Miracle-dz/UniFAR-FTUs huggingface dataset:
Definition and Goal of Graph Clustering -Motivation to Explore a New Algorithm

In recent years, one of the popular method for mining evolving data, is to convert it to a network, in fact an adjacency matrix, where data units are nodes and their relations/similarity are the link weights. The network is then partitioned into communities to explore information hidden in the data. For last 50 years, various graph partitioning algorithms are proposed. Depending on the application, the optimization objective of the partitioning and the resulting clusters are different. Different algorithms, based on Linear Algebra, heuristics with different greedy optimization criterion, agglomerative algorithms, are proposed to meet different optimization criterion suitable for target network and application.In this work we proposed a genetic algorithm (GA) based dynamic clustering algorithm. Genetic algorithm (GA) for graph clustering, with fitness function as the modularity index, is already proposed, in our previous works. In this work, we propose a multimodal GA. The algorithm starts with modularity index (Q) as the optimization criterion. Once converged, we add another term in the fitness function which will balance the cardinalities of the partitions. The parameters are computed based on the partitions after the first stage of convergence. We continue to run the Genetic search with modified fitness function until a second convergence is achieved. In all our experiments, not only could we achieve better balance of the size of different clusters, in many experiments it actually improved the modularity index. For a highly modular graph, with Q ≥ 0.7, most of the algorithms produce the same result. When the optimum modularity index of the graph is low, GA with only modularity index as optimization criterion usually converges in local minimum. With the proposed modification, we could always find clustering with an improved Q value. We run popular partitioning algorithms on known real-world networks and found that the proposed algorithm could find better partitioning, closest to reality.
Low Cost Greenhouse Monitoring System Based on Internet of Things

It is a well-known situation that nowadays people are opting for organic food grown in an ecological way to reduce carbon footprint and damage to the environment. Ecology is suffering because of the use of pesticides and crop protection chemicals due to the damage caused to the flora and fauna in the environment where the crops are being cultivated. Combined with this, people are trying to get the freshest vegetables, fruits, and even flowers. Because of these situations, it is more common nowadays to find people trying to grow their food, or plants that they can't get in the place they live. For this, it is useful to use tools that can help the growth of these plants and make the job easier, but usually, greenhouses controllers are expensive and unnecessary due to the size of the home plantations. The creation of a low-cost greenhouse prototype that can be used in houses or small farms to help the people grow their crops and its comparison with current commercial systems are presented.
The Evolution of the Schooling-Smoking Gradient

We explore how the schooling-smoking gradient has evolved over time. Using data from 11 Gallup Surveys conducted between 1954 and 1999, we find that the schooling-smoking gradient first emerged in tandem with a schooling-health knowledge gradient. As early as 1957, a schooling-knowledge gradient developed, with 62 percent of college graduates agreeing that smoking was a cause of lung cancer, compared to only 46 percent of those with less than a college degree. After the mid-1970s, the schooling-knowledge gradient began to flatten but the schooling-smoking gradient did not. To further explore the role of health knowledge, we tighten our focus to the schooling-smoking cessation gradient. We use contemporaneous and retrospective information on smoking cessation from six cycles of the Tobacco Use Supplements to the Current Population Survey (TUS-CPS). We again document the evolution of a strong gradient: In the 1950s, smoking cessation rates were low and similar across schooling categories, but by 1987 the smoking cessation rate for smokers with a college education was about double the rate for high school dropouts. We test two hypotheses based on possible roles health knowledge may have played in the schooling-cessation gradient. Our first hypothesis is that the gradient should be steeper in the cohorts who initiated smoking before the spread of knowledge about the health consequences. In these cohorts, there will be more people who started smoking only because of their lack of health knowledge, so we expect that as the knowledge spreads they will decide to quit. Our second hypothesis is that the gradient steepens after the introduction of effective pharmaceutical products for smoking cessation. Schooling may have helped smokers learn about and adopt the new products more quickly.
Consensus Based State of Charge Balancing Control for Multiple DESUs in Islanded Microgrid

Distributed energy source units (DESUs) is indispensable for microgrid to compensate for the intermittent and randomness of distributed energy sources (DES). Therefore, use time of batteries in DESUs is a crucial issue. In order to avoid decreasing the cycle life of DESUs, consensus based SoC balancing control is raised. The intermediate control variables SoCave is got by adopting neighbor communication based on consensus algorithm. With applying suggested approach, SoC could realize balance which each DESU has disparate states (such as initial SoC and sizes). Simulation results present the validities of the suggested approach for multiple DESUs in islanded microgrid.
Optimal Design of Double Stator Permanent Magnet Motors Used in Electric Vehicle

