Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluid Stochastic Petri Nets : Theory , Applications , and Solution 1

In this paper we introduce a new class of stochastic Petri nets in which one or more places can hold uid rather than discrete tokens. We de ne a class of uid stochastic Petri nets in such a way that the discrete and continuous portions may a ect each other. Following this de nition we provide equations for their transient and steady-state behavior. We present several examples showing the utility of the construct in communication network modeling and reliability analysis, and discuss important special cases. We then discuss numerical methods for computing the transient behavior of such nets. Finally, some numerical examples are presented. A preliminary version of this paper appeared in the Proceedings of the 1993 Petri Net Conference, Chicago, Illinois, under the title FSPNs: Fluid Stochastic Petri Nets. This research was partially supported by the National Aeronautics and Space Administration under NASA contract NAS1-19480 while the rst, third and fourth authors were in residence at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA 23681-0001. This research was supported in part by the National Science Foundation under Grant CCR-9201195 and by NASA Grant NAG-1332. This research was supported in part by the National Science Foundation under Grant NSF-EEC-94-18765.
Brain-inspired neuronal silencing mechanism to enable high-precision sequence identification

Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing methods apply recurrent neural networks, which suffer from training difficulties; however, performing this function without feedback loops remains a challenge. Here, we present an experimental neuronal long-term plasticity mechanism for high-precision feedforward
Assessing Vehicle Emissions from a Multi-Lane to Turbo Roundabout Conversion Using a Microsimulation Tool

The development of urban strategies for the reduction of environmental impacts and decarbonization requires ongoing monitoring from the local scale and further deployment of actions to improve transport demand (user characteristics and modal choice) and supply (infrastructure and services). The analysis of pollution sources and the evaluation of possible scenarios are preliminary to the mitigation of impacts. In particular, the study of geometrical and functional characteristics of infrastructures through micro-simulation allows understanding of which schemes can support the reduction of emissions and guarantee high levels of service (LOS), reducing the problem of vehicular congestion in urban areas. The present work focuses on the small-scale analysis of vehicular traffic emissions at a multi-lane roundabout road intersection and the comparison of geometric schemes (current and design) and use with a turbo roundabout scheme as traffic volumes changes. These volumes have plummeted due to the current COVID-19 pandemic. The results show that the geometric-functional modification of the roundabout intersection from a multi-lane to a turbo-roundabout intersection allows a reduction of up to 30% of the emissions considering the current composition of the traffic fleet in the city of Rzeszow in Poland. The proposed comparative analysis methodology can contribute to the drafting of sustainable urban mobility plans (SUMPs) proposing a set of investments for new road works and considering a number of scenarios with interventions that can be implemented in the medium and long term that can provide the incentive to reduce road congestion and vehicular emissions.
A MATLAB toolbox and Excel workbook for calculating the densities, seismic wave speeds, and major element composition of minerals and rocks at pressure and temperature

To interpret seismic images, rock seismic velocities need to be calculated at elevated pressure and temperature for arbitrary compositions. This technical report describes an algorithm, software, and data to make such calculations from the physical properties of minerals. It updates a previous compilation and Excel® spreadsheet and includes new MATLAB® tools for the calculations. The database of 60 mineral end‐members includes all parameters needed to estimate density and elastic moduli for many crustal and mantle rocks at conditions relevant to the upper few hundreds of kilometers of Earth. The behavior of α and β quartz is treated as a special case, owing to its unusual Poisson's ratio and thermal expansion that vary rapidly near the α‐β transition. The MATLAB tools allow integration of these calculations into a variety of modeling and data analysis projects.
Part 1 : Large-Scale Dynamic Human Eye Movement Datasets and Consistency Studies for Visual Action Recognition

