cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Creators: name: Pacchiardi, Lorenzo
name: Adewoyin, Rilwan
name: Dueben, Peter
name: Dutta, Ritabrata
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (stat.ML)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Probabilistic forecasting relies on past observations to provide a probability distribution for a future outcome, which is often evaluated against the realization using a scoring rule. Here, we perform probabilistic forecasting with generative neural networks, which parametrize distributions on high-dimensional spaces by transforming draws from a latent variable. Generative networks are typically trained in an adversarial framework. In contrast, we propose to train generative networks to minimize a predictive-sequential (or prequential) scoring rule on a recorded temporal sequence of the phenomenon of interest, which is appealing as it corresponds to the way forecasting systems are routinely evaluated. Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting. Further, we prove consistency of the minimizer of our objective with dependent data, while adversarial training assumes independence. We perform simulation studies on two chaotic dynamical models and a benchmark data set of global weather observations; for this last example, we define scoring rules for spatial data by drawing from the relevant literature. Our method outperforms state-of-the-art adversarial approaches, especially in probabilistic calibration, while requiring less hyperparameter tuning.
Effect of hole geometry on charge sharing and other parameters in GEM-based detectors
Creators: name: Roy, Promita
name: Bhattacharya, Purba
name: Rout, Prasant Kumar
name: Mukhopadhyay, Supratik
name: Majumdar, Nayana
name: Sarkar, Sandip
Publisher: name: arXiv
Year: 2021
Subjects: subject: Instrumentation and Detectors (physics.ins-det)
subject: FOS: Physical sciences

description: Gas Electron Multipliers (GEM) are among the more prominent Micro-Pattern Gaseous Detectors (MPGDs) and widely used in high energy particle physics experiments and various related applications. Adoption of different production techniques lead to holes of varying geometries in GEM foils. Since the response of a GEM-based detector is closely related to the hole geometry through the influence of the latter on charge sharing and transport through GEM foils, attempts have been made to relate hole configurations to different figures of merit of a detector. Numerical simulations have been performed to study the effects of hole geometry on important parameters such as charge sharing, collection efficiency, extraction efficiency, gain, possibility of transition from avalanche to streamer modes for single, double and triple layer GEM detectors. The numerical estimates have been compared to available experimental data. The comparisons, although not always in agreement, are found to be generally encouraging.
description: 19 pages, 18 figures, preprint prepared for JINST. v3 was an incorrect replacement and this reverts to the latest correct version V2
A randomized measurement toolbox for an interacting Rydberg-atom quantum simulator
Creators: name: Notarnicola, Simone
name: Elben, Andreas
name: Lahaye, Thierry
name: Browaeys, Antoine
name: Montangero, Simone
name: Vermersch, Benoit
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantum Physics (quant-ph)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Atomic Physics (physics.atom-ph)
subject: Computational Physics (physics.comp-ph)
subject: FOS: Physical sciences

description: We present a toolbox to probe quantum many-body states implemented on Rydberg-atoms quantum hardware via randomized measurements. We illustrate the efficacy of this measurement toolbox in the context of probing entanglement, via the estimation of the purity, and of verifying a ground-state preparation using measurements of the Hamiltonian variance. To achieve this goal, we develop and discuss in detail a protocol to realize independent, local unitary rotations. We benchmark the protocol by investigating the ground state of the one-dimensional SSH model, recently realized on a chain of Rydberg atom, and the state resulting after a sudden quench in a staggered XY chain. We probe the robustness of our toolbox by taking into account experimental imperfections, such as pulse fluctuations and measurement errors.
description: 10 pages, 8 figures
Topologically semiperfect topological rings
Creators: name: Positselski, Leonid
name: Stovicek, Jan
Publisher: name: arXiv
Year: 2022
Subjects: subject: Rings and Algebras (math.RA)
subject: Category Theory (math.CT)
subject: FOS: Mathematics

