Miracle-dz/UniFAR-FTUs huggingface dataset:
On Mean Field Limit and Quantitative Estimates with a Large Class of Singular Kernels: Application to the Patlak-Keller-Segel Model

In this note, we propose a new relative entropy combination of the methods developed by P.--E. Jabin and Z. ~Wang [Inventiones (2018)] and by S. Serfaty [Proc. Int. Cong. of Math, (2018) and references therein] to treat more general kernels in mean field limit theory. This new relative entropy may be understood as introducing appropriate weights in the relative entropy developed by P.-E. Jabin and Z. Wang (in the spirit of what has been recently developed by D.~Bresch and P.--E. Jabin [Annals of Maths (2018)]) to cancel the more singular terms involving the divergence of the flow. As an example, a full rigorous derivation (with quantitative estimates) of the Patlak-Keller-Segel model in some subcritical regimes is obtained. Our new relative entropy allows to treat singular potentials which combine large smooth part, small attractive singular part and large repulsive singular part.
Multiscale modelling via split-step methods in neural firing

ABSTRACT Neuronal models based on the Hodgkin–Huxley equation form a fundamental framework in the field of computational neuroscience. While the neuronal state is often modelled deterministically, experimental recordings show stochastic fluctuations, presumably driven by molecular noise from the underlying microphysical conditions. In turn, the firing of individual neurons gives rise to an electric field in extracellular space, also thought to affect the firing pattern of nearby neurons. We develop a multiscale model which combines a stochastic ion channel gating process taking place on the neuronal membrane, together with the propagation of an action potential along the neuronal structure. We also devise a numerical method relying on a split-step strategy which effectively couples these two processes and we experimentally test the feasibility of this approach. We finally also explain how the approach can be extended with Maxwell’s equations to allow the potential to be propagated in extracellular space.
Intercalibration of wave and wind data from TOPEX/POSEIDON and moored buoys off the west coast of Canada

Significant wave height (SWH) and wind speed data from the merged geophysical data record (MGDR) of the TOPEX/POSEIDON satellite altimeters, for the first 47 cycles of satellite coverage (September 1992 to December 1993), are compared with measurements from an array of instrumented buoys moored along and off the west coast of Canada. SWH values from the satellite and buoys cover a range of 0.1 to 8 m, in one case exceeding 12 m. The comparison shows TOPEX waves, on average, low by 5% relative to the buoys, with rms scatter about the mean relation of 30 cm. For buoys lying within 10 km of the satellite tracks, this scatter is reduced to about 15 cm. TOPEX tracks pass sufficiently close to each of the buoys for the satellite altimeter to be used as a “transfer standard,” showing a small difference between the two types of buoys in the array and occasional problems with the buoy measurements. A significant difference was found between the satellite and buoy wind speeds, with NASA altimeter winds being high by about 10% relative to the buoys. This can be explained by known factors affecting the buoy data. The small amount of POSEIDON data shows the winds in the MGDR to be 12% low compared with the buoys. The difference between POSEIDON and NASA altimeter winds is confirmed by computing large-area spatial averages over the Pacific Ocean. The rms scatter for wind speed about the best fit regression was near 2 m/s, reducing to 1.4 m/s when data were restricted to distances less than 10 km from deep water buoys. Atmospheric pressure values, inserted in the satellite data from a global weather model, were found to agree with buoy measurements to within about 1 mbar. Apart from the discrepancy in wind speeds, the results are consistent with both buoy and satellite data meeting or exceeding their design specifications. The effects of spatial variations of wave climate and changes in atmospheric stability are assessed. The comparison demonstrates how satellite and buoy observations can be used for mutual calibration and performance monitoring.
Multispectral derivation of bathymetry in Singapore's shallow, turbid waters

In very shallow waters, active sensing determinations of bathymetry are often expensive and unwieldy. Sea depth estimation using passive remote-sensing methods is an attractive alternative, especially using cheap multispectral imagery with high spatial resolution. Three models for the determination of bathymetry from multispectral imagery were utilized with new eight-band images from DigitalGlobe's Worldview-2 satellite platform. All three were trained with electronic navigational chart data and evaluated for accuracy in Singapore's turbid shallow coastal waters. These waters are characterized by high turbidity, suspended sediment, and vehicle traffic. Of the three models, a linear band algorithm performed best, with a root-mean-square error (RMSE) of 0.48 m. A look-up table classification provided a precision of 0.64 m, but was limited by a training set that did not fully represent variance in water column and benthic properties. Possibly owing to the domination of particle backscatter over pigment absorption in these turbid waters, a linear ratio algorithm did not perform as well as the linear band algorithm, achieving an RMSE of only 0.56 m. Analysis found that the usual relationship between ratios of low-absorption to high-absorption bands and depth does not hold as well for these waters, likely due to backscatter dominating leaving-water signals, masking relative absorption effects. High turbidity, with a Secchi disk depth of 1.9 m, limited analysis to shallow reefs and coastline and likely impacted the sensitivity of the bathymetric algorithms. A larger validation data set containing water quality and benthic data is required for further investigation to determine specific sources of error.
Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances

The main scope of this chapter is to serve as an introduction to face presentation attack detection, including key resources and advances in the field in the last few years. The next pages present the different presentation attacks that a face recognition system can confront, in which an attacker presents to the sensor, mainly a camera, a Presentation Attack Instrument (PAI), that is generally a photograph, a video, or a mask, to try to impersonate a genuine user. First, we make an introduction of the current status of face recognition, its level of deployment, and its challenges. In addition, we present the vulnerabilities and the possible attacks that a face recognition system may be exposed to, showing that way the high importance of presentation attack detection methods. We review different types of presentation attack methods, from simpler to more complex ones, and in which cases they could be effective. Then, we summarize the most popular presentation attack detection methods to deal with these attacks. Finally, we introduce public datasets used by the research community for exploring vulnerabilities of face biometrics to presentation attacks and developing effective countermeasures against known PAIs.
The Distance to the Isolated Neutron Star RX J0720.4–3125

We have used a set of dedicated astrometric data from the Hubble Space Telescope to measure the parallax and proper motion of the nearby neutron star RX J0720.4-3125. At each of eight epochs over 2 years, we used the High Resolution Camera of the Advanced Camera for Surveys to measure the position of the B = 26.6 target to a precision of ~2 mas (~0.07 pixels) relative to 22 other stars. From these data we measure a parallax of π = 2.8 ± 0.9 mas (for a distance of 360 pc) and a proper motion of μ = 107.8 ± 1.2 mas yr-1. Exhaustive testing of every stage of our analysis suggests that it is robust, with a maximum systematic uncertainty on the parallax of 0.4 mas. The distance is compatible with earlier estimates made from scaling the optical emission of RX J0720.4-3125 relative to the even closer neutron star RX J1856.5-3754. The distance and proper motion imply a transverse velocity of 180 km s-1, comparable to velocities observed for radio pulsars. The speed and direction suggest an origin for RX J0720.4-3125 in the Trumpler 10 OB association ~0.7 Myr ago, with a possible range of 0.5-1.0 Myr given by the uncertainty in the distance.
$(t, n)$ Threshold $d$-level quantum secret sharing based on quantum Fourier transformation

Quantum secret sharing (QSS) is an important branch of secure multiparty quantum computation. Several schemes for $(n, n)$ threshold QSS based on quantum Fourier transformation (QFT) have been proposed. Inspired by the flexibility of $(t, n)$ threshold schemes, Song {\it et al.} (Scientific Reports, 2017) have proposed a $(t, n)$ threshold QSS utilizing $QFT$. Later, Kao and Hwang (arXiv:1803.00216) have identified a loophole in the scheme but have not suggested any remedy. In this present study, we have proposed a $(t, n)$ threshold QSS scheme to share a $d$ dimensional classical secret. This scheme can be implemented using local operations (such as $QFT$, generalized Pauli operators and local measurement) and classical communication. Security of the proposed scheme is described against outsider and participants' eavesdropping.
The short‐term economic consequences of COVID‐19: Occupation tasks and mental health in Canada

Abstract In this paper, we study the effect of COVID‐19 on the labour market and reported mental health of Canadians. To better understand the effect of the pandemic on the labour market, we build indexes for whether workers: (i) are relatively more exposed to disease, (ii) work in proximity to co‐workers, (iii) are essential workers and (iv) can easily work remotely. Our estimates suggest that the impact of COVID‐19 was significantly more severe for workers that work in proximity to co‐workers and those more exposed to disease who are not in the health sector, while the effects are less severe for essential workers and workers that can work remotely. Last, using the Canadian Perspective Survey Series, we observe that reported mental health is significantly lower among some of the most affected workers such as women and less‐educated workers. We also document that those who were absent from work because of COVID‐19 are more concerned with meeting their financial obligations and with losing their job than those who continue working outside their home.
Multiwinner Analogues of Plurality Rule: Axiomatic and Algorithmic Perspectives