Computer vision is making rapid progress fueled by machine learning and large-scale annotated datasets. While the ultimate visual recognition result – the label of an image or a video, the layout of an object and its category – is currently the main target of annotation, in this technical report we complement existing state-of-the art large-scale dynamic computer vision datasets like Hollywood-2[1] and UCF Sports[2] with human eye movements collected under the ecological constraints of the visual action recognition task. To our knowledge these are the first massive human eye tracking datasets to be collected for video. Besides the many person-month effort of collecting the data (497,107 frames, each viewed by 16 subjects), unique in terms of its (a) large scale and computer vision relevance, (b) dynamic, video stimuli, (c) task control, as opposed to free-viewing, which will be made available to the research community, we introduce novel dynamic consistency and alignment measures. The studies, both static and dynamic, underline the remarkable stability of patterns of visual search among subjects. Additionally, in part 2 of this technical report we show that effective models for predicting human fixations can be trained and evaluated using our dataset, and such models can boost state of the art action recognition performance in automatic end-to-end systems. We hope that our studies and the eye-tracking data that we will publicly release will foster quantitative research into feature descriptors and computational vision models and will stimulate inter-disciplinary research in computer vision, visual attention and machine learning.
Petrogenesis of late Hercynian calc-alkaline dykes of mid-eastern Sardinia: petrographical and geochemical data constraining hybridization process

During the final stages of the emplacement of the Hercynian batholith, a calc-alkaline dyke swarm intersected the crystalline basement of Sardinia. In mid-eastern Sardinia, these dykes range in composition from basaltic andesite to dacites-rhyolites with oligophyric and porphyritic textures, respectively. Plagioclase is the dominant phase among the phenocrysts, with subordinate brown and green amphibole; biotite, K-feldspar and quartz are abundant in more differentiated compositions. The biotite Rb-Sr age of this magmatism ranges between 291 and 271 Ma. Compositional data can be modelled using two separate trends: 1) AFC-type processes with fractionation of An-rich plagioclase, orthopyroxene and Cr-rich clinopyroxene are responsible for compositional variations amongst the most basic rocks; 2) andesitic to rhyolitic magmas evolve starting from basic magmas which are aligned along the first trend due to hybridization process. The complex and long-lived mafic magma and the crust interaction indicate a MASH-like scenario characterized by different magma chambers, as proposed for the late-Hercynian magmatism in other areas.
Generalised Structural CNNs ( SCNNs ) for time series data with arbitrary graph-topologies

Deep Learning methods, specifically convolutional neural networks (CNNs), have seen a lot of success in the domain of image-based data, where the data offers a clearly structured topology in the regular lattice of pixels. This 4-neighbourhood topological simplicity makes the application of convolutional masks straightforward for time series data, such as video applications, but many high-dimensional time series data are not organised in regular lattices, and instead values may have adjacency relationships with non-trivial topologies, such as small-world networks or trees. In our application case, human kinematics, it is currently unclear how to generalise convolutional kernels in a principled manner. Therefore we define and implement here a framework for general graph-structured CNNs for time series analysis. Our algorithm automatically builds convolutional layers using the specified adjacency matrix of the data dimensions and convolutional masks that scale with the hop distance. In the limit of a lattice-topology our method produces the well-known image convolutional masks. We test our method first on synthetic data of arbitrarily-connected graphs and human hand motion capture data, where the hand is represented by a tree capturing the mechanical dependencies of the joints. We are able to demonstrate, amongst other things, that inclusion of the graph structure of the data dimensions improves model prediction significantly, when compared against a benchmark CNN model with only time convolution layers.
Poisson calculus for spatial neutral to the right processes

Neutral to the right (NTR) processes were introduced by Doksum in 1974 as Bayesian priors on the class of distributions on the real line. Since that time there have been numerous applications to models that arise in survival analysis subject to possible right censoring. However, unlike the Dirichlet process, the larger class of NTR processes has not been used in a wider range of more complex statistical applications. Here, to circumvent some of these limitations, we describe a natural extension of NTR processes to arbitrary Polish spaces, which we call spatial neutral to the right processes. Our construction also leads to a new rich class of random probability measures, which we call NTR species sampling models. We show that this class contains the important two parameter extension of the Dirichlet process. We provide a posterior analysis, which yields tractable NTR analogues of the Blackwell-MacQueen distribution. Our analysis turns out to be closely related to the study of regenerative composition structures. A new computational scheme, which is an ordered variant of the general Chinese restaurant processes, is developed. This can be used to approximate complex posterior quantities. We also discuss some relationships to results that appear outside of Bayesian nonparametrics.
Adaptive Detection of Structured Signals in Low-Rank Interference

