Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW LUMINOSITY RELATION FOR GAMMA-RAY BURSTS AND ITS IMPLICATION

Gamma-ray bursts (GRBs) are the most luminous astrophysical events observed so far. They are conventionally classified into long and short ones depending on their time duration, T90. Because of the advantage that their high redshifts offer, many efforts have been made to apply GRBs to cosmology. The key to this is to find correlations between some measurable properties of GRBs and the energy or the luminosity of GRBs. These correlations are usually referred to as luminosity relations and are helpful in understanding the GRBs themselves. In this paper, we explored such correlations in the X-ray emission of GRBs. The X-ray emission of GRBs observed by Swift has the exponential functional form in the prompt phase and relaxes to a power-law decay at time Tp. We have assumed a linear relation between log LX,p (with LX,p being the X-ray luminosity at Tp) and log [Tp/(1 + z)], but there is some evidence for curvature in the data and the true relationship between LX,p and Tp/(1 + z) may be a broken power law. The limited GRB sample used in our analysis is still not sufficient for us to conclude whether the break is real or just an illusion caused by outliers. We considered both cases in our analysis and discussed the implications of the luminosity relation, especially on the time duration of GRBs and their classification.
A pilot observational study of horsemanship skills training for veterans with posttraumatic stress disorder.

OBJECTIVES Pilot assessment of an equine-assisted services intervention for Veterans with posttraumatic stress disorder. DESIGN Prospective cohort. SETTING A large Veterans Administration healthcare system. INTERVENTION Two sessions of instruction in horsemanship skills and two sessions of trail rides. MAIN OUTCOME MEASURES Safety and feasibility of recruitment, retention, providing the intervention, and measuring outcomes, as well as acceptability of the intervention to the population studied were the main outcome measures. RESULTS There were no injuries among the 18 participants, 6 staff, or 11 equines over the 3 months of the study. Eleven of 12 (92 %) planned intervention sessions were provided, with one being canceled due to weather. Out of approximately 1800 potential subjects, 21 were enrolled within three months and three dropped out before the first session. The mean number of sessions attended was 3 % and 52 % of participants completed all sessions. The mean PACES score for all sessions together was 109 (SD = 12), with a range of 50-126. Pre- to one-month post-intervention exploratory results indicated increased psychological flexibility (p = 0.008) and positive affect (p = 0.008) as measured by the Acceptance and Action Questionnaire II and the Positive and Negative Affect Scale, respectively as well as decreased PTSD (p = 0.001) and depressive symptoms (p = 0.017) as measured by the PTSD Checklist for DSM 5 and the Beck Depression Inventory, respectively. CONCLUSIONS Results suggest the intervention can be conducted with minimal risk. Further, it was feasible to recruit participants as well as provide the intervention and measure exploratory outcomes. Session attendance and PACES scores indicate acceptability to the Veteran population. The exploratory results will provide sample size estimates for future randomized controlled studies of this intervention Future studies should plan for missed sessions due to weather and consider compensating subjects to enhance retention. Finally, this work provides preliminary evidence that this intervention, without a mental health treatment component, might benefit the mental health of Veterans with PTSD.
Hydrodynamics of rapidly rotating superfluid neutron stars with mutual friction

We study time evolutions of superfluid neutron stars, focussing on the nature of the oscillation spectrum, the effect of mutual friction force on the oscillations and the hydrodynamical spin-up phase of pulsar glitches. We linearise the dynamical equations of a Newtonian two-fluid model for rapidly rotating backgrounds. In the axisymmetric equilibrium configurations, the two fluid components corotate and are in �-equilibrium. We use analytical equations of state that generate stratified and non-stratified stellar models, which enable us to study the coupling between the dynamical degrees of freedom of the system. By means of time evolutions of the linearised dynamical equations, we determine the spectrum of axisymmetric and non-axisymmetric oscillation modes, accounting for the contribution of the gravitational potential perturbations, i.e. without adopting the Cowling approximation. We study the mutual friction damping of the superfluid oscillations and consider the effects of the non-dissipative part of the mutual friction force on the mode frequencies. We also provide technical details and relevant tests for the hydrodynamical model of pulsar glitches discussed by Sidery, Passamonti & Andersson (2010). In particular, we describe the method used to generate the initial data that mimic the pre-glitch state, and derive the equations that are used to extract the gravitational-wave signal.
Botnet Attack Detection in IoT Using Machine Learning

