July 2023 arXiv papers — page 85

Iman Saberi, Fatemeh Fard, Fuxiang Chen

Software Engineering (SE) Pre-trained Language Models (PLMs), such as CodeBERT, are pre-trained on large code corpora, and their learned knowledge has shown success in transferring into downstream tasks (e.g., code clone detection) through the fine-tuning of PLMs. In Natural Language Processing (NLP), an alternative in transferring the knowledge of PLMs is e

Huan Long, Linwei Sang, Zaijun Wu, Wei Gu

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The

Roberto Mottola, Gianni Buser, Philipp Treutlein

Scalability presents a central platform challenge for the components of current quantum network implementations that can be addressed by microfabrication techniques. We demonstrate a high-bandwidth optical memory using a warm alkali atom ensemble in a microfabricated vapor cell compatible with wafer-scale fabrication. By applying an external tesla-order magn

Motoko Kotani, Hisashi Naito

A discrete harmonic surface is a trivalent graph which satisfies the balancing condition in the 3-dimensional Euclidean space and achieves energy minimizing under local deformations. Given a topological trivalent graph, a holomorphic function, and an associated discrete holomorphic quadratic form, a version of the Weierstrass representation formula for discr

Baihong Lin, Zengrong Lin, Yulan Guo, Yulan Zhang

RGB-T semantic segmentation has been widely adopted to handle hard scenes with poor lighting conditions by fusing different modality features of RGB and thermal images. Existing methods try to find an optimal fusion feature for segmentation, resulting in sensitivity to modality noise, class-imbalance, and modality bias. To overcome the problems, this paper p

Marcel Beetz, Abhirup Banerjee, Julius Ossenberg-Engels, Vicente Grau

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and phys

N. Grittini

We prove that, in a finite group, if every rational irreducible character has odd degree, then all rational elements are 2-elements, as it was originally conjectured by Tiep and Tong-Viet.

Mustafa Yildirim, Niyazi Ulas Dinc, Ilker Oguz, Demetri Psaltis

Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficie

Chao Wang, Yiran Zhang, Qing Li, Ao Zhou

The on-orbit processing of massive satellite-native data relies on powerful computing power. Satellite computing has started to gain attention, with researchers proposing various algorithms, applications, and simulation testbeds. Unfortunately, a practical platform for deploying satellite computing is currently lacking. As a result, the industry needs to mak

Tianyi Li, Luca Biferale, Fabio Bonaccorso, Martino Andrea Scarpolini

Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans, and astrophysics. Despite exceptional theoretical, numerical, and experimental efforts conducted over the past thirty years, no existing models are capable of faithfully reproducin

Ekaterina Iakovleva, Karteek Alahari, Jakob Verbeek

Deep convolutional networks are ubiquitous in computer vision, due to their excellent performance across different tasks for various domains. Models are, however, often trained in isolation for each task, failing to exploit relatedness between tasks and domains to learn more compact models that generalise better in low-data regimes. Multi-domain learning aim

V. S. Akhmetov, B. Bucciarelli, M. Crosta, M. G. Lattanzi

This work presents the results of a kinematic analysis of the Galaxy that uses a new model as applied to the newest available Gaia data. We carry out the Taylor decomposition of the velocity field up to second order for 18 million high luminosity stars (i.e. OBAF-type stars, giants and subgiants) from the Gaia DR3 data. We determine the components of mean st

Shiye Lei, Hao Chen, Sen Zhang, Bo Zhao

With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become a common practice to train models on synthetic data due to data-scarcity and privacy leakage problems. Owing to massive and diverse information conveyed in real images, it is challenging for text-to-image generative models to synthesize informative training data wit

Marc Goerigk, Mohammad Khosravi

In robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model this uncertainty set has a decisive influence on the complexity of the corresponding robust problem. For this reason, budgeted uncertainty sets are often studied, as they enable us t

