Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Numerical Technique for the Nonlinear Fractional Kolmogorov–Petrovskii–Piskunov Equation

The q -homotopy analysis transform method ( q -HATM) is employed to find the solution for the fractional Kolmogorov–Petrovskii–Piskunov (FKPP) equation in the present frame work. To ensure the applicability and efficiency of the proposed algorithm, we consider three distinct initial conditions with two of them having Jacobi elliptic functions. The numerical simulations have been conducted to verify that the proposed scheme is reliable and accurate. Moreover, the uniqueness and convergence analysis for the projected problem is also presented. The obtained results elucidate that the proposed technique is easy to implement and very effective to analyze the complex problems arising in science and technology.
A Study on the Spatial Determination of Red Tide Occurrence Area Using GSIS

Few researches related to the spatial determination of areas where red tide occurs have been performed, so accurately determining the area where red tide occurs and disappears poses difficulties. Therefore, a more objective and scientific method is necessary to analyze the occurrence and movement of red tide based on a geo-spatial information system. In this study, the coastline was extracted using a digital topographic map in order to examine areas where red tide occurs each year. An analysis of red tide occurrence areas, which were determined based on red tide data for the last several years, showed that only Yeoja bay had almost a zero case of red tide of the areas studied, whereas Dolsando in the South Sea and NamHaedo Aenggang bay areas exhibited the highest frequency of red tide occurrences. Based on these results, by using a system that determines the geo-spatial distribution status of areas repeatedly hit by red tide every year, it would be possible to predict the course of red tide and prevent consequent damages.
Automatic Skull Stripping and Brain Segmentation with U-Net in MRI Database

Skull stripping has an important in neuroimaging workflow. Skull stripping is a time-consuming process in the Magnetic resonance imaging (MRI). For this reason, skull stripping and brain segmentation are aimed in this study. For the this purpose, the U-NET architecture design, which is one of the frequently used models in the field of medical image segmentation, was used. Also, different loss functions such as Cross Entropy (CE), Dice, IoU, Tversky, Focal Tversky and their compound forms were tested on U-Net architecture design. The compound loss function of CE and Dice loss functions achieved the best performace with the average dice score of 0.976, average IoU score of 0.964, sensitivity of 0.972, specificity of 0.985, precision of 0.960 and accuracy of 0.981. As a result, skull stripping was performed to facilitate the detection of brain diseases.
The Effect of Information on Scheduling Performance in Multi-Hop Wireless Networks

Previous research has estimated the performance of wireless networks by assuming that nodes in the network can obtain precise network information. However, in reality, available network information is mostly imprecise and incomplete. In this paper, we study the relationship between wireless network performance and available network information. It is assumed that each node in the network can obtain the information about other nodes within its information collection range, and a distributed graph coloring algorithm is employed to perform scheduling with the available information. The analytical result on the quantitative relationship between the information collection range and the network throughput is derived. We also consider the communication overhead of collecting information, and analyze the tradeoff between network capacity improvement and information collection overhead. Based on the derived result, an optimal information collection range which maximizes the net data rate can be found. Since wireless networks are typically mobile, and the collected information may be inaccurate due to the dynamics of the networks, we analyze the effect of information for mobile wireless networks by considering the information updating rate, and the result can be used to determine the information collection range as well as the information updating period.
Extracting scientific results from research articles

From April to September I’ve been doing my research internship with Pierre Senellart in École Normale Supérieure computer science department’s VALDA team 1 . My goal is to develop and test methods to perform information extraction from PDF articles, in particular focusing on theoretical statements such as theorems or deﬁnitions. We tackle the topic of data management and extraction, performing research on hierarchical feature extraction, conditional random ﬁelds for document segmentation, software engineering for accelerated dataset creation, and leveraging deep-learning methods for information extraction. While no groundbreaking state-of-the-art results have been obtained here, this internship results in ﬂexible reusable software and promising methods paving the way for future research. The software is available on Github, in the following repository: github.com/PierreSenellart/theoremkb Acknowledgements I would like to thank my advisor Pierre Senellart for his guidance throughout this internship, in spite of these hectic times. I’m also thanking Théo Delemazure with whom I collaborated on the project. I’m grateful for the hospitality of my friends Camille Gobert and Leila Slaoui who hosted me and helped me bear the COVID-19 lockdown in better conditions than in my little parisian appartment. At last, I’m thanking Maëlle Puéchoultres who has always been there, even in my hard times.
Sloth Bear ( Melursus ursinus ) Habitat in the Forests of North Bilaspur Forest Division, Chhattisgarh

