Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallelization of 2 D Fast Multipole Method

for each i = 1, 2, . , n, where K is a given (usually smooth away from origin) kernel. For example, for gravitaional interactions in 3D, K(x, y) = 1 |x−y| ; for 2D Laplacian equation, K(x, y) = log |x− y|. The direct computation has a cost of O(n). In recent decades, many algorithm has been invented and developed to accelerate the quadratic time complexity. Fast multipole method (FMM), first introduced by V. Rokhlin and L. Greengard in 1985[1], is probably the most famous and prominent one – it achieves the linear complexity O(n), with constant factor depends on precision. This fact makes FMM one of the first choice in simulations of large-scale physical problems, and thus, an efficient parallelization of FMM is crucial in many applications. Many papers[2][3] have explored different methods/platforms to parallel different versions of FMM. In this project, we will explore the parallelization of 2D Laplacian FMM using OpenMP on a shared memory computing structure. We will first make a brief introduction to the FMM algorithm (section 2); after that we will discuss about the fork-join model (section 3.1) – a natural but clever way to parallel FMM, and then we will explore yet another parallelization of FMM – the interleaving model (section 3.2). Finally we will show some numerical result about the scaling of the parallel FMM (section 4). The code we use in this project is based on FMM2D Libraries of Courant Mathematics and Computing Laboratory (CMCL). The original version of code is available at http://www.cims. nyu.edu/cmcl/ fmm2dlib/fmm2dlib.html. Our parallel FMM code is available on the project github https://github. com/tsl665/fmm2d_omp.
Using remote sensing to detect the polarized sunglint reflected from oil slicks beyond the critical angle

The critical angle at which the brightness of oil slicks and oil-free seawater is reversed occurs under sunglint and is often shown as an area of uncertainty due to different roughness and surface Fresnel reflection parameters. Consequently, differentiating oil slicks from the seawater in these areas using optical sensors is a challenge. Polarized optical remote sensing techniques provide complementary information for intensity imagery with different physical properties and, thus, possess the ability to resolve this difficult problem. In the polarized reflectance model, the degree of linear polarization (DOLP) of sunglint depends on accurately knowing the Stokes parameter for the reflected light, and varies with the refractive index of the surface layer and the viewing angles. For the polarized detection of oil slicks, the highest sensitivity of the DOLP to the refractive index is located within the specular reflection direction where the sum of the solar and sensor zenith angles is 82.6°. The modeled results clearly indicate that the DOLP of oil slicks is weaker in comparison with oil-free seawater under sunglint. Using measurements from the space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) over the Deepwater Horizon oil spill in the Gulf of Mexico, we illustrate that the PARASOL-derived DOLP difference between the oil spill and seawater is obvious and is in accordance with the modeled results. These preliminary results suggest that the potential of multi-angle measurement and feasibility of deriving refractive index of ocean surface from space-borne sensors need further researches.
Methodological rigour within a qualitative framework.

AIM This paper discusses the literature on establishing rigour in research studies. It describes the methodological trinity of reliability, validity and generalization and explores some of the issues relating to establishing rigour in naturalistic inquiry. BACKGROUND Those working within the naturalistic paradigm have questioned the issue of using validity, reliability and generalizability to demonstrate robustness of qualitative research. Triangulation has been used to demonstrate confirmability and completeness and has been one means of ensuring acceptability across paradigms. Emerging criteria such as goodness and trustworthiness can be used to evaluate the robustness of naturalistic inquiry. DISCUSSION It is argued that the transference of terms across paradigms is inappropriate; however, if we reject the concepts of validity and reliability, we reject the concept of rigour. Rejection of rigour undermines acceptance of qualitative research as a systematic process that can contribute to the advancement of knowledge. Emerging criteria for demonstrating robustness in qualitative inquiry, such as authenticity, trustworthiness and goodness, need to be considered. Goodness, when not seen as a separate construct but as an integral and embedded component of the research process, should be useful in assuring quality of the entire study. Triangulation is a tried and tested means of offering completeness, particularly in mixed-method research. When multiple types of triangulation are used appropriately as the 'triangulation state of mind', they approach the concept of crystallization, which allows for infinite variety of angles of approach. CONCLUSION Qualitative researchers need to be explicit about how and why they choose specific legitimizing criteria in ensuring the robustness of their inquiries. A shift from a position of fundamentalism to a more pluralistic approach as a means of legitimizing naturalistic inquiry is advocated.
Automatic documentation inference for exceptions

