Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing and Evaluating a Cytoscape App for Graph-Based Clustering

Cluster analysis is the task of grouping objects so that objects in a group are similar to each other and dissimilar to objects of other groups. This technique is commonly used in many areas as a preprocessing step to analyze data. The Yoshiko algorithm is an algorithm for cluster analysis that utilizes a graph-based approach. It groups objects into clusters by transforming their relationships into a graph, reducing it using six reduction rules and then solving Weighted Cluster Editing (WCE). WCE is the problem of choosing a subset of edges in an undirected complete weighted graph so that the edge-induced subgraph consists of disjointed cliques and the sum of edge weights is maximized. This is achieved by either applying a heuristic approach or solving an integer linear program (ILP) to calculate an exact solution. In this thesis we describe an implementation of the Yoshiko algorithm as an application for the network visualization software Cytoscape. Thereby, we provide an intuitive tool for cluster analysis. It allows the user to set edge-weights directly or via mapping and allows edges to be explicitly included in or excluded from the solution. We then evaluate the algorithm in terms of running time and quality and compare it to TransClust, another clustering algorithm based on WCE. In addition, we analyze the eﬀect of the reduction rules on the running time of the algorithm. For this analysis we use the clustering evaluation framework ClustEval with 16 real world and synthetic data sets. We identiﬁed two rules suitable for reducing real-valued instances and found that – on the data tested – reduction is ineﬃcient when using the heuristic mode as it increases running time. While the running time of Yoshiko in heuristic mode and TransClust was found to be similar, the running time of the ILP mode was highly dependent on the reduction, with good reductions ( > 95% of input size) reducing the running time by several orders of magnitude. Overall, we found that both modes of the Yoshiko algorithm generate solutions of similar or identical quality compared to TransClust. From this we draw the conclusion that, at least for the instances tested, exact algorithms for WCE do not result in a solution quality
Logic Models to Classify Cancer Types based on Tumor DNA Samples

Correctly predicting the cancer type of a tumor DNA sample can aid in cancer research, diagnostics, and therapy. In order to create explainable logic models with high credibility and interpretability that predict these cancer types, we introduce “LOCATOR”, which uses boolean logical formulas in disjunctive normal form (DNF) for predictions, with each literal standing for the presence or absence of a specific mutation. One DNF formula is constructed for each cancer type, and if a DNF formula is true for a tumor DNA sample, the sample is classified as the corresponding cancer type. LOCATOR uses recursive feature elimination for linear support-vector machines (SVM-RFE) to reduce the high dimensionality of cancer data, and integer linear programs (ILPs) to find the best DNF formulas. In addition, we try to predict cancer types by finding a set of potential driver mutations for each cancer type, while considering the hypothesis that driver mutations tend to be mutually exclusive. Therefore, we introduce “CATMEME”, which also uses SVM-RFE on cancer data and ILPs for finding these sets of potential cancer-type specific, mutually exclusive driver mutations. We used cancer data from the online genome database cBioPortal. The SVM-RFE we use reduced the dimensionality of the cancer data from 13150 to 250 . We evaluated CATMEME based on the Cancer Gene Census (CGC) from the Catalogue Of Somatic Mutations In Cancer, which lists known driver mutations for several cancer types. Among the eight tested CATMEME variants, the best one did not find many known driver mutations that are listed in the CGC, and we deem the other potential ones found improbable to be actual cancer drivers. Accordingly, classification with CATMEME does not yield good results either. We evaluated LOCATOR based on accuracy, precision, and recall using stratified 4 -fold cross-validation. Among the 16 tested LOCATOR variants, the best one in terms of accuracy and recall achieves an accuracy of 71.7 % ± 0.8 , an average precision of 77.7 % ± 0.9 , and an average recall of 65.6 % ± 1.3 , whereas the best one in terms of precision achieves an accuracy of 62.8 % ± 1.0 ,
A decision support tool for optimising groundwater-level monitoring networks using an adaptive genetic algorithm

