Miracle-dz/UniFAR-FTUs huggingface dataset:
Average‐tempered stable subordinators with applications

In this paper the running average of a subordinator with a tempered stable distribution is considered. We investigate a family of previously unexplored infinite-activity subordinators induced by the probability distribution of the running average process and determine their jump intensity measures. Special cases including gamma processes and inverse Gaussian processes are discussed. Then we derive easily implementable formulas for the distribution functions, cumulants, and moments, as well as provide explicit estimates for their asymptotic behaviors. Numerical experiments are conducted for illustrating the applicability and efficiency of the proposed formulas. Two important extensions of the running average process and its equi-distributed subordinator are examined with concrete applications to structural degradation modeling and financial derivatives pricing, where their advantages relative to several existing models are highlighted together with the mention of Euler discretization and compound Poisson approximation techniques.
On the spectrum of some Bloch–Torrey vector
operators

We consider the Bloch-Torrey operator in $L^2(I,{\mathbb R}^3)$ where $I\subseteq{\mathbb R}$. In contrast with the $L^2(I,{\mathbb R}^2)$ (as well as the $L^2({\mathbb R}^k,{\mathbb R}^2)$) case considered in previous works. We obtain that ${\mathbb R}_+$ is in the continuous spectrum for $I={\mathbb R}$ as well as discrete spectrum outside the real line. For a finite interval we find the left margin of the spectrum. In addition, we prove that the Bloch-Torrey operator must have an essential spectrum for a rather general setup in ${\mathbb R}^k$, and find an effective description for its domain.
Improving the background estimation technique in the GstLAL inspiral pipeline with the time-reversed template bank

Background estimation is important for determining the statistical significance of a gravitational-wave event. Currently, the background model is constructed numerically from the strain data using estimation techniques that insulate the strain data from any potential signals. However, as the observation of gravitational-wave signals become frequent, the effectiveness of such insulation will decrease. Contamination occurs when signals leak into the background model. In this work, we demonstrate an improved background estimation technique for the searches of gravitational waves (GWs) from binary neutron star coalescences by time-reversing the modeled GW waveforms. We found that the new method can robustly avoid signal contamination at a signal rate of about one per 20 seconds and retain a clean background model in the presence of signals.
Edge effect removal in Fourier ptychographic microscopy via perfect Fourier transformation (PFT)

Edge effect may degrade the imaging precision and is caused by the aperiodic image extension of fast Fourier transform (FFT). In this letter, a perfect Fourier transform algorithm termed PFT was reported to remove the artifacts with comparable efficiency to FFT. Although we demonstrated the performance of PFT in Fourier ptychographic microscopy (FPM) only, it can be expanded in any occasion where the conventional FFT is used.
Rare Event Algorithm Study of Extreme Warm Summers and Heatwaves Over Europe

The analysis of extremes in climate models is hindered by the lack of statistics due to the computational costs required to run simulations long enough to sample rare events. We demonstrate how rare event algorithms can improve the statistics of extreme events in state‐of‐the‐art climate models. We study extreme warm summers and heatwaves over France and Scandinavia with CESM1.2. 2 in present‐day climate. The algorithm concentrates the simulations on events of importance, and shifts the probability distributions of regional temperatures such that warm summers become common. We estimate return times of extremes orders of magnitude larger than what feasible with direct sampling, and we compute statistically significant composite maps of dynamical quantities conditional on the occurence of the extremes. We show that extreme warm summers are associated to wavenumber 3 hemispheric teleconnection patterns, and that the most extreme summers are related to the succession of rare subseasonal heatwaves.
A Theory for the Maximum Magnitude versus Rate of Decline Relation of Classical Novae

We propose a theory for the maximum magnitude versus rate of decline (MMRD) relation of novae using free–free emission model light curves built on the optically thick wind theory. We calculated for various sets of , where is the peak absolute V magnitude, t3 is the 3 mag decay time from the peak, and is the mass accretion rate onto the white dwarf (WD) of mass MWD. The model light curves are uniquely characterized by , where Menv is the hydrogen-rich envelope mass and Msc is the scaling mass at which the wind has a certain wind mass-loss rate. For a given ignition mass Mig, we can specify the first point on the model light curve and calculate the corresponding peak brightness and t3 time from this first point. Our points cover well the distribution of existing novae. The lower the mass accretion rate, the brighter the peak. The maximum brightness is limited to by the lowest mass accretion rate of yr−1. A significant part of the observational MMRD trend corresponds to the yr−1 line with different WD masses. A scatter from the trend line indicates a variation in their mass accretion rates. Thus, the global trend of an MMRD relation does exist, but its scatter is too large for it to be a precision distance indicator of individual novae.
Diffusive search for a stochastically-gated target with resetting

