Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesizing Geologically Coherent Cave Networks

We present a geologically‐based method to generate complex karstic networks. Karsts are a type of landscape formed by the dissolution of highly soluble rocks (generally limestones). In particular, they are characterized by complex underground networks made of varieties of tunnels and breakout chambers with stalagmites and stalactites. Our method computes skeletons of karstic networks by using a gridless anisotropic shortest path algorithm according to field data of the underground system (such as inlets and outlets), geomorphological features and parameters such as faults, inception horizons, fractures, and permeability contrasts. From this skeleton, we define the geometry of the conduits as a signed distance function construction tree combining primitives with blending and warping operators. Our framework provides multiple levels of control, allowing us to author both the structure of the karstic network and the geometric cross‐section shapes and details of the generated conduits.
A Statistically-Assisted Sketch-Based Interface for Creating Arbitrary 3D Faces

Creating faces is important in a number of application areas. Faces can be constructed using commercial modelling tools, existing faces can be transferred to a digital form using equipment such as laser scanners, and law enforcement agencies use sketch artists and photo-ﬁt software to produce faces of suspects. We present a technique that can create a 3-dimensional head using intuitive, artistic 2-dimensional sketching techniques. Our work involves bringing together two types of graphics applications: sketching interfaces and systems used to create 3-dimensional faces, through the mediation of a statistical model. We present our results where we sketch a nose and search for a geometric face model in a database whose nose best matches the sketched nose.
Novel Reinforcement Learning Methods for Robotics

—This work aims to evaluate a set of similar Deep Reinforcement Learning algorithms in a set of diversiﬁed of environments. The adaptations required by the vanilla algorithms to get to the presented results in each environment are a key aspect of this work. The experiments are performed by using agents described with hyper-parameter tables and neural network schematics to evaluate differences in the documented conﬁgurations, in spite of the stochastic results. The goal is to use the presented results to support a more generic description for agents, environments and their interaction, an exercise which can fall outside the traditional scope of optimization
A Neutrosophic Cognitive Map Based Approach to Explore the Health Deterioration Factors

: Neutrosophy logic has gained an exponential fame in recent years among researchers and academician specially when there is need to deal with the different and difficult situations. Out of several neutrosophy-sets related concepts, the researchers have used neutrosophic cognitive maps for identifying the hidden and indeterminate factors that can influence a particular situation or can significantly affect any problems which involve making decisions. In our study we have used neutrosophic cognitive maps to explore the factors which can lead to health deterioration. The present method not only illustrates the way neutrosophic cognitive maps can be used but also suggests how we can help the masses to find out the health affecting factors and to control it. It is believed that the proposed method can be helpful for analyzing many such a situation and can set a bench mark in using soft computing for healthcare.
Tracking resistance by the RWDT and flashover characteristics of polymeric materials of outdoor HV insulator

This paper describes the results of a study on the tracking performance of high voltage polymeric insulating materials based on the rotating wheel dip test (RWDT). The influence of surface degradation was evaluated through measurement of the flashover performance after and before tracking test. It is concluded that the surface degradations of polymeric insulating materials are directly related to the partial discharge and weight loss. Leakage current on the surface of specimens is useful in evaluating degradation aspects. In the RWDT, the breakdown specimen is not affected by the dry flashover voltage, despite the fact the that the surface degradation of tracking test has different state on each specimen.
Supplementary Material: Video Instance Segmentation with a Propose-Reduce Paradigm

Training We follow the training setup as in [2]. We train our model for 4 epochs in the main-training stage and 5 Algorithm 1: Sequence Proposals Reduction Input: Input sequence set S = {So k}; Its classification score C(S) = {C(So k)}; Its mask sequence set M(S) = {M(So k)}; where k = 0, 1, . ,K 1 and o = 0, 1, . , O 1. IoU threshold ✓. Output: Final sequence set b S b S {}; while S 6= ? do (k0, o0) argmax C(S); V So k0 ; b S b S [ V ;S S V ; for So k in S do if IoU(M(V ), M(So k)) ✓ then S S So k; C(S) C(S) C(So k); M(S) M(S) M(So k); end end end return b S;
CAPACITATED FUZZY TWO-STAGE LOCATION-ALLOCATION PROBLEM