description: We define topologically semiperfect (complete, separated, right linear) topological rings and characterize them by equivalent conditions. We show that the endomorphism ring of a module, endowed with the finite topology, is topologically semiperfect if and only if the module is decomposable as an (infinite) direct sum of modules with local endomorphism rings. Then we study structural properties of topologically semiperfect topological rings and prove that their topological Jacobson radicals are strongly closed and the related topological quotient rings are topologically semisimple. For the endomorphism ring of a direct sum of modules with local endomorphism rings, the topological Jacobson radical is described explicitly as the set of all matrices of nonisomorphisms. Furthermore, we prove that, over a topologically semiperfect topological ring, all finitely generated discrete modules have projective covers in the category of modules, while all lattice-finite contramodules have projective covers in both the categories of modules and contramodules. We also show that the topological Jacobson radical of a topologically semiperfect topological ring is equal to the closure of the abstract Jacobson radical, and present a counterexample demonstrating that the topological Jacobson radical can be strictly larger than the abstract one. Finally, we discuss the problem of lifting idempotents modulo the topological Jacobson radical and the structure of projective contramodules for topologically semiperfect topological rings.
description: LaTeX 2e with mathrsfs, xy-pic, and graphicx.sty, 38 pages, 3 commutative diagrams; v.3: references to Gregorio's paper added, new Section 6 inserted, what is now Section 7 reshuffled with Theorem 7.11 obtained as a new result, former Proposition 9.3 is now Proposition 7.10; v.4: small corrections, numbering of sections shifted to agree with the journal version
Using embedding theorems to account for the extreme properties of traversable wormholes
Creators: name: Kuhfittig, Peter K. F.
Publisher: name: arXiv
Year: 2022
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: Embedding theorems, which have a long history in the general theory of relativity, are used in this paper to account for two of the most troubling aspects of Morris-Thorne wormholes, (1) the origin of exotic matter and the amount needed to sustain a wormhole, and (2) the enormous radial tension that is characteristic of wormholes with moderately-sized throats. Attributing the latter to exotic matter ignores the fact that exotic matter was introduced for a completely different reason and is usually present in only small quantities.
description: 8 pages, no figures. arXiv admin note: text overlap with arXiv:2202.00051
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
Creators: name: Lee, Doksoo
name: Chan, Yu-Chin
name: Chen, Wei Wayne
name: Wang, Liwei
name: van Beek, Anton
name: Chen, Wei
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computational Engineering, Finance, and Science (cs.CE)
subject: Databases (cs.DB)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric research trend, however, lacks principled frameworks dedicated to data acquisition, whose quality propagates into the downstream tasks. Often built by naive space-filling design in shape descriptor space, metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we present t-METASET: an active-learning-based data acquisition framework aiming to guide both diverse and task-aware data generation. Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (~O(10^4 )) shape-only library has been prepared with no properties evaluated. The key idea is to harness a data-driven shape descriptor learned from generative models, fit a sparse regressor as a start-up agent, and leverage metrics related to diversity to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three deployment cases, which encompass general use, task-specific use, and tailorable use. Two large-scale mechanical metamaterial datasets are used to demonstrate the efficacy. Applicable to general image-based design representations, t-METASET could boost future advancements in data-driven design.
description: This preprint has been submitted as a manuscript to ASME Journal of Mechanical Design
Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study
Creators: name: Dayarathna, Thisun
name: Muthukumarana, Thamidu
name: Rathnayaka, Yasiru
name: Denman, Simon
name: de Silva, Chathura
name: Pemasiri, Akila
name: Ahmedt-Aristizabal, David
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Recently, in-bed human pose estimation has attracted the interest of researchers due to its relevance to a wide range of healthcare applications. Compared to the general problem of human pose estimation, in-bed pose estimation has several inherent challenges, the most prominent being frequent and severe occlusions caused by bedding. In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings. First, we explore the effective fusion of information from different imaging modalities for better pose estimation. Secondly, we propose a framework that can estimate in-bed pose estimation when visible images are unavailable, and demonstrate the applicability of fusion methods to scenarios where only LWIR images are available. We analyze and demonstrate the effect of fusing features from multiple modalities. For this purpose, we consider four different techniques: 1) Addition, 2) Concatenation, 3) Fusion via learned modal weights, and 4) End-to-end fully trainable approach; with a state-of-the-art pose estimation model. We also evaluate the effect of reconstructing a data-rich modality (i.e., visible modality) from a privacy-preserving modality with data scarcity (i.e., long-wavelength infrared) for in-bed human pose estimation. For reconstruction, we use a conditional generative adversarial network. We conduct ablative studies across different design decisions of our framework. This includes selecting features with different levels of granularity, using different fusion techniques, and varying model parameters. Through extensive evaluations, we demonstrate that our method produces on par or better results compared to the state-of-the-art.
Continuous Human Action Recognition for Human-Machine Interaction: A Review
Creators: name: Gammulle, Harshala
name: Ahmedt-Aristizabal, David
name: Denman, Simon
name: Tychsen-Smith, Lachlan
name: Petersson, Lars
name: Fookes, Clinton
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Human-Computer Interaction (cs.HC)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams. Recognising actions and detecting action transitions within an input video are challenging but necessary tasks for applications that require real-time human-machine interaction. By reviewing a large body of recent related work in the literature, we thoroughly analyse, explain and compare action segmentation methods and provide details on the feature extraction and learning strategies that are used on most state-of-the-art methods. We cover the impact of the performance of object detection and tracking techniques on human action segmentation methodologies. We investigate the application of such models to real-world scenarios and discuss several limitations and key research directions towards improving interpretability, generalisation, optimisation and deployment.
description: Preprint submitted to ACM Computing Surveys
Approximate localised dihedral patterns near a Turing instability
Creators: name: Hill, Dan J.
name: Bramburger, Jason J.
name: Lloyd, David J. B.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Dynamical Systems (math.DS)
subject: Pattern Formation and Solitons (nlin.PS)
subject: FOS: Mathematics
subject: FOS: Physical sciences