The purpose of this paper is to propose a novel double stator permanent magnet machine (DSPMM) for in-wheel electric-vehicle situation. At first, the structure of in-wheel machine drive system and DSPMM are introduced which composes of inner stator, outer stator and cup rotor. Then the cooling water circuit are design, based on the inner size and its power. In addition, the proposed structure of rotor and its performance on mechanical property are analyzed in detail. Finally, the results of simulations and experiments, including back EMF, cogging torques, torques and flux density are given to verify the validity of the model.
Ramping Services from Grid-interactive Buildings

Ancillary services to the power grids are in demand due to a rapid growth of renewable energy in recent years. Demand-side flexible loads have the potential to provide reliable and affordable operational reserve services to the grid. However, this must be achieved without compromising the resources’ delivery of quality service to the end users. In this paper, we present demand flexibility estimator and ramping service controls for multi-zone commercial building’s air distribution system and chiller plant. The building flexibility is represented by a simple virtual battery model and a model-based optimization algorithm is used to estimate the parameters of the virtual battery in a receding horizon manner. The proposed ramping controller varies the Heating, Ventilation and Air Conditioning (HVAC) systems’ power consumption so that the deviation from its baseline power profile tracks the grid reference signal while maintaining comfortable indoor climate. This is achieved by modifying the zones temperature set-points within a given comfort bounds and adjusting the temperature set-point of the chilled water. The controller’s performance is demonstrated with a high-fidelity simulation model representing a medium sized office building to meet the ARPA-E NODES project targets for response time, ramp time, reserve magnitude, variability tolerance and duration of service. Overall, the results validate the capability of buildings to provide high quality grid ramping reserves with minimal indoor comfort impact.
Performance Evaluation of Spectrum Sensing for OFDM Systems Using Parallel Combination of Comb Filter and Autocorrelator

In this paper, we have proposed a spectrum sensing method which exploits the combination of a comb filter with autocorrelation function in a parallel form. The primary user information is not required, but the noise variance is used, which makes the proposed method a semiblind one. This approach enhances the sensing capability of the cyclic prefix (CP) based orthogonal frequency division multiplexing (OFDM) signals in severe noise environments. The detection performance is investigated for various important parameters for OFDM based applications. A comprehensive evaluation by simulations shows that the proposed method is useful under low levels of signal-to-noise ratio (SNR).
Semi-Automatic Creation of an Anime-Like 3D Face Model from a Single Illustration

In this paper, we propose a method for semi-automatically creating an anime-like 3D face model from a single illustration. In the proposed method, principal component analysis (PCA) is applied to existing anime-like 3D models to obtain base models for generating natural 3D models. To align the dimensions of the data and make geometric correspondence, a template model is deformed using a modified Nonrigid Iterative Closest Point (NICP) method. Then, the coefficients of the linear combination of the base models are estimated by minimizing the difference between the rendered image of the 3D model with the coefficients and the input illustration using edge-based matching. We confirmed that our method was able to generate a natural anime-like 3D face models which has similar eye and face shapes to those of the input illustration.
Multiclass Breast Cancer Classification Using Convolutional Neural Network

Nowadays, the quality of classification systems depends on the presentation of the dataset, a process that takes time to use in-depth knowledge to produce specific characteristics. Meanwhile, deep learning can extract features from a dataset, without having to design feature extractors. Convolutional Neural Network (CNN) is a special type of deep learning that achieves many accomplishments in speech recognition, image recognition and classification. In this paper, we use CNN to classify and recognize breast cancer images from public BreakHis dataset. This dataset includes 7,909 breast cancer (BC) histopathology images with four benign subclasses and four malignant subclasses. Our new task with this dataset is the automated multi-classification of these breast cancer images in eight classes, which can help reducing death rates and saving people’s lives in the world. Our method based on the resizing original images for building CNN model and classifying breast cancer classes.
Active Disturbances Rejection Controller for Position Servo Control of PMSM

In PMSM position servo control system, Active Disturbances Rejection Controller (ADRC) is introduced to meet the requirement of high performance position tracking because of the disadvantages of slow response and large error of traditional PID control. In this paper, the friction damping and load torque are taken as disturbance. The load torque compensation of PMSM is realized with the ADRC, which improves the anti-interference ability of the system. Simulation and experiment results show that this method can improve the response speed of the system, reduce the steady-state error, and effectively suppress the influence of load change on the position-tracking accuracy.
Graft steatosis - where to stop?