Exception handling is a powerful and widely-used programming language abstraction for constructing robust software systems. Unfortunately, it introduces an inter-procedural flow of control that can be difficult to reason about. Failure to do so correctly can lead to security vulnerabilities, breaches of API encapsulation, and any number of safety policy violations. We present a fully automated tool that statically infers and characterizes exception-causing conditions in Java programs. Our tool is based on an inter-procedural, context-sensitive analysis. The output of this tool is well-suited for use as human-readable documentation of exceptional conditions. We evaluate the output of our tool by comparing it to over 900 instances of existing exception documentation in almost two million lines of code. We find that the output of our tool is at least as good as existing documentation 85% of the time and is better 25% of the time.
The case of severe weather in Greece on 16 and 17 July 2017 - Evaluation of the prognostic guidance provided by ECMWF and COSMO numerical weather models

Nowadays there is a profound increase in the number of natural disasters attributed to extreme weather events which is significantly impeding progress towards sustainable development. To deal with a risk of an emergency threatening life or property, a weather-forecast office would use a range of forecast tools to assess the threat and provide the necessary forecasts and warnings. In this paper, using as case study the severe weather occurred in Greece on 16 and 17 July 2017, we discuss the capability of the ECMWF/EPS and the COSMO-LEPS forecasts, as also the ΕCMWF/HRES and COSMO.GR7 deterministic forecasts to provide forecasters with reliable prognostic guidance. To do so, we used these models’ consecutive runs from Friday 14-7-2017 (two days before the event) until Monday 17 -7-2017 (last day of the event). The effectiveness of these forecasts (rainfall spatiotemporal distribution and intensity) was then evaluated with the accumulated precipitation at ground (H-SAF/PR-OBS-5), with MSG products (RGB Airmass, Cloud Top Height) provided by EUMETSAT and with weather radar products.
Performance analysis issues for parallel implementations of propagation algorithm

We present a theoretical study to evaluate the performance of a family of parallel implementations of the propagation algorithm. The propagation algorithm is used to an image interpolation application. The theoretical performance analysis is based on the construction of generic models using stochastic automata networks (SAN) formalism to describe each implementation scheme. The prediction results can be compared to the achieved performance in some real test cases to verify the accuracy of our modeling technique. The main contribution is to point out the advantages and problems of our approach to the development of generic models of parallel implementations.
An analytic performance model of disk arrays

As disk arrays become widely used, tools for understanding and analyzing their performance become increasingly important. In particular, performance models can be invaluable in both configuring and designing disk arrays. Accurate analytic performance models are preferable to other types of models because they can be quickly evaluated, are applicable under a wide range of system and workload parameters, and can be manipulated by a range of mathematical techniques. Unfortunately, analytic performance models of disk arrays are difficult to formulate due to the presence of queueing and fork-join synchronization; a disk array request is broken up into independent disk requests which must all complete to satisfy the original request. In this paper, we develop and validate an analytic performance model for disk arrays. We derive simple equations for approximating their utilization, response time and throughput. We validate the analytic model via simulation, investigate the error introduced by each approximation used in deriving the analytic model, and examine the validity of some of the conclusions that can be drawn from the model.
Reliable Stream Scheduling with Minimum Latency for Wireless Sensor Networks

As sensor networks are increasingly deployed for critical applications, reliability and latency guarantee become more important than ever to meet industrial requirements. In this paper, we investigated the impact of link burstiness on stream scheduling using a data trace of 3,600,000 packets collected from an indoor testbed. We demonstrate that a good tradeoff between reliability and latency can be achieved by allocating certain time slots on each link for stream transmissions based on its burst length and frequency distributions. With this observation, we design transmission scheduling and routing algorithms for data streams to meet a specified reliability requirement while minimizing end-to- end latency. For the multi-stream scheduling problem, we prove its NP-hardness and design an algorithm that achieves the reliability guarantee and an $O(\log n)$ approximation of minimizing the maximum end-to-end latency for any stream. Trace- driven simulations show that our solution meets specified end-to-end reliability requirements with latency up to 9.18 times less than existing solutions.
Comparison of univariate and multivariate gene set analysis in acute lymphoblastic leukemia.