description: Fully localised patterns involving cellular hexagons or squares have been found experimentally and numerically in various continuum models. However, there is currently no mathematical theory for the emergence of these localised cellular patterns from a quiescent state. A key issue is that standard techniques for one-dimensional patterns have proven insufficient for understanding localisation in higher dimensions. In this work, we present a comprehensive approach to this problem by using techniques developed in the study of axisymmetric patterns. Our analysis covers localised patterns equipped with a wide range of dihedral symmetries, avoiding a restriction to solutions on a predetermined lattice. The context in this paper is a theory for the emergence of such patterns near a Turing instability for a general class of planar reaction-diffusion equations. Posing the reaction-diffusion system in polar coordinates, we carry out a finite-mode Fourier decomposition in the angular variable to yield a large system of coupled radial ordinary differential equations. We then utilise various radial spatial dynamics methods, such as invariant manifolds, rescaling charts, and normal form analysis, leading to an algebraic matching condition for localised patterns to exist in the finite-mode reduction. This algebraic matching condition is nontrivial, which we solve via a combination of by-hand calculations and Gröbner bases from polynomial algebra to reveal the existence of a plethora of localised dihedral patterns. These results capture the essence of the emergent localised hexagonal patterns witnessed in experiments. Moreover, we combine computer-assisted analysis and a Newton-Kantorovich procedure to prove the existence of localised patches with 6m-fold symmetry for arbitrarily large Fourier decompositions. This includes the localised hexagon patches that have been elusive to analytical treatment.
description: 60 pages, 15 figures
SVD-разложение и его практические приложения
Creators: name: Kolesnikov, Eugene
Publisher: name: figshare
Year: 2015
Subjects: subject: 10301 Numerical Analysis
subject: FOS: Mathematics
subject: Geometry
subject: Algebra

description: SVD-разложение и его практические приложения с исходным кодом на языке Матлаб и подробными комментариями.
Intention to Use Internet of Things for Remote Monitoring to Predictive Maintenance of Machines in Manufacturing Industry
Creators: name: Aksara Aksonsit
Publisher: name: MSMIS Thammasat University
Year: 2563
Subjects: Internet of Things, Remote monitoring, Predictive maintenance, Manufacturing industry, Intention to use

description: Journal of information systems in Business JISB, 6, 1, 69
Riemannian Interior Point Methods for Constrained Optimization on Manifolds
Creators: name: Lai, Zhijian
name: Yoshise, Akiko
Publisher: name: arXiv
Year: 2022
Subjects: subject: Optimization and Control (math.OC)
subject: FOS: Mathematics
subject: 90C51, 90C53, 90C46

description: We extend the classical primal-dual interior point method from the Euclidean setting to the Riemannian one. Our method, named the Riemannian interior point method, is for solving Riemannian constrained optimization problems. We establish its local superlinear and quadratic convergence under the standard assumptions. Moreover, we show its global convergence when it is combined with a classical line search. Our method is a generalization of the classical framework of primal-dual interior point methods for nonlinear nonconvex programming. Numerical experiments show the stability and efficiency of our method.
description: 25pages
Neuroprotective effects of Tilia americana var. mexicana on damage induced by cerebral ischaemia in mice
Creators: name: Angeles-López, Guadalupe E.
name: Ma. Eva González-Trujano
name: Gómez, Claudia
name: Ma. Elena Chánez-Cárdenas
name: Ventura-Martinez, Rosa
Publisher: name: Taylor & Francis
Year: 2015
Subjects: subject: Cell Biology
subject: Neuroscience
subject: Pharmacology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: Science Policy