In this paper, we consider the problem of detecting the presence (or absence) of an unknown but structured signal from the space-time outputs of an array under strong, non-white interference. Our motivation is the detection of a communication signal in jamming, where often the “training” portion is known but the “data” portion is not. We assume that the measurements are corrupted by additive white Gaussian noise of unknown variance and a few strong interferers, whose number, powers, and array responses are unknown. We also assume the desired signal‘s array response is unknown. To address the detection problem, we propose two GLRT-based detection schemes that employ a probabilistic signal model and use the EM algorithm for likelihood maximization. Numerical experiments are presented to assess the performance of the proposed schemes.
Vehicle Detection in Aerial Images Based on Lightweight Deep Convolutional Network and Generative Adversarial Network

Vehicle detection in aerial images is a challenging task and plays an important role in a wide range of applications. Traditional detection algorithms are based on sliding-window searching and shallow-learning-based features, which limits the ability to represent features and generates a lot of computational costs. Recently, with the successful application of convolutional neural network in computer vision, many state-of-the-art detectors have been developed based on deep CNNs. However, these CNN-based models still face some difficulties and challenges in vehicle detection in aerial images. Firstly, the CNN-based detection model requires extensive calculations during training and detection, and the accuracy of detection for small objects is not high. In addition, deep learning models often require a large amount of sample data to train a robust detection model, while the annotated data of aerial vehicles is limited. In this study, we propose a lightweight deep convolutional neural network detection model named LD-CNNs. The detection algorithm not only greatly reduces the computational costs of the model, but also significantly improves the accuracy of the detection. What’s more, in order to cope with the problem of insufficient training samples, we develop a multi-condition constrained generative adversarial network named MC-GAN, which can effectively generate samples. The detection performance of the proposed model has been evaluated on the Munich public dataset and the collected dataset respectively. The results show that on the Munich dataset, the proposed method achieves 86.9% on mAP (mean average precision), F1-score is 0.875, and the detection time is 1.64s on Nvidia Titan XP. At present, these detection indicators have reached state-of-the-art level in vehicle detection of aerial images.
Ranking Indirect Connections in Literature-Based Discovery: The Role of Medical Subject Headings (MeSH)

Arrowsmith, a computer-assisted process for literature-based discovery, takes as input two disjoint sets of records (A,C) from the Medline database. It produces a list of title words and phrases, B, that are common to A and C, and displays the title context in which each B-term occurs within A and within C. Subject experts then can try to find A-B and B-C title-pairs that together may suggest novel and plausible indirect A-C relationships (via B-terms) that are of particular interest in the absence of any known direct A-C relationship. The list of B-terms typically is so large that it is difficult to find the relatively few that contribute to scientifically interesting connections. The purpose of the present paper is to propose and test several techniques for improving the quality of the B-list. These techniques exploit the Medical Subject Headings that are assigned to each input record. A MesH-based concept of literature cohesiveness is defined and plays a key role. The proposed techniques are tested on an earlier published example of indirect connections between migraine and magnesium deficiency. The tests demonstrate how the earlier results can be replicated with a more efficient and more systematic computer-aided process.
A Corpus-based Investigation of Definite Description Use a Corpus-based Investigation of Deenite Description Use a Corpus-based Investigation of Deenite Description Use