In this paper, we analyze the mean first passage time (MFPT) for a single Brownian particle to find a stochastically-gated target under the additional condition that the position of the particle is reset to its initial position x 0 at a rate r. The gate switches between an open (absorbing) and closed (reflecting) state according to a two-state Markov chain and can only be detected by the searcher in the open state. One possible example of such a target is a protein switching between different conformational states. As expected, the MFPT with or without resetting is an increasing function of the fraction of time ρ 0 that the gate is closed or reflecting. However, the interplay between stochastic resetting and stochastic gating has non-trivial effects with regards the optimization of the search process under resetting. First, by considering the diffusive search for a gated target at one end of an interval, we show that the ratio Δ(r) = T(r)/T(0), where T(r) is the MFPT at a resetting rate r, exhibits a non-monotonic dependence on ρ 0 even though T(r) and T(0) increase monotonically with ρ 0. In particular, the value of Δ(r) at the optimal resetting rate (when it exists) decreases with ρ 0 up to some critical value, after which it increases and eventually approaches unity. Second, in the case of a spherical target in Rd , the dependence of the MFPT on the spatial dimension d is significantly amplified in the presence of stochastic gating.
Are the Milky Way and Andromeda unusual? A comparison with Milky Way and Andromeda analogues

Our Milky Way provides a unique test case for galaxy evolution models, thanks to our privileged position within the Milky Way's disc. This position also complicates comparisons between the Milky Way and external galaxies, due to our inability to observe the Milky Way from an external point of view. Milky Way analog galaxies offer us a chance to bridge this divide by providing the external perspective that we otherwise lack. However, over-precise definitions of "analog" yield little-to-no galaxies, so it is vital to understand which selection criteria produce the most meaningful analog samples. To address this, we compare the properties of complementary samples of Milky Way analogs selected using different criteria. We find the Milky Way to be within 1$\sigma$ of its analogs in terms of star-formation rate and bulge-to-total ratio in most cases, but we find larger offsets between the Milky Way and its analogs in terms of disc scale length; this suggests that scale length must be included in analog selections in addition to other criteria if the most accurate analogs are to be selected. We also apply our methodology to the neighbouring Andromeda galaxy. We find analogs selected on the basis of strong morphological features to display much higher star-formation rates than Andromeda, and we also find analogs selected on Andromeda's star-formation rate to over-predict Andromeda's bulge extent. This suggests both structure and star-formation rate should be considered when selecting the most stringent Andromeda analogs.
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation

As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit. There has been considerable research on recommendation fairness. However, we argue that the previous literature has been based on simple, uniform and often uni-dimensional notions of fairness assumptions that do not recognize the real-world complexities of fairness-aware applications. In this paper, we explicitly represent the design decisions that enter into the trade-off between accuracy and fairness across multiply-defined and intersecting protected groups, supporting multiple fairness metrics. The framework also allows the recommender to adjust its performance based on the historical view of recommendations that have been delivered over a time horizon, dynamically rebalancing between fairness concerns. Within this framework, we formulate lottery-based mechanisms for choosing between fairness concerns, and demonstrate their performance in two recommendation domains.
A Visual Analytics Approach to Scheduling Customized Shuttle Buses via Perceiving Passengers’ Travel Demands

Shuttle buses have been a popular means to move commuters sharing similar origins and destinations during periods of high travel demand. However, planning and deploying reasonable, customized service bus systems becomes challenging when the commute demand is rather dynamic. It is difficult, if not impossible to form a reliable, unbiased estimation of user needs in such a case using traditional modeling methods. We propose a visual analytics approach to facilitating assessment of actual, varying travel demands and planning of night customized shuttle systems. A preliminary case study verifies the efficacy of our approach.
A SETI survey of the Vela region using the Murchison Widefield Array: Orders of magnitude expansion in search space

Abstract Following the results of our previous low-frequency searches for extraterrestrial intelligence (SETI) using the Murchison Widefield Array (MWA), directed towards the Galactic Centre and the Orion Molecular Cloud (Galactic Anticentre), we report a new large-scale survey towards the Vela region with the lowest upper limits thus far obtained with the MWA. Using the MWA in the frequency range 98–128 MHz over a 17-h period, a $400\,\textrm{deg}^{2}$ field centred on the Vela Supernova Remnant was observed with a frequency resolution of 10 kHz. Within this field, there are six known exoplanets. At the positions of these exoplanets, we searched for narrow-band signals consistent with radio transmissions from intelligent civilisations. No unknown signals were found with a 5 $\sigma$ detection threshold. In total, across this work plus our two previous surveys, we have now examined 75 known exoplanets at low frequencies. In addition to the known exoplanets, we have included in our analysis the calculation of the Effective Isotropic Radiated Power (EIRP) upper limits towards over 10 million stellar sources in the Vela field with known distances from Gaia (assuming a 10-kHz transmission bandwidth). Using the methods of Wright, Kanodia, & Lubar (2018) to describe an eight-dimensional parameter space for SETI searches, our survey achieves the largest search fraction yet, two orders of magnitude higher than the previous highest (our MWA Galactic Anticentre survey), reaching a search fraction of $\ \sim2\,{\times}\,10^{-16}$ . We also compare our results to previous SETI programs in the context of the $\mbox{EIRP}_{\textrm{min}}$ —Transmitter Rate plane. Our results clearly continue to demonstrate that SETI has a long way to go. But, encouragingly, the MWA SETI surveys also demonstrate that large-scale SETI surveys, in particular for telescopes with a large field-of-view, can be performed commensally with observations designed primarily for astrophysical purposes.
Running Neural Networks on the NIC