Maria Papageorgiou, Doreen Fraser

Arguments by Sorkin arXiv:gr-qc/9302018 and Borsten, Jubb, and Kells arXiv:1912.06141 establish that a natural extension of quantum measurement theory from non-relativistic quantum mechanics to relativistic quantum theory leads to the unacceptable consequence that expectation values in one region depend on which unitary operation is performed in a spacelike

Thiago Felicissimo

Bidirectional typing is a discipline in which the typing judgment is decomposed explicitly into inference and checking modes, allowing to control the flow of type information in typing rules and to specify algorithmically how they should be used. Bidirectional typing has been fruitfully studied and bidirectional systems have been developed for many type theo

Yuchao He, Yongli Song, Yonghui Xia

The constant vorticity {\bf two-layer water wave} in the $\beta$-plane approximation with centripetal forces is investigated in this paper. Different from the works (Chu and Yang\cite[JDE, 2020]{chu} and Chu and Yang \cite[JDE, 2021]{chu2}) on the singe-layer wave flows, we consider the two-layer water wave model containing a free surface and an interface. T

Jacobus Conradi, Anne Driemel, Benedikt Kolbe

We consider the $(1+\varepsilon)$-Approximate Nearest Neighbour (ANN) Problem for polygonal curves in $d$-dimensional space under the Fr\'echet distance and ask to what extent known data structures for doubling spaces can be applied to this problem. Initially, this approach does not seem viable, since the doubling dimension of the target space is known to be

Jennifer Elder, Nadia Lafrenière, Erin McNicholas, Jessica Striker

Interval-closed sets of a poset are a natural superset of order ideals. We initiate the study of interval-closed sets of finite posets from enumerative and dynamical perspectives. In particular, we use the generalized toggle group to define rowmotion on interval-closed sets as a product of these toggles. Our main theorem is an intricate global characterizati

Jake Fawkes, Robin J. Evans

In this paper we provide a theoretical analysis of counterfactual invariance. We present a variety of existing definitions, study how they relate to each other and what their graphical implications are. We then turn to the current major question surrounding counterfactual invariance, how does it relate to conditional independence? We show that whilst counter

D. Anchishkin, V. Gnatovskyy, D. Zhuravel, V. Karpenko

The thermodynamics of a system of interacting bosonic particles and antiparticles in the presence of the Bose-Einstein condensate is studied in the framework of the Skyrme-like mean-field model. It is assumed that the total charge density (isospin density) is conserved at all temperatures. Two cases are explicitly considered: zero and nonzero isospin charge

Luan Alberto Ferreira

We prove that given $\lambda \in \mathbb{R}$ such that $0 < \lambda < 1$, then $\pi(x + x^\lambda) - \pi(x) \sim \displaystyle \frac{x^\lambda}{\log(x)}$. This solves a long-standing problem concerning the existence of primes in short intervals. In particular, we give a positive answer (for all sufficiently large number) to some old conjectures about prime n

Lukas Trottner

Covariate shift in regression problems and the associated distribution mismatch between training and test data is a commonly encountered phenomenon in machine learning. In this paper, we extend recent results on nonparametric convergence rates for i.i.d. data to Markovian dependence structures. We demonstrate that under H\"older smoothness assumptions on the

Michal Jablonowski

We introduce an invariant of alternating knots and links (called here WRP), namely a pair of integer polynomials associated with their two checkerboard planar graphs from their minimal diagram. We prove that the invariant is well-defined and give its values obtained from calculations for some knots in the tables. This invariant is strong enough to distinguis

Haowen Zhang

Over function fields of p-adic curves, we construct stably rational varieties in the form of homogeneous spaces of SL_n with semisimple simply connected stabilizers and we show that strong approximation away from a non-empty set of places fails for such varieties. The construction combines the Lichtenbaum duality and the degree 3 cohomological invariants of

Dan Frumin, Amin Timany, Lars Birkedal

We present guarded interaction trees -- a structure and a fully formalized framework for representing higher-order computations with higher-order effects in Coq, inspired by domain theory and the recently proposed interaction trees. We also present an accompanying separation logic for reasoning about guarded interaction trees. To demonstrate that guarded int

