Miracle-dz/UniFAR-FTUs huggingface dataset:
Perturbative Expansion of the Chern-Simons Integral (量子場の数理とその周辺)

The necessity of the asymptotic expansion in infinite dimensions arises from the pioneering work of the Chern-Simons theory by Witten [10] in 1989. However, the exponentia13rd term of the Chern-Simons theory is less likely to be handled by techniques known at present, so that we will challenge a new method called the Fujiwara-Kumano-go method [5, 8] as a possibility. Let $M$ be a compact oriented smooth 3-manifold, $G$ a simply connected, connected compact simple Lie group, and $Parrow M$ a principal $G$-bundle over
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

We identify various sports-related challenges pertinent to teams, player selections, and event management. We examine both technical and managerial decisions made during player promotion, and various factors influenced the matches. We conceptualize that the game strategies, player and team selections, performance-based sports ecology management entities can blend into an informatics paradigm for which sports data modelling and analytics are required. In addition, reviews of players in social media have significance in weighing the economic value of players. With a motivation to systematize and franchise the branded teams and players to generate revenues, we aim at articulating a logical Sports Information System (SIS), for which various design-science guided artefacts are proposed. The research evaluates game strategies, the promotion of promising players, team-based performance, and economic indicators. Documentation of sports facts and logical storage models are vital to generate metadata and assess how players performed and what factors can motivate future matches.
Locating corners in noisy curves by delineating imperfect sequences

Line-based image analysis and area-based image analysts are rarely combined, although it is quite clear that these two approaches complement each other. One reason for the infrequency of the combination is that automatic segmentation procedures usually produce lines of poor quality, and such lines are hard to analyze. This paper addresses the problem of locating right angle corners in unsmooth digital lines, employing the concept of imperfect sequences, that has proved useful in various applications, including road finding and RNA homology detection. A detector of imperfect sequences is used to create a line analysis method that is fast and simple, yet robust enough to produce reasonable corner localization even in noisy lines. We have found that using this algorithm gave us a powerful tool for combining the line-based and area-based approaches. Subject Terms: Digital Corners, Imperfect Sequences.
The X-ray Asynchronous Optical Afterglow of GRB 060912A and Tentative Evidence of a 2175-A Host Dust Extinction Feature

We present optical photometry of the GRB 060912A afterglow obtained with ground-based telescopes, from about 100 sec after the GRB trigger till about 0.3 day later, supplemented with the Swift optical afterglow data released in its official website. The optical light curve (LC) displays a smooth single power-law decay throughout the observed epochs, with a power-law index of about -1 and no significant color evolution. This is in contrast to the X-ray LC which has a plateau phase between two normal power-law decays of a respective index of about -1 and -1.2. It is shown by our combined X-ray and optical data analysis that this asynchronous behavior is difficult to be reconciled with the standard afterglow theory and energy injection hypothesis. We also construct an optical-to-X-ray spectral energy distribution at about 700 sec after the GRB trigger. It displays a significant flux depression in the B-band, reminding us of the possibility of a host-galaxy (at z=0.937) 2175-A dust absorption similar to the one that characterizes the Milky Way extinction law. Such an identification, although being tentative, may be confirmed by our detailed analysis using both template extinction laws and the afterglow theory. So far the feature is reported in very few GRB afterglows. Most seem to have a host galaxy either unusually bright for a GRB, just like this one, or of an early type, supporting the general suggestion of an anti-correlation between the feature and star-forming activities.
On the second order statistics for GPS ionospheric scintillation modeling

Equatorial ionospheric scintillation is a phenomenon that occurs frequently, typically during nighttime, affecting radio signals that propagate through the ionosphere. Depending on the temporal and spatial distribution, ionospheric scintillation can represent a problem in the availability and precision for the Global Navigation Satellite System's users. This work is concerned with the statistical evaluation of the amplitude ionospheric scintillation fading events, namely, level crossing rate (LCR) and average fading duration (AFD). Using α‐μ model, the LCR and AFD are validated against experimental data obtained in São José dos Campos (23.1°S; 45.8°W; dip latitude 17.3°S), Brazil, a station located near the southern crest of the ionospheric equatorial ionization anomaly. The amplitude scintillation data were collected between December 2001 and January 2002, a period of high solar flux conditions. The obtained results with the proposed model fitted quite well with the experimental data and performed better when compared to the widely used Nakagami‐m model. Additionally, this work discusses the estimation of α and μ parameters, and the best fading coefficients found in this analysis are related to scintillation severity. Finally, for theoretical situations in which no set of experimental data are available, this work also presents parameterized equations to describe these fading statistics properly.
Visions of a City for All. Resources, Choices and Factors Supporting and Impeding Universal Design in the Urban Development Process