The practical way of identifying potential habitat of a species is to have a thorough examination of land use patterns (e.g. natural forests, plantations, water bodies, road network, human habitations, etc.) in an area from the remotelysensed data. The framework of a species habitat suitable model also envisage to ascertain features such as compact forest blocks especially those along the courses of the least fragmentation, smallest stretches of discontinuity and of the adjacent poorly covered forests communicating with the large blocks for gene flow and terrain, availability of forage and water in such areas specifically, during dry season and its distance from the human habitations with a view to ascertain its amenability to revival besides least biotic interferences. We, therefore, incorporated all such variables into our habitat suitability model for sloth bear ( Melursus ursinus ). A statistically desirable proportion of sloth bear geographic locations based on direct sightings during the course of study were randomly selected and were laid over the spatial layers so as to generate potential available habitat for sloth in North Bilaspur Forest Division. Logistic regression model was used to develop the probability distribution map.
MPPT for PV systems based on Optimization

— The output power of a photovoltaic (PV) array, which is highly dependent on both solar radiation and cell temperature, is continuously maximized using Maximum Power Point Tracking (MPPT) techniques. The complexity of non-traditional approaches and the PV power fluctuations around the maximum power point (MPP) caused by conventional methods are compelling arguments for the need for new MPPT techniques. In order to increase the convergence, speed, and accuracy of the PV system, this study discusses numerous MPPT algorithms, including hill climbing methods like the Perturbation and Observe method (P&O), Incremental Conductance (INC), Genetic Algorithm (GA), Evolutionary Algorithms like Cuckoo Search Algorithm (CSA) and Grey Wolf Algorithm (GWO). In the typical test conditions, the suggested technique may also effectively track the global MPP. The optimum performance of the PV panel during MPPT was achieved by employing GWO more frequently to get the quick reaction of the power transient's reduction. This was done to address the oscillations and spikes problem in the output power. To extract the most power possible from the PV resource, the suggested controllers used a DC/DC boost converter. The softwar MATLAB/SIMULINK is used for system programming and modeling. Comparisons are made between the acquired results and those from other MPPT algorithms. To identify the global maximum power point (MPP) for PV arrays, numerous evolutionary algorithms (EA) hav also been taken into consideration. The following EA algorithms are used: grey wolf algorithm, teaching learning-based technique, mine blast optimization, genetic algorithm, particle swarm optimization (PSO), ant colony, and PSO algorithm.Even though the aforementioned EAs in terms of MPPT function satisfactorily, there is still a need to advance contemporary EAs. The aforementioned GWO results are contrasted with current optimization methods like CSA, P&
SUMMER FLOUNDER, SCUP, AND BLACK SEA BASS FISHERY MANAGEMENT PLAN Alternative Set 2: Commercial Quota Allocation Proposed New Alternative: Dynamic Transboundary Approach

This proposal oﬀers a new alternative for modifying the allocation of the commercial summer ﬂounder quota. It involves a dynamic approach for gradually adjusting state-speciﬁc allocations using a combination of resource utilization (historical allocations) and current levels of resource distribution. The alternative is modeled after the Transboundary Management Guidance Committee (TMGC) approach, which was developed and used for the management of shared Georges Bank resources between the United States and Canada. As noted by Gulland (1980)
Posting behaviour Dynamics and Active Filtering for Content Diversity in Social Networks

—In this paper, we have two objectives: First we model the posting behaviour of publishers in Social Networks which have externalities, and the second objective is to propose content active ﬁltering in order to increase content diversity from different publishers. By externalities, we mean that when the quantity of posted contents from a speciﬁc publisher impacts the popularity of other posted contents. We introduce a dynamical model to describe the posting behaviour of publishers taking into account these externalities. Our model is based on stochastic approximations and sufﬁcient conditions are provided to ensure its convergence to a unique rest point. We provide a close form of this rest point and show that it can be obtained as the unique equilibrium of a non-cooperative game. Content Active Filtering (CAF) are actions taken by the administrator of the Social Network in order to promote some objectives related to the quantity of contents posted in various contents. As objective of the CAF we shall consider maximizing the diversity of posted contents. Finally, we illustrate our results through numerical simulations and we validate them with real data extracted from social networks.
Common Workflow Language, v1.0