There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A complete machine learning pipeline was proposed, including exploratory data analysis, which provides detailed insights into the data, followed by preprocessing. During this process, the data passes through six fundamental steps. A decision tree, an XgBoost model, and a logistic regression model are proposed, trained, tested, and evaluated on the dataset. In addition to model accuracy, F1-score, recall, and precision are also considered. Based on all experiments, it is concluded that the decision tree outperformed with 94% test accuracy.
Sub-volt broadband hybrid plasmonic-vanadium dioxide switches

The insulator-metal phase transition of a correlated-electron material, vanadium dioxide, is used to demonstrate electrically controlled, compact, broadband, and low voltage plasmonic switches. The devices are micron-scale in length and operate near a wavelength of 1550 nm. The switching bandwidths exceed 100 nm and 400 mV is sufficient to attain extinction ratios in excess of 20 dB. The results illustrate the promise of using phase transition materials for efficient and ultra-compact plasmonic switches and modulators.
Automated machine learning for analysis and prediction of vehicle crashes

This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support-vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places.
Impact of mergers on LISA parameter estimation for nonspinning black hole binaries

We investigate the precision with which the parameters describing the characteristics and location of nonspinning black hole binaries can be measured with the Laser Interferometer Space Antenna (LISA). By using complete waveforms including the inspiral, merger, and ringdown portions of the signals, we find that LISA will have far greater precision than previous estimates for nonspinning mergers that ignored the merger and ringdown. Our analysis covers nonspinning waveforms with moderate mass ratios, q{>=}1/10, and total masses 10{sup 5} < or approx. M/M{sub {center_dot}}< or approx. 10{sup 7}. We compare the parameter uncertainties using the Fisher-matrix formalism, and establish the significance of mass asymmetry and higher-order content to the predicted parameter uncertainties resulting from inclusion of the merger. In real-time observations, the later parts of the signal lead to significant improvements in sky-position precision in the last hours and even the final minutes of observation. For comparable-mass systems with total mass M/M{sub {center_dot}{approx}1}0{sup 6}, we find that the increased precision resulting from including the merger is comparable to the increase in signal-to-noise ratio. For the most precise systems under investigation, half can be localized to within O(10 arcmin), and 10% can be localized to within O(1 arcmin).
Development of a Simulation Environment to Track Key Metrics to Support Trajectory Energy Management of Electric Aircraft

Growing concerns worldwide about anthropogenic climate change are leading to signiﬁcant research in ways to reduce greenhouse gas emissions. Technologies are investigated to improve the overall energy eﬃciency of ﬂying vehicles, and among these, new powertrain technologies less reliant on fossil fuels are especially promising. Concurrently, the expected growth of new market segments, such as urban air mobility and regional air mobility where vehicles are envisioned to operate over densely populated areas, will lead to increased scrutinity regarding the vehicle emissions and the vehicle safety. In this context, signiﬁcant research has been carried out in the ﬁeld of electric and hybrid-electric aircraft propulsion. Driven by signiﬁcant strides made by the automotive industry regarding electric battery technology, the aspirational goal of useful electric ﬂight is now within reach. Signiﬁcant challenges nonetheless remain regarding the certiﬁcation of these new vehicles to ensure an equivalent level of safety. Indeed, the behavior of electric powertrains is more complex than that of traditional powertrains and features additional thermal and ageing constraints that need to be contended with. Moreover, the ability of many of these vehicles to ﬂy both on their wing or on their rotors brings another level of sophistication that will increase the workload of ﬂight crews. Combined, these might adversely impact the safety of ﬂight. This research aims to elucidate some of these challenges by providing insights into the behavior and idiosyncracies of new electriﬁed vehicles and by identifying visual cues that should be provided to ﬂight crews to support safe decision-making in the cockpit. Besides these visual cues, we explore functionalities that a Trajectory Energy Management system could feature to improve ﬂight safety by providing insights into the management of stored usable energy and by monitoring critical parameters of electriﬁed powertrains. This paper includes two use-cases in which the functionality of the Trajectory Energy Management system is explored for pre-ﬂight planning and
A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Speciﬁcation