Matteo Lucca

This thesis treats the topic of CMB Spectral Distortions (SDs), which represent any deviation from a pure black body shape of the CMB energy spectrum. As such, they can be used to probe the inflationary, expansion and thermal evolution of the universe both within $\Lambda$CDM and beyond it. The currently missing observation of this rich probe of the universe

Tristan J. Calay, Basheer Qolomany, Aos Mulahuwaish, Liaquat Hossain

Rooted in collaborative efforts, cybersecurity spans the scope of cyber competitions and warfare. Despite extensive research into team strategy in sports and project management, empirical study in cyber-security is minimal. This gap motivates this paper, which presents the Collaborative Cyber Team Formation (CCTF) Simulation Framework. Using Agent-Based Mode

Andrés E. Piatti

The age distribution of the open cluster system is a key piece of information to decipher the star formation history of the Milky Way disk. Recently, a remarkable earlier drop of its older end was found, which caught our attention. Precisely, we analyzed in detail the population of open clusters older than 1 Gyr located inside a circle of 2.0 kpc from the Su

Peter Carragher, Lynnette Hui Xian Ng, Kathleen M. Carley

In this work, we analyze the circumstances under which social influence operations are likely to succeed. These circumstances include the selection of Confederate agents to execute intentional perturbations and the selection of Perturbation strategies. We use Agent-Based Modelling (ABM) as a simulation technique to observe the effect of intentional stance pe

Maja S. Scharnagl, Timm Kielinski, Klemens Hammerer

We study a variational class of generalised Ramsey protocols that include two one-axis twisting (OAT) operations, one performed before the phase imprint and the other after. In this framework, we optimise the axes of the signal imprint, the OAT interactions, and the direction of the final projective measurement. We distinguish between protocols that exhibit

Anthony Ozier-Lafontaine, Camille Fourneaux, Ghislain Durif, Polina Arsenteva

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell populat

Thomas G. Rizzo

As is well-known, Trinification, \ie, the extension of the Standard Model (SM) to $[SU(3)]^3=SU(3)_c\times SU(3)_L\times SU(3)_R$ as occurs in $E_6$ models, allows for a partial unification of the gauge forces even though quarks and leptons remain in separate multiplets so that no heavy gauge or scalar fields exist which can generate proton decay. The extens

Mete Kemertas, Allan D. Jepson, Amir-massoud Farahmand

We propose Mirror Descent Optimal Transport (MDOT), a novel method for solving discrete optimal transport (OT) problems with high precision, by unifying temperature annealing in entropic-regularized OT (EOT) with mirror descent techniques. In this framework, temperature annealing produces a sequence of EOT dual problems, whose solution gradually gets closer

Chen Sun, Calvin Luo, Xingyi Zhou, Anurag Arnab

We aim to investigate whether end-to-end learning of visual reasoning can be achieved with general-purpose neural networks, with the help of visual pretraining. A positive result would refute the common belief that explicit visual abstraction (e.g. object detection) is essential for compositional generalization on visual reasoning, and confirm the feasibilit

Daniel Atzberger, Tim Cech, Willy Scheibel, Matthias Trapp

Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corp

Rahul Kumar Gautam, Anjeneya Swami Kare, S. Durga Bhavani

Given a graph $G=(V, E)$, the problem of Graph Burning is to find a sequence of nodes from $V$, called a burning sequence, to burn the whole graph. This is a discrete-step process, and at each step, an unburned vertex is selected as an agent to spread fire to its neighbors by marking it as a burnt node. A burnt node spreads the fire to its neighbors at the n

Chaoya Jiang, Haiyang Xu, Wei Ye, Qinghao Ye

Vision Transformer (ViT) based Vision-Language Pre-training (VLP) models have demonstrated impressive performance in various tasks. However, the lengthy visual token sequences fed into ViT can lead to training inefficiency and ineffectiveness. Existing efforts address the challenge by either bottom-level patch extraction in the ViT backbone or top-level patc

Sandeep Angara, Sharath Thirunagaru

The COVID-19 pandemic has led to a global health crisis, highlighting the need for rapid and accurate virus detection. This research paper examines transfer learning with vision transformers for COVID-19 detection, known for its excellent performance in image recognition tasks. We leverage the capability of Vision Transformers to capture global context and l