description: Tilia americana var. mexicana (T. americana) is a plant widely used in Mexico for its medicinal properties on the central nervous system. In the present study, we designed a protocol to investigate the neuroprotective effects of non-polar and polar extracts of T. americana on damage induced by cerebral ischaemia in mice. Vehicle or extracts were administered immediately after ischaemia. Functional neurological deficit, survival percentage and infarct area were determined in each experimental group. Results showed that groups treated with non-polar or polar extracts of T. americana had increased survival rate, improved neurological deficits and diminished the infarct area in relation to the ischaemic group. In conclusion, this study confirms the neuroprotective activity of T. americana, suggests a possible synergism between non-polar and polar constituents and supports its potential as a useful aid in the clinical management of stroke.
Reflective obstructions of unitary modular varieties
Creators: name: Maeda, Yota
Publisher: name: arXiv
Year: 2022
Subjects: subject: Algebraic Geometry (math.AG)
subject: Number Theory (math.NT)
subject: Representation Theory (math.RT)
subject: FOS: Mathematics
subject: Primary:14G35, Secondary:11F03, 11E39, 14E22

description: To prove that a modular variety is of general type, there are three types of obstructions: reflective, cusp and elliptic obstructions. In this paper, we give a quantitative estimate of the reflective obstructions for the unitary case. This shows in particular that the reflective obstructions are small enough in higher dimension, say greater than $138$. Our result reduces the study of the Kodaira dimension of unitary modular varieties to the construction of a cusp form of small weight in a quantitative manner. As a byproduct, we formulate and partially prove the finiteness of Hermitian lattices admitting reflective modular forms, which is a unitary analog of the conjecture by Gritsenko-Nikulin in the orthogonal case. Our estimate of the reflective obstructions uses Prasad's volume formula.
description: 45 pages
Lagrangian Descriptors and the Action Integral of Classical Mechanics
Creators: name: García-Garrido, V. J.
name: Wiggins, S.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Dynamical Systems (math.DS)
subject: Chaotic Dynamics (nlin.CD)
subject: FOS: Mathematics
subject: FOS: Physical sciences

description: In this paper we bring together the method of Lagrangian descriptors and the principle of least action, or more precisely, of stationary action, in both deterministic and stochastic settings. In particular, we show how the action can be used as a Lagrangian descriptor. This provides a direct connection between Lagrangian descriptors and Hamiltonian mechanics, and we illustrate this connection with benchmark examples.
Shock interactions for the Burgers-Hilbert Equation
Creators: name: Bressan, Alberto
name: Galtung, Sondre T.
name: Grunert, Katrin
name: Nguyen, Khai T.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics
subject: 35L65, 35L60, 35L03

description: This paper provides an asymptotic description of a solution to the Burgers-Hilbert equation in a neighborhood of a point where two shocks interact. The solution is obtained as the sum of a function with $H^2$ regularity away from the shocks plus a corrector term having an asymptotic behavior like |x|ln|x| close to each shock. A key step in the analysis is the construction of piecewise smooth solutions with a single shock for a general class of initial data.
description: 47 pages
Initial Survey of IT Chargeback: Management Perspective
Creators: name: Ratchanee Kulsolkookiet And Nitaya Wongpinunwatana
Publisher: name: MSMIS Thammasat University
Year: 2563
Subjects: IT policies, IT administratives, IT chargeback, End-user computing

description: Journal of information systems in Business JISB, 6, 1, 40
Reflections on a lab rotation integrating science writing and experiment work
Creators: name: Ng, Wenfa
Publisher: name: figshare
Year: 2015
Subjects: Education
Biotechnology
Biological Engineering
Cell Biology
Biological Techniques

description: Project and advisor selection are critical decisions in a graduate student’s career. To help students and advisors make an informed decision, many universities instituted lab rotation programmes, where students cycle through a few labs to assess the relative fit of projects and lab environment. Nevertheless, how should a lab rotation be designed to deliver maximum educational benefit without significantly affecting lab operations, while fulfilling the goal of matching projects to students? By combining science writing with experiment work, this article reflects on one possible approach for structuring a lab rotation experience. Specifically, writing a commentary on a published article introduced the general theme of the lab’s research, while providing information on the student’s critical thinking and journal critique capabilities. On the other hand, a literature review of a prospective research topic allowed the student time to critically assess the current state of the field, and possible paths forward. Breadth and depth of the analysis offered glimpses into the student’s comprehension of the field, and capability at identifying knowledge gaps and conceptualizing innovative approaches for problem solution. Immunoblotting was chosen as a focused technique for experiment work and introduced student to a new technique. More important, multi-step nature of western blots highlighted the importance of experiment precision and rigour. Collectively, integrating scientific writing and experiment work afforded student’s a holistic understanding of the lab’s research thrust and practical experience of an important experiment technique, while providing advisor with important information on student’s attitude, motivation, experimental skills and thinking capabilities.
How Not to Handle False or Misleading Data: Greetings to the Food System Dashboard and the World Bank Data Catalog (Still a Big Fan!)
Creators: name: Maurer, Moritz
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 300202
subject: Agricultural land management
classificationCode: 300210
subject: Sustainable agricultural development