In financial literature, Value-at-Risk (VaR) and Expected Shortfall (ES) modelling is focused on producing 1-step ahead conditional variance forecasts. The present paper provides a methodological contribution to the multi-step VaR and ES forecasting through a new adaptation of the Monte Carlo simulation approach for forecasting multi-period volatility to a fractionally integrated GARCH framework for leptokurtic and asymmetrically distributed portfolio returns. Accounting for long memory within the conditional variance process with skewed Student- t (skT) conditionally distributed innovations, accurate 95% and 99% VaR and ES forecasts are calculated for multi-period time horizons. The results show that the FIGARCH-skT model has a superior multi-period VaR and ES forecasting performance.
Magnetic field evolution of accreting neutron stars

The flow of a matter, accreting onto a magnetized neutron star, is accompanied by an electric current. The closing of the electric current occurs in the crust of a neutron stars in the polar region across the magnetic field. But the conductivity of the crust along the magnetic field greatly exceeds the conductivity across the field, so the current penetrates deep into the crust down up to the super conducting core. The magnetic field, generated by the accretion current, increases greatly with the depth of penetration due to the Hall conductivity of the crust is also much larger than the transverse conductivity. As a result, the current begins to flow mainly in the toroidal direction, creating a strong longitudinal magnetic field, far exceeding an initial dipole field. This field exists only in the narrow polar tube of $r$ width, narrowing with the depth, i.e. with increasing of the crust density $\rho$, $r\propto \rho^{-1/4}$. Accordingly, the magnetic field $B$ in the tube increases with the depth, $B\propto \rho^{1/2}$, and reaches the value of about $10^{17}$ Gauss in the core. It destroys super conducting vortices in the core of a star in the narrow region of the size of the order of ten centimeters. Because of generated density gradient of vortices they constantly flow into this dead zone and the number of vortices decreases, the magnetic field of a star decreases as well. The attenuation of the magnetic field is exponential, $B=B_0(1+t/\tau)^{-1}$. The characteristic time of decreasing of the magnetic field $\tau$ is equal to $\tau\simeq 10^3$ years. Thus, the magnetic field of accreted neutron stars decreases to values of $10^8 - 10^9$ Gauss during $10^7-10^6$ years.
METHODS AND MEANS OF INTELLIGENT ANALYSIS OF TEXT DOCUMENTS The paper reviews the methods of analysis and processing of electronic documents. Methods of analysis of text documents to solve the problem of determining the thematic affinity of texts

The paper reviews the methods of analysis and processing of electronic documents. Methods of analysis of text documents to solve the problem of determining the thematic affinity of texts are analyzed. An overview of existing approaches to solving the classification problem is performed. The main approaches used in the task of text classification are described; the stages of the classification process are determined and the most common methods of classifying text documents are considered. The main approaches to text pre-processing, such as: lower case, root correction, stemming, lemmatization, stop word removal, normalization, are considered. Advantages and disadvantages of each approach are considered. The procedure for reducing the dimension of a set of features with a division into sub-processes: selecting features and highlighting features is considered.
Re-examining the Eﬀects of Switching Costs

Consumers often incur costs when switching from one product to another. Recently there has been renewed debate within the literature about whether these switching costs lead to higher prices. We build a theoretical model of dynamic competition and solve it analytically for a wide range of switching costs. We provide a simple condition which determines whether switching costs raise or lower long-run prices. We also show that switching costs are more likely to increase prices in the short-run. Finally switching costs redistribute surplus across time, and as such are shown to sometimes increase consumer welfare. at the conference (Valencia) and the EARIE conference (Rome). I to Nijs, Fabra, Klemperer, Parakhonyak, Thanassoulis, and Chris Wilson for their helpful comments, as at the International Industrial Organization Conference (Washington), Royal Economic Society (Royal Holloway), and the Second Workshop on Search and Switching Costs. Financial assistance from the ESRC (UK) and British gratefully acknowledged. Any remaining errors are my own.
Weisfeiler and Leman Return with Graph Transformations

We propose novel graph transformations that allow standard message passing to achieve state-of-the-art expressiveness and predictive performance. Message passing graph neural networks are known to have limited expressiveness in distinguishing graphs. To mitigate this, one can either change message passing or modify the graphs. Changing message passing is powerful but requires signiﬁcant changes to existing implementations and cannot easily be combined with other approaches. Modifying the graph requires no changes to the learning algorithm and works directly with oﬀ-the-shelf implementations. In this paper, we propose novel graph transformations and compare them to the state-of-the-art. We prove that they are at least as expressive as corresponding message passing algorithms when combined with the Weisfeiler-Leman test or a suﬃciently powerful graph neural network. Furthermore, we empirically demonstrate that these transformations lead to competitive results on molecular graph datasets.
Beyond the Standard Model with noncommutative geometry, strolling towards quantum gravity

