Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of genes involved in the unusual genitals of the bear macaque, Macaca arctoides

Genital divergence is thought to contribute to reproductive barriers by establishing a “lock- and-key” mechanism for reproductive compatibility. One such example, Macaca arctoides, the bear macaque, has compensatory changes in both male and female genital morphology as compared to close relatives. Macaca arctoides also has a complex evolutionary history, having extensive introgression between the fascicularis and sinica macaque species groups. Here, phylogenetic relationships were analyzed via whole genome sequences from five species, including M. arctoides, and two species each from the putative parental species groups. This analysis revealed ∼3x more genomic regions supported placement in the sinica species group as compared to the fascicularis species group. Additionally, introgression analysis of the M. arctoides genome revealed it is a mosaic of recent polymorphisms shared with both species groups. To examine the evolution of their unique genital morphology further, the prevalence of candidate genes involved in genital morphology were compared against genome-wide outliers in various population genetic metrics, while accounting for background variation in recombination rate. This analysis identified 66 outlier genes, including several genes that influence baculum morphology in mice, which were of interest since the bear macaque has the longest primate baculum. The mean of several metrics was statistically different in the candidate genes as compared to the rest of the genome, suggesting that genes involved in genital morphology have increased divergence and decreased diversity beyond expectations. These results highlight how extensive introgression may have contributed to reproductive isolation and shaped the unique genital morphology in the bear macaque.
Mass Media as a Mirror of the COVID-19 Pandemic

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.
Spammer Detection Based on Task Completion Time Variation in Crowdsourcing

Many existing spammer detection methods uses dependency between workers’ answers and the true answers. These methods may regard a diligent but low-skilled worker as a spammer. Our method uses correlation between workers’ task completion time and the difficulty of the tasks. Our experimental result suggests that this approach is potentially useful, but the selection of tasks seems a key for success.
Interpretable Mathematical Model-guided Ultrasound Prostate Contour Extraction Using Data Mining Techniques

Among all image features, the contour is one of the most critical features for displaying the shape of the object intuitively. Due to unseen/missing regions of transrectal ultrasound images caused by imaging artifacts and limited field of view, accurate and robust ultrasound prostate contour extraction is challenging. Hence, we propose a triple cascaded framework for ultrasound prostate contour extraction using a few existing points as the prior. The proposed scheme contains two types of data mining: principal curve-based and machine learning-based methods. The first stage is using an improved polygonal segment method to obtain a contour composed of line segments connected by sorted vertices, where only a few radiologist-defined seed points are used as the prior. The second stage is to achieve an optimal machine learning-based approach based on an improved differential evolution-based method. The third stage is to find a map function (realized by the machine learning-based method) to generate the smooth contour represented by the output of neural network (i.e., optimized vertices) to match the ground truth contour. Our results demonstrated that the performance of the proposed method outperformed several other state-of-the-art methods.
VA-PRT: A Visualization Tool for Analyzing Post-translational Modification Retention Times

Post-translational modifications (PTMs) play a critical role in many aspects of cell biology including cell growth, differentiation, and survival. Accurate identifications of PTM peptides and their PTM locations are very important in proteomics. The retention time information of PTM peptides can be used to improve the accuracy of PTM identification and experimental proteomic strategies. Therefore, understanding PTM effects on retention times will be beneficial for future research.Here, we present VA-PRT (Visualization tool for Analyzing Post-translational modification Retention Times) that systematically investigates the PTM effects on retention times using visualization techniques and advanced statistical methods. The usefulness of VA-PRT is demonstrated using a large synthetic proteomic and phosphoproteomic dataset. VA-PRT is implemented in R and will be freely available for public use.
Robust Metabolic Syndrome Risk Index Based on Triangular Areal Similarity