Despite laws, policies and visions to create cities and societies for all, barriers still exclude persons with disabilities from using buildings and public places. Our study aimed to identify choices made during the urban development process that include or exclude users in the built environment; how and when these choices arise during the process; and what is needed to implement universal design (UD) as a strategy and tool to secure all users equal opportunities in the built environment. The study involved employees and private actors in city development processes. The participants were asked to identify impediments and support of UD in completed building projects to shed light on choices made during the process and on conditions needed to implement UD along the process. Four workshops were followed by qualitative interviews with key players. The analysis was based on qualitative data from workshops and interviews. Aspects impeding and supporting UD and conflicting visions and goals were identified in all phases, as well as the need for tools to implement UD. The findings show that accessibility for all users is dealt with (too) late in the process, often giving rise to special solutions. Urban trends such as densification and high exploitation can cause the exclusion of some users, and an unbalanced view of sustainable development prioritising ecological aspects puts high demands on users’ abilities. The findings also show how UD appears more clearly in remodelling projects than in new constructions. A strong vision from the start to build for all users clearly supports UD throughout the process. Other factors such as pre-studies that include human diversity, allocation of resources and experts’ early opinions also prove to be clear drivers for UD. Overall, findings reveal a demand for solutions that can maintain early visions and goals throughout the processes. We conclude by providing seven recommendations for addressing these challenges.
The Research of Users' Continuance Intention in Relationship-Based Virtual Communities from the Perspective of Quality

: With the increasing number of virtual communities, it was challenging to retain existing users and encourage their continued participation in recent years. A research framework to investigate virtual community users' continuance intention was proposed from the perspective of quality. Based on the empirical study, it was found that information and system quality directly affected functional benefits and social benefits, which ultimately determined users’ continuance intention to get and to provide information. Furthermore, by modeling information quality and system quality as multifaceted constructs, the results revealed key quality concerns in relationship-based virtual communities. The conclusions had a certain significance on theoretical research and management practice.
Moment-functional based spectral density-functional theory

We describe a density-functional method which aims at computing the ground state electron density and the spectral function at the same time. One basic ingredient of our method is the construction of the spectral function from the first four spectral moment matrices. The second basic ingredient is the construction of the spectral moment matrices from density functionals. We call our method moment-functional based spectral density-functional theory (MFbSDFT), because it is based on density-functionals for the spectral moments and because it allows us to compute the spectral function. If it is implemented in second variation our method consumes only a fraction more computer time than a standard DFT calculation with the PBE functional. We show that MFbSDFT captures correlation effects such as the valence-band satellite in Ni and the formation of lower and upper Hubbard bands in SrVO$_3$. For the purpose of constructing the spectral function from the first four $N\times N$ spectral moment matrices we describe an efficient algorithm based on the diagonalization of one hermitean $2N\times 2N$ matrix.
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents \textit{GRAND}, a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions. RDF2vec first generates graph walks and then uses a language model to obtain embeddings for each node in the graph. This study shows that the walk generation strategy and the embedding model have a significant effect on the performance of the entity typing task. The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes. The results show that the combination of order-aware RDF2vec variants together with the contextual embeddings of the textual entity descriptions achieve the best results.
Differentially Private Learning of Hawkes Processes

Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history going back decades, some of their properties, such as sample complexity for learning the parameters and releasing differentially private versions, are yet to be thoroughly analyzed. In this work, we study standard Hawkes processes with background intensity $\mu$ and excitation function $\alpha e^{-\beta t}$. We provide both non-private and differentially private estimators of $\mu$ and $\alpha$, and obtain sample complexity results in both settings to quantify the cost of privacy. Our analysis exploits the strong mixing property of Hawkes processes and classical central limit theorem results for weakly dependent random variables. We validate our theoretical findings on both synthetic and real datasets.
Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings

Localization in aerial imagery-based maps offers many advantages, such as global consistency, geo-referenced maps, and the availability of publicly accessible data. However, the landmarks that can be observed from both aerial imagery and on-board sensors is limited. This leads to ambiguities or aliasing during the data association. Building upon a highly informative representation (that allows efficient data association), this paper presents a complete pipeline for resolving these ambiguities. Its core is a robust self-tuning data association that adapts the search area depending on a pseudo-entropy of the measurements. Additionally, to smooth the final result, we adjust the information matrix for the associated data as a function of the relative transform produced by the data association process. We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany. We compare state-of-the-art outlier mitigation methods with our self-tuning approach, demonstrating a considerable improvement, especially for outer-urban scenarios.
BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

We developed and validated a rating scale to assess the aesthetic pleasure (or beauty) of a visual data representation: the BeauVis scale. With our work we offer researchers and practitioners a simple instrument to compare the visual appearance of different visualizations, unrelated to data or context of use. Our rating scale can, for example, be used to accompany results from controlled experiments or be used as informative data points during in-depth qualitative studies. Given the lack of an aesthetic pleasure scale dedicated to visualizations, researchers have mostly chosen their own terms to study or compare the aesthetic pleasure of visualizations. Yet, many terms are possible and currently no clear guidance on their effectiveness regarding the judgment of aesthetic pleasure exists. To solve this problem, we engaged in a multi-step research process to develop the first validated rating scale specifically for judging the aesthetic pleasure of a visualization (osf.io/fxs76). Our final BeauVis scale consists of five items, “enjoyable,” “likable,” “pleasing,” “nice,” and “appealing. ” Beyond this scale itself, we contribute (a) a systematic review of the terms used in past research to capture aesthetics, (b) an investigation with visualization experts who suggested terms to use for judging the aesthetic pleasure of a visualization, and (c) a confirmatory survey in which we used our terms to study the aesthetic pleasure of a set of 3 visualizations.
Second quantization of open quantum systems in Liouville space

We present a theoretical framework based on second quantization in Liouville space to treat open quantum systems. We consider an ensemble of identical quantum emitters characterized by a discrete set of quantum states. The second quantization is performed directly at the level of density matrices, thereby significantly reducing the size of the Liouville space. In contrast to conventional Hilbert space techniques, statistically mixed states and dissipation are naturally incorporated. As a particular example of application, we study the effect of incoherent processes and statistical mixing of emitters' initial states in the interaction with quantum light. Moreover, we link our framework to a phase-space description of the dynamics, which can overcome the computational limitations of our method with the increasing number of particles.
The Rapid Rotation of the Strongly Magnetic Ultramassive White Dwarf EGGR 156

The distribution of white dwarf rotation periods provides a means for constraining angular momentum evolution during the late stages of stellar evolution, as well as insight into the physics and remnants of double degenerate mergers. Although the rotational distribution of low-mass white dwarfs is relatively well constrained via asteroseismology, that of high-mass white dwarfs, which can arise from either intermediate-mass stellar evolution or white dwarf mergers, is not. Photometric variability in white dwarfs due to rotation of a spotted star is rapidly increasing the sample size of high-mass white dwarfs with measured rotation periods. We present the discovery of 22.4 minute photometric variability in the light curve of EGGR 156, a strongly magnetic, ultramassive white dwarf. We interpret this variability as rapid rotation, and our data suggest that EGGR 156 is the remnant of a double degenerate merger. Finally, we calculate the rate of period change in rapidly-rotating, massive, magnetic WDs due to magnetic dipole radiation. In many cases, including EGGR 156, the period change is not currently detectable over reasonable timescales, indicating that these WDs could be very precise clocks. For the most highly-magnetic, rapidly-rotating massive WDs, such as ZTF J1901+1450 and RE J0317−853, the period change should be detectable and may help constrain the structure and evolution of these exotic white dwarfs.
One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares

While deep neural networks are capable of achieving state-of-the-art performance in various domains, their training typically requires iterating for many passes over the dataset. However, due to computational and memory constraints and potential privacy concerns, storing and accessing all the data is impractical in many real-world scenarios where the data arrives in a stream. In this paper, we investigate the problem of one-pass learning, in which a model is trained on sequentially arriving data without retraining on previous datapoints. Motivated by the increasing use of overparameterized models, we develop Orthogonal Recursive Fitting (ORFit), an algorithm for one-pass learning which seeks to perfectly fit every new datapoint while changing the parameters in a direction that causes the least change to the predictions on previous datapoints. By doing so, we bridge two seemingly distinct algorithms in adaptive filtering and machine learning, namely the recursive least-squares (RLS) algorithm and orthogonal gradient descent (OGD). Our algorithm uses the memory efficiently by exploiting the structure of the streaming data via an incremental principal component analysis (IPCA). Further, we show that, for overparameterized linear models, the parameter vector obtained by our algorithm is what stochastic gradient descent (SGD) would converge to in the standard multi-pass setting. Finally, we generalize the results to the nonlinear setting for highly overparameterized models, relevant for deep learning. Our experiments show the effectiveness of the proposed method compared to the baselines.
Sectored DRAM: An Energy-Efficient High-Throughput and Practical Fine-Grained DRAM Architecture

work in fine-grained DRAM, Sectored DRAM greatly reduces DRAM energy consumption, does not reduce DRAM throughput, and can be implemented with low hardware cost. We evaluate Sectored DRAM using 41 work-loads from widely-used benchmark suites. Compared to a system with coarse-grained DRAM, Sectored DRAM reduces the DRAM energy consumption of highly-memory-intensive workloads by up to (on average) 33% (20%) while improving their performance by up to (on average) 36% (17%). Sectored DRAM’s DRAM energy savings, combined with its system performance improvement, allows system-wide energy savings of up to 23%.
Fundamental Theory and R-linear Convergence of Stretch Energy Minimization for Equiareal parameterizations

In this paper, we first extend the finite distortion problem from the bounded domains in $\mathbb{R}^2$ to the closed genus-zero surfaces in $\mathbb{R}^3$ by the stereographic projection. Then we derive a theoretical foundation for spherical equiareal parameterizations between a simply connected closed surface $\mathcal{M}$ and a unit sphere $\mathbb{S}^2$ via minimizing the total area distortion energy on $\overline{\mathbb{C}}$. Provided we determine the minimizer of the total area distortion energy, the minimizer composed with the initial conformal map determines the equiareal map between the extended planes. Taking the inverse stereographic projection, we can derive the equiareal map between $\mathcal{M}$ and $\mathbb{S}^2$. The total area distortion energy can be rewritten into the sum of Dirichlet energies associated with the southern and northern hemispheres, respectively, and can be decreased by alternatingly solving the corresponding Laplacian equations. Based on this foundational theory, we develop a modified stretch energy minimization for the computation of the spherical equiareal parameterization between $\mathcal{M}$ and $\mathbb{S}^2$. In addition, under some mild conditions, we verify that our proposed algorithm has asymptotically R-linear convergence or forms a quasi-periodic solution. Numerical experiments on various benchmarks validate the assumptions for convergence always hold and indicate the efficiency, reliability and robustness of the developed modified stretch energy minimization.
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.
Cosmic ray interstellar propagation tool using Itô Calculus (criptic): software for simultaneous calculation of cosmic ray transport and observational signatures

We present criptic, the Cosmic Ray Interstellar Propagation Tool using Itô Calculus, a new open-source software package to simulate the propagation of cosmic rays through the interstellar medium and to calculate the resulting observable non-thermal emission. Criptic solves the Fokker-Planck equation describing transport of cosmic rays on scales larger than that on which their pitch angles become approximately isotropic, and couples this to a rich and accurate treatment of the microphysical processes by which cosmic rays in the energy range ∼MeV to ∼PeV lose energy and produce emission. Criptic is deliberately agnostic as to both the cosmic ray transport model and the state of the background plasma through which cosmic rays travel. It can solve problems where cosmic rays stream, diffuse, or perform arbitrary combinations of both, and the coefficients describing these transport processes can be arbitrary functions of the background plasma state, the properties of the cosmic rays themselves, and local integrals of the cosmic ray field itself (e.g. the local cosmic ray pressure or pressure gradient). The code is parallelised using a hybrid OpenMP-MPI paradigm, allowing rapid calculations exploiting multiple cores and nodes on modern supercomputers. Here we describe the numerical methods used in the code, our treatment of the microphysical processes, and the set of code tests and validations we have performed.