BACKGROUND Gene set analysis (GSA) incorporates biological with statistical knowledge to identify gene sets which are differentially expressed that between two or more phenotypes. MATERIALS AND METHODS In this paper gene sets differentially expressed between acute lymphoblastic leukaemia (ALL) with BCR-ABL and those with no observed cytogenetic abnormalities were determined by GSA methods. The BCR-ABL is an abnormal gene found in some people with ALL. RESULTS The results of two GSAs showed that the Category test identified 30 gene sets differentially expressed between two phenotypes, while the Hotelling's T2 could discover just 19 gene sets. On the other hand, assessment of common genes among significant gene sets showed that there were high agreement between the results of GSA and the findings of biologists. In addition, the performance of these methods was compared by simulated and ALL data. CONCLUSIONS The results on simulated data indicated decrease in the type I error rate and increase the power in multivariate (Hotelling's T2) test as increasing the correlation between gene pairs in contrast to the univariate (Category) test.
A language model for highly inflective non-agglutinative languages

This paper proposes a method of creating language models for highly inflective non-agglutinative languages. Three types of language models were considered - a common n-gram model, an n-gram model of lemmas and a class n-gram model. The last two types were specially designed for the Serbian language reflecting its unique grammar structure. All the language models were trained on a carefully collected data set incorporating several literary styles and a great variety of domain-specific textual documents in Serbian. Language models of the three types were created for different sets of textual corpora and evaluated by perplexity values they have given on the test data. A log-linear combination of the common, lemma-based and class n-gram models that was also created shows promising results in overcoming the data sparsity problem. However, the evaluation of this combined model in the context of a large vocabulary continuous speech recognition system (LVCSR) is yet to be done in order to establish the improvement in terms of word error rate (WER).
Assessment of Heavy Metals Level of Soils around Sacks and Packaging Company, Akwanga Nasarawa State, Nigeria

Increasing industrial activity around the world has left behind large number of contaminants such as heavy metals which can easily get into food chain and bio-cumulate in tissues of living organism with detrimental effect. This study was carried out to assess the impact of activities in a sack production and packaging company on the level of heavy metal on soils around the industrial layout. The results obtained from the soil analysis reveals that the pH, organic carbon and organic matter content of the test and control soils were (8.40 ± 0.20 and 8.51 ± 0.01), (1.76 ± 0.030 and 0.92 ± 0.02%) and (3.03 ± 0.33 and 0.55 ± 0.05%) respectively. While the available phosphorus content, nitrogen and effective cation exchange capacity of the test and control soil samples were (3.62 ± 0.02 and 4.11 ± 0.10%), (0.251 ± 0.01 and 0.078 ± 0.001%) and (65.59 ± 0.05 and 14.78 ± 0.01 Meq/100 g) respectively which were within the limits of normal agronomical soil. The mean ± standard deviation of heavy metal concentrations in the test and control soil were Fe (4625.32 ± 0.252 mg/kg and 3676.44 ± 0.57 mg/kg), Ni (48.20 ± 0.128 mg/kg and 27.50 ± 0.11 mg/kg) Co (36.85 ± 0.046 mg/kg and 37.05 ± 0.044 mg/kg) and Pb (321.45 ± 0.038 mg/kg and 174.23 ± 0.088 mg/kg) respectively where the mean concentrations of the heavy metals in the soil are in the order: Fe>Pb>Ni>Co while Cd and Cr were below detection limit. The impact of the industrial activities determines using indices such as Geo-accumulation index and contamination factor indicates high contamination of the soil with lead, a non-essential element responsible for lead poisoning. Also, the statistical analysis showed a general significant difference between the mean content of heavy metal between the soil around the industry and the soil around the control site suggesting that metal enrichment is likely due to anthropogenic activity since the metal level in the test soil were generally higher with the exception of cobalt.
Assessing relevance of tweets for risk communication

ABSTRACT Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.
A General Framework for Predictive Business Process Monitoring

As organizations gain awareness of the potential business value locked in their process execution event logs, evidence-based” business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise. In previous work, various approaches have been proposed to address typical predictive process monitoring problems, such as whether a running process instance will meet its performance targets, or when will an instance be finally finished. However, these approaches are rather ad-hoc and lack generality, as they tackle only particular, pre-defined aspects of predictive monitoring and often only work with specific characteristics of the dataset. The proposed research project aims at developing a general and robust framework for predictive process monitoring that will address a variety of process monitoring tasks such as predicting the outcome of individual activities or of the whole process instance, or predicting the completion path of an instance.
A Term Frequency Distribution Approach for the DUC-2007 Update Task