Mapping of the spatial variability of sparse groundwater-level measurements is usually achieved by means of geostatistical methods. This work tackles the problem of deficient sampling of an aquifer, by employing an innovative integer adaptive Genetic Algorithm (iaGA) coupled with geostatistical modelling by means of ordinary kriging, to optimise the monitoring network. Fitness functions based on three different errors are used for removing a constant number of boreholes from the monitoring network. The developed methodology has been applied to the Mires basin in Crete, Greece. The methodological improvement proposed concerns the adaptive method for the GA, which affects the crossover–mutation fractions depending on the stall parameter, aiming at higher accuracy and faster convergence of the GA. The initial dataset consists of 70 monitoring boreholes and the applied methodology shows that as many as 40 boreholes can be removed, while still retaining an accurate mapping of groundwater levels. The proposed scenario for optimising the monitoring network consists of removing 30 boreholes, in which case the estimated uncertainty is considerably smaller. A sensitivity analysis is conducted to compare the performance of the standard GA with the proposed iaGA. The integrated methodology presented is easily replicable for other areas for efficient monitoring networks design.
A SURVEY FOR REAL TIME IMAGE PROCESSING AND APPLICATIONS

: This paper is a complete survey of different image processing techniques and large number of related application in diverse disciplines, including medical, pedestrian protection, biometrics, moving object tracking, vehicle detection and monitoring and Traffic queue detection algorithm for processing various real time image processing challenges. Techniques discussed are FPGA, Focal plane, cloud, segmentation, edge detection, A3 methodology, SURE engine, corner detection. At last we relate various techniques with application areas and also explain their future scope. We intend this paper to be useful to researchers and practitioners interested in real time image processing.
Native OCaml compiler support on the ESP32 microcontrollers

From March to July 2018 I’ve been doing my research internship with Anil Madhavapeddy in the University of Cambridge Computer Laboratory’s OCaml Labs . This internship was about systems and compilation for micro-controllers, working with the OCaml programming language, the Mirage operating system library and ESP32 as the micro-controller target. My goal was to extend and test OCaml capabilities on embedded devices by developing a new compilation backend targeting ESP32 processors and porting Mirage operating system unikernel framework to this platform. As this has been achieved with success, this internship results in a proof-of-concept for safe and portable programming on a cross-compiled software environment targeting constrained devices. In the end I’ve been able to get an OCaml compilation backend for ESP32 micro-controllers and a fully working Mirage system including network features, and this work will be presented in a talk at ICFP 2018 OCaml Workshop 1 . I warmly thank Anil Madhavappedy and Gemma Gordon for having me there, as well as the whole OCaml Labs staﬀ and interns with whom I had really good times and for some helped me in my project.
Native Language Identiﬁcation of Spoken Language Using Recurrent Neural Networks

Native language identiﬁcation (NLI) involves the identiﬁcation of a persons native language (L1) based on data gathered in a secondary language (L2). Traditional NLI methods involve the extraction of lexical and/or audio features, which are then used to train a traditional clas-sifer such as an SVM. In this project, we attempt a different approach and instead use extracted features to train a recurrent neural network. Using the spoken language dataset of the 2017 NLI Shared Task, which contains speech transcriptions and audio i-vectors from 11 different L1 classes, we train a variety of RNN architectures. Through this approach, we were unable to surpass the baseline accuracy of a word-unigram SVM model, leading us to hypothesize that 1) RNNs are not well suited for the task of NLI and 2) audio features play a more signiﬁcant role in NLI than speech transcriptions.
Identification of Bird Species Using a Deep Learning Technology

—Numerous human beings go to bird sanctuaries to get a relief from stress by seeing various birds and enjoying the beauty of colours and traits of the birds. For ensuring biodiversity, species information is a fundamental factor. In many scenarios, it is very difficult to identify the species of the birds due to the similarities existing in between the intraclass and interclass varieties of bird species. Recently, with the help of convolutional neural network (CNN), many state-of-the art algorithms on image classification achieved remarkable successes. This paper has been discussed to develop a deep learning platform to recognize the bird species by its image. Convolutional neural network is used for both the classification and prediction processes. In order to improve the feature extraction, a skip connection oriented neural network model is being introduced. The proposed method achieved 99.00% of classification accuracy for the training image. By using the proposed method, the amateur bird watchers can easily identify the bird species from the captured bird image.
Identifying the Main Risk Factors for CVD Prediction Using Machine Learning Algorithms

CVDs are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. Since effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc – using the train-test split technique and k-fold cross-validation. Our study identifies the top two and four attributes from each CVD diagnosis/prediction dataset. As our main findings, the ten MLAs exhibited appropriate diagnosis and predictive performance; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.
Chapter 8: Scenarios in Dataﬂow Modelling and Analysis

