Miracle-dz/UniFAR-FTUs huggingface dataset:
The Immobilization of ChEMBL474807 Molecules Using Different Classes of Nanostructures

Indirubin derivatives and analogues are a large group of compounds which are widely and successfully used in treatment of many cancer diseases. In particular, the ChEMBL474807 molecule, which has confirmed inhibiting abilities against CDK2 and GSK3B enzymes, can be included in this group. The immobilization of inhibitors with the use of nanocarriers is an often used strategy in creation of targeted therapies. Evaluations were made of the possibility of immobilizing ligand molecules on different types of nanocarrier, such as carbon nanotubes (CNT), functionalized fullerene C60 derivatives (FF_X), and functionalized cube rhombellanes, via the use of docking methods. All results were compared with a reference system, namely C60 fullerene. The realized calculations allowed indication of a group of compounds that exhibited significant binding affinity relative to the ligand molecule. Obtained data shows that structural modifications, such as those related to the addition of functional groups or changes of structure symmetry, realized in particular types of considered nanostructures, can contribute to increases of their binding capabilities. The analysis of all obtained nano complexes clearly shows that the dominant role in stabilization of such systems is played by stacking and hydrophobic interactions. The realized research allowed identification of potential nanostructures that, together with the ChEMBL474807 molecule, enable the creation of targeted therapy.
Surfaces Modelling Using Isotropic Fractional-Rational Curves

The problem of building a smooth surface containing given points or curves is actual due to development of industry and computer technology. Previously used for those purposes, shells of zero Gaussian curvature and minimal surfaces based on isotropic analytic curves are restricted in their consumer properties. To expand the possibilities regarding the shaping of surfaces we propose the method of constructing surfaces based on isotropic fractional-rational curves. The surfaces are built using flat isothermal and orthogonal grids and on the basis of the Weierstrass method. In the latter case, the surfaces are minimal. Examples of surfaces that were built according to the proposed method are given.
Approche multicritère de recherche d’information structurée basée sur l’apprentissage d’ordonnancement

Il est connu que, dans la recherche d'information, des meilleures performances sont obtenues lorsque plusieurs sources de pertinence sont combinees en utilisant des methodes d'apprentissage d'ordonnancement. Dans cet article, nous proposons une approche multicritere pour la recherche d'information dans les documents structurees basee sur les methodes d'apprentissage d'ordonnancement pour apprendre automatiquement un modele de Ranking et mesurer l'impact de chaque source de pertinence. Des (.)
Video to Skill Tagging using Transcripts under Weak Supervision

Delivering micro content (small pieces of learning materials) from a course catalog has become important as it helps learners engage quickly with a learning platform on a need-to-know basis. In order to recommend videos that can teach skills relevant to a learner, we propose an approach for tagging videos with skills using transcripts. Since, there is no labeled data to train a video-to-skill model directly, we leverage a bunch of techniques that fall under the umbrella of weak supervision. We evaluate our approach using offline metrics like Precision, Recall, F-1 score and online metrics via large-scale A/B testing.
Efficient Rock-Mass Point Cloud Registration Using $n$ -Point Complete Graphs

The surfaces of rock masses are arbitrary and complex. Moreover, the point clouds of rock-mass surfaces acquired via terrestrial laser scanning typically span large distances and have high resolutions. These characteristics cause difficulties in registration between scans. To address these difficulties, an efficient method using <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-point complete graphs is proposed. To handle massive point clouds, a step-by-step strategy is adopted to reduce the number of points involved in the computation. First, the Gaussian curvature of each point of the initial data is estimated, and points with low Gaussian curvatures are filtered out such that only the interesting points are preserved. Second, these interesting points are clustered, and the centroid of each cluster is calculated. Finally, a descriptor is built from the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-point complete graph formed by each centroid and its <inline-formula> <tex-math notation="LaTeX">$n-1$ </tex-math></inline-formula> nearest neighbors. By matching the descriptors generated from two point clouds, corresponding point pairs can be obtained, thus achieving alignment. In addition, this strategy inherently incorporates denoising, outlier handling, and filtration, thereby endowing the method with strong adaptability to various conditions without incurring any additional cost. Experiments on data sets with varying degrees of outliers, noise and overlap were conducted to demonstrate the robustness of the proposed method. The results show that, with point span <inline-formula> <tex-math notation="LaTeX">$r~\approx ~1$ </tex-math></inline-formula> cm, the output root mean square error is around 0.5 cm, which is comparable with that of the Iterative Closest Point algorithm. A runtime analysis shows that the total processing time of the proposed method grows nearly linearly with increasing data size.
Application of Generative Adversarial Network on Image Style Transformation and Image Processing