We present the results of a study of deenite descriptions use in written texts aimed at assessing the feasibility of annotating corpora with information about deenite description interpretation. We ran two experiments, in which subjects were asked to classify the uses of definite descriptions in a corpus of 33 newspaper articles, containing a total of 1412 deenite descriptions. We measured the agreement among annotators about the classes assigned to deenite descriptions, as well as the agreement about the antecedent assigned to those deenites that the annotators classiied as being related to an antecedent in the text. The most interesting result of this study from a corpus annotation perspective was the rather low agreement (K=0.63) that we obtained using versions of Hawkins' and Prince's classiication schemes; better results (K=0.76) were obtained using the simpliied scheme proposed by Fraurud that includes only two classes, rst-mention and subsequent-mention. The agreement about antecedents was also not complete. These ndings raise questions concerning the strategy of evaluating systems for deenite description interpretation by comparing their results with a standardized annotation. From a linguistic point of view, the most interesting observations were the great number of discourse-new deenites in our corpus (in one of our experiments, about 50% of the deenites in the collection were classiied as discourse-new, 30% as anaphoric, and 18% as associative/bridging) and the presence of deenites which did not seem to require a complete disambiguation.
Critical Review of Scientific Basis for Lowering Coal Mine Dust Exposure Level. iii. Exposure-Response Studies of Radiographic CWP

Implementation of the coal mine dust (“CMD”) interim standard of 3.0 milligrams per cubic meter (“mg/m 3 ”) in 1970 was reduced to 2.0 mg/m3 in 1972 and produced a steady decline in dust levels and prevalence of coal workers pneumoconiosis (“CWP”). Beginning in the mid-1990s, an apparent increase was reported in what was thought to be severe and rapidly progressive CWP and PMF despite stability in CMD levels. These “sentinel health” events led to further investigation and stimulated the 2010 MSHA proposal to lower the current CMD standard from 2.0 mg/m 3 to 1.0 mg/m 3 . The purpose of this study is to summarize exposure-response associations between CMD and CWP to evaluate evidence for a revised standard. There are five cohorts of UK and US coalminers with exposure-response analyses useful for assessing a proposed coal mine dust standard. Two major biases confound such an evaluation. Exposure misclassification bias occurs for exposures occurring before standards and before sampling surveillance was initiated. This bias is most obvious in the US studies where sample results collected after initiation of the standard were back-extrapolated to pre-standard time periods. The bias introduced produces a spurious steeper exposure-response slope due to overestimation of risk at higher exposure levels (>4 mg/m 3 ) and under-estimation of risk at lower exposures. Participation bias occurs in one of the US studies when survey participation rates dropped below 50% in rounds 1-4. Exposure-response evidence suggests a coal mine dust standard of 2 mg/m 3 appears to be protective from occurrence of CWP ≥2 for low rank coals. However there is excess CWP ≥2 in miners exposed to high rank coal, suggesting a lower exposure standard is needed to protect these miners.
Simultaneous Support Recovery in High Dimensions: Benefits and Perils of Block $\ell _{1}/\ell _{\infty} $-Regularization

Given a collection of <i>r</i> ≥ 2 linear regression problems in <i>p</i> dimensions, suppose that the regression coefficients share partially common supports of size at most <i>s</i>. This set-up suggests the use of ℓ<sub>1</sub>/ℓ<sub>∞</sub>-regularized regression for joint estimation of the <i>p</i>×<i>r</i> matrix of regression coefficients. We analyze the high-dimensional scaling of ℓ<sub>1</sub>/ℓ<sub>∞</sub>-regularized quadratic programming, considering both consistency rates in ℓ<sub>∞</sub>-norm, and how the minimal sample size <i>n</i> required for consistent variable selection scales with model dimension, sparsity, and overlap between the supports. We first establish bounds on the ℓ<sub>∞</sub>-error as well sufficient conditions for exact variable selection for fixed design matrices, as well as for designs drawn randomly from general Gaussian distributions. Specializing to the case <i>r</i> = 2 linear regression problems with standard Gaussian designs whose supports overlap in a fraction α ∈ [0,1] of their entries, we prove that ℓ<sub>1</sub>/ℓ<sub>∞</sub>-regularized method undergoes a phase transition characterized by the rescaled sample size θ<sub>1,∞</sub>(<i>n</i>, <i>p</i>, <i>s</i>, α) = <i>n</i>/{(4 - 3 α) <i>s</i> log(<i>p</i>-(2- α) <i>s</i>)}. An implication is that the use of ℓ<sub>1</sub>/ℓ<sub>∞</sub>-regularization yields improved statistical efficiency if the overlap parameter is large enough ( α >; 2/3), but has worse statistical efficiency than a naive Lasso-based approach for moderate to small overlap (α <; 2/3 ). Empirical simulations illustrate the close agreement between theory and actual behavior in practice. These results show that caution must be exercised in applying <i>ℓ</i><sub>1</sub>/ℓ<sub>∞</sub> block regularization: if the data does not match its structure very closely, it can impair statistical performance relative to computationally less expensive schemes.
graphs Velvet : Algorithms for de novo short read assembly using de Bruijn Material Supplemental