The Common Workflow Language (CWL) is an informal, multi-vendor working group consisting of various organizations and individuals that have an interest in portability of data analysis workflows. Our goal is to create specifications that enable data scientists to describe analysis tools and workflows that are powerful, easy to use, portable, and support reproducibility.CWL builds on technologies such as JSON-LD and Avro for data modeling and Docker for portable runtime environments. CWL is designed to express workflows for data-intensive science, such as Bioinformatics, Medical Imaging, Chemistry, Physics, and Astronomy.This is v1.0 of the CWL tool and workflow specification, released on 2016-07-08.
M ACHINE L EARNING R EPORTS Extending the concept of soft sensors to green sensing

There are many innovative technical developments that are denoted ”green”, ranging for example from very generic green power to more specific green AI. In the context of soft sensors, this paper describes its extension to green sensing , taking into account and implementing various aspects of efficiency and sustainability in addition to more traditional performance criteria, such as precision, robustness, and interoperability.
Relational Query Synthesis (cid:90) Decision Tree Learning

We study the problem of synthesizing a core fragment of relational queries called select-project-join (SPJ) queries from input-output examples. Search-based synthesis techniques are suited to synthesizing projections and joins by navigating the network of relational tables but require additional supervision for synthesizing comparison predicates. On the other hand, decision tree learning techniques are suited to synthesizing comparison predicates when the input database can be summarized as a single labelled relational table. In this paper, we adapt and interleave methods from the domains of relational query synthesis and decision tree learning, and present an end-to-end framework for synthesizing relational queries with categorical and numerical comparison predicates. Our technique guarantees the completeness of the synthesis procedure and strongly encourages minimality of the synthesized program. We present Libra, an implementation of this technique and evaluate it on a benchmark suite of 1,475 instances of queries over 159 databases with multiple tables. Libra solves 1,361 of these instances in an average of 59 seconds per instance. It outperforms state-of-the-art program synthesis tools Scythe and PatSQL in terms of both the running time and the quality of the synthesized programs.
A Study on Consumer Animosity, Negative WOM (nWOM), and Boycott Behaviours of Rival Football Fans

This study aims to determine the effects of consumer animosity on boycott motivation through Negative WOM. The sample population of this study, which was carried out on rival football fans, consists of 408 people selected by a convenient sampling method. Considering the research analyses, the relationship levels between the variables were determined. Reliability and validity analyses were applied. Then, the effect and mediation levels between the variables were determined by Structural Equation Modelling. According to the results, it was found that consumer animosity had a positive and significant effect on boycott motivation through negative WOM
Visualizing Viral Cartography with MapReverse

Social media provide a fast-moving ecosystem for the rapid dissemination of digital maps. Some maps become viral, reaching massive audiences across diverse networks. A new challenge for cartographers is to characterize the design and dissemination of viral maps in social media. Here, we present a prototype visualization tool called MapReverse which allows users to explore collections of map images that are similar to a given viral map. Similar maps are identified using a combination of reverse image search and machine learning image analysis. In some cases, we can identify original sources for maps; more commonly we can identify many modifications of the original viral maps. This system provides a glimpse into the evolution of cartographic design as found in viral social media
Seismic tomography constraints on reconstructing the Philippine Sea Plate and its margin

Seismic Tomography Constraints on Reconstructing the Philippine Sea Plate and Its Margin. (December 2004) Lina Handayani, B.S., Institut Teknologi Bandung; M.S., Texas A&M University Chair of Advisory Committee: Dr. Thomas W.C. Hilde The Philippine Sea Plate has been surrounded by subduction zones throughout Cenozoic time due to the convergence of the Eurasian, Pacific and Indian-Australian plates. Existing Philippine Sea Plate reconstructions have been made based primarily on magnetic lineations produced by seafloor spreading, rock magnetism and geology of the Philippine Sea Plate. This dissertation employs seismic tomography model to constraint the reconstruction of the Philippine Sea Plate. Recent seismic tomography studies show the distribution of high velocity anomalies in the mantle of the Western Pacific, and that they represent subducted slabs. Using these recent tomography data, distribution maps of subducted slabs in the mantle beneath and surrounding the Philippine Sea Plate have been constructed which show that the mantle anomalies can be related to the various subduction zones bounding the Philippine Sea Plate. The high velocity mantle anomalies are clearly coincident with WadatiBenioff zones in the upper mantle. The lower mantle anomalies, although distributed in the “transition zone” (500-1000 km) as stagnant slabs in some cases, can clearly be
Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data

Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer’s disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance (MR) images, and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data, were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion.
AccuNGS: detecting ultra-rare variants in viruses from clinical samples