Author(s): Wang, Anshu | Advisor(s): Wu, Yingnian | Abstract: Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping between two different domains or multiple domains. Recent research in computer vision and deep learning produced powerful tools for the task. Conditional adversarial net- works serve as a general-purpose solution for image-to-image translation problems. Deep Convolutional Neural Networks can learn an image representation that can be applied for recognition, detection, and segmentation. Generative Adversarial Networks (GANs) has gained success in image synthesis. However, traditional models that require paired training data might not be applicable in most situations due to lack of paired data.Here we review and compare two different models for learning unsupervised image to im- age translation: CycleGAN and Unsupervised Image-to-Image Translation Networks (UNIT). Both models adopt cycle consistency, which enables us to conduct unsupervised learning without paired data. We show that both models can successfully perform image style trans- lation. The experiments reveal that CycleGAN can generate more realistic results, and UNIT can generate varied images and better preserve the structure of input images.
Output‐based resilient event‐triggered control for networked control systems under denial of service attacks

This study is concerned with the output-based resilient event-triggered control for networked control systems (NCSs) subject to denial of service (DoS) attacks, where 'resilient' is interpreted as that the NCSs can tolerate a larger time delay with a certain degree of performance degradation when there are DoS attacks. Otherwise, the NCSs run with $H-{\infty }$H∞ performance. In order to characterise DoS attacks, an additional positive term which implies the effects of DoS attacks is imposed on the pre-designed triggering condition. By considering the fact that only partial output measurements can be obtained, the output-based security performance analysis as well as an output feedback controller design under the proposed event-triggered strategy is conducted with Lyapunov-Krasovskii method and linear matrix inequalities technique. At last, the simulation results show that the designed controller is resilient to DoS attacks with less dependent on the transmission packets while sacrificing some control performance.
Early morning peaks in the diurnal cycle of precipitation over the northern coast of West Java and possible influencing factors

Abstract. The diurnal cycles of precipitation over the northern coast of West Java have been studied using the Tropical Rainfall Measuring Mission (TRMM) Real Time Multi-satellite Precipitation Analyses (TMPA-RT) products with records spanning from 2000 to 2016, with emphasis on the occurrences of early morning precipitation peaks. Diurnal precipitation over the study area during November to March is basically characterized by precipitation peaks that occur in the afternoon to evening time (15:00–21:00 LT), but secondary peaks in the night to morning time (01:00–07:00 LT) are also pronounced in January and February. The harmonic analysis method was then applied to data of January and February to objectively determine the diurnal phase and classify the timing of precipitation for each day into three categories, i.e. afternoon-to-evening precipitation (AEP), early morning precipitation (EMP), and late morning precipitation (LMP) with peaks that occur in the time windows of 13:00–24:00, 01:00–04:00, and 05:00–12:00 LT, respectively. In terms of frequency of occurrence, AEP, EMP, and LMP constitute 55 %, 18.9 %, and 26.1 % of total samples of precipitation events. In spite of the smallest percentage, EMP events are characterized by seaward (as well as landward) propagation, flat phase distribution, and large mean amplitudes. The propagating characteristics of EMP are more prominent, with indications of stronger connectivity between precipitation systems over land and ocean, when data are composited by taking the 99th percentile values in each grid to represent extreme precipitation events. The flat phase distribution of EMP events suggests that the timing of coastal precipitation is not necessarily locked to the phase of land–sea breezes, thus allowing precipitation to occur more randomly. Furthermore, the role of the South China Sea cold tongue (SCS-CT) and cross-equatorial northerly surge (CENS) as influencing factors for the occurrences of the EMP event have also been investigated. In agreement with previous studies, we confirmed that the SCS-CT generally prevails in January and February, and morning precipitation events over the northern coast of West Java mainly occurred when there was more enhanced sea surface temperature (SST) cooling in the South China Sea. Additionally, we found that CENS is the most differential factor with regard to the phase of coastal precipitation. In this case, CENS is positively correlated with SCS-CT and, when associated with EMP events, concurrent enhancement of CENS and SCS-CT is connected to a narrow channelling of strong surface northerly wind anomalies just offshore of Indochina and the Malay Peninsula.
ANALYSIS OF SPRINT START TRANSITION PHASES AND THEIR ASSOCIATIONS WITH PERFORMANCE