This paper presents a new class of two-stage minimum risk location and allocation model based on credibility theory. We ﬁ rst discuss an approximation approach to the location-allocation problem as well as the convergence of the approach. Then, we design a hybrid algorithm, which integrates the approximation approach, neural network and simulated annealing, to solve the proposed location-allocation problem. Finally, we provide a numerical example to test the e ﬀ ectiveness of the hybrid algorithm
Multi wall carbon nanotubes as a top electrode for perovskite light-emitting electrochemical cells

Carbon nanotubes are promising materials for use as flexible and transparent electrodes for a wide variety of optoelectronic devices like solar cells and light-emitting diodes. Moreover, the chemical inertness of carbon nanotubes matches with the operation of light-emitting electrochemical cells where ion migration is a main working principle especially based on halide perovskite, in which ions corrode metal electrodes. Here we demonstrate how the removal of synthesis residuals via synthesis enables the use of multiwall carbon nanotubes as a transparent electrode.
Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.
Conflict Resolution Advisory Service in autonomous aircraft operations

Recent advances in air traffic Communication Navigation and Surveillance (CNS) have encouraged investigations into the concept following which Air Traffic Control functions can be delegated to the flight deck, allowing more freedom of movement to airspace users. In late 1995, an initiative, known under the project name FREER, for Free-Route Experimental Encounter Resolution, was introduced at EUROCONTROL to investigate the feasibility of this concept. In this paper, we present the initial results obtained from the Autonomous Aircraft Operations study (FREER-I) that targets full delegation of ATC to aircraft operating in low-density airspace. Specific issues of Autonomous Aircraft Operations (AAO) supporting free-route, free flight and user-preferred routing are discussed. A description of the current prototype illustrating the Airborne Interactive Conflict Resolution Advisory service is included.
Total angular momenta quantization of dielectric sphere modes

Spherical particles both dielectric and metallic are essential building blocks in nanophotonics. During the recent rapid development of Mie-tronic — nanophotonics devices heavily using various features of the Mie-resonances — the deep fundamental investigation of the eigenmodes of such particles by using the novel tools is still relevant and currently important. Moreover, eigenmodes of a sphere are closely related to the Vector Spherical Harmonics (VSH) which are widely used in the multipolar decomposition to analyze less symmetric structures. In this work, we study in detail the canonical spin and angular momenta (AM), helicity, and other properties of the eigenmodes of dielectric (nondispersive) and metallic (dispersive) spheres. We show that the canonical momentum density of the AM is quantized and has a close relation to the quantum picture of a single photon. Our work provides a solid platform for future studies and applications of the AM transfer from near fields of spherical particles to the matter in its vicinity.
Phase reconstruction of living human embryonic kidney 293 cells based on two off-axis holograms

In this paper, we validate experimentally the potential of off-axis digital micro-holography for 3D image reconstruction of a live Human Embryonic Kidney 293(HEK293) cell that is widely used as transfection and expression. A subtraction method of two off-axis holograms to reconstruct the phase of the live microscopic object is discussed. The presented subtraction method can remove some main noise, for example, the quadratic phase aberration introduced by microscope objective (MO), other phase aberration introduced by the liquid in tank and other interference noise introduced by the optical parts. Thus an improvement in the measurement precision of live cells in aqueous solution is observed. The potential of this method is demonstrated by providing the phase reconstruction results of a phase grating and a single HEK293 cell. The results showed good correspondence to the actual character of HEK293 cell prove the capability of digital micro-holography as a tool to monitor the dynamic transfection process of the living HEK 293 cells.
A Novel Approach for Image Denoising and Performance Analysis using SGO and APSO

Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).
Genetic differentiation of Coregonus muksun in natural populations and broodstocks of fish rearing farms “Forvat” and “Sobsky” in relation to the problem of restoring the species population in Western Siberia

With regard to the problem of restoring the muksun (Coregonus muksun) population in Western Siberia by means of artificial regeneration, the genetic differentiation of natural populations and broodstocks of fish rearing farms “Forvat” and “Sobsky” was studied. Eighty-four sequences of mtDNA (3,006 bp) containing the highly polymorphic genes ND1 and ND2 were analysed. The differences between the populations of Western and Eastern Siberia were revealed. These findings must be taken into account when attempting to restore the species population in each particular region. The genetic closeness of the muksun from fish rearing farms “Forvat” and “Sobsky” with muksun from the Ob-Taz basin gives grounds for using these broodstocks for seeding the Ob and Taz rivers.
Amplification rate of matK and rbcL genes in three types of durian