Dataﬂow models can be used to model and program concurrent systems and applications. Static timed dataﬂow models commonly abstract the temporal behavior of systems in terms of their worst-case behaviors. This may lead to models that are very pessimistic. The scenario methodology can be applied to the dataﬂow modelling approach to group similar dynamic behaviors into static dataﬂow behaviors that abstract the system scenarios in a tight fashion. Constraints on the possible scenario transitions in the system can be modelled, among other options, by a ﬁnite state automaton. This approach leads to a model called Scenario-Aware Dataﬂow (SADF) that is presented in this chapter. We introduce the model and its semantics and discuss its fundamental analyis techniques. We discuss a parametrized extension and its analysis. We discuss a dataﬂow programming model and its implementation challenges. We give an overview of reﬁned analysis techniques and run-time exploitation possibilities of SADF.
Performance Analysis of CMOS Circuits using Shielded Channel Dual Gate Stack Silicon on Nothing Junctionless Transistor

In this paper it has been demonstrated that a shielded channel made by varying the side gate length in silicon-on-nothing junctionless transistor not only improves the short channel effect but also improve the performance of CMOS circuits of this device. The proposed device shielded channel dual gate stack silicon on nothing junctionless transistor (SCDGSSONJLT) drain induced barrier lowering (DIBL), cut-off frequency and subthreshold slope are improved by 20%, 39% and 20% respectively over the single material gate silicon on insulator junctionless transistor (SMG SOI JLT). The proposed device CMOS inverter fall time Tf (pS) and noise margin improves by 50% and 10% compare to shielded channel silicon on insulator junctionless transistor (SCSOIJLT). It has been observed that circuit simulation of CMOS inverter, NAND and NOR of proposed device. The static power dissipation in the case of proposed SCDGSSONJLT device are reduced by 45%, 81% and 83% respectively over the SMGSOIJLT. Thus, significant improvement in DIBL, cut-off frequency, propagation delay and static power dissipation at low power supply voltage shows that the proposed device is more suitable for low power CMOS circuits.
DDDT_A_298552 2469..2481

1Department of Neurology, Heyuan People’s Hospital, Heyuan, Guangdong Province, 517000, People’s Republic of China; 2Department of Neurology, Hainan Hospital of Chinese People’s Liberation Army General Hospital, Sanya, Hainan Province, 572013, People’s Republic of China; 3Beijing Institute of Radiation Medicine, Beijing, 100850, People’s Republic of China Purpose: To investigate the prevalence of shortand long-term benzodiazepine and z-drugs (BZD) for the treatment of post-stroke subjective sleep disturbance (SSD) and to evaluate the risk factors associated with prolonged BZD treatment in this patient body. Patients and Methods: Between 1st January 2018 and 1st December 2018, we identified 542 inpatients suffering from acute stroke in Heyuan People’s Hospital. Of these, 290 inpatients were included in our final analysis. These patients were divided into three groups according to the treatment they received: non/occasional BZD (non-BZD), short-term BZD (short-term) and prolonged-term BZD (prolonged-term) treatment. We investigated the prevalence of each BZD treatment term and identified differences between the groups. Univariate logistic regression analysis was used to identify potential predictors for the prolonged use of BZD. Multinomial logistic regression analysis was used to assess the correlation between the prolonged use of BZD and potential predictors. Results: The prevalence of cases receiving short and prolonged BZD treatments were 40.35% and 31.72%, respectively; none of the patients received polysomnography (PSG) screening from obstructive sleep apnoea (OSP). Treatment strategies were limited to BZD and traditional Chinese medicine; none of the patients received cognitive-behavioral treatment (CBT) or other forms of treatment. Logistic regression analysis showed that the short-term use was associated with z-drugs (odds ratio [OR]: 2.189, 95% confidence interval [CI]: 1.419–3.378), non-communication barriers (OR =0.535, 95% CI: 0.325– 0.880) and posterior circulation infarct (POCI) (OR =2.199, 95% CI: 1.112–4.349). The prolonged-term use was associated with z-drugs (OR =3.012, 95% CI: 1.637–5.542), noncommunication barriers (OR =0.530, 95% CI: 0.307–0.916), partial anterior circulation infarct (PACI) (OR =0.455, 95% CI: 0.250–0.827), and non pain after stroke (OR =0.315, 95% CI: 0.207–0.480). Conclusion: The status of BZD abuse for post-stroke SSD is worrying. Additional research attention and treatment options are needed for the treatment of post-stroke SSD. In particular, the potential combination of stroke and OSP appears to be underestimated and neglected. Post-stroke SSD patients should receive more comprehensive assessment and rigid follow-up to avoid the prolonged use of BZD. Additional and effective therapeutic strategies (such as positive pressure ventilation treatment or CBT) are urgently needed for cause-specific intervention.
Contrasitive Learning for 3D Point Clouds Classification and Shape Completion