description: I’m a big fan of the Food System Dashboard, but I’ve noticed one missing feature: the ability to plot data across all countries within a selected dataset, aggregated by sum or mean. To test this, I downloaded the dataset “Fertilizer consumption (kg/ha of arable land).” However, the output I received was quite confusing. This prompted me to investigate the data further. I’m sharing my findings here because they tell an interesting story about how data deficiencies can go unnoticed when datasets are reused without sufficient attention to their original limitations.My operations on the dataset can be viewed and downloaded via this github-repository. You can as well view, run and manipulate the code with an online notebook such as google colab. There, enter this “https://github.com/AgrarMo/Checking-Food-System-Data/tree/master/FSStats” GitHub URL, Choose the “master” branch and pick the FSStats_forGooglColab.ipyng.
Preclinical Assessment of Zinc Ferrite Nanoparticles Synthesized Using D-Glucose by Hydrothermal Method
Creators: name: C S Vicas
name: K Namratha
name: K Byrappa
name: H S Yathirajan
Publisher: name: figshare
Year: 2015
Subjects: Toxicology

description: Eloquent hydrothermal synthesis of Zinc ferrite nanoparticles was carried out using dextrose as a reducing agent. Nanoparticles were characterized using dynamic light scattering technique, scanning electron microscopy, Fourier transform infrared spectroscopy, powder X-ray diffraction and energy dispersive spectroscopy. The nanoparticles obtained were of an average size of ~47 nm. Microbial toxicology, Haemolysis of blood, teratology and embryotoxicology on chick embryos were performed. The material was found to be non haematotoxic and non-teratogenic. Determination of total protein content using Bradford’s assay showed that the level remained to be insignificantly altered in the treated embryos. Synthesized ZnFe2O4 also demonstrated an excellent antioxidant property of ~90% when studied using DPPH free radical scavenging assay. The nanoparticles synthesized have shown significant inhibition of skin and soft tissue infection (SSTI) causing bacteria such as Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Bacillus subtilis. Based on the current evidences compared with the known ferrites, it was inferred that the biocompatibility and the ability of zinc ferrite nanoparticles to inhibit the growth of the selected SSTI causing bacteria, opens the doors to a new arena for the usage of zinc ferrite nanoparticles as potent antibacterial agents for treating skin infections. They can also be considered for use as ointments and antibacterial creams.
Type of Facebook Advertising Affecting to Online Purchase Intention of Customers in Bangkok
Creators: name: Romcharee Laohasatchaporn
Publisher: name: MSMIS Thammasat University
Year: 2563
Subjects: Trust in advertising, Influencer marketing, Retargeting, Brand equity, Advertising format on Facebook, Online purchase intention

description: Journal of information systems in Business JISB, 6, 1, 52
Dust release from cold ring particles as a mechanism of spoke formation in Saturn's rings
Creators: name: Hirata, Naoyuki
name: Kimura, Hiroshi
name: Ohtsukia, Keiji
Publisher: name: arXiv
Year: 2022
Subjects: subject: Earth and Planetary Astrophysics (astro-ph.EP)
subject: FOS: Physical sciences

description: Spokes in Saturn's rings are radially-extended structures consisting of dust grains. Although spacecraft and space telescope observations have revealed various detailed features of the spokes and their time variation, their formation mechanism is still under debate. Previous models examined charging mechanisms to attempt at explaining dust release from cm-sized ring particles; however, the attempt has been unsuccessful, because the electrostatic force caused by such charging mechanisms is much weaker than the cohesive force acting on dust grains at ordinary conditions in the ring environment. Here we propose a novel model for the formation of the spokes, where the temperature dependence of cohesion plays an essential role. Ring particles with a temperature below 60K adsorb an O2 ring atmosphere, which facilitates release of dust grains from them by a reduction in the cohesive force between the grains and the particles on the morning ansa. Then, intense electrostatic forces sufficient to overcome the cohesive force are generated on the surface of ring particles and the released dust grains form the structure of spokes. Our model explains observational features of the spokes including their longitudinal location, lifetime, radial expansion velocity, and seasonality.
description: 37 pages, 7 figures, Submission History: This paper was submitted in Nature Geoscience in May 6, 2018 and rejected after very long reviews. This paper was resubmitted in Icarus in May 8, 2021 and accepted in January 30, 2022
Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging
Creators: name: Ahmedt-Aristizabal, David
name: Nguyen, Chuong
name: Tychsen-Smith, Lachlan
name: Stacey, Ashley
name: Li, Shenghong
name: Pathikulangara, Joseph
name: Petersson, Lars
name: Wang, Dadong
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Graphics (cs.GR)
subject: FOS: Computer and information sciences