Faizuddin Ahmed

This study investigates the geodesic motion of test particles, both massless and massive, within a Schwarzschild-Klinkhamer (SK) wormhole space-time. We specifically consider the influence of cosmic strings on the system and analyze the effective potential, and observing that the presence of a cosmic string parameter alters it for null and time-like geodesic

Zhixue He, Chen Shen, Lei Shi, Jun Tanimoto

The critical mass effect is a prevailing topic in the study of complex systems. Recent research has shown that a minority of zealots can effectively drive widespread cooperation in social dilemma games. However, achieving a critical mass of cooperation in the prisoner's dilemma requires stricter conditions. The underlying mechanism behind this effect remains

Borui Zhao, Renjie Song, Jiajun Liang

Distilling knowledge from convolutional neural networks (CNNs) is a double-edged sword for vision transformers (ViTs). It boosts the performance since the image-friendly local-inductive bias of CNN helps ViT learn faster and better, but leading to two problems: (1) Network designs of CNN and ViT are completely different, which leads to different semantic lev

Zhiling Yue, Howard H. Yang, Meng Zhang, Nikolaos Pappas

We consider a random access network consisting of source-destination pairs. Each source node generates status updates and transmits this information to its intended destination over a shared spectrum. The goal is to minimize the network-wide Age of Information (AoI). We develop a frame slotted ALOHA (FSA)-based policy for generating and transmitting status u

Cangyuan Li

In the absence of sensitive race and ethnicity data, researchers, regulators, and firms alike turn to proxies. In this paper, I train a Bidirectional Long Short-Term Memory (BiLSTM) model on a novel dataset of voter registration data from all 50 US states and create an ensemble that achieves up to 36.8% higher out of sample (OOS) F1 scores than the best perf

Parisa Arabameri, Zahra Davari, Nima Khosravi

With the emersion of precise cosmology and the emergence of cosmic tensions, we are faced with the question of whether the simple model of cold dark matter needs to be extended and whether doing so can alleviate the tensions and improve our understanding of the properties of dark matter. In this study, we investigate one of the generalized models of dark mat

Yixi Cai, Fanze Kong, Yunfan Ren, Fangcheng Zhu

Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution LiDAR sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping.

Zhe Zhu, Honghua Chen, Xing He, Weiming Wang

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures. Current methods either perceive shape patterns using only 3D coordinates or import extra images with well-calibrated intrinsic param

V. S. Beskin, V. I. Krauz, S. A. Lamzin

Jets from young stars are used as an example to review how laboratory modeling enables advancement in understanding the main physical processes responsible for the formation and stability of these amazing objects. The discussion focuses on the options for modeling jet emissions in a laboratory experiment at the PF-3 facility at the National Research Center K

Khwaja Zubair Sediqi, Anja Feldmann, Oliver Gasser

BGP exchanges reachability information in the form of prefixes, which are usually originated by a single Autonomous System (AS). If multiple ASes originate the same prefix, this is referred to as a Multiple Origin ASes (MOAS) prefix. One reason for MOAS prefixes are BGP prefix hijacks, which are mostly short-lived and have been studied extensively in the pas

Mario Pérez-Escribano, Salvador Moreno-Rodríguez, Carlos Molero, Juan F. Valenzuela-Valdés

This paper presents an analytical framework, based on Floquet modal expansions of the electromagnetic fields and equivalent circuits, to model reconfigurable metasurfaces loaded with generic lumped elements (resistors, capacitors, inductors, varactors, etc.). The analytical approach is computationally efficient compared to full-wave solvers. Additionally, it

Jeremy Echeverria, Iván Schmidt

The renormalization group equations of massive $\mathcal{N}=1$ supersymmetric quantum electrodynamics (SQED) are studied using the functional renormalization group approach. A non-perturbative form of the beta function has been computed via a derivative expansion of the effective action. In the local potential approximation, the functional form of the non-pe