Durian is a tropic fruit having numerous variations on its fruits. Its variations are not only in its shape but also in its aril fruit, aril color, flavor, and aril thickness. In addition to its fruit variations, the genus Durio also has many species which quite hard to distinguish morphologically, except during flowering and fruiting times. This study aimed to determine the genetic relationship among Durian, Pelangi Atuturi Variety Durian, Durio graveolent, and Durio zibetinus based on chloroplast genes (RbcL and matK genes). The primers were previously designed for amplifying matK and rbcL genes based on the Durio zibethinus sequence. Both genes were used because of having great competence to describe genetic relationships between plant species. The rbcL primer could amplify all evaluated samples. Meanwhile, matK primer generated a smeared band in Durian Pelangi; thus, we did not obtain any sequence of this plant. Sequence analysis showed no variation of rbcL sequence in these evaluated species. A similar result was also observed on D. zibethinus and D. graveolent. Overall, both genes could not describe the genetic relationship among the evaluated durians, and they were grouped in the same cluster in phylogenetic.
Generative Adversarial Networks for Facial Image Inpainting and Super-Resolution

Inpainting helps to fill in the lost data in visual images. Inpainting techniques also refer to unusual image editing in distorted regions. These include areas that are noisy, blurred and watery areas. The most appropriate pixel values must be replaced in these regions to achieve good performance. Artists used to play it, and still now, pieces that are not in the picture can be inpainted in the same manner, though it takes more time. In the present age of automation, inpainting can be automated to obtain quicker and better outcomes by deep learning technologies. In this area, many of the latest techniques have been created, however, many methods produce blurred findings and data loss. Two adversarial networks are used to achieve this task, where first network aims at inpainting and the second network aims at super-resolution. The input generated as a part of first stage network is passed on to the second stage super-resolution network to overcome blurriness that is caused in the initial inpainting network. The network efficiency is determined in terms of increased PSNR obtained which is 28.19 dB with less training period of approximately 14 hours in comparison with other network models which performs similar task.
Social Crime or Spatial Crime? Exploring the Effects of Social, Economical, and Spatial Factors on Burglary Rates

Space-crime research has primarily focused on crime prevention through environmental design, based on the ideology of defensible space, territoriality, target-hardening factors, and so on. Much of the research has attempted to determine ideological imperatives within the scope of either architectural buildings or urban areas. Few studies can be found dealing with whether there exists a significant influence of spatial configuration or CPTED-based building designs or social and economical reasons for the patterns of residential burglary distribution. This research focuses on three major factors: social factor of building use patterns, economical factor of building values, and two spatial factors, namely, the configurational factor of urban space structures and the design factor of architectural buildings. To examine the effect of the latter factors on burglary distribution patterns, configurational factor of urban space structures will be examined by investigating the configurational factors of intelligibility and integration. Other factors in consideration are road types, the types of buildings that witness high crime rates, and visibility issues. For the effect of building design factors, issues such as the number of entrances, building height, and target-hardening facilities will be examined. Our findings across six areas in a metropolitan city suggest that intelligible areas that can be easily accessible by passersby, and thus, enable more people to enter the area, are deemed to be less vulnerable than less intelligible areas. The urban space structure configures residential burglary patterns and building-use patterns significantly affect burglary rates, followed by road type and design factor issues.
Development of an Industry 4.0 Big Data Processing and Management System

Industry 4.0, also known as the fourth industrial revolution, refers to the enhancement of automation, connectivity and intelligence of machines within a production environment. Its objectives are increasing reactiveness, asset monitoring, decision making and value creation of manufacturing. Industry 4.0 solutions are however challenging to implement as they rely on data systems capable of handling massive amounts of data with low latency, high delivery guarantee, high fault tolerance and high information throughput. This generally implies satisfying Big Data, cyber physical system, Internet of Things and distributed computing system constraints. Understanding the high potential associated to Industry 4.0 solutions, AKKA Technologies, an Engineering and Technology consulting ﬁrm, decided to launch project ZORRO who’s initial aim was to develop a data system capable of detecting manufacturing anomalies in real-time and to serve as a source of internal Industry 4.0 know-how. As a contribution to project ZORRO, in this work an Industry 4.0 Big Data solution, capable of satisfying Industry 4.0 and project ZORRO’s objectives for automatic anomaly detection, is proposed providing end-point solutions consisting in machine learning platforms for automatic anomaly detection, data visualisation platforms for real-time dashboarding and monitoring, data science tools for extraction of important production insights, and batch and stream processing engines to implement virtual sensors, online predictions and other real-time calculations. The architecture was designed to respond to the various constraints associated with these types of Big Data systems whilst remaining modular, aﬀordable using commodity hardware, scalable and microservice based.
A Comprehensive Study of Multiflare GRB Spectral Lag

