelectron spectrum (PES), which can be written in terms of intra- and int

Xiao Tang, Hongrui Zhang, Ruonan Zhang, Deyun Zhou

Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processin

Hui Yuan, Linbin Huang, Huisheng Gao, Jikui Xing

The interaction among inverter-based resources (IBRs) and power network may cause small-signal stability issues, especially in low short-circuit-level grids. Besides, integrating static synchronous compensators (STATCOMs) in a multi-IBR system for voltage support can deteriorate small-signal stability. However, it is still challenging to fully understand the

Hakan Pabuccu, Serdar Ongan, Ayse Ongan

Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (SVM), the Artificial Ne

N. Myrzakulov, M. Koussour, Dhruba Jyoti Gogoi

In this paper, we propose a new parametrization of dark energy based on the $Om(z)$ diagnostic tool behavior. For this purpose, we investigate a functional form of the $Om(z)$ that predicts the popular dark energy dynamical models, namely phantom and quintessence. We also found the famous cosmological constant for specified values of the model's parameters.

Arnab Roy, Matthias Johann Steiner, Stefano Trevisani

In this paper we propose the (keyed) permutation Arion and the hash function ArionHash over $\mathbb{F}_p$ for odd and particularly large primes. The design of Arion is based on the newly introduced Generalized Triangular Dynamical System (GTDS), which provides a new algebraic framework for constructing (keyed) permutation using polynomials over a finite fie

Sanju Xaviar, Xin Yang, Omid Ardakanian

The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple sensors with different modalities. While there are promising deep neural network architectures for multimodal fusion, their performance falls apart quickly in the presence of consecutiv

Jose M. Peña

We present two methods for bounding the probabilities of benefit and harm under unmeasured confounding. The first method computes the (upper or lower) bound of either probability as a function of the observed data distribution and two intuitive sensitivity parameters which, then, can be presented to the analyst as a 2-D plot to assist her in decision making.

Florence Regol, Mark Coates

Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuous data. Amongst them are the recently emerging diffusion probabilistic models, which have the observ

Renato Sortino, Simone Palazzo, Concetto Spampinato

Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation, respectively. In this work, we show how employing multi-head attention to encod