Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Analysis of Urban Heatwaves Using Tukey G-and-H Random Field Models

The statistical quantification of temperature processes for the analysis of urban heat island (UHI) effects and local heat-waves is an increasingly important application domain in smart city dynamic modelling. This leads to the increased importance of real-time heatwave risk management on a fine-grained spatial resolution. This study attempts to analyze and develop new methods for modelling the spatio-temporal behavior of ground temperatures. The developed models consider higher-order stochastic spatial properties such as skewness and kurtosis, which are key components for understanding and describing local temperature fluctuations and UHI's. The developed models are applied to the greater Tokyo metropolitan area for a detailed real-world data case study. The analysis also demonstrates how to statistically incorporate a variety of real data sets. This includes remotely sensed imagery and a variety of ground-based monitoring site data to build models linking city and urban covariates to air temperature. The air temperature models are then used to capture high-resolution spatial emulator outputs for ground surface temperature modelling. The main class of processes studied includes the Tukey g-and-h processes for capturing spatial and temporal aspects of heat processes in urban environments.
σ
 and 
ρ
 coupling constants for the charmed and beauty mesons

We investigate the $\sigma$ and $\rho$ coupling constants for the $DD$ and $D^*D^*$ interactions, based on correlated $2\pi$ exchange in the $DD$ and $D^*D^*$ interactions. Starting from the $D\bar{D}\to \pi\pi$ and $D^*\bar{D}^*\to \pi\pi$ amplitudes derived in the pseudophysical region ($4m_\pi^2\le t \le 52m_\pi^2$) with the $S$- and $P$-wave $2\pi$ correlations considered, we obtain the spectral function for the $DD\to DD$ and $D^*D^*\to D^*D^*$ amplitude with correlated $S$- and $P$-wave $2\pi$ exchange. Using the pole approximation, we estimate the $DD\sigma$, $DD\rho$, $D^*D^*\sigma$, and $D^*D^*\rho$ coupling constants. The results are compared with those of other models. We also present the results of the $BB\sigma$, $BB\rho$, $B^*B^*\sigma$, and $B^*B^*\rho$ coupling constants. We observe that it is unlikely that the $\sigma$ and $\rho$ coupling constants for the $B$ and $B^*$ mesons are the same as those for the $D$ and $D^*$ mesons. On the contrary, they are quite larger than those for the charmed mesons.
The properties of inside-out assembled galaxies at z < 0.1

Abstract In this work, we study the properties of galaxies that are showing the inside-out assembly (which we call inside-out assembled galaxies; IOAGs), with the main aim to understand better their properties and morphological transformation. We analysed a sample of galaxies from the Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8), with stellar masses in the range log M* = 10.73 – 11.03 M⊙ at at z < 0.1, and analyze their location in the stellar mass-SFR and the color-stellar mass diagram. We found that IOAGs have different spectroscopic properties, most of them being classified either as AGN or composite. We found that the majority of our sources are located below the main sequence of star formation in the SFR-stellar mass diagram, and in the green valley or red sequence in the color-stellar mass diagram. We argue that IOAGs seem to correspond to the transition area where the galaxies are moving from star-forming to quiescent, and from the blue cloud to the red sequence and/or to recently quenched galaxies.
How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context

Abstract We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect that uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modeling of text and discourse.
RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s

Evolving graphs in the real world are large-scale and constantly changing, as hundreds of thousands of updates may come every second. Monotonic algorithms such as Reachability and Shortest Path are widely used in real-time analytics to gain both static and temporal insights and can be accelerated by incremental computing. Existing streaming systems adopt the incremental computing model and achieve either low latency or high throughput, but not both. However, both high throughput and low latency are required in real scenarios such as financial fraud detection. This paper presents RisGraph, a real-time streaming system that provides low-latency analysis for each update with high throughput. RisGraph addresses the challenge with localized data access and inter-update parallelism. We propose a data structure named Indexed Adjacency Lists and use sparse arrays and Hybrid Parallel Mode to enable localized data access. To achieve inter-update parallelism, we propose a domain-specific concurrency control mechanism based on the classification of safe and unsafe updates. Experiments show that RisGraph can ingest millions of updates per second for graphs with several hundred million vertices and billions of edges, and the P999 processing time latency is within 20 milliseconds. RisGraph achieves orders-of-magnitude improvement on throughput when analyses are executed for each update without batching and performs better than existing systems with batches of up to 20 million updates.
Radiation forces constrain the FRB mechanism