The gap between the availability of livers from organ donors and the increased demand has led many centers to apply strategies to reduce this deficit. Splitting of cadaveric organs for use in 2 recipients; domino transplantation; and organs from living donors, non–heartbeating donors, and extended-criteria donors (ECDs) are all currently used in expanding the liver graft availability for orthotopic liver transplantation (OLT). Fatty changes in the donor liver are a risk factor for poor function after OLT, especially for primary non-function; however, the presence of steatosis, frequently present in livers from ECDs, does not exclude the use of these organs. The aims were to study the steatosis as a cause of nonacceptance of a liver for transplantation after exploration by the donor surgical team. All donor harvesting procedures performed by our unit from 2008 to 2014 were retrospectively studied. Donors were divided in those accepted and harvested and those discarded by the donor surgical team. Donor characteristics of accepted and used liver grafts were compared with those rejected due to steatosis. Five hundred sixty-one donor liver procurements were performed, out of which 60 (10.6%) demonstrated on liver biopsy macroscopic steatosis. Out of these 44 had mild steatosis (>30% but <60%) and 16 had severe steatosis (>60%). The main indication for biopsy was macroscopic doubt about the degree of steatosis. The main indication for discarding the liver was macrovesicular steatosis above 60%.
Consensus of Second-Order Multi-Agent Systems with Uncertainties Using Only Position Measurements under a Directed Graph

This paper focuses on the consensus problem for second-order multi-agent systems with parametric uncertainties using only the position measurements under a general directed graph. In order to remove the necessity of velocity measurements, we propose a consensus algorithm for the system without relative velocity measurements, in which the velocity feedback is replaced by a filter based on a passivity approach. Based on an auxiliary variable, the consensus problem of original system is converted into the consensus problem for a double integrator multi-agent system with a vanishing term. The consensus convergence is then analyzed via the Lyapunov stability and input-to-state stability theory.
Evolution of Translational Omics: Lessons Learned and the Path Forward

Sequencing the human genome opened a new era in biomedical science. Researchers have begun to untangle the complex roles of biology and genetics in specific diseases, and now better understand why particular therapies do or do not work in individual patients. New technologies have made it feasible to measure an enormous number of molecules within a tissue or cell; for example, genomics investigates thousands of DNA sequences, and pro-teomics examines large numbers of proteins. Collectively, these technologies are referred to as omics. Patients look to the scientific community to develop innovative omics-based tests to more reliably detect disease and to predict their likelihood of responding to specific drugs. However, transforming the great promise of these new technologies into clinical laboratory tests that can help patients directly has happened more slowly than anticipated. The process to translate omics-based discoveries into clinically useful tests is much more demanding than has been widely recognized. For example , verification of the complex computational procedures used to develop omics-based tests requires adequate access to the data, computer code, and computational steps used to develop that test. Also, regulatory oversight of clinical laboratory tests differs from that of drugs. Thus far, the Food and Drug Administration (FDA) has chosen not to review most of these clinical tests. These challenges converged during a recent case involving premature use of omics-based tests in clinical trials at Duke University. Flawed gene-expression tests developed by cancer researchers at Duke were used in three clinical trials to determine which chemotherapy treatment patients with lung or breast cancer would receive. For three years, the Duke investigators rebuffed Patients look to the scientific community to develop innovative omics-based tests to more reliably detect disease and to predict their likelihood of responding to specific drugs.
Analysis and Control of Current Harmonics in Multiphase Machines in Fault-tolerant Operation against Open-phase Faults

Low-order current harmonics exist in multiphase drives due to machine and converter nonlinearity. In the healthy condition, the characteristics of current harmonics and controllers for harmonic compensation or injection has been well addressed in previous literatures. However, these problems have not been clearly clarified under fault-tolerant operation. This paper analyzed the characteristics of current harmonics under fault-tolerant operation against open-phase faults. It was found that both positive and negative sequence current harmonics exist in all the machine subspaces under asymmetrical open-phase faults, therefore the performance of traditional current harmonics control structure is degraded. Thus, we proposed an improved current harmonics control structure, in which the current harmonics control was performed on both positive and negative harmonic components in all the machine subspaces. Experiments were done on a nine-phase induction machine. The experimental results showed the proposed structure had much better harmonic control performance compared to the traditional one in open-phase conditions, which verified the analysis result and the effectiveness of the proposed structure. With the proposed current harmonics control scheme, torque ripples and winding losses can be reduced for distributed-winding machines, or torque density can be enhanced by harmonic injection for concentrated-winding machines even under open-phase faults.
Using Asynchronous Simulation Approach for Interactive Simulation

This paper discusses about the advantage of using asynchronous simulation in the case of interactive simulation in which user can steer and control parameters during a simulation in progress. Asynchronous models allow to compute each iteration faster to address the issues of performance needed in an highly interactive context, and our hypothesis is that get partial results faster is better than getting synchronized and final results to take a decision, in a interactive simulation context.
A Novel Two Degrees-of-Freedom Rotary-linear Flux-Switching Permanent Magnet Machine