description: Advanced artificial intelligence and machine learning have great potential to redefine how skin lesions are detected, mapped, tracked and documented. Here, We propose a 3D whole-body imaging system known as 3DSkin-mapper to enable automated detection, evaluation and mapping of skin lesions. A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles. Based on the images, we developed algorithms for 3D model reconstruction, data processing and skin lesion detection and tracking based on deep convolutional neural networks. We also introduced a customised, user-friendly, and adaptable interface that enables individuals to interactively visualise, manipulate, and annotate the images. The proposed system is developed for skin lesion screening, the focus of this paper is to introduce the system instead of clinical study. Using synthetic and real images we demonstrate the effectiveness of the proposed system by providing multiple views of a target skin lesion, enabling further 3D geometry analysis and longitudinal tracking. It takes only a few seconds to capture the entire skin surface, and about half an hour to process and analyse the images. Our experiments show that the proposed system allow fast and easy whole body 3D imaging. It can be used by dermatological clinics to conduct skin screening, detect and track skin lesions over time, identify suspicious lesions, and document pigmented lesions. The system can potentially save clinicians time and effort significantly. The 3D imaging and analysis has the potential to change the paradigm of whole body photography with many applications in skin diseases, including inflammatory and pigmentary disorders.
description: In Computer Methods and Programs in Biomedicine
Kernel Normalized Convolutional Networks
Creators: name: Nasirigerdeh, Reza
name: Torkzadehmahani, Reihaneh
name: Rueckert, Daniel
name: Kaissis, Georgios
Publisher: name: arXiv
Year: 2022
Subjects: subject: Machine Learning (cs.LG)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model. BatchNorm, however, performs poorly with small batch sizes, and is inapplicable to differential privacy. To address these limitations, we propose the kernel normalization (KernelNorm) and kernel normalized convolutional layers, and incorporate them into kernel normalized convolutional networks (KNConvNets) as the main building blocks. We implement KNConvNets corresponding to the state-of-the-art ResNets while forgoing the BatchNorm layers. Through extensive experiments, we illustrate that KNConvNets achieve higher or competitive performance compared to the BatchNorm counterparts in image classification and semantic segmentation. They also significantly outperform their batch-independent competitors including those based on layer and group normalization in non-private and differentially private training. Given that, KernelNorm combines the batch-independence property of layer and group normalization with the performance advantage of BatchNorm.
Metric Optimization in Penner Coordinates
Creators: name: Capouellez, Ryan
name: Zorin, Denis
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computational Geometry (cs.CG)
subject: Graphics (cs.GR)
subject: FOS: Computer and information sciences

description: Many parametrization and mapping-related problems in geometry processing can be viewed as metric optimization problems, i.e., computing a metric minimizing a functional and satisfying a set of constraints, such as flatness. Penner coordinates are global coordinates on the space of metrics on meshes with a fixed vertex set and topology, but varying connectivity, making it homeomorphic to the Euclidean space of dimension equal to the number of edges in the mesh, without any additional constraints imposed. These coordinates play an important role in the theory of discrete conformal maps, enabling recent development of highly robust algorithms with convergence and solution existence guarantees for computing such maps. We demonstrate how Penner coordinates can be used to solve a general class of optimization problems involving metrics, including optimization and interpolation, while retaining the key solution existence guarantees available for discrete conformal maps.
Symmetry breaking of three self-organization rules:A general theory for the origin of complexity
Creators: name: Wu, Wen-Hao
name: Li, Ze-Zheng
name: Wang, Wen-Xu
Publisher: name: arXiv
Year: 2024
Subjects: subject: Biological Physics (physics.bio-ph)
subject: Chaotic Dynamics (nlin.CD)
subject: FOS: Physical sciences

