Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensorflex: Tensorflow bindings for the Elixir programming language

Recently, with the advent of programmatic and practical machine learning tools, programmers have been able to successfully integrate applications for the web and the mobile with artificial intelligence capabilities. This trend has largely been possible because of major organizations and software companies releasing their machine learning frameworks to the public– such as Tensorflow (Google), MXnet (Amazon) and PyTorch (Facebook). Python has been the de facto choice as the programming language for these frameworks because of it’s versatility and ease-ofuse. In a similar vein, Elixir is the functional programming language equivalent of Python and Ruby, in that it combines the versatility and ease-of-use that Python and Ruby boast of, with functional programming paradigms and the Erlang VM’s fault tolerance and robustness. However, despite these obvious advantages, Elixir, similar to other functional programming languages, does not provide developers with a machine learning toolset which is essential for equipping applications with deep learning and statistical inference features. To bridge this gap, we present Tensorflex, an open source framework that allows users to leverage pre-trained Tensorflow models (written in Python, C or C++) for Inference (generating predictions) in Elixir. Moreover, Tensorflex was written as part of a Google Summer of Code (2018) project by Anshuman Chhabra, and José Valim was the mentor for the same.
Effect of seismic operations on cetaceans sightings off-shore Akwa Ibom State, south-south, Nigeria

The possible behavioral and physical problems involving cetaceans and marine seismic surveys have attracted the interest and concern of several authors. However, studies on cetaceans frequenting Nigerian waters have not been detailed in literature. This research work is appropriately driven by these requirements. Opportunistic data was collected on board six different marine vessels belonging to ExxonMobil. Data collected were analyzed using descriptive statistics, t-test and ANOVA at p=0.05. A total of 8,327 hours 36 minutes observation effort was accomplished during twenty-six months, representing almost 800 km 2 of coverage. Two hundred and eighty-three (283) sightings were recorded comprising of 189 (66.78%) sightings of whales and 94 (33.22%) sightings of dolphins. Four species of cetaceans were identified, bottlenose dolphins ( Tursiops truncatus ) were the most abundant followed by pantropical spotted dolphins ( Stenella attenuata ). These two species occurred throughout the sampling months, while the predominant whales specie were the humpback whale (Megaptera novaeangliae) and killer whales ( Orcinus orca ) which seem to be present in just a part of the year. Whales were sighted more when the guns were active. Conversely, less of the dolphins were sighted while the guns were active. There were significant differences (P=0.05) regarding the air-gun operational state between the number of whales and dolphins sightings. The result suggests that swimming behavior was dependent of air-gun operational state. Keywords : Cetaceans, seismic survey, Sightings, Akwa Ibom state, south-south Nigeria.
Computing Polygon Similarity From Raster Signatures.

Computing similarity of spatial objects is not a trivial task. It considers complex algorithms, which have high cost to compute. This work proposes a simple algorithm to compute similarity between polygons through their Four-Color Raster Signatures (4CRS) based on Jaccard index. The algorithm was implemented in SECONDO, an extensible DBMS platform. Experimental tests were conduct in order to evaluate algorithm precision and execution time compared to computing polygon similarity through real representations of polygons. Results demonstrated that raster similarity computation is three times faster than exact computation, and raster similarity precision is higher for objects with high similarity and lower for objects that are not very similar. Therefore, we point the use of the proposal when the intention is to process objects that seem to have high similarity. On the other hand, other algorithm must be employed for objects with low similarity, e.g., compute similarity on objects real representation.
Thematic Text Graph: A Text Representation Technique for Keyword Weighting in Extractive Summarization System

Keyword extraction approaches based on directed graph representation of text mostly use word positions in the sentences. A preceding word points to a succeeding word or vice versa in a window of N consecutive words in the text. The accuracy of this approach is dependent on the number of active voice and passive voice sentences in the given text. Edge direction can only be applied by considering the entire text as a single unit leaving no importance for the sentences in the document. Otherwise words at the initial or ending positions in each sentence will get less connections/recommendations. In this paper we propose a directed graph representation technique (Thematic text graph) in which weighted edges are drawn between the words based on the theme of the document. Keyword weights are identified from the Thematic text graph using an existing centrality measure and the resulting weights are used for computing the importance of sentences in the document. Experiments conducted on the benchmark data sets SemEval-2010 and DUC 2002 data sets shown that the proposed keyword weighting model is effective and facilitates an improvement in the quality of system generated extractive summaries.
Flow: Open Source Reinforcement Learning for Traffic Control