In this paper we show that the data plane of commodity programmable (Network Interface Cards) NICs can run neural network inference tasks required by packet monitoring applications, with low overhead. This is particularly important as the data transfer costs to the host system and dedicated machine learning accelerators, e.g., GPUs, can be more expensive than the processing task itself. We design and implement our system -- N3IC -- on two different NICs and we show that it can greatly benefit three different network monitoring use cases that require machine learning inference as first-class-primitive. N3IC can perform inference for millions of network flows per second, while forwarding traffic at 40Gb/s. Compared to an equivalent solution implemented on a general purpose CPU, N3IC can provide 100x lower processing latency, with 1.5x increase in throughput.
Power-law energy distributions of small-scale impulsive events on the active Sun: results from IRIS

Numerous studies have analysed inferred power-law distributions between frequency and energy of impulsive events in the outer solar atmosphere in an attempt to understand the predominant energy supply mechanism in the corona. Here, we apply a burst detection algorithm to high-resolution imaging data obtained by the Interface Region Imaging Spectrograph to further investigate the derived power-law index, $\gamma$, of bright impulsive events in the transition region. Applying the algorithm with a constant minimum event lifetime (of either $60$ s or $110$ s) indicated that the target under investigation, such as Plage and Sunspot, has an influence on the observed power-law index. For regions dominated by sunspots, we always find $\gamma 2$ in the energy range [$\sim10^{23}$, $\sim10^{26}$] erg. Applying the algorithm with a minimum event lifetime of three timesteps indicated that cadence was another important factor, with the highest cadence datasets returning $\gamma >2$ values. The estimated total radiative power obtained for the observed energy distributions is typically 10-25 % of what would be required to sustain the corona indicating that impulsive events in this energy range are not sufficient to solve coronal heating. If we were to extend the power-law distribution down to an energy of $10^{21}$ erg, and assume parity between radiative energy release and the deposition of thermal energy, then such bursts could provide 25-50 % of the required energy to account for the coronal heating problem.
Profiling US Restaurants from Billions of Payment Card Transactions

A payment card (such as debit or credit) is one of the most convenient payment methods for purchasing goods and services. Hundreds of millions of card transactions take place across the globe every day, generating a massive volume of transaction data. The data render a holistic view of cardholder-merchant interactions, containing insights that can benefit various applications, such as payment fraud detection and merchant recommendation. However, utilizing these insights often requires additional information about merchants missing from the data owner’s (i.e., payment company’s) perspective. For example, payment companies do not know the exact type of product a merchant serves. Collecting merchant attributes from external sources for commercial purposes can be expensive. Motivated by this limitation, we aim to infer latent merchant attributes from transaction data. As proof of concept, we concentrate on restaurants and infer the cuisine types of restaurants from transactions. To this end, we present a framework for inferring the cuisine types of restaurants from transaction data. Our proposed framework consists of three steps. In the first step, we generate cuisine labels for a limited number of restaurants via weak supervision. In the second step, we extract a wide variety of statistical features and neural embeddings from the restaurant transactions. In the third step, we use deep neural networks (DNNs) to infer the remaining restaurants’ cuisine types. The proposed framework achieved a 76.2% accuracy in classifying the US restaurants. To the best of our knowledge, this is the first framework to infer the cuisine types of restaurants by analyzing transaction data as the only source.
Owl Eyes: Spotting UI Display Issues via Visual Understanding