description: Complex spatiotemporal patterns in nature significantly challenge reductionism-based modern science. The lack of a paradigm beyond reductionism hinders our understanding of the emergence of complexity. The diversity of countless patterns undermines any notion of universal mechanisms. Here, however, we show that breaking the symmetry of three simple and self-organization rules give rise to nearly all patterns in nature, such as a wide variety of Turing patterns, fractals, spiral, target and plane waves, as well as chaotic patterns. The symmetry breaking is rooted in basic physical quantities, such as positive and negative forces, space, time and bounds. Besides reproducing the hallmarks of complexity, we discover some novel phenomena, such as abrupt percolation of Turing patterns, phase transition between fractals and chaos, chaotic edge in travelling waves, etc. Our asymmetric self-organization theory established a simple and unified framework for the origin of complexity in all fields, and unveiled a deep relationship between the first principles of physics and the complex world
description: 97 pages, 60 figures, for full article, see https://doi.org/10.1142/S021812742430012X
Spectroscopic evidence for engineered hadron formation in repulsive fermionic $\textrm{SU}(N)$ Hubbard Models
Creators: name: Werner, Miklós Antal
name: Moca, Cătălin Paşcu
name: Kormos, Márton
name: Legeza, Örs
name: Dóra, Balázs
name: Zaránd, Gergely
Publisher: name: arXiv
Year: 2022
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: Particle formation represents a central theme in various branches of physics, often associated to confinement. Here we show that dynamical hadron formation can be spectroscopically detected in an ultracold atomic setting within the most paradigmatic and simplest model of condensed matter physics, the repulsive $\textrm{SU}(N)$ Hubbard model. By starting from an appropriately engineered initial state of the ${{\textrm{SU}(3)}}$ Hubbard model, not only mesons (doublons) but also baryons (trions) are naturally generated during the time evolution. In the strongly interacting limit, baryons become heavy and attract each other strongly, and their residual interaction with mesons generates meson diffusion, as captured by the evolution of the equal time density correlation function. Hadrons remain present in the long time limit, while the system thermalizes to a negative temperature state. Our conclusions extend to a large variety of initial conditions, all spatial dimensions, and for SU($N>2$) Hubbard models.
description: 7 pages, 5 figures
Associative Memory Based Experience Replay for Deep Reinforcement Learning
Creators: name: Li, Mengyuan
name: Kazemi, Arman
name: Laguna, Ann Franchesca
name: Hu, X. Sharon
Publisher: name: arXiv
Year: 2022
Subjects: subject: Hardware Architecture (cs.AR)
subject: Emerging Technologies (cs.ET)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Experience replay is an essential component in deep reinforcement learning (DRL), which stores the experiences and generates experiences for the agent to learn in real time. Recently, prioritized experience replay (PER) has been proven to be powerful and widely deployed in DRL agents. However, implementing PER on traditional CPU or GPU architectures incurs significant latency overhead due to its frequent and irregular memory accesses. This paper proposes a hardware-software co-design approach to design an associative memory (AM) based PER, AMPER, with an AM-friendly priority sampling operation. AMPER replaces the widely-used time-costly tree-traversal-based priority sampling in PER while preserving the learning performance. Further, we design an in-memory computing hardware architecture based on AM to support AMPER by leveraging parallel in-memory search operations. AMPER shows comparable learning performance while achieving 55x to 270x latency improvement when running on the proposed hardware compared to the state-of-the-art PER running on GPU.
description: 9 pages, 9 figures. The work was accepted by the 41st International Conference on Computer-Aided Design (ICCAD), 2022, San Diego
Random Colorings in Manifolds
Creators: name: Even-Zohar, Chaim
name: Hass, Joel
Publisher: name: arXiv
Year: 2022
Subjects: subject: Geometric Topology (math.GT)
subject: Combinatorics (math.CO)
subject: Probability (math.PR)
subject: FOS: Mathematics

description: We develop a general method for constructing random manifolds and submanifolds in arbitrary dimensions. The method is based on associating colors to the vertices of a triangulated manifold, as in recent work for curves in 3-dimensional space by Sheffield and Yadin (2014). We determine conditions on which submanifolds can arise, in terms of Stiefel-Whitney classes and other properties. We then consider the random submanifolds that arise from randomly coloring the vertices. Since this model generates submanifolds, it allows for studying properties and using tools that are not available in processes that produce general random subcomplexes. The case of 3 colors in a triangulated 3-ball gives rise to random knots and links. In this setting, we answer a question raised by de Crouy-Chanel and Simon (2019), showing that the probability of generating an unknot decays exponentially. In the general case of k colors in d-dimensional manifolds, we investigate the random submanifolds of different codimensions, as the number of vertices in the triangulation grows. We compute the expected Euler characteristic, and discuss relations to homological percolation and other topological properties. Finally, we explore a method to search for solutions to topological problems by generating random submanifolds. We describe computer experiments that search for a low-genus surface in the 4-dimensional ball whose boundary is a given knot in the 3-dimensional sphere.
Stochastic algebraic Riccati equations are almost as easy as deterministic ones theoretically
Creators: name: Guo, Zhen-Chen
name: Liang, Xin
Publisher: name: arXiv
Year: 2022
Subjects: subject: Optimization and Control (math.OC)
subject: Numerical Analysis (math.NA)
subject: FOS: Mathematics
subject: 93B11, 65F45, 49N10, 93E03, 93E20