Noncommutative geometry in its many incarnations appears at the crossroad of many researches in theoretical and mathematical physics: from models of quantum spacetime(with or without breaking of Lorentz symmetry) to loop gravity and string theory, from early considerations on UV-divergenciesin quantum field theory to recent models of gauge theories on noncommutatives pacetime, from Connes description of the standard model of elementary particles to recent Pati-Salam like extensions. We list several of these applications, emphasizing also the original point of view brought by noncommutative geometry on the nature of time. This text serves as an introduction to the volume of proceedings of the parallel session “Noncommutative geometry and quantum gravity”, as a part of the conference “Conceptual and technical challenges in quantum gravity” organized at the University of Rome La Sapienza sin September 2014.
Factors of Muscle Quality and Determinants of Muscle Strength: A Systematic Literature Review

Muscle quality defined as the ratio of muscle strength to muscle mass disregards underlying factors which influence muscle strength. The aim of this review was to investigate the relationship of phase angle (PhA), echo intensity (EI), muscular adipose tissue (MAT), muscle fiber type, fascicle pennation angle (θf), fascicle length (lf), muscle oxidative capacity, insulin sensitivity (IS), neuromuscular activation, and motor unit to muscle strength. PubMed search was performed in 2021. The inclusion criteria were: (i) original research, (ii) human participants, (iii) adults (≥18 years). Exclusion criteria were: (i) no full-text, (ii) non-English or -German language, (iii) pathologies. Forty-one studies were identified. Nine studies found a weak–moderate negative (range r: [−0.26]–[−0.656], p < 0.05) correlation between muscle strength and EI. Four studies found a weak–moderate positive correlation (range r: 0.177–0.696, p < 0.05) between muscle strength and PhA. Two studies found a moderate-strong negative correlation (range r: [−0.446]–[−0.87], p < 0.05) between muscle strength and MAT. Two studies found a weak-strong positive correlation (range r: 0.28–0.907, p < 0.05) between θf and muscle strength. Muscle oxidative capacity was found to be a predictor of muscle strength. This review highlights that the current definition of muscle quality should be expanded upon as to encompass all possible factors of muscle quality.
A journey into localization, integrability and thermalization

We present here the results obtained during my PhD work. We report the study of the many body localization transition in a spin chain and the breaking of ergodicity measured in terms of return probability in a state evolution. Moreover, we introduce the Richardson model, an exactly solvable model, that turns out to be suitable for investigating the many-body localized phase. Then we turn to the analysis of the quench problem in an ensemble of random matrices. We analyze the thermalization properties and the validity of the Eigenstate Thermalization Hypothesis for the typical case, where the quench parameter explicitly breaks a Z_2 symmetry.
Maximizing the information gain of a single ion microscope using bayes experimental design

We demonstrate nanoscopic transmission microscopy, using a deterministic single particle source, and compare the resulting images in terms of signal-to-noise ratio with those of conventional Poissonian sources. Our source is realized by deterministic extraction of laser-cooled calcium ions from a Paul trap. Gating by the extraction event allows for the suppression of detector dark counts by six orders of magnitude. Using the Bayes experimental design method, the deterministic characteristics of this source are harnessed to maximize information gain, when imaging structures with a parametrizable transmission function. We demonstrate such optimized imaging by determining parameter values of one and two dimensional transmissive structures.
Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts

Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle. We introduce MaRCo, a detoxification algorithm that combines controllable generation and text rewriting methods using a Product of Experts with autoencoder language models (LMs). MaRCo uses likelihoods under a non-toxic LM (expert) and a toxic LM (anti-expert) to find candidate words to mask and potentially replace. We evaluate our method on several subtle toxicity and microaggressions datasets, and show that it not only outperforms baselines on automatic metrics, but MaRCo’s rewrites are preferred 2.1 times more in human evaluation. Its applicability to instances of subtle toxicity is especially promising, demonstrating a path forward for addressing increasingly elusive online hate.