Graphical User Interface (GUI) provides a visual bridge between a software application and end users, through which they can interact with each other. With the development of technology and aesthetics, the visual effects of the GUI are more and more attracting. However, such GUI complexity posts a great challenge to the GUI implementation. According to our pilot study of crowdtesting bug reports, display issues such as text overlap, blurred screen, missing image always occur during GUI rendering on different devices due to the software or hardware compatibility. They negatively influence the app usability, resulting in poor user experience. To detect these issues, we propose a novel approach, OwlEye, based on deep learning for modelling visual information of the GUI screenshot. Therefore, OwlEye can detect GUIs with display issues and also locate the detailed region of the issue in the given GUI for guiding developers to fix the bug. We manually construct a large-scale labelled dataset with 4,470 GUI screenshots with UI display issues and develop a heuristics-based data augmentation method for boosting the performance of our OwlEye. The evaluation demonstrates that our OwlEye can achieve 85% precision and 84% recall in detecting UI display issues, and 90% accuracy in localizing these issues. We also evaluate OwlEye with popular Android apps on Google Play and F-droid, and successfully uncover 57 previously-undetected UI display issues with 26 of them being confirmed or fixed so far.
Linking Stakeholders’ Viewpoint Concerns with Microservices

Widespread adoption of agile project management , independent delivery with microservices, and automated deployment with DevOps has tremendously speedup the systems development. The real game-changer is continuous integration (CI) , continuous delivery and continuous deployment (CD) . Organizations can do multiple releases a day, shortening the test, release, and deployment cycles from weeks to minutes. Maturity of container technologies like Docker and container orchestration platforms like Kubernetes has promoted microservices architecture, especially in the cloud-native developments. Various tools are available for setting up CI/CD pipelines. Organizations are moving away from monolith applications and moving towards microservices-based architectures. Organizations can quickly accumulate hundreds of such microservices accessible via application programming interfaces (APIs). The primary purpose of these modern methodologies is agility, speed, and reusability. While DevOps offers speed and time to market, agility and reusability may not be guaranteed unless mi-croservices and API’s are linked to enterprise-wide stakeholders’ needs. The link between business needs and microservices/APIs is not well captured nor adequately defined. In this publication, we describe a structured method to create a logical link among APIs and microservices-based agile developments with enterprise stakeholders’ needs and viewpoint concerns . This method enables capturing and documenting enterprise-wide stakeholders’ needs, whether these are business owners, planners (product owners, architects), designers (developers, DevOps engineers), or the partners and subscribers of an enterprise.
Low-cost Active Dry-Contact Surface EMG Sensor for Bionic Arms

Surface electromyography (sEMG) is a popular bio-signal used for controlling prostheses and finger gesture recognition mechanisms. Myoelectric prostheses are costly, and most commercially available sEMG acquisition systems are not suitable for real-time gesture recognition. In this paper, a method of acquiring sEMG signals using novel low-cost, active, dry-contact, flexible sensors has been proposed. Since the active sEMG sensor was developed to be used along with a bionic arm, the sensor was tested for its ability to acquire sEMG signals that could be used for real-time classification of five selected gestures. In a study of 4 subjects, the average classification accuracy for real-time gesture classification using the active sEMG sensor system was 85%. The common-mode rejection ratio of the sensor was measured to 59 dB, and thus the sensor’s performance was not substantially limited by its active circuitry. The proposed sensors can be interfaced with a variety of amplifiers to perform fully wearable sEMG acquisition. This satisfies the need for a low-cost sEMG acquisition system for prostheses.
Two close binaries across the hydrogen-burning limit in the Praesepe open cluster

We present Keck I/OSIRIS and Keck II/NIRC2 adaptive optics imaging of two member candidates of the Praesepe stellar cluster (d=186.18$\pm$0.11 pc; 590-790 Myr), UGC J08451066+2148171 (L1.5$\pm$0.5) and UGCS J08301935$+$2003293 (no spectroscopic classification). We resolved UGCS J08451066$+$2148171 into a binary system in the near-infrared, with a $K$-band wavelength flux ratio of 0.89$\pm$0.04, a projected separation of 60.3$\pm$1.3 mas (11.2$\pm$0.7 au; 1$\sigma$). We also resolved UGCS J08301935$+$2003293 into a binary system with a flux ratio of 0.46$\pm$0.03 and a separation of 62.5$\pm$0.9 mas. Assuming zero eccentricity, we estimate minimum orbital periods of $\sim$100 years for both systems. According to theoretical evolutionary models, we derive masses in the range of 0.074-0.078 M$_{\odot}$ and 0.072-0.076 M$_{\odot}$ for the primary and secondary of UGCS J08451066$+$2148171 for an age of 700$\pm$100 Myr. In the case of UGCS J08301935$+$2003293, the primary is a low-mass star at the stellar/substellar boundary (0.070-0.078 M$_{\odot}$) while the companion candidate might be a brown dwarf (0.051-0.065 M$_{\odot}$). These are the first two binaries composed of L dwarfs in Praesepe. They are benchmark systems to derive the location of the substellar limit at the age and metallicity of Praesepe, determine the age of the cluster based on the lithium depletion boundary test, derive dynamical masses, and improve low-mass stellar and substellar evolutionary models at a well-known age and metallicity.