description: Stochastic algebraic Riccati equations, also known as rational algebraic Riccati equations, arising in linear-quadratic optimal control for stochastic linear time-invariant systems, were considered to be not easy to solve. The-state-of-art numerical methods most rely on differentiability or continuity, such as Newton-type method, LMI method, or homotopy method. In this paper, we will build a novel theoretical framework and reveal the intrinsic algebraic structure appearing in this kind of algebraic Riccati equations. This structure guarantees that to solve them is almost as easy as to solve deterministic/classical ones, which will shed light on the theoretical analysis and numerical algorithm design for this topic.
description: 17 pages
Overwhelmed Software Developers
Creators: name: Michels, Lisa-Marie
name: Petkova, Aleksandra
name: Richter, Marcel
name: Farley, Andreas
name: Graziotin, Daniel
name: Wagner, Stefan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Software Engineering (cs.SE)
subject: Computers and Society (cs.CY)
subject: FOS: Computer and information sciences

description: We have conducted a qualitative psychology study to explore the experience of feeling overwhelmed in the realm of software development. Through the candid confessions of two participants who have recently faced overwhelming challenges, we have identified seven distinct categories: communication-induced, disturbance-related, organizational, variety, technical, temporal, and positive overwhelm. While most types of overwhelm tend to deteriorate productivity and increase stress levels, developers sometimes perceive overwhelm as a catalyst for heightened focus, self-motivation, and productivity. Stress was often found to be a common companion of overwhelm. Our findings align with previous studies conducted in diverse disciplines. However, we believe that software developers possess unique traits that may enable them to navigate through the storm of overwhelm more effectively.
description: 8 pages. Published at IEEE Software. Based on the technical report arxiv:2401.02780
Together or Apart? Investigating a mediator bot to aggregate bot's comments on pull requests
Creators: name: Ribeiro, Eric
name: Nascimento, Ronan
name: Steinmacher, Igor
name: Xavier, Laerte
name: Gerosa, Marco
name: de Paula, Hugo
name: Wessel, Mairieli
Publisher: name: arXiv
Year: 2022
Subjects: subject: Software Engineering (cs.SE)
subject: FOS: Computer and information sciences

description: Software bots connect users and tools, streamlining the pull request review process in social coding platforms. However, bots can introduce information overload into developers' communication. Information overload is especially problematic for newcomers, who are still exploring the project and may feel overwhelmed by the number of messages. Inspired by the literature of other domains, we designed and evaluated FunnelBot, a bot that acts as a mediator between developers and other bots in the repository. We conducted a within-subject study with 25 newcomers to capture their perceptions and preferences. Our results provide insights for bot developers who want to mitigate noise and create bots for supporting newcomers, laying a foundation for designing better bots.
description: 5 pages, to be published at the New Ideas and Emerging Results (NIER) Track of ICSME 2022
title: Central leaves and EKOR strata on Shimura varieties with parahoric reduction
Creators: name: Hesse, Jens
Publisher: name: UNSPECIFIED
Year: 2020

description: We investigate the geometry of the special fiber of the integral model of a Shimura variety with parahoric level at a given prime place. To be more precise, we deal with, firstly, the definition of central leaves in this situation, their local closedness, and the relationship between the folations for varying parahoric level. This is connected to the verification of axioms for integral models formulated by He and Rapoport. Secondly, we deal with the EKOR stratification which interpolates between the Ekedahl-Oort and Kottwitz-Rapoport stratifications. In the Siegel case we give a geometric description by suitably generalizing the theory of G-zips of Moonen, Wedhorn, Pink and Ziegler to our context.
Vision-Based Activity Recognition in Children with Autism-Related Behaviors
Creators: name: Wei, Pengbo
name: Ahmedt-Aristizabal, David
name: Gammulle, Harshala
name: Denman, Simon
name: Armin, Mohammad Ali
Publisher: name: arXiv
Year: 2022
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems have been introduced to enable comprehensive analysis of neuro-developmental conditions such as Autism Spectrum Disorder (ASD). This condition affects children from their early developmental stages onwards, and diagnosis relies entirely on observing the child's behavior and detecting behavioral cues. However, the diagnosis process is time-consuming as it requires long-term behavior observation, and the scarce availability of specialists. We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. For this purpose, we adopt and enhance a dataset for analyzing autism-related actions using videos of children captured in uncontrolled environments (e.g. videos collected with consumer-grade cameras, in varied environments). The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames and classifying autism-related behaviors by analyzing the relationships between frames in a video. Through extensive evaluations on the feature extraction and learning strategies, we demonstrate that the best performance is achieved with an Inflated 3D Convnet and Multi-Stage Temporal Convolutional Networks, achieving a 0.83 Weighted F1-score for classification of the three autism-related actions, outperforming existing methods. We also propose a light-weight solution by employing the ESNet backbone within the same system, achieving competitive results of 0.71 Weighted F1-score, and enabling potential deployment on embedded systems.