Metabolic syndrome is a cluster of risk factors associated with certain cardiovascular diseases. It is a chronic disease and has become an important public health issue in many countries, as it is steadily increasing owing to urbanized diets and lifestyles. The current medical diagnostic methods focus on the diagnosis of metabolic syndrome itself; however, they are not suitable for measuring its progression. Several methods have been proposed for measuring the risk of metabolic syndrome aiming to solve this problem, including the "Areal Similarity Degree" method. In this study, a robust risk index based on radar charts is proposed to overcome the weaknesses of the Areal Similarity Degree method. In the proposed method, the examination values are expressed as two types of triangles on 10 types of three-axis radar charts, and each area of the triangle is synthesized and used as a risk index. The performance of the proposed risk index is evaluated based on the data of 31,070 Korean adults obtained from the Korea Genome and Epidemiology Study health examinee (KoGES HEXA) dataset. The proposed risk index shows a high correlation with current medical diagnostic criteria, and a good diagnostic performance. It produces more stable and generalized risk index values than those from the basic Areal Similarity Degree method. In addition, its sample-independent characteristics are confirmed.
EEG Emotion Recognition based on Hierarchy Graph Convolution Network

Emotion recognition has become a research focus in the field of human-computer interaction (HCI). As an excellent physiological signal, electroencephalographic (EEG) is considered to be a favorable tool for emotion recognition. Most traditional methods focus on extracting features in time domain and frequency domain but the adjacent information and asymmetric information from adjacent and asymmetric channels are often ignored. Although several graph neural network (GNN) models are utilized to learn EEG features, most of the emotion recognition studies of GNN ignore the information existing between adjacent electrodes. In this paper, we propose an EEG emotion recognition method based on hierarchy graph convolution network (HGCN) named ERHGCN. Firstly, six different features including power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), asymmetry (ASM) and differential caudality (DCAU) from five frequency bands are extracted. Secondly, to improve graph convolution network (GCN) shortcoming of only extracting time and frequency features, HGCN is applied to extract deeper spatial feature by treating the longitudinal and transverse adjacent electrode pairs in different ways. Finally, six extracted features are fed into the HGCN model, then all features are integrated by two full connection layers. We conducted extensive experiments on DEAP dataset and experimental results show that the proposed method can obtain 90.56% and 88.79% recognition accuracies for valence and arousal classification tasks.
FES-RF: A Feature Ensemble Selection Based Random Forest Method For Accurate Cancer Screening

The diagnosis and analysis of cancer are usually roughly judged through the accumulation of professional knowledge, which is difficult to deal with a large number of patient samples and a variety of causes and symptoms. Moreover, most of the existing machine learning methods are black-box, and can not give reasonable diagnosis basis. Therefore, an accurate and interpretable method is urgently required for cancer diagnosis. In this paper, a total of 700 serum samples consisting of three groups of patients and one group of healthy individuals were collected and subjected to SERS measurements. We rank the Raman spectra of 700 human SERA according to the feature importance, and construct the feature importance vector reflecting the spectral feature importance. We further construct candidate feature sets based on importance selection, so as to construct a random forest model based on feature ensemble selection. On the one hand, we compare the proposed method with the popular machine learning methods to verify the effectiveness in the task of cancer screening. On the other hand, we conduct qualitative and quantitative analysis of cancer characteristics, and give model basis and biomedical explanation for the impact of different important cancer characteristics on the final classification and diagnosis. Some more experimental results and discussions are included in the appendix. Our source code and appendix are available under https://github.com/liujiatong429/BIBM2021
Scotty: Fast a priori Structure-based Extraction from Time Series

The recognition and extraction of data-driven pat- terns is a challenging task. In vast amounts of data, suitable techniques should prepare data that match the user’s intentions. For example, "an increasing daily price of a stock, is generally followed by a sudden fall due to the endorsed profit gains". Such a statement should effortlessly be transferred as input to a data mining task, without the need for an extensive training phase building an extremely complicated model and the usage of threshold-based approaches. We propose a method for extracting lexical representations directly from the raw data, enabling others not directly compatible with real-valued data. We provide evidence that our method is fast and accurate. We use as evaluation a preliminary step for a classification task compared to state-of-the-art classifiers applying it on a publicly available dataset.
Cancer Incidence & Cancer Mortality vis-à-vis Correlation, Co-integration and Causation