The sprint start consists of three transitions defined by the instants the hands, rear leg and front leg leave the ground, which delimit three phases. Ground reaction forces produced by 57 sprinters during the block phase were analysed to investigate the performance (average horizontal external power) implications of transition timing and the force production and centre of mass (CM) displacements between them. Better sprint starters exhibited higher horizontal (r = 0.47 for phase two), but lower vertical (r = -0.40), CM displacement across shorter time periods. Additionally, more horizontally-orientated force vectors to reduce CM projection angle at each transition were favourable (r range = -0.62 to -0.45). Coaches should encourage high anteroposterior bilateral force production even though this may compromise (r = -0.41) subsequent front block force production.
Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

Intel recently introduced the Heterogeneous Architecture Research Platform, HARP. In this platform, the Central Processing Unit and a Field-Programmable Gate Array are connected through a high-bandwidth, low-latency interconnect and both share DRAM memory. For this platform, Open Computing Language (OpenCL), a High-Level Synthesis (HLS) language, is made available. By making use of HLS, a faster design cycle can be achieved compared to programming in a traditional hardware description language. This, however, comes at the cost of having less control over the hardware implementation. We will investigate how OpenCL can be applied to implement a real-time guided image filter on the HARP platform. In the first phase, the performance-critical parameters of the OpenCL programming model are defined using several specialized benchmarks. In a second phase, the guided image filter algorithm is implemented using the insights gained in the first phase. Both a floating-point and a fixed-point implementation were developed for this algorithm, based on a sliding window implementation. This resulted in a maximum floating-point performance of 135 GFLOPS, a maximum fixed-point performance of 430 GOPS and a throughput of HD color images at 74 frames per second.
Temporal and spatial dynamics of willow grouse Lagopus lagopus

This study is a first attempt to analyze large-scale willow grouse (Lagopus lagopus) population dynamics. In this thesis I studied the population dynamics of willow grouse in the Swedish mountain range. I estimated the scale of synchrony in both breeding success, and the adult segment between populations throughout the mountain range by using time-series of harvest data and population densities estimated from line transect counts. Little evidence of regular fluctuations/cycles in juveniles in autumn was found, and chick production in willow grouse appears to fluctuate more irregularly than was previously believed. Breeding success estimated as number of juveniles per two adults in autumn, showed a spatial correlation between sites up to 200 km apart, but there was no spatial correlation in the adult segment of the populations. When analyzing the presence and strength of time-lags in density dependent processes, a first order process was detected in the southernmost region, and a combination of weak first and second order processes in the northern part of the mountain range. There was a positive relationship between breeding success the previous year and the intrinsic rate of increase in the northernmost region where first and second order density dependence was weak and of equal strength. No such relationship was found in the southern region where density dependence was stronger and dominated by a first order process. Natal dispersal resulted in 80% of the juvenile females dispersing more than 5 km from their summer area, with a mean dispersal distance of 10.2 km, three times further than for juvenile males (3.4 km). Adult females were migratory between wintering areas used as a juvenile and their first breeding site. At landscape scales, only juvenile females and migratory adult females are important in exchange of individuals between populations. Hunters’ effort provided a useful relationship with harvest rate. Setting limits to the totally allowable effort within an area has a stronger potential for controlling harvest than daily bag limits or adjusting the length of the open season. Another simple harvest strategy would be to prohibit harvest in parts of the total area hunted. By setting aside a part of the total area as a buffer, and placing limits on the harvest effort in the open parts of the hunting area, it would be possible to achieve a cost-efficient system with a small risk of over-harvesting.
Multiscale Full-Waveform Dual-Parameter Inversion Based on Total Variation Regularization to On-Ground GPR Data