Huachuan Qiu, Shuai Zhang, Anqi Li, Hongliang He

Considerable research efforts have been devoted to ensuring that large language models (LLMs) align with human values and generate safe text. However, an excessive focus on sensitivity to certain topics can compromise the model's robustness in following instructions, thereby impacting its overall performance in completing tasks. Previous benchmarks for jailb

Janine Strotherm, Alissa Müller, Barbara Hammer, Benjamin Paaßen

The more AI-assisted decisions affect people's lives, the more important the fairness of such decisions becomes. In this chapter, we provide an introduction to research on fairness in machine learning. We explain the main fairness definitions and strategies for achieving fairness using concrete examples and place fairness research in the European context. Ou

Shree Charran R, Sandipan Das Mahapatra

Interpretability is a crucial aspect of machine learning models that enables humans to understand and trust the decision-making process of these models. In many real-world applications, the interpretability of models is essential for legal, ethical, and practical reasons. For instance, in the banking domain, interpretability is critical for lenders and borro

Stefan Buijsman

In order to monitor and prevent bias in AI systems we can use a wide range of (statistical) fairness measures. However, it is mathematically impossible to optimize for all of these measures at the same time. In addition, optimizing a fairness measure often greatly reduces the accuracy of the system (Kozodoi et al, 2022). As a result, we need a substantive th

Yunqiang Li, Jan C. van Gemert, Torsten Hoefler, Bert Moons

Deep learning algorithms are increasingly employed at the edge. However, edge devices are resource constrained and thus require efficient deployment of deep neural networks. Pruning methods are a key tool for edge deployment as they can improve storage, compute, memory bandwidth, and energy usage. In this paper we propose a novel accurate pruning technique t

Tim S. Lyon, Sebastian Rudolph

This paper establishes alternative characterizations of very expressive classes of existential rule sets with decidable query entailment. We consider the notable class of greedy bounded-treewidth sets (gbts) and a new, generalized variant, called weakly gbts (wgbts). Revisiting and building on the notion of derivation graphs, we define (weakly) cycle-free de

Zoë Broad, Daniel Nicholls, Jack Wells, Alex W. Robinson

Despite advancements in electron backscatter diffraction (EBSD) detector speeds, the acquisition rates of 4-Dimensional (4D) EBSD data, i.e., a collection of 2-dimensional (2D) diffraction maps for every position of a convergent electron probe on the sample, is limited by the capacity of the detector. Such 4D data enables computation of, e.g., band contrast

Dan Zwillinger, Paul San Clemente

In classical game theory, optimal strategies are determined for games with complete information; this requires knowledge of the opponent's goals. We analyze games when a player is mistaken about their opponents goals. For definitiveness, we study the (common) bimatrix formulation where both player's payoffs are matrices. While the payoff matrix weights are a

Vadim Petruhanov, Alexander Pechen

The GRadient Ascent Pulse Engineering (GRAPE) method is widely used for optimization in quantum control. GRAPE is gradient search method based on exact expressions for gradient of the control objective. It has been applied to coherently controlled closed and open quantum systems. In this work, we adopt GRAPE method for optimizing objective functionals for op

Juyoung Jeong, Muddappa Gowda

A Fan-Theobald-von Neumann system is a triple $(V,W,\lambda)$, where $V$ and $W$ are real inner product spaces and $\lambda:V\to W$ is a norm-preserving map satisfying a Fan-Theobald-von Neumann type inequality together with a condition for equality. Examples include Euclidean Jordan algebras, systems induced by certain hyperbolic polynomials, and normal dec

Alan R. Bowman, Álvaro Rodríguez Echarri, Fatemeh Kiani, Fadil Iyikanat

Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its microscopic origin strongly debated, and its potential for unravelling nanoscale carrier dynamics large

Yun Tang, Antonio A. Bruto da Costa, Jason Zhang, Irvine Patrick

Engineering knowledge-based (or expert) systems require extensive manual effort and domain knowledge. As Large Language Models (LLMs) are trained using an enormous amount of cross-domain knowledge, it becomes possible to automate such engineering processes. This paper presents an empirical automation and semi-automation framework for domain knowledge distill