Cancer is the leading cause of death in Australia. It is estimated that more than 130,000 cases will be diagnosed with cancer in 2017 and further, that the estimated number of deaths will be around 50,000. If we look at the time series data of cancer incidence and cancer mortality it seems there is a very high significant correlation between these variables. This may be spurious and misinterpreted which is quite often the case in epidemiological studies. In this paper we have introduced the concept of co-integration and shown that although cancer incidences are increasing very fast, cancer mortality is not increasing that fast. This paper demonstrated that there is no long term relationship or co-integration between these two variables. However, there exists a short-run causal relationship from cancer to mortality for the cases of lung and prostate cancers. The impulse response function reveals that the mortality peaks at second year and the effect gradually disappear after 8 years.
RStream: Simple and Efficient Batch and Stream Processing at Scale

Distributed data processing platforms aim to provide a balance between ease of use and performance. The question is: do they succeed? Systems like Apache Spark or Apache Flink offer a high-level programming model that results in simple and concise definition of the processing tasks, abstracting away most of the concerns associated to concurrency and distribution but at the cost of a large performance gap with custom programs that use low-level primitives to control distribution and resource usage. May we fill this gap? May alternative design choices yield better performance without sacrificing simplicity? This paper answers the above questions by introducing RStream, a novel data processing platform written in Rust. RStream provides a high-level programming model similar to that of mainstream data processing systems, which supports batch and stream processing, data transformations, grouping, aggregation, iterative computations, and time-based analytics, incurring in a much lower overhead, closer to that of custom, low-level code. In numerical terms, our evaluation shows that RStream programs present nearly identical complexity as similar programs written in Flink, delivering from 2× to 20× the throughput of Flink, rivaling custom MPI implementations.
Analysis of Students’ Concentration Levels for Online Learning Using Webcam Feeds

Tracking the concentration of students during online learning offers great benefits. For examples, distracted students can be suggested to do a brief exercise to refresh their brains; or the teacher can be notified when too many students have difficulties on concentration so the class could take a short break. Traditionally, mental states like concentration levels can be analyzed using Electroencephalogram (EEG) or Functional Near-Infrared Spectroscopy (fNIRS). However, methods that utilize these data require specialized equipment which is not feasible to deploy on a large scale. On the other hand, recent breakthroughs in deep learning provide possibilities of scalable solutions to detect concentration levels using only webcam. Leveraging this advancement, we investigate the task of tracking students’ concentration levels during online learning using facial data coupled with deep learning based computer vision technologies. More specifically, we examine the performances of different representations of facial data integrated with various deep architectures to empirically determine a solution balanced between prediction accuracy and time efficiency that is suitable for real-time application. Our experimental study shows that the proposed solution achieves over 91% accuracy while keeping execution time low enough for real-time deployment.
Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods

Single cell transcriptomics measures gene expression data of large number of genes, concurrently, from tens of thousands of cells present in a studied biological sample. It is difficult to obtain good classification results due to high data dimensionality and variability of biological states. We performed a preliminary study to assess the feasibility of using supervised machine learning methods to classify peripheral blood mononuclear cell (PBMC) types from single cell gene expression data. We analyzed a large PBMC data set $(\sim 120,000$ PBMC cells), selected 47 genes (from 30698 features) suitable as SML classification features, and performed classification using 20 machine learning algorithms. Data sets represented three sample processing strategies: PBMC separation (two data sets), and experimental cell sorting by (two data sets). The accuracy in 5-class classification among 20 methods was 91-97% (PBMC separation), 97-100% (magnetic-activated cell sorting), and 82-99% (fluorescence-activated cell sorting). Our results indicate the feasibility of supervised machine learning for classification of cells into major PBMC cell types using a small number of classification features from single cell gene expression data.
Analysis of IM Asymmetry in MOSFET Small-Signal Amplifiers