Next generation sequencing is widely used to characterize genetic diversity in a sample, yet is hindered by its relatively low resolution. Particularly, detecting rare genetic variants in clinical samples of viruses is still nearly impossible. Here we describe AccuNGS, an approach that combines error reduction in each sequencing stage with in silico error elimination, which enables detection of variants as rare as 1:10,000 or lower. We thoroughly explore AccuNGS background errors and reveal they are mostly generated in the sequencer itself. We demonstrate that as opposed to common assumptions, Illumina paired-end reads are not independent. After applying AccuNGS to an HIV sample taken during acute infection, we reveal that the vast majority of transition variants in the sample segregate at ultra-low frequencies, rendering them undetectable by standard sequencing. These results highlight the early rich accumulation of genetic diversity during viral infection at depths previously unseen. strand-bias error of is 7.83x10 -5 when filtering for and 5.80x10 -5 when filtering for Q38. error rates to a cutoff of 1.35x10 -4 and 1.16x10 -4 based on the 95 percentile of a fitted gamma distribution of all transition errors. when focusing on specific types of transitions (mostly T>C and A>G) the cutoff drops to below 1x10
A System for Analyzing Human Capability at Scale using AI

Over 80% of corporate value is now comprised of intangibles, of which a large component is human capability (HC). Reflecting this, the SEC has recently mandated HC reporting requirements (SEC, Q4 2020). We use machine learning to build a prototype system to analyze HC using SEC filings and applied it to 5,760 companies. The approach algorithmically generates lexicons for HC concepts, and then applies machine learning to extract the relevant text on HC and business outcomes from annual reports, to create a dashboard for each firm on the quantity of reporting over four dimensions of HC: talent, leadership, organization, and human resources operations. This system links HC reporting to measurable business outcomes such as revenue per employee, earnings, Tobin’s Q, and social citizenship. This will enable companies to improve the quality of reporting and governance of HC as well as guide investments in specific areas of HC.
The role of the organizational climate in the effect of workplace incivility on revenge

Workplace incivility and revenge are considered to be negative for organizations. The climate of the organization is considered important because of its impact on organizational goals and outputs. The aim of this study is to investigate the effect of workplace indecency on revenge. In addition, the role of the organization's climate as an intermediary in this relationship has been examined. Therefore, a survey has been conducted with 153 flight crew members in the civil aviation sector. According to the results of the study, incivility positively affects the intention of revenge and negatively affects the climate of the organization. The climate of the organization negatively affects the intention of revenge. In addition, partial mediation has been found in the relationship between indecency and revenge
Twisting Clay Creative Research to Explore the Complex Rheology in Ceramic Extrusion

This paper details practice-based research exploring new creative possibilities involving the ceramic extrusion process. The paper begins by providing a short overview of the extrusion technique, its characteristics and some contextual coverage of the process. The paper then describes how both tacit knowledge and theoretical material understanding have been used to overcome technical challenges through iterative research cycles and how, ultimately, the aesthetic qualities of the extrusion process have been used to develop a body of creative work. A key theme of the research is how digital fabrication technologies can be used in toolmaking scenarios to deliver innovation with a process that has long been used in craft ceramics but has remained somewhat underutilised.
Earnings conservatism and litigation exposure in the banking industry

ABSTRACT This paper examines the effect of managers' liability exposure on earnings conservatism in the banking industry. Focusing on a wide international sample of commercial banks and using TIER1 as a proxy of bank managers' exposure to litigation, our results show a negative relationship between the level of TIER1 and earnings conservatism. We interpret these results as evidence of an increase in bank managers' liability exposure increasing earnings conservatism. Moreover, we find that this negative relationship holds for both, those banks with a TIER1 below the median country level of TIER1 (low-TIER1 banks) and those with a TIER1 above the median country level of TIER1 (high-TIER1 banks), even though it is less pronounced for the former group. Thus, although it is expected that higher public scrutiny scenarios in the banking industry (low-TIER1 banks) trigger a higher degree of unconditional conservatism, they do not prevent managers from resorting to earnings conservatism in an attempt to minimize not only litigation costs but also the likelihood of adverse political action.
Neural signals encoded by specific frequencies of closed-loop electrical stimulation structurally and functionally reconstruct spinal sensorimotor circuits after spinal cord injury

20 Epidural electrical stimulation restores locomotion in animals and humans with spinal 21 . CC-BY 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted May 12, 2022. ; https://doi.org/10.1101/2022.05. 10.491346 doi: bioRxiv preprint