This paper proposes and investigates a novel two degrees of freedom rotary-linear flux-switching permanent magnet (2DoFRL-FSPM) machine, which consists of two arc-shaped stators and a long solid salient-poles (rotor) mover. Firstly, the geometric topology and operation principle of a 2DoFRL-FSPM machine are introduced and explained. Then, based on three-dimensional finite element model (3D-FEM), both the static and dynamic performances, including air-gap magnetic field, PM flux-linkage, back-EMF, cogging torque/force, and electromagnetic torque/thrust force, etc., are predicted. The simulated results reveal that the 2DoFRL-FSPM is capable of rotary, linear and helical motions.
New design of analog and mixed-signal cells using back-gate cross-coupled structure

Analog integrated circuits, in particular passive components, never follow the Moore’s law. FDSOI (Fully Depleted Silicon On Insulator) technology allows to reduce the SCE (Short Channel Effect) and to design new mixed-signal topologies in order to remove passive component. To illustrate this SCE problem, a current mirror was chosen and a new design is proposed. First, a RO (Ring Oscillator) based on back-gate cross-coupled structure and complementary logic, has been designed, optimized and measured. Using the same technique, a current mirror is studied and finally implemented to a VCRO (Voltage Controlled RO). The concept is validated by simulations and measurements.
Using Ultrasonic Mid-air Haptic Patterns in Multi-Modal User Experiences

Ultrasonic mid-air tactile displays offer a unique combination of high spatial and temporal resolution and can stimulate a wide range of tactile frequencies. Leveraging those features, a new modulation technique producing spatially distributed tactile sensations has recently been introduced. This new approach, referred to as Spatiotemporal Modulation (STM), draws lines, curves and shapes on users’ palm by moving a mid-air tactile point rapidly and repeatedly along the path. STM parameters and their impact on tactile perception are yet to be studied systematically. In this work, we first study how varying the draw frequency and the size of a simple shape affects the participants perception of texture and their emotional responses. In the second part of our study, we used the most salient tactile patterns of the first study to extend the results within a multimodal context. We found that tactile patterns’ perception was consistent within both studies. We also found instances when the tactile patterns could alter the perception of the audio and visual stimuli. Finally, we discuss the benefits of our findings and conclude with implications for future work.
Average Data Rate Analysis for Hierachical Cell Structure under Nakagami-m Fading Channel with a Two-layer Feed-Forward Neural Network

A hierarchical cell structure is a cell structure that consists of small-cells and macro-cells to support high data rate and to solve the shadow area problem. To avoid intercell interference in a hierarchical cell structure, it would be preferable to assign different spectrums. Otherwise, to improve spectral efficiency, it would be preferable to assign same spectrum. To balance this trade-off, the performance of the system should be analyzed first. However, this is difficult because of the complexity of the calculations required especially with multiple interferences under Nakagami-m fading channel model. A neural network undergoes a process of adjusting the weight of each connection through learning to approximate arbitrary functions, and the advantage of the neural network is that once the learning is complete, a simple linear operation can be used to approximate the function and obtain the output for the input easily. In this paper, we examined the applicability of a two-layer feed-forward neural network to analyze the average data rate of the spectrum sharing system under Nakagami-m fading channel.
A Spatio-Temporal Flow Model of Dockless Shared Bikes

With the advantages of convenient access and free parking, dockless shared bikes are favored by the public. However, the irregular flow of dockless shared bikes poses a challenge for related research. In this paper, a clustering method based on the point of interests is proposed to find the aggregation areas of dockless shared bikes. Through the analysis of the time series location data of ofo shared bikes in Beijing, a model is proposed to describe the spatio-temporal flow of urban dockless shared bikes. The model is evaluated based on the topological features of the complex network. The results can be used to analyze the mobility characteristics of citizens, prevent emergencies and improve the urban traffic management system.
Live Demonstration: Smart tracker and gesture capturer for people with Parkinson’s diseases

Parkinson’s disease is classified as a chronic movement disorder whose incidence is proportional to the age. It is a common progressive neurodegenerative condition associated with significant disability and negative impact on the quality of life. Its manifestations involve difficulty with coordinated movements such as asymmetric resting tremor, rigidity, and bradykinesia. These symptoms and their response to levodopa constitute the basis for a clinical diagnosis of Parkinson's disease (PD) [1] . A recent study of this disease in North America showed the prevalence of PD among those aged ⩾45 years to be 572 per 100,000, with 680,000 individuals in the US aged ⩾ 45 years with PD in 2010 and the estimative to rise to approximately 930,000 in 2020 and 1,238,000 in 2030 based on the US Census Bureau’s populational projections [2] .