This work presents Flow, an open-source Python library enabling the application of distributed reinforcement learning (RL) to mixed-autonomy traffic control tasks, in which autonomous vehicles, human-driven vehicles, and infrastructure interact. Flow integrates SUMO, a traffic microsimulator, with RLlib, a distributed reinforcement learning library. Using Flow, researchers can programatically design new traffic networks, specify experiment configurations, and apply control to autonomous vehicles and intelligent infrastructure. We have used Flow to train autonomous vehicles to improve traffic flow in a wide variety of representative traffic scenarios; the results and scripts to generate the networks and perform training have been integrated into Flow as benchmarks. Community use of Flow is central to its design; extensibility and clarity were considered throughout development. Flow is available for open-source use at flow-project.github. io and github.com/flow-project/flow.
Exploiting Adaptive Background Image and Dynamic Search Window for Fast Object Tracking

Recently, due to the interest for personal safety, intelligent image recognition technology using the CCTV has received a lot of attention in many areas. Real-time video or image tracking technology is the technology to continuously track the movement of the target object and maintain the specified information of the object. However, the problem is that unnecessary noise or environmental changes of the input video from CCTV make it difficult to accurately detect and track the target object. In this paper, we propose a fast object tracking algorithm, which is divided into two parts: i) object detection process and ii) object tracking process. In the background image replacement technique, we compute the difference between the initial input image and input video image. Then, it replaces N×M blocks based on the changed pixels, finally leading to replacing the background images. If it changes over the pre-defined threshold value, we set the block size and generate the variable search window for object tracking. Since the variable search window only generates the minimum search area for object tracking, our proposed method is quickly able to compute the variable region for object tracking. Through various experiments, our proposed scheme outperforms the other object detection and tracking methods in terms of object tracking counts and tracking error counts.
Multispectral Image Analysis for Astaxanthin Coating Classification

1. Technical University of Denmark (DTU),Department of Informatics and Mathematical Modelling2. Technical University of Denmark (DTU),National Food Institute, Division of Industrial Food TechnologyAbstract. Industrial quality inspection using image analysis on astax-anthin coating in aquaculture feed pellets is of great importance for au-tomatic production control. The pellets were divided into two groups,one with pellets coated using synthetic astaxanthin in fish oil, and theother with pellets coated only with fish oil. In this study multi-spectralimage analysis of pellets captured reflection in 20 wavelengths (385–1050 nm). Linear Discriminant Analysis (LDA), Principal ComponentAnalysis (PCA), Support Vector Machine (SVM) were used as statisti-cal analysis. The features extracted from the multi-spectral images waspixel spectral values as well as using summary statistics such as the meanor median value of each pellet. Classification using LDA on pellet meanor median values showed overall good results. Multi-spectral imagingis a promising technique for non-invasive on-line quality food and feedproducts with optimal use of pigment and minimum amount of waste.Keywords: NIR, astaxanthin, multi-spectral image, image analysis
Production and dispersion of coccolithophores are assessed within their ecologic and hydrographic context across enhanced spring chlorophyll production in the surface eastern North Atlantic

[1] Production and dispersion of coccolithophores are assessed within their ecologic and hydrographic context across enhanced spring chlorophyll production in the surface eastern North Atlantic. Within a 4 day period from 12 to 16 March 2004, a N–S transect from 47°N to 33°N was sampled along 20°W. Water samples from defined depths down to 200 m were analyzed for coccolithophores from 0.45 mm polycarbonate filters by scanning electron microscopy. At 47°N coccolithophores flourished when euphotic conditions allowed new production at deep mixing, low temperatures, and high nutrient concentrations. Emiliania huxleyi flourished at high turbulence during an early stage of the phytoplankton succession and contributed half of the total coccolithophore assemblage, with up to 150 × 10 cells L and up to 12 × 10 cells m when integrated over the upper 200 m of the water column. Maximum chlorophyll concentrations occurred just north of the Azores Front, at 37°N–39°N, at comparatively low numbers of coccolithophores. To the south, at 35°N–33°N, coccolithophores were abundant within calm and stratified Subtropical Mode Waters, and E. huxleyi was the dominant species again. Although the cell densities of coccolithophores observed here remained below those typical of plankton blooms visible from satellite images, the depth‐integrated total mass makes them significant producers of calcite and contributors to the total carbon sedimentation at a much wider range of ecological conditions during late winter and early spring than hitherto assumed.
How Do Hypothesis Tests Provide Scientific Evidence ? Reconciling