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.
Assignment of partial charges to identical atom neighborhoods of drug-like molecules

Here we present an approach to assign identical partial charges to atoms with chemically equivalent environments. We show how atoms of a molecule can be grouped into sets of atoms with equivalent environments by comparing the atom neighborhoods with subgraph isomorphism and graph canonization. As an extension to the charge assignment problem, we introduce the symmetric charge assignment problem where the task is to select a charge for every atom of a molecule from a range of candidate charges. Charges have to be selected such that chemically equivalent atoms receive the same charge and such that the sum of partial charges matches the total known charge of the molecule with a slight margin for error. Each candidate charge has a score that is based on the observed frequency of that charge in a database of already parameterized molecules. By selecting charges that maximize the sum of scores under these conditions we assign charges that are frequently assigned in similar chemical environments. We show that the symmetric charge assignment problem can be modeled as a multiple-choice knapsack problem variant and show that our variant remains weakly NP-complete. We give an Integer Linear Programming formulation and design a transformation algorithm to assign identical partial charges using an existing pseudo-polynomial time Dynamic Programming algorithm. We evaluate our method on a database of about 261.000 parameterized molecules and compare our computed charges to quantum-mechanically (QM) derived charges. We show that our method results in very similar average charge errors, but increases the usability of the computed charges in molecular dynamics simulations.
The Adaptation and Validation of Resilience Scale for HIV Patients

Objectives: The aim of this study was to adapt and validate the Resilience scale for HIV positive adults in Pakistan. The psychometric properties of the scale were also established. Methods: Cross-sectional research design was used for data collection from HIV patients residing in nine different cities of Punjab during 2019-2020. The Resilience Scale was adapted for HIV positive adults. The psychometric properties and fitness of model in indigenous culture was assessed by performing Exploratory Factor Analysis and Confirmatory Factor Analysis on the adapted scale. Convergent and Discriminant validity of the adapted was also measured by measuring its correlation with General Resilience Scale. Results: The results of EFA revealed that the adapted version of The Resilience Scale is valid while results of CFA revealed that the scale possessed a good model fit in the indigenous culture. Results of convergent validity showed significant positive correlation between resilience in HIV patients and in normal population measured by adapted scale. Results of discriminant validity also displayed significant negative correlation between depression and resilience in HIV positive patients. Conclusion: The adapted version of The Resilience Scale was fit for the HIV patients in Pakistan. Corresponding Author | Faiza Nayyer, PhD Scholar, Department of Psychology, Government College University, Lahore. Email: faizanayyer@hotmail.com
Crystal structure of tris(cyano-(hydrogen tris(3,5-dimethylpyrazolyl)borate))-iron(III) 4-methoxypyridinium monohydrate, C24H32BN10O2Fe

Abstract C24H32BN10O2Fe, orthorhombic, Pca21 (no. 29), a = 19.053(4) Å, b = 9.0666(18) Å, c = 16.439(3) Å, V = 2839.8(10) Å3, Z = 4, Rgt(F) = 0.0482, wRref(F2) = 0.1194, T = 123(2) K.
Evolving Cooperation in Multi-Agent Systems

Evolving Cooperation in Multi-Agent Systems by Aglaia Elli GALATA Applications of deep reinforcement learning in multi-agent systems is a rapidly developing scientific field that focuses on constructing computational frameworks and models for how various agents can interact efficiently to achieve their goals. In this thesis we tackle the challenging coordination problem in which the agents have to cooperate to achieve a common task. First we evaluate the robustness of the state-of-the-art algorithms: VDN and MADDPG in more complex scenarios of higher agent cardinality. We will analyse if the agent populations are able to discover various physical and informational coordination strategies, since the variance of the models increases proportionally to the number of the agents. To overcome the limitations derived from their low cooperation and their slow convergence, we introduce MAGAT model. This new actor-critic model, incorporates graph neural layers to encourage a better representation of the agent relations. The MADDPG algorithm demonstrates a poor performance on the initial scenario, motivating us to modify the current rewarding scheme. Although our model yielded some promising preliminary results, it seemed more challenging to fine-tune the self-attention architecture and requires further analysis. Designing optimal policies for coordination problems is a highly active and open research area. Through our insights, we hope that we will contribute to the evolution of the cooperation analysis and our work will be the building block for future studies.