We provide constraints on fast radio burst (FRB) models by careful considerations of radiation forces associated with these powerful transients. We find that the induced Compton scatterings of the coherent radiation by electrons/positrons accelerate particles to very large Lorentz factors (LFs) in and around the source of this radiation. This severely restricts those models for FRBs that invoke relativistic shocks and maser-type instabilities at distances less than about 1013 cm of the neutron star. Radiation travelling upstream, in these models, forces particles to move away from the shock with an LF larger than the LF of the shock front. This suspends the photon generation process after it has been operating for less than ∼0.1 ms (observer frame duration). We show that masers operating in shocks at distances larger than 1013 cm cannot simultaneously account for the burst duration of 1 ms or more and the observed ∼GHz frequencies of FRBs without requiring an excessive energy budget (>1046 erg); the energy is not calculated by imposing any efficiency consideration, or other details, for the maser mechanism, but is entirely the result of ensuring that particle acceleration by induced Compton forces upstream of the shock front does not choke off the maser process. For the source to operate more or less continuously for a few ms, it should be embedded in a strong magnetic field – cyclotron frequency ≫ wave frequency – so that radiation forces do not disperse the plasma and shut off the engine.
Safe Zero-Shot Model-Based Learning and Control: A Wasserstein Distributionally Robust Approach

This paper explores distributionally robust zero-shot model-based learning and control using Wasserstein ambiguity sets. Conventional model-based reinforcement learning algorithms struggle to guarantee feasibility throughout the online learning process. We address this open challenge with the following approach. Using a stochastic model-predictive control (MPC) strategy, we augment safety constraints with affine random variables corresponding to the instantaneous empirical distributions of modeling error. We obtain these distributions by evaluating model residuals in real time throughout the online learning process. By optimizing over the worst case modeling error distribution defined within a Wasserstein ambiguity set centered about our empirical distributions, we can approach the nominal constraint boundary in a provably safe way. We validate the performance of our approach using a case study of lithium-ion battery fast charging, a relevant and safety-critical energy systems control application. Our results demonstrate marked improvements in safety compared to a basic learning model-predictive controller, with constraints satisfied at every instance during online learning and control.
Learning to cooperate: Emergent communication in multi-agent navigation

Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.
Beyond pharmacies: new perspectives in ORS marketing.

Social marketing uses private sector marketing concepts and technologies to meet public sector social goals. The approach has been highly successful in family planning interventions preventing AIDS teen drug use and reducing heart disease. This document discusses the nature of social marketing and how it may be applied as well as steps made by PRITECH to help foster the use of social marketing in developing countries with special emphasis upon diarrheal disease control. Results are presented from a workshop on innovative marketing strategies of oral rehydration solution (ORS); PRITECHs basic approach is outlined; and implementation suggestions are offered from Merck Woodward and Searle. The paper further explains to private sector marketers how they may enter social marketing projects while demonstrating the benefits which could accrue in terms of new profit opportunities improved corporate image and exposure to new technology. It also explains to public sector administrators how social marketing may help achieve goals which are important to society while building lasting government-business relationships. All involved parties stand to benefit from the social marketing approach. Sections discuss expanding ORS marketing ORS market size rural marketing reaching rural doctors in India targeting rural retailers in Pakistan community health promotions in Pakistan and using mass media in the Philippines.
Kernel autocovariance operators of stationary processes: Estimation and convergence

We consider autocovariance operators of a stationary stochastic process on a Polish space that is embedded into a reproducing kernel Hilbert space. We investigate how empirical estimates of these operators converge along realizations of the process under various conditions. In particular, we examine ergodic and strongly mixing processes and prove several asymptotic results as well as finite sample error bounds with a detailed analysis for the Gaussian kernel. We provide applications of our theory in terms of consistency results for kernel PCA with dependent data and the conditional mean embedding of transition probabilities. Finally, we use our approach to examine the nonparametric estimation of Markov transition operators and highlight how our theory can give a consistency analysis for a large family of spectral analysis methods including kernel-based dynamic mode decomposition.
Repetition-Based NOMA Transmission and Its Outage Probability Analysis

In this paper, we discuss a non-orthogonal multiple access (NOMA) scheme to exploit a high diversity gain using repetition, namely repetition-based NOMA. Unlike conventional power-domain NOMA, all the users can have the same transmit power, but different number of repetitions. Thanks to a high diversity gain, a low outage probability can be achieved without instantaneous channel state information (CSI) feedback for power allocation. A closed-form expression for an upper-bound on the outage probability is derived so that the values of key parameters can be decided to maintain the outage probability below a target value. We also consider the average error probability for finite-length codes. Simulation results are compared with the derived bounds and it is shown that the bounds are reasonably tight and can be used to decide key parameters (e.g., code rates) to guarantee target error probabilities.
On the expected number of components in a finite admixture model.