We present our system used in the DUC 2007 update task, which is our first entry in any of the DUC evaluations. We make use of ideas within our existing FreqDistSumm text summarizer, which has been shown to perform well in biomedical text summarization. Our system submitted to the DUC Update Task, called FreqDistUpdate, uses a context sensitive approach to scoring sentences based on a frequency distribution model. FreqDistUpdate performed in the middle of all systems in three out of the four evaluations. We believe the frequency distribution method is a promising approach for the update task, and that improvements in implementation and approach will lead to better performance in the future DUC
Arc-Standard Spinal Parsing with Stack-LSTMs

We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees. The parser uses Stack-LSTMs that compose constituent nodes with dependency-based derivations. In experiments, we show that this model adapts to different styles of dependency relations, but this choice has little effect for predicting constituent structure, suggesting that LSTMs induce useful states by themselves.
Mobile manipulator control based on voice and visual signal

In this paper, two kinds of methods are used to control the mobile manipulator in terms of voice signal control approach and visual signal control approach. In voice signal control approach, the mobile manipulator can perform some actions according to the operator's speaking voice input through a microphone. For example, the robot can be commanded to go forward or backward in specified distance, turn left or right with specified angle, even can follow up “8” shape trajectory and catch a ball. In visual approach, the mobile robot can accomplish some actions according to the operator's gestures of two hands using Kinect sensor information. It gets the depth image of two hands in spatial coordinates and calculates the relative distance of them to take some actions by an onboard computer. An embedded single board computer is installed in mobile robot Pioneer2 DX for enhancing remote control and running program's ability.
The Institute For Research In Cognitive Science Disambiguation of Super Parts of Speech ( or Supertags ) : Almost Parsing by Aravind

In a lexicalized grammar formalism such as Lexicalized Tree-Adjoining Grammar (LTAG), each lexical item is associated with at least one elementary structure (supertag) that localizes syntactic and semantic dependencies. Thus a parser for a lexicalized grammar must search a large set of supertags to choose the right ones to combine for the parse of the sentence. We present techniques for disambiguating supertags using local information such as lexical preference and local lexical dependencies. The similarity between LTAG and Dependency grammars is exploited in the dependency model of supertag dis-ambiguation. The performance results for various models of supertag disambiguation such as unigram, trigram and dependency-based models are presented.
Towards the Chilean solar thermal potential knowledge for solar power tower plants

The present paper presents a very simple energy yield model fitted using the annual DNI and the latitude as main inputs, considering a solar tower CSP plant, with 100 MW of net energy output and 6 hours of thermal storage. Furthermore, a mask of suitable areas for CSP power tower installations in Chile is also shown. The mapping of solar radiation components has been calculated from multi-regressive models based on ground based measurements, existing maps of solar resources and atmospheric parameters. An analysis of the available data bases in Chile is also done in order to obtain useful information for the development of the work.
Controlling for systematic selection in retrospective analyses: an application to fluoxetine and sertraline prescribing in the United Kingdom.

BACKGROUND Criticism has been made of observational studies in clinical practice because of their failure to control for unobserved factors that correlate with both initial treatment selection and observed outcomes. METHOD A two-stage statistical model was applied to data obtained from a large general practitioner medical records database (DIN-LINK) to estimate the effect of initial antidepressant selection on the duration of antidepressant therapy and on the likelihood of being prescribed an average daily dose above the minimum recommended dose. The statistical model controlled for unobserved factors correlated with initial treatment selection and the observed outcomes as well as for observed confounders. RESULTS Unobserved factors correlated with treatment selection were not a statistically significant determinant of the number of days of antidepressant therapy. However, unobserved factors correlated with treatment selection were a statistically significant determinant of the likelihood of receiving an average dose during therapy greater than the minimum recommended. After controlling for relevant confounders, those patients who began treatment with sertraline as opposed to fluoxetine had fewer days of antidepressant therapy and were more likely to receive average doses greater than the minimum recommended during therapy. CONCLUSION Unobserved factors correlated with treatment selection can impact outcomes in observational studies and should be tested and controlled for whenever possible.
Mobile Robot 3D Perception and Mapping without Odometry Using Multi-Resolution Occupancy Lists

Many real-world applications require mobile robots to have 3D perception and mapping capabilities. This paper proposes a novel mechanism for augmenting a traditional 2D laser range finder to produce almost omni-directional 3D scans at degree resolution in 3 seconds. Also introduced is a multi-resolution sampling based algorithm for aligning generated range data stored as occupancy lists. This algorithm can align a sequence of scans to produce maps and localize within a prior global map. A 200 square meter office environment is mapped at 0.02 m resolution, resulting in a 200,000 voxel occupancy list. Global localization within this entire map with no prior pose estimate is done in 5 seconds on a 2 GHz processor.