Testing a null hypothesis is the most common activity in statistical data analysis. Yet, the proper interpretation of such tests is still controversial. I argue that this is not only a central question of philosophy of statistics, but also at the heart of the disagreement between Popperians and Bayesians about scientific method. After elaborating on the shortcomings of the prevalent interpretations, including the Fisherian and the subjective Bayesian approach, I propose to regard frequentist hypothesis tests as stylized Bayesian decision problems, thereby solving refractory problems such as Lindley’s paradox. The crucial tool is the reference Bayesian approach, developed by the statistician José Bernardo. By exploring its philosophical implications, the paper provides (i) a foundationally sound account of hypothesis testing, (ii) a way to explicate Popper’s account of empirical corroboration, and (iii) a partial reconciliation of Popperian and Bayesian perspectives on scientific inference.
Reaching Resilience: Protective Factors and Adult Children of Divorce

Counseling Association by Dr. Garry R. Walz and Dr. Jeanne C. Bleuer of Counseling Outfitters, LLC. Its purpose is to provide a means of capturing the ideas, information and experiences generated by the annual ACA Conference and selected ACA Division Conferences. Papers on a program or practice that has been validated through research or experience may also be submitted. This digital collection of peer-reviewed articles is authored by counselors, for counselors. VISTAS Online contains the full text of over 500 proprietary counseling articles published from 2004 to present.
Stochastic stability on general state spaces 1

This paper studies stochastic stability methods applied to processes on general state spaces. This includes settings in which agents repeatedly interact and choose from an uncountable set of strategies. Dynamics exist for which the stochastically stable states differ from those of any reasonable finite discretization. When there are a finite number of rest points of the unperturbed dynamic, sufficient conditions for analogues of results from the finite state space literature are derived and studied. Illustrative examples are given.
Self-organization of polymeric lipids with hydrophilic spacers in side groups and main chain: investigation in monolayers and multilayers

Several polymerizable lipids were synthesized and polymerized to amphiphilic homopolymers and to copolymers with the help of hydrophilic comonomers. The self-organization of these polymeric lipids was investigated in monolayers and Langmuir-Blodgett multilayers. The self-organization of these polymers in model membranes is due to hydrophilic spacer groups in the amphiphilic side groups as well as to hydrophilic spacer groups in the polymer backbone. Thus, highly ordered monolayers and LB-multilayers are easily obtained. Scheme I. Schematic Representation of Amphiphilic Polymers Containing Hydrophilic Spacergroups"
A statistical explanation of MaxEnt for ecologists

MaxEnt is a program for modelling species distributions from presence‐only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence‐only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south‐west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.
Scales of justice: Is there a geographic bias in environmental equity analysis?

Abstract Many empirical environmental justice (EJ) studies lack a systematic framework in which to undertake research and interpret results. This paper characterizes the conventional EJ study and examines how results can be influenced by the choice of the spatial scale and scope of analysis. After thoroughly reviewing a sample of prominent EJ studies, a conventional EJ study was performed for (Superfund) National Priorities List sites at multiple scales and scopes. It was found that evidence of environmental injustice could be sensitive to scale and scope chosen, which partly explains the observed inconsistency in the empirical literature. Implications for interpreting existing EJ research and conducting future EJ research are discussed.
The science case for the Planet Formation Imager (PFI)

Among the most fascinating and hotly-debated areas in contemporary astrophysics are the means by which planetary systems are assembled from the large rotating disks of gas and dust which attend a stellar birth. Although important work has already been, and is still being done both in theory and observation, a full understanding of the physics of planet formation can only be achieved by opening observational windows able to directly witness the process in action. The key requirement is then to probe planet-forming systems at the natural spatial scales over which material is being assembled. By definition, this is the so-called Hill Sphere which delineates the region of influence of a gravitating body within its surrounding environment. The Planet Formation Imager project (PFI; http://www.planetformationimager.org) has crystallized around this challenging goal: to deliver resolved images of Hill-Sphere-sized structures within candidate planethosting disks in the nearest star-forming regions. In this contribution we outline the primary science case of PFI. For this purpose, we briefly review our knowledge about the planet-formation process and discuss recent observational results that have been obtained on the class of transition disks. Spectro-photometric and multi-wavelength interferometric studies of these systems revealed the presence of extended gaps and complex density inhomogeneities that might be triggered by orbiting planets. We present detailed 3-D radiation-hydrodynamic simulations of disks with single and multiple embedded planets, from which we compute synthetic images at near-infrared, mid-infrared, far-infrared, and sub-millimeter wavelengths, enabling a direct comparison of the signatures that are detectable with PFI and complementary facilities such as ALMA. From these simulations, we derive some preliminary specifications that will guide the array design and technology roadmap of the facility.
Self-Efficacy and Anxiety among EFL Learners with Different Kinds of Multiple Intelligences