We characterize the class of committee scoring rules that satisfy the fixed-majority criterion. In some sense, the committee scoring rules in this class are multiwinner analogues of the single-winner Plurality rule, which is uniquely characterized as the only single-winner scoring rule that satisfies the simple majority criterion. We define top-$k$-counting committee scoring rules and show that the fixed majority consistent rules are a subclass of the top-$k$-counting rules. We give necessary and sufficient conditions for a top-$k$-counting rule to satisfy the fixed-majority criterion. We find that, for most of the rules in our new class, the complexity of winner determination is high (that is, the problem of computing the winners is NP-hard), but we also show examples of rules with polynomial-time winner determination procedures. For some of the computationally hard rules, we provide either exact FPT algorithms or approximate polynomial-time algorithms.
New method of underwater passive navigation based on gravity gradient

A new method of underwater passive navigation based on gravity gradient is proposed in this paper. In comparison with some other geophysical characteristics such as gravity or gravity anomaly, gravity gradient which is the second derivative of gravitational potential has better spatial resolution and more sensitive to terrain changes. Through it, the digitally stored gravity gradient maps and real-time gravity gradient measurements can be taken as input information, with gravity gradient linearization techniques and extended Kalman filter, the navigation errors of INS are estimated by using gravity gradient error, therefore the output in the inertial navigation system are corrected. Simulation test has been done and the results show that, the method is effective and efficient for the positioning precision improvement.
Model Checking Temporal Properties of Recursive Probabilistic Programs

Probabilistic pushdown automata (pPDA) are a standard operational model for programming languages involving discrete random choices and recursive procedures. Temporal properties are useful for specifying the chronological order of events during program execution. Existing approaches for model checking pPDA against temporal properties have focused mostly on $\omega$-regular and LTL properties. In this paper, we give decidability and complexity results for the model checking problem of pPDA against $\omega$-visibly pushdown languages that can be described by specification logics such as CaRet. These logical formulae allow specifying properties that explicitly take the structured computations arising from procedural programs into account. For example, CaRet is able to match procedure calls with their corresponding future returns, and thus allows to express fundamental program properties such as total and partial correctness.
Mechanical Stress Inference for Two Dimensional Cell Arrays

Many morphogenetic processes involve mechanical rearrangements of epithelial tissues that are driven by precisely regulated cytoskeletal forces and cell adhesion. The mechanical state of the cell and intercellular adhesion are not only the targets of regulation, but are themselves the likely signals that coordinate developmental process. Yet, because it is difficult to directly measure mechanical stress in vivo on sub-cellular scale, little is understood about the role of mechanics in development. Here we present an alternative approach which takes advantage of the recent progress in live imaging of morphogenetic processes and uses computational analysis of high resolution images of epithelial tissues to infer relative magnitude of forces acting within and between cells. We model intracellular stress in terms of bulk pressure and interfacial tension, allowing these parameters to vary from cell to cell and from interface to interface. Assuming that epithelial cell layers are close to mechanical equilibrium, we use the observed geometry of the two dimensional cell array to infer interfacial tensions and intracellular pressures. Here we present the mathematical formulation of the proposed Mechanical Inverse method and apply it to the analysis of epithelial cell layers observed at the onset of ventral furrow formation in the Drosophila embryo and in the process of hair-cell determination in the avian cochlea. The analysis reveals mechanical anisotropy in the former process and mechanical heterogeneity, correlated with cell differentiation, in the latter process. The proposed method opens a way for quantitative and detailed experimental tests of models of cell and tissue mechanics.
Pull Request Decision Explained: An Empirical Overview

Context: Pull-based development model is widely used in open source, leading the trends in distributed software development. One aspect which has garnered significant attention is studies on pull request decision - identifying factors for explanation. Objective: This study builds on a decade long research on pull request decision to explain it. We empirically investigate how factors influence pull request decision and scenarios that change the influence of factors. Method: We identify factors influencing pull request decision on GitHub through a systematic literature review and infer it by mining archival data. We collect a total of 3,347,937 pull requests with 95 features from 11,230 diverse projects on GitHub. Using this data, we explore the relations of the factors to each other and build mixed-effect logistic regression models to empirically explain pull request decision. Results: Our study shows that a small number of factors explain pull request decision with the integrator same or different from the submitter as the most important factor. We also noted that some factors are important only in special cases e.g., the percentage of failed builds is important for pull request decision when continuous integration is used.
Diffusive boundary layer influenced by bottom boundary hydrodynamics in tidal flows