Full-waveform inversion (FWI) in the time domain of ground-penetrating radar (GPR) data involves a vast number of calculations; thus, it requires a large amount of memory and is difficult to calculate on a personal computer (PC). In this paper, GPR data are analyzed with multiscale FWI using two parameters (permittivity and conductivity) based on total variation (TV) regularization, which is implemented on a PC using a graphics processing unit (GPU) parallel acceleration strategy. The inverse problem is considered to be a nonlinear optimization problem and is solved with limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) process, which is a quasi-Newton method. The gradient of the objective function is calculated using the adjoint-state method, and the finite-difference method is required to solve the forward problem many times. A multiscale serial inversion strategy is applied to optimize the inversion algorithm and to decompose the inversion problem into 2–3 frequency bands to search in the direction of the global minimum point instead of local minimums. Taking the complex model as an example, experiments are carried out to assess the parameter adjustment factor and regularization parameter. The appropriate parameter adjustment factor and regularization parameter can effectively guarantee the convergence speed and stability of dual-parameter inversion method and improve the accuracy of GPR data inversion. Finally, FWI of the noise-free and 25-dB signal-to-noise ratio (SNR) noise data of the overthrust model is performed. The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface.
Global Nested PID Control of Strict-Feedback Nonlinear Systems With Prescribed Output and Virtual Tracking Performance

This brief proposes a global nested PID control method for a class of strict feedback nonlinear systems with unknown system nonlinearities, which guarantees that the output and virtual tracking errors are confined in some prescribed performance regions and globally bounded closed loop signals. No linearized approximators are required to compensate for the unknown nonlinearities. The proposed nested PID control is capable of ensuring prescribed output and virtual tracking performance independent of control parameters and unknown system nonlinearities with low controller complexity. Finally, simulation results are given to demonstrate the effectiveness of proposed method.
Three-dimensional coherent ladar based on FMCW and its flight demonstration

A scanning three-dimensional coherent laser radar (ladar) based on the frequency modulated continuous wave (FMCW) is proposed and demonstrated, which can obtain many three-dimensional high-quality images. The system utilizes an electro-optic modulator and an optical filter to output a linear FMCW with a bandwidth of 2 GHz. The flexible and variable rotating double prism is used for beam scanning. The images of flight demonstration are formed by attitude compensation correction. The experiment result validates the performance of our system for airborne three-dimensional scanning imaging.
Design of MIMO Antenna Isolation Structure Based on a Hybrid Topology Optimization Method

A hybrid topology optimization (HTO) method is utilized to optimize the isolation structure of a multiple-input multiple-output (MIMO) antenna. The HTO method, in which the scalar isotropic material with penalization (SIMP) and the level set method (LSM) are integrated, is based on the gradient optimization method with fast convergence speed. It combines the advantages of strong topology-searching ability of SIMP and smooth structure boundary of LSM. The proposed HTO method is adopted to optimize the isolation structure for good impedance matching, high isolation and high polarization purity of the MIMO antenna simultaneously. By setting the optimization structure as a symmetric one, the number of optimization variables is reduced by half. The optimization is validated by the full-wave simulation and the measurement. At the expected frequency of 5.8 GHz, the measured mutual coupling between the MIMO antenna elements with a center-to-center distance of $0.25\lambda _{0}$ is reduced by more than 24 dB, and the cross-polarization ratio reaches more than 27 dB. Furthermore, the reason for the isolation improvement is analyzed as well.
Modified phase locked loop for grid connected single phase inverter

Connecting a single-phase or three-phase inverter to the grid in distributed generation applications requires synchronization with the grid. Synchronization of an inverter-connected distributed generation units in its basic form necessitates accurate information about the frequency and phase angle of the utility grid. Phase Locked Loop (PLL) circuit is usually used for the purpose of synchronization. However, deviation in the grid frequency from nominal value will cause errors in the PLL estimated outputs, and that’s a major drawback. Moreover, if the grid is heavily distorted with low order harmonics the estimation of the grid phase angle deteriorates resulting in higher oscillations (errors) appearing in the synchronization voltage signals. This paper proposes a modified time delay PLL (MTDPLL) technique that continuously updates a variable time delay unit to keep track of the variation in the grid frequency. The MTDPLL is implemented along a Multi-Harmonic Decoupling Cell (MHDC) to overcome the effects of distortion caused by gird lower order harmonics. The performance of the proposed MTDPLL is verified by simulation and compared in terms of performance and accuracy with recent PLL techniques.
Prediction of Left Ventricular Mechanics Using Machine Learning

The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 ± 0.584 ml, XGBoost pressure: 1.544 ± 0.298 mmHg, Cubist volume: 1.495 ± 0.260 ml, Cubist pressure: 1.623 ± 0.191 mmHg, myofiber stress: 0.334 ± 0.228 kPa, cross myofiber stress: 0.075 ± 0.024 kPa, and shear stress: 0.050 ± 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.