In Bayesian non-parametric density estimation a question of interest is how the number of components in the model grows with the number of observations. We state the growth rate of the number of components for a finite admixture model both in expectation and in distribution. The tools we use in our analysis combine the concept of a Choquet measure with classic results in stochastic geometry on the number of extrema of random polytopes. We show that if our admixture weights are probability vectors from a unit $(J-1)$-simplex then the number of admixture components grows as $(\log n)^{J-1}$; in the standard mixture case we recover the $\log n$ rate. We also state a central limit theorem for the number of mixture components. In addition, we state the convergence of the sequence of the empirical measures generated by our model to the Choquet measure. Lastly, we relate our model to a classical non-parametric density estimator based on a Polya tree.
A robust optimization and an area decomposition approach to large-scale network planning under uncertainty

This paper describes an area decomposition and a robust optimization strategy for the planning of large-scale transmission networks considering uncertainty in variable renewable energy (VRE) and hydropower generation. The strategy is illustrated with a study of Brazil's power grid expansion from 2027 to 2036. The country's network has over 10 thousand buses and 14 thousand circuits and covers an area equivalent to the USA. A clustering scheme based on bus marginal costs is used to decompose the grid into seven regions. Each regional planning is then solved by a heuristic and Benders decomposition methods to produce a robust least-cost plan for 3,000 dispatch scenarios.
Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences

Light-weight camera localization in existing maps is essential for vision-based navigation. Currently, visual and visual-inertial odometry (VO&VIO) techniques are well-developed for state estimation but with inevitable accumulated drifts and pose jumps upon loop closure. To overcome these problems, we propose an efficient monocular camera localization method in prior LiDAR maps using direct 2D-3D line correspondences. To handle the appearance differences and modality gaps between LiDAR point clouds and images, geometric 3D lines are extracted offline from LiDAR maps while robust 2D lines are extracted online from video sequences. With the pose prediction from VIO, we can efficiently obtain coarse 2D-3D line correspondences. Then the camera poses and 2D-3D correspondences are iteratively optimized by minimizing the projection error of correspondences and rejecting outliers. Experimental results on the EurocMav dataset and our collected dataset demonstrate that the proposed method can efficiently estimate camera poses without accumulated drifts or pose jumps in structured environments.
Robust Single Rotation Averaging

We propose a novel method for single rotation averaging using the Weiszfeld algorithm. Our contribution is threefold: First, we propose a robust initialization based on the elementwise median of the input rotation matrices. Our initial solution is more accurate and robust than the commonly used chordal $L_2$-mean. Second, we propose an outlier rejection scheme that can be incorporated in the Weiszfeld algorithm to improve the robustness of $L_1$ rotation averaging. Third, we propose a method for approximating the chordal $L_1$-mean using the Weiszfeld algorithm. An extensive evaluation shows that both our method and the state of the art perform equally well with the proposed outlier rejection scheme, but ours is $2-4$ times faster.
k-Median clustering under discrete Fréchet and Hausdorff distances

We give the first near-linear time $(1+\eps)$-approximation algorithm for $k$-median clustering of polygonal trajectories under the discrete Frechet distance, and the first polynomial time $(1+\eps)$-approximation algorithm for $k$-median clustering of finite point sets under the Hausdorff distance, provided the cluster centers, ambient dimension, and $k$ are bounded by a constant. The main technique is a general framework for solving clustering problems where the cluster centers are restricted to come from a \emph{simpler} metric space. We precisely characterize conditions on the simpler metric space of the cluster centers that allow faster $(1+\eps)$-approximations for the $k$-median problem. We also show that the $k$-median problem under Hausdorff distance is \textsc{NP-Hard}.
An ephemerally self-exciting point process