[1] Field measurements of the diffusive boundary layer (DBL) and bottom boundary layer (BBL) in two distinctly different coastal ocean environments are analyzed. The dynamic conditions of the BBL have a strong influence on the DBL thickness (δDBL) and oxygen diffusive fluxes at the sediment-water interface. Three different estimates of the Batchelor length (LB) in the BBL are obtained from the measured dissipation rate of turbulent kinetic energy (em), turbulent friction velocity (u*), and tidal velocity (Um) and bottom roughness length (z0). The two estimates of LB from em and u* have low correlations with δDBL. The estimate of LB from Um and z0 has a higher correlation with δDBL at both sites, suggesting a simple estimation of δDBL.
Rehabilitation of lands mined for limestone in the Indian desert

In the Indian desert, the economics of mining is second only to agriculture in importance. However, research on the rehabilitation of land disturbed by mining has only recently received serious attention. An attempt has been made to determine both the qualitative and quantitative success of rehabilitation plans used to revegetate limestone mine spoils in an area near Barna, northwest arid India. Rehabilitation success was achieved using a combination of rainwater harvesting techniques, soil amendment application approaches, plant establishment methods and the selection of appropriate germplasm material (trees, shrubs and grasses). It is expected that the resulting vegetative cover will be capable of self-perpetuation under natural conditions while at the same time meeting the land-use needs of the local people. The minespoils have adequate levels of the major nutrients (except P, Mo and Se) for proper plant and grazing animal health. Levels of organic matter are low whereas total B concentrations are exceptionally high. Also, the population of soil fungi, Azotobactor, and nitrifying bacteria is negligible. Enhanced plant growth was achieved in treated plots, compared to control plots, where spoil moisture storage was improved by 5 – 45 per cent. Due to the decomposition of farmyard manure and nitrogen fixation by planted leguminous plant species, the electrical conductance of treated mine spoils increased threefold, CaCO3 content decreased from 20·0 to 5·2 per cent, and organic carbon, P, K, and biological activity increased significantly. The rehabilitation protocol used at the site appears to have been successful because plant self-regeneration is occurring. The increased diversity of woody perennials resulted in ‘dominance’ being better shared among species and ‘evenness’ being increased within the plant community elements. The early to mid-successional trends are continuing for six years following initial rehabilitation. This study developed methods for the rehabilitation of lands mined for limestone and has also resulted in an understanding of rehabilitation processes in arid regions with an emphasis on the long-term monitoring of rehabilitation success. Copyright © 2000 John Wiley & Sons, Ltd.
Integral statistical eco-indices – effective complementary tool for assessment of ecological state of and ecological risks for water ecosystems

Eco-indices are successfully used for assessment of the ecological state and risks of water reservoirs. They allow, already at early stages, to detect negative effects on water ecosystems caused by progressive anthropogenic impacts and widening of the spectrum of pollutants, and to quantitatively evaluate ecological risks and damage for water reservoirs. Implementing these modern tools to water quality assessment is one of the lines to make decisions concerning challenging environmental problems.
Upper Limits on the Isotropic Gravitational-Wave Background from Advanced LIGO's and Advanced Virgo's Third Observing Run

We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO's and Advanced Virgo's third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density $\Omega_{\rm GW}\leq 5.8\times 10^{-9}$ at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20-76.6 Hz; $\leq 3.4 \times 10^{-9}$ at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20-90.6 Hz; and $\leq 3.9 \times 10^{-10}$ at 25 Hz for a spectral index of 3, in the band 20-291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries. Finally, we combine our results with observations of individual mergers andshow that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at $z \lesssim 2$ than can be achieved with individually resolved mergers alone. [abridged]
Scene Parsing by Weakly Supervised Learning with Image Descriptions

This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations). We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixel-wise object labeling and ii) a recursive neural network (RsNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative annotations (e.g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and apply these tree structures to discover the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RsNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments show that our model is capable of producing meaningful and structured scene configurations, and achieving more favorable scene labeling results on PASCAL VOC 2012 and SYSU-Scenes datasets compared to other state-of-the-art weakly-supervised deep learning methods. In particular, SYSU-Scenes is a dedicated dataset released by us to facilitate further research on scene parsing, which contains more than 5000 scene images with their sentence-based semantic descriptions.