The present study was an attempt to investigate the relationship between the self-efficacy and anxiety of EFL learners with different kinds of multiple intelligences. Accordingly, three questionnaires were used: the General Self-Efficacy Scale (GSE), the Endler Multidimensional Anxiety Scales (EMAS), and the Multiple Intelligence Development Assessment Scales (MIDAS). A total of 246 undergraduate and graduate students aged 19 to 27 participated in this study. The researchers carried out a series of both descriptive and inferential statistical analyses and the results indicated that there is a positive correlation between anxiety and self-efficacy. The results also showed a significant relationship between linguistically intelligent EFL learners’ self-efficacy and anxiety and that anxiety cannot be a predictor for EFL learners’ self-efficacy.
Response of Late Cretaceous Migrating Deltaic Facies Systems to Sea Level, Tectonics, and Sediment Supply Changes, New Jersey Coastal Plain, U.S.A.

Abstract Paleogeographic, isopach, and deltaic lithofacies mapping of thirteen depositional sequences establish a 35 myr high resolution (> 1 Myr) record of Late Cretaceous wave- and tide-influenced deltaic sedimentation. We integrate sequences defined on the basis of lithologic, biostratigraphic, and Sr-isotope stratigraphy from cores with geophysical log data from 28 wells to further develop and extend methods and calibrations of well-log recognition of sequences and facies variations. This study reveals the northeastward migration of depocenters from the Cenomanian (ca. 98 Ma) through the earliest Danian (ca. 64 Ma) and documents five primary phases of paleodeltaic evolution in response to long-term eustatic changes, variations in sediment supply, the location of two long-lived fluvial axes, and thermoflexural basement subsidence: (1) Cenomanian–early Turonian deltaic facies exhibit marine and nonmarine facies and are concentrated in the central coastal plain; (2) high sediment rates, low sea level, and high accommodation rates in the northern coastal plain resulted in thick, marginal to nonmarine mixed-influenced deltaic facies during the Turonian–Coniacian; (3) comparatively low sediment rates and high long-term sea level in the Santonian resulted in a sediment-starved margin with low deltaic influence; (4) well-developed Campanian deltaic sequences expand to the north and exhibit wave reworking and longshore transport of sands; and (5) low sedimentation rates and high long-term sea level during the Maastrichtian resulted in the deposition of a sediment-starved glauconitic shelf. Our study illustrates the widely known variability of mixed-influence deltaic systems, but also documents the relative stability of deltaic facies systems on the 106–107 yr scale, with long periods of cyclically repeating systems tracts controlled by eustasy. Results from the Late Cretaceous further show that although eustasy provides the template for sequences globally, regional tectonics (rates of subsidence and accommodation), changes in sediment supply, proximity to sediment input, and flexural subsidence from depocenter loading determines the regional to local preservation and facies expression of sequences.
Combination of Penalty Function, Lagrange Multiplier and Conjugate Gradient Methods for the Solution of Constrained Optimization Problems

In this paper, we combined Langrage Multiplier, Penalty Function and Conjugate Gradient Methods (CPLCGM), to enable Conjugate Gradient Method (CGM) to be employed for solving constrained optimization problems. In the year past, Langrage Multiplier Method (LMM) has been used extensively to solve constrained optimization problems likewise Penalty Function Method (PFM). However, with some special features in CGM, which makes it unique in solving unconstrained optimization problems, we see that this features we be advantageous to solve constrained optimization problems if it can be properly amended. This, then call for the CPLCGM that is aimed at taking care of some constrained optimization problems, either with equality or inequality constraint but in this paper, we focus on equality constraints. The authors of this paper desired that, with the construction of the new Algorithm, it will circumvent the difficulties undergone using only LMM and as well as PFM to solve constrained optimization problems and its application will further improve the result of the Conjugate Gradient Method in solving this class of optimization problem. We applied the new algorithm to some constrained optimization problems and compared the results with the LMM and PFM.