Hong Yan, Yang Liu, Yushen Wei, Zhen Li

Skeleton sequence representation learning has shown great advantages for action recognition due to its promising ability to model human joints and topology. However, the current methods usually require sufficient labeled data for training computationally expensive models, which is labor-intensive and time-consuming. Moreover, these methods ignore how to util

Maja Lujan Niemeyer, José Luis Bernal, Eiichiro Komatsu

We present the Simple Intensity Map Producer for Line Emission (SIMPLE), a public code for quickly simulating mock line-intensity maps, and an analytical framework for modeling intensity maps including observational effects. SIMPLE can be applied to any spectral line sourced by galaxies. The SIMPLE code is based on lognormal mock catalogs of galaxies includi

Jianqiu Wu, Zhongyi Yu, Jianxiong Guo, Zhiqing Tang

Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient offloading of computational tasks from user electronic devices to Unmanned Aerial Vehicles or local execution

Jiacheng Ruan, Mingye Xie, Jingsheng Gao, Ting Liu

Transformer and its variants have been widely used for medical image segmentation. However, the large number of parameter and computational load of these models make them unsuitable for mobile health applications. To address this issue, we propose a more efficient approach, the Efficient Group Enhanced UNet (EGE-UNet). We incorporate a Group multi-axis Hadam

Maksym Ersteniuk, Timothy Banks, Edwin Budding, Michael D. Rhodes

Model orbits have been fitted to 27 physical double stars listed in a 1922 catalogue. A Markov Chain Monte Carlo technique was applied to estimate best fitting values and associated uncertainties for the orbital parameters. Dynamical masses were calculated using parallaxes from the Hipparcos mission, and are presented in this paper together with the estimate

Josua Spisak, Matthias Kerzel, Stefan Wermter

Multimodal integration is a key component of allowing robots to perceive the world. Multimodality comes with multiple challenges that have to be considered, such as how to integrate and fuse the data. In this paper, we compare different possibilities of fusing visual, tactile and proprioceptive data. The data is directly recorded on the NICOL robot in an exp

Samuel Blitz, A. Rod Gover, Jarosław Kopiński, Andrew Waldron

We study the non-linear Dirichlet-to-Neumann map for the Poincar\'e-Einstein filling problem. For even dimensional manifolds the range of this non-local map is described in terms of a rank two "Dirichlet-to Neumann tensor" along the boundary determined by the Poincar\'e-Einstein metric. This tensor is proportional to the variation of renormalized volume alon

Alexandros Alexakis

How locally injected turbulence, spreads in space is investigated with direct numerical simulations. We consider a turbulent flow in a long channel generated by a forcing that is localised in space. The forcing is such that it does not inject any mean momentum in the flow. We show that at long times a statistically stationary state is reached where the turbu

Cesar Damian, Oscar Loaiza-Brito

We compute the Galois group of a polynomial whose roots are determined by the critical points of a scalar potential in type IIB compactifications. We focus our study on certain perturbative models where it is feasible to construct a de Sitter vacuum within the effective theory by introducing non-geometric fluxes, D-branes, or non-BPS states. Our findings cle

Aditya Raghavan, Fabio Di Troia, Mark Stamp

Effective and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has been used widely in the field of pattern matching in general-and malware d

Tin Barisin, Jesus Angulo, Katja Schladitz, Claudia Redenbach

Scattering networks yield powerful and robust hierarchical image descriptors which do not require lengthy training and which work well with very few training data. However, they rely on sampling the scale dimension. Hence, they become sensitive to scale variations and are unable to generalize to unseen scales. In this work, we define an alternative feature r

Steffen Seitz, Thomas Götz, Christopher Lindenberg, Ronald Tetzlaff

Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophist

Federico Matteucci, Vadim Arzamasov, Klemens Boehm

Categorical encoders transform categorical features into numerical representations that are indispensable for a wide range of machine learning models. Existing encoder benchmark studies lack generalizability because of their limited choice of (1) encoders, (2) experimental factors, and (3) datasets. Additionally, inconsistencies arise from the adoption of va