Genome Res. May , 2008 18: 802-809 David Hernandez, Patrice François, Laurent Farinelli, et al. on a desktop computer De novo bacterial genome sequencing: Millions of very short reads assembled Genome Res. May , 2008 18: 810-820 Jonathan Butler, Iain MacCallum, Michael Kleber, et al. ALLPATHS: De novo assembly of whole-genome shotgun microreads Genome Res. November , 2007 17: 1697-1706 Juliane C. Dohm, Claudio Lottaz, Tatiana Borodina, et al. novo genomic sequencing SHARCGS, a fast and highly accurate short-read assembly algorithm for de
The Dynamic Phase Transition for Decoding Algorithms

The state-of-the-art error correcting codes are based on large random constructions (random graphs, random permutations, . . . ) and are decoded by linear-time iterative algorithms. Because of these features, they are remarkable examples of diluted mean-field spin glasses, both from the static and from the dynamic points of view. We analyze the behavior of decoding algorithms using the mapping onto statistical-physics models. This allows to understand the intrinsic (i.e. algorithm independent) features of this behavior. UMR 8549, Unité Mixte de Recherche du Centre National de la Recherche Scientifique et de l’ Ecole Normale Supérieure.
Dimension Detection with Local Homology

Detecting the dimension of a hidden manifold from a point sample has become an important problem in the current data-driven era. Indeed, estimating the shape dimension is often the first step in studying the processes or phenomena associated to the data. Among the many dimension detection algorithms proposed in various fields, a few can provide theoretical guarantee on the correctness of the estimated dimension. However, the correctness usually requires certain regularity of the input: the input points are either uniformly randomly sampled in a statistical setting, or they form the so-called $(\varepsilon,\delta)$-sample which can be neither too dense nor too sparse. Here, we propose a purely topological technique to detect dimensions. Our algorithm is provably correct and works under a more relaxed sampling condition: we do not require uniformity, and we also allow Hausdorff noise. Our approach detects dimension by determining local homology. The computation of this topological structure is much less sensitive to the local distribution of points, which leads to the relaxation of the sampling conditions. Furthermore, by leveraging various developments in computational topology, we show that this local homology at a point $z$ can be computed \emph{exactly} for manifolds using Vietoris-Rips complexes whose vertices are confined within a local neighborhood of $z$. We implement our algorithm and demonstrate the accuracy and robustness of our method using both synthetic and real data sets.
Analysis of Sorting Algorithms by Kolmogorov Complexity ( A Survey )

Recently, many results on the computational complexity of sorting algorithms were obtained using Kolmogorov complexity (the incompressibility method). Especially, the usually hard average-case analysis is ammenable to this method. Here we survey such results about Bubblesort, Heapsort, Shellsort, Dobosiewiczsort, Shakersort, and sorting with stacks and queues in sequential or parallel mode. Especially in the case of Shellsort the uses of Kolmogorov complexity surprisingly easily resolved problems that had stayed open for a long time despite strenuous attacks.