This paper describes and analyzes IM asymmetry issues for weakly nonlinear MOSFET Common-Gate(CG) and Common-Source(CS) architectures. Using a Volterra series anal- ysis, the cause of IM asymmetry in CG and CS amplifiers is explained. The asymmetry between high-side and low-side IM products in the CG amplifier exhibits a larger amplitude differ- ence at low-frequency offset than at high frequency offset, while the CS amplifier shows more asymmetry at a high frequency offset. The magnitude of the IM product asymmetry in the CS amplifier can be significantly higher than the CG amplifier. Methods of mitigating IM asymmetry are suggested for both CG and CS amplifiers. A 65 nm Si CMOS technology is used for the simulation verification of the results derived in this paper.
Improving the Awareness of Sustainable Smart Cities by Analyzing Lifelog Images and IoT Air Pollution Data

Currently, air pollution has become the tremendous problem humankind has ever faced. Countries governments have struggled with conflicting demands from industries benefits and people/natural health. Scientists have investigated to find solutions that can balance these demands. Along with these directions, this research introduces a convenient and economical solution for estimating PM2.5 at the current time and predicting PM2.5 in a short- and medium-term period just by using images captured from personal devices (e.g., smartphones, cameras, lifelog cameras). The proposed method aims to leverage the association between urban nature (e.g., street greenness, street building), urban traffic (e.g., the volume of vehicles), and air pollution (e.g., PM2.5) to discover a set of periodic-frequent patterns and to build the PM2.5 estimation model. The estimated PM2.5, together with a set of patterns, is utilized to predict the PM2.5 in the short-term future. Evaluation running on different datasets collected from India and Vietnam shows the productivity of the proposed method.
Small Microstrip Antennas Realized with Periodically L-C Loaded Transmission Lines

The properties of periodically L-C loaded microstrip transmission lines are utilized to shrink the physical dimensions of patch antennas. A transmission line model is employed to analyze the electrical behavior of antenna configurations with a varying number of L-C loaded unit cells. Two representative antenna structures have been selected and subsequently optimized with full wave electromagnetic analysis. Return loss and radiation pattern measurements of these antennas proof the developed concept for compact microstrip antennas in the lower GHz frequency range. By Martin Schüßler', JensFreese*, Index Terms and Rolf Jakoby* Microstrip antennas. Compact antennas, Small antennas, Left handed materials, Metamaterials Frequenz 58(2004) 7-8
Combining Deductive and Statistical Explanations in the FRANK Query Answering System

Both symbolic and sub-symbolic AI have their limi-tations, but their combination can be more than the sum of their parts. For instance, statistical machine learning has been hugely successful at classification and decision-making tasks, but not so good at deliberative systematic reasoning nor at explanation. We argue that by combining symbolic and sub-symbolic reasoning into hybrid systems, the whole will be more than the sum of its parts. To illustrate the potential of hybrid AI system, we describe the FRANK query answering system. FRANK infers new knowledge from the diverse and immense knowledge sources on the Web, using a combination of both deductive and statistical reasoning. This enables it to make predictions. For instance, to answer the question “Which country in Europe will have the highest GDP growth rate by 2032? ”, it (i) decomposes Europe into its constituent countries, (ii) then for each country uses regression over their previous GDP growth rates to extrapolate each to 2032 and (iii) then returns the country which is predicted to then have the maximum value. The decompositions are explained deductively and the regressions by a prediction model that can be rendered graphically. This explanation of FRANK's reasoning merges deduction and statistics. In this paper, we highlight recent work on FRANK that focus on leveraging hybrid AI to tackle question answering with emphasis on explainability of the inference process and its inferred answers. We aim for whole system reasoning; that is, we are automating the choices of knowledge sources and the planning that constructs the inference process from the facts found in these knowledge sources. We intend that these ‘engineering’ choices are also explained to the user.
An Experience-Based Approach to Teaching Product Design

This paper presents a new experienced-based approach to teaching product design. The goal is to address specific issues that are difficult to teach to students effectively in a purely theoretical setting, as students need to have experienced in some way the behaviours involved in managing a product design process. The backbone of the approach is a design game that allows students to experience themselves some of the most important challenges in product design. Later during the course, the concepts of product design are taught by always referring to the specific part of the game in which that aspect was relevant.