Naomi M. McClure-Griffiths, Snezana Stanimirovic, Daniel R. Rybarczyk

Atomic hydrogen (HI) is a critical stepping stone in the gas evolution cycle of the interstellar medium (ISM) of the Milky Way. Hi traces both the cold, premolecular state before star formation and the warm, diffuse ISM before and after star formation. This review describes new, sensitive HI absorption and emission surveys, which, together with high angular

Somnath Chakraborty

We prove that counting the analytic Brouwer degree of rational coefficient polynomial maps in $\operatorname{Map}(\mathbb C^d, \mathbb C^d)$ -- presented in degree-coefficient form -- is hard for the complexity class $\operatorname{\sharp P}$, in the following sense: if there is a randomized polynomial time algorithm that counts the Brouwer degree correctly

Yasin Uzun

Gene regulatory networks are powerful tools for modeling interactions among genes to regulate their expression for homeostasis and differentiation. Single-cell sequencing offers a unique opportunity to build these networks with high-resolution data. There are many proposed computational methods to build these networks using single-cell data and different app

Yi Zheng, Cheng-Jie Zhang, Zheng-Hao Liu, Jian-Wei Shao

As a quantum resource, quantum coherence plays an important role in modern physics. Many coherence measures and their relations with entanglement have been proposed, and the dynamics of entanglement has been experimentally studied. However, the knowledge of general results for coherence dynamics in open systems is limited. Here we propose a coherence factori

Anahid Jalali, Anita Graser, Clemens Heistracher

This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal graph neural networks (GNNs) and counterfactuals. We review the existing GeoXAI studies, argue the need for comprehensible explanations wit

Luca Ciambelli, Alessandra D'Alise, Vittorio D'Esposito, Dušan Đorđevic

After introducing the covariant phase space calculus, Noether's theorems are discussed, with particular emphasis on Noether's second theorem and the role of gauge symmetries. This is followed by the enunciation of the theory of asymptotic symmetries, and later its application to gravity. Specifically, we review how the BMS group arises as the asymptotic symm

Max Gheorghiu

We characterize planar graphs and graph minors among other graph theoretic notions in terms of right-angled Artin groups (RAAGs). For this, we determine all sets of elements in RAAGs with ears as underlying graphs that are exactly the sets of vertex generators. Generalizing ear decompositions of graphs to loose ear decompositions, we characterize both decomp

Yuling Xue, Songbai Guo

The monotonicity properties of remainder of Stirling's formula for the gamma function are simply obtained by using the integral transforms with series.

Snehasish Roy Chowdhury, Subhendu B. Ghosh, Tathagata Gupta, Anandamay Das Bhowmik

Discrimination of quantum states under local operations and classical communication (LOCC) is an intriguing question in the context of local retrieval of classical information, encoded in the multipartite quantum systems. All the local quantum state discrimination premises, considered so far, mimic a basic communication set-up, where the spatially separated

Owen Crystal, Pejman J. Maralani, Sandra Black, Alan R. Moody

Lateral ventricular volume (LVV) is an important biomarker for clinical investigation. We present the first transfer learning-based LVV segmentation method for fluid-attenuated inversion recovery (FLAIR) MRI. To mitigate covariate shifts between source and target domains, this work proposes an domain adaptation method that optimizes performance on three targ

Fan Qin

These notes are mainly based on arXiv:2003.13674 and a series of talks given in the workshop CARTEA. For any symmetrizable Kac-Moody algebra $\mathfrak{g}$ and any Weyl group element $w$, the corresponding quantum unipotent subgroup $A_{q}[N_{-}(w)]$ possesses the dual canonical basis $\mathbf{B}^*$. We show that the dual canonical basis is the (common) tria

M. A. Vasiliev

A new efficient approach to the analysis of nonlinear higher-spin equations, that treats democratically auxiliary spinor variables $Z_A$ and integration homotopy parameters in the non-linear vertices of the higher-spin theory, is developed. Being most general, the proposed approach is the same time far simpler than those available so far. In particular, it i