Abstract Across a wide variety of applications, the self-exciting Hawkes process has been used to model phenomena in which the history of events influences future occurrences. However, there may be many situations in which the past events only influence the future as long as they remain active. For example, a person spreads a contagious disease only as long as they are contagious. In this paper, we define a novel generalization of the Hawkes process that we call the ephemerally self-exciting process. In this new stochastic process, the excitement from one arrival lasts for a randomly drawn activity duration, hence the ephemerality. Our study includes exploration of the process itself as well as connections to well-known stochastic models such as branching processes, random walks, epidemics, preferential attachment, and Bayesian mixture models. Furthermore, we prove a batch scaling construction of general, marked Hawkes processes from a general ephemerally self-exciting model, and this novel limit theorem both provides insight into the Hawkes process and motivates the model contained herein as an attractive self-exciting process in its own right.
Association of Obesity with the Lower Limbs Osteoarthritis in a Community of Women from El Jadida Province in Morocco

Background: In the last years, obesity became of interest because of its association with osteoarthritis (OA) which is increasing with the increase of both life expectancy and the prevalence of obesity. Aims: The objective was to assess the association of obesity with the susceptibility of the lower limbs OA (LLOA) occurrence in women. Subjects and Methods: The symptomatic susceptibility of the onset of LLOA was evaluated on a sample of women from El Jadida (Morocco) using the Moroccan version of the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) for the lower limbs. Sociodemographic and anthropometric data were collected too using a questionnaire. Results: The surveyed female population was 45±13 years old mostly obese (77%) with an average BMI: 29.18 ± 5.29 and a WHR: 0.93 ± 0.9. The susceptibility of the LLOA in both forms (knee and coccyx osteoarthritis) was highly prevalent in women older than 50 years (22%). The three dimensions of WOMAC (pain, stiffness and functional impairment) are felt much more among postmenopausal 44.37±26.67 (with a rate of 49%) than procreating women 29.58±22.44 (with a rate of 51%) among women with morbid obesity (69.40±8.27) than normal weight (31.67±4.40) and in women having android (86.13%) than those with a gynoid obesity morphotype (5.10%). Conclusions: The results report an association of obesity with the risk of osteoarthritis occurrence varying with age and OA location hence the importance of preventing osteoarthritis issues through the management of obesity. Keywords: Knee osteoarthritis, Coccyx osteoarthritis, WOMAC, Obesity, BMI, WHR.
Neuroaesthetics of Art Vision: An Experimental Approach to the Sense of Beauty

Objective: NEVArt research aims to study the correlation between a set of neurophysiological/emotional reactions and the level of aesthetic appreciation of around 500 experimental subjects, during the observation of 18 different paintings from the XVI-XVIII century, in a real museum context. Methods: Several bio-signals have been recorded to evaluate the participants’ reactions during the observation of paintings. Among them: (a) neurovegetative, motor and emotional biosignals were recorded using wearable tools for EEG (electroencephalogram), ECG (electrocardiogram) and EDA (electrodermal activity); (b) gaze pattern during the observation of art works, while (c) data of the participants (age, gender, education, familiarity with art, etc.) and their explicit judgments about paintings have been obtained. Participants were invited to respond during the observation of paintings, reporting the degree of pleasantness, perceived movement and familiarity with the painted subject. Results: Each recorded bio-signal will be correlated with the explicit evaluations obtained by participants during the museum experience. These results may contribute to enlarge the theoretical framework on the physiological, cognitive and emotional responses of people when viewing pictorial artworks. Conclusion: The NEVArt research, both in term of technical skills and upgrade in neuroscientific awareness, can be the basis to proceed with a set of further research topics in the near future. Ground-breaking and statistically significant observations can be derived from the present research, mainly at the biological, medical and didactical point of view, by paving the way for many other multidisciplinary research developments on art exhibit, architecture, etc.
GREEN CONGLOMERATE AND INVESTIGATION USING AQUEOUS EXTRACTION FROM DATURA METAL LEAVES FOR CUPROUS OXIDE NANOPARTICLES

-The Cuprous Oxide (Cu2O) nanoparticles were synthesized by green synthesis method using copper as precursor and Ammonium chloride with Lactobacillus bulgaricus as reducing agent in presence of Datura metal leaf extract as biosurfactant in an aqueous medium. The bio synthesized nanoparticles were describe the distinctive natured by X-ray diffraction (XRD) and the pattern unveil face centered cubic structure. The size of Cu2O nanoparticles were observed by scanning electron microscopy (SEM). The results indicate that the Cu2O nanoparticles have high purity and the average particles size is 32 nm. The elemental anatomy of matals has been identified by Energy Dispersive X-Ray Analysis (EDX). Also conducting Inductive Couple Plasma (ICP) for its propensity to identify and quantify all elements with the exception of Argon