We select 48 multiflare gamma-ray bursts (GRBs) (including 137 flares) from the Swift/XRT database and estimate the spectral lag with the discrete correlation function. It is found that 89.8% of the flares have positive lags and only 9.5% of the flares show negative lags when fluctuations are taken into account. The median lag of the multiflares (2.75 s) is much greater than that of GRB pulses (0.18 s), which can be explained by the fact that we confirm that multiflare GRBs and multipulse GRBs have similar positive lag–duration correlations. We investigate the origin of the lags by checking the E peak evolution with the two brightest bursts and find the leading models cannot explain all of the multiflare lags and there may be other physical mechanisms. All of the results above reveal that X-ray flares have the same properties as GRB pulses, which further supports the observation that X-ray flares and GRB prompt-emission pulses have the same physical origin.
Environment-Conscious Urban Design Simulator "Clean Mobility Simulator" - Traffic Simulator that Includes Electric Vehicles -

For discussion and proliferation of smart community projects around the world that strive for a low-carbon society through the effective use of energy and electric power-demand management, there is a growing demand of technology for the quantitative evaluation of urban comfort and energy management. Mitsubishi Heavy Industries, Ltd. (MHI) has focused its attention on traffic, which is difficult to evaluate quantitatively. MHI has developed a traffic evaluation simulator that can examine the introduction of infrastructure related to charging equipment and transportation measures for the spread of electric vehicles. This paper presents an overview of the simulator and several examples of its use |1. Introduction Smart community projects that intend to establish a social system based on electricity and realize low-carbon society are discussed and executed around the world in response to attention-drawing large scale environmental issues such as global warming. In these projects, as well as renewable energy and more-efficient fossil fuel applications, the efficient use of electricity through electric power-demand management has a significant role in achieving a low-carbon society. These projects often include the adoption of electric vehicles (EVs) for transportation. Thus there is a growing need to study the required charging infrastructure and traffic measures for electric power-demand management in the context of the accompanying increase in the demand for electric power. However, quantitative evaluation of the electric power demand of transportation is difficult because the demand is significantly influenced by social behavior of each person. This requires an evaluation approach that can consider such factors. We have developed a Clean Mobility Simulator, a traffic evaluation model that can simulate city traffic flows including the charging of EVs. The simulator can also quantitatively evaluate traffic-related urban comfort and energy management. This paper presents an overview of the simulator.
Recent Advances in Tin-based Hybrid Organic-Inorganic PSCs: Additives for Improved Stability and Performance

Perovskite solar cells (PSCs) materials are progressing as potential candidates for the future generation of photovoltaics. Despite the most efficient photovoltaic performance, lead-based perovskite materials are not considered for commercialization due to the high toxicity of lead. One of the promising alternatives is tin (Sn)-based perovskites, which exhibits equivalent ionic size as lead and outstanding absorption properties. However, Sn-based perovskite materials have stability and low-performance issues because of the easy oxidation of Sn2+ to Sn4+and fast crystallization. This paper gives a focused overview of the notable recent studies to address the stability and low-performance challenges of Sn-based hybrid organic-inorganic perovskite (Sn-HOIP) materials for solar cells by using functional additives. To date, the addition of SnF2 additive in the methylammonium tin iodide-based PSCs has shown the highest efficiency of 7.78% and maintains 70% of original efficiency over 200-hours. In the case of formamidinium tin iodide-based PSCs, the addition of phenylhydrazine hydrochloride significantly increases the power conversion efficiency to 11.40% from 5.60% for a pristine device. However, further improvement in the stability and efficiency of Sn-based PSCs requires a molecular-level understanding of the role of existing and new candidates of additives tailored for evolving Sn-HOIP materials.