Xinzhi Zhao, Jianwei Shao, Yi Zheng, Wen-Zhao Zhang

Quantum coherence plays an important role in quantum resource theory, which is strongly related with entanglement. Similar to the entanglement evolution equation, we find the coherence evolution equation of quantum states through fully and strictly incoherent operation (FSIO) channels. In order to quantify the full coherence of qudit states, we define G-cohe

Étienne Bamas, Alexander Lindermayr, Nicole Megow, Lars Rohwedder

In this paper we study the relation of two fundamental problems in scheduling and fair allocation: makespan minimization on unrelated parallel machines and max-min fair allocation, also known as the Santa Claus problem. For both of these problems the best approximation factor is a notorious open question; more precisely, whether there is a better-than-2 appr

Mayalen Etcheverry, Michael Levin, Clément Moulin-Frier, Pierre-Yves Oudeyer

Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes. Biological networks like gene regulatory networks and protein pathways are key drivers of embryogenesis and physiological processes. Comprehending their diverse behaviors is essenti

Misba Afrin, Sushant G. Ghosh

The Event Horizon Telescope (EHT) collaboration unveiled event-horizon-scale images of the supermassive black holes (SMBHs) M87* and Sgr A*, revealing a dark brightness depression, namely the black hole shadow, whose shape and size may encode the parameters of the SMBHs, and the shadow is consistent with that of a Kerr black hole. It furnishes another encour

Maksat Satybaldiev, Ilya Mereminskiy, Alexander Lutovinov, Dmitri Karasev

We present the results of the analysis of the SRG/ART-XC observation of the Supergiant Fast X-ray Transient IGR J16195-4545 performed on March 3, 2021. Six bright flares are present in the light curve, with no significant change in hardness occuring during these flares. The spectrum is described with an absorbed power law model with a high energy exponential

Pierre Thodoroff, Markus Kaiser, Rosie Williams, Robert Arthern

Computer simulations are becoming an essential tool in many scientific fields from molecular dynamics to aeronautics. In glaciology, future predictions of sea level change require input from ice sheet models. Due to uncertainties in the forcings and the parameter choices for such models, many different realisations of the model are needed in order to produce

Luozhou Wang, Shuai Yang, Shu Liu, Ying-cong Chen

Conditional diffusion models have demonstrated impressive performance in image manipulation tasks. The general pipeline involves adding noise to the image and then denoising it. However, this method faces a trade-off problem: adding too much noise affects the fidelity of the image while adding too little affects its editability. This largely limits their pra

Aki Mori

It will be proved that a $k$-clique in the $1$-skeleton of either the order polytope or the chain polytope corresponds to the $(k-1)$-face, which is a simplex, in each polytope. These results generalize the known explicit descriptions of edges and triangular $2$-faces of each polytope.

Jun Wu, Hao Fu, Mingzheng Zhu, Haiyue Zhang

Quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoder, we introduce the model of Quantum Circuit Aut

Faisal Ahmed, Miguel Heredia Conde, Paula López Martínez

Passive Time-of-Flight (ToF) imaging can be enabled by optical wireless communication (OWC). The lighting infrastructure is the backbone of emerging light-based wireless communication. To this end, communication sources are used as opportunity illuminators to probe the scene, and an array of time-resolved pixels are exploited to demodulate the return, provid

Oliver Busch, Franziska Ziolkowski, Börge Göbel, Ingrid Mertig

The orbital Hall effect can generate currents of angular momentum more efficiently than the spin Hall effect in most metals. However, so far, it has only been understood as a steady state phenomenon. In this theoretical study, the orbital Hall effect is extended into the time domain. We investigate the orbital angular momenta and their currents induced by a

Chris Halcrow, Egor Babaev

We predict a topological defect in ferroelectric barium titanate which we call a skyrme line. These are line-like objects characterized by skyrmionic topological charge. As well as configurations with integer charge, the charge density can split into well-localized fractional parts. We show that under certain conditions the fractional skyrme lines are stable