Miracle-dz/UniFAR-FTUs huggingface dataset:
A Parameter Division Based Method for the Geometrical Calibration of X-Ray Industrial Cone-Beam CT

For X-ray cone-beam computed tomography (CBCT), non-calibrated system geometry causes indistinct and blurring artifacts in reconstructed images, which impacts on the image quality badly. This paper presents a practical industrial CBCT geometric calibration method, which aims to determine the geometric parameter without extra phantom and benefit to efficiency for calibration. In our method, the geometric parameter is divided into system parameter and imaging parameter to be calibrated, respectively. First, the system parameter was determined precisely at the first beginning via average for multiple measurements. Next, the imaging parameter was determined via a designed algorithm in this paper based on the invariance of rotation axis. Furthermore, the proposed method only utilizes the projection data without reconstruction iteration to calibrate geometric parameter, which benefits to efficiency and practical industrial application. 3-D simulation data have been through test with and without noise, and the results show robust and accuracy on the proposed algorithm. In addition, it is also validated with various real data include printed circuit board and industrial components acquired at actual industrial CBCT. The results of reconstructed images demonstrate that the proposed method can achieve comparable image quality in the reconstruction as some phantom-based methods. Furthermore, the advantages and obstacle of the proposed method are analyzed and discussed in this paper.
Optimal Task Scheduling for Distributed Cluster With Active Storage Devices and Accelerated Nodes

With advancements in compute-intensive and memory-bound applications, the need for faster and more energy-efficient processing platforms continues. In support of these advancements, heterogeneous platforms have been proposed to enhance the performance and efficiency in the cloud. These platforms include field programmable gate arrays and graphical processing units in addition to general-purpose processors. Furthermore, there is a strong interest in advancing active solid-state drives to support both storage and computation. In this paper, we present a generic formulation to support the modeling of such a heterogeneous cloud environment, without being specific to a particular cloud platform such as Spark or Hadoop. We represent the cloud as a collection of clients, middleware control nodes, and high performance compute nodes (HPN), where the HPNs represent the options of advanced compute technologies in a heterogeneous cloud. The objective of the paper is to present a simple and efficient formulation for scheduling applications in such a heterogeneous cloud. Consistent with recent software modeling of artificial intelligence applications, we propose to map applications into directed acyclic graph representations of tasks. The optimization problem is then formulated to infer the best scheduling of tasks on the HPNs, while minimizing the overall execution time and data communication delays between nodes. Unlike existing scheduling algorithms that assume equal performance across nodes, our formulation explicitly takes into account the different compute capabilities of the heterogeneous nodes. The resulting task scheduling is then evaluated to provide insights into the performance gains with the proposed advanced heterogeneous cloud computing environment. The results show improved performance when comparing the proposed task-scheduling algorithm with the genetic algorithm and heterogeneous earliest finish time algorithms. We also show the performance gains achieved with the optimal task scheduling on a heterogeneous cloud system as compared with a conventional CPU-only cloud system.
An Approach to Clustering Models Estimation

There are numerous clustering algorithms and clustering quality criteria present at the moment. According to clustering tasks, different criteria can be used as a ground for choosing one or another clustering model. But choosing of clustering quality criteria is in turn not based on any formal approach and depends only on an analyst's intuition. We propose a systematic approach to clustering quality estimation by analyzing the structure of clustering models and extracting the factors of clustering quality. Further, we believe that clustering quality estimation requires estimation of knowledge or information produced in result of clustering. However, we show that existing information criteria can not be used as criteria for choosing clustering models and propose a new information criterion for clustering and examine it experimentally.
Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting

Electricity is of great significance for national economic, social, and technological activities, such as material production, healthcare, and education. The nationwide electricity demand has grown rapidly over the past few decades. Therefore, efficient electricity demand estimation and management are required for better strategies planning, energy utilization, waste management, improving revenue, and maintenance of power systems. In this paper, we propose an empirical mode decomposition (EMD)-based deep learning approach which combines the EMD method with the long short-term memory network model to estimate electricity demand for the given season, day, and time interval of a day. For this purpose, the EMD algorithm decomposes a load time series signal into several intrinsic mode functions (IMFs) and residual. Then, a LSTM model is trained separately for each of the extracted IMFs and residual. Finally, the prediction results of all IMFs are combined by summation to determine an aggregated output for electricity demand. To demonstrate the applicability of the proposed approach, it is applied to electricity consumption data of city Chandigarh. Furthermore, the performance of the proposed approach is evaluated by comparing the prediction results with recurrent neural network (RNN), LSTM, and EMD-based RNN (EMD+RNN) models.
Buffer-Aware Data Migration Scheme for Hybrid Storage Systems

Since solid-state drives (SSD) have high read speed, they are integrated into a traditional hard disk drive (HDD)-based storage systems for improving the overall performance. The data migration, which is responsible for moving the data between the HDD and SSD so as to maximize the I/O performance, may result in the overhead in terms of unnecessary page migrations. To deal with it, we develop a novel buffer-aware data migration scheme to boost the performance for hybrid storage systems consisting of HDD and SSD by exploiting the content information in the buffer cache. It first introduces two new data states to reclassify the data, which are placed on the hybrid storage systems equipped with HDD and SSD. Then, it tries to improve the efficiency of data migration by copying the newly updated data in the buffer cache instead of the old version in the HDD or SSD into the other device. We also model the process of data migration operation and assess the effectiveness of the buffer-aware data migration scheme theoretically. Experimental results reveal that the buffer-aware data migration scheme can reduce the runtime time by up to 18% and the write count by up to 14% over the buffer-unaware data migration scheme under three benchmarks and a real workload.
Dance Exergame System for Health Using Wearable Devices

This paper proposed a dance training game applied using game theory by using wearable devices. The system extracts the teacher’s motions by using motion capture sensor technology. The pressure on the foot was sensed while dancing, and the motions of the ankle were extracted. The exergame enables students to watch the teacher’s dance in all directions by using an avatar and gaining the effects of both exercise and education by repeatedly practicing avatar’s dancing. To overcome the space constraints of the special equipment in the course of acquiring and measuring the students’ movements and to allow students to practice at any place, the movements of the user’s foot were measured by using a sock type wearable device that the user wears in their lives normally.
Nomograms for predicting long-term overall survival and disease-specific survival of patients with clear cell renal cell carcinoma

Objectives The aim of this study was to establish comprehensive and practical nomograms, based on significant clinicopathological parameters, for predicting the overall survival (OS) and the disease-specific survival (DSS) of patients with clear cell renal cell carcinoma (ccRCC). Patients and methods The data of 35,151 ccRCC patients, diagnosed between 2004 and 2014, were obtained from the database of the Surveillance, Epidemiology, and End Results (SEER) program. The Kaplan–Meier method and Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinicopathological variables on survival. Based on Cox models, a nomogram was constructed to predict the probabilities of OS and DSS for an individual patient. The predictive performance of nomograms was evaluated using the concordance index (C-index) and calibration curves. Results According to univariate and multivariate analyses, age at diagnosis, sex, race, marital status, surgical approach, tumor node metastasis (TNM) stage, and Fuhrman grade significantly correlated with the survival outcomes. These characteristics were used to establish nomograms. The nomograms showed good accuracy in predicting 3-, 5-, and 10-year OS and DSS, with a C-index of 0.79 (95% CI, 0.79–0.80) for OS and 0.87 (95% CI, 0.86–0.88) for DSS. All calibration curves revealed excellent consistency between predicted and actual survival. Conclusion Nomograms were developed to predict death from ccRCC treated with nephrectomy. These new prognostic tools could aid in improving the predictive accuracy of survival outcomes, thus leading to reasonable individualized treatment.
Features to Detect Android Malware

Android is one the most used mobile operating system worldwide. Due to its technological impact, its open source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android’s vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security. To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities. In this article, we propose to consider Android application layer and network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community. Finally, our models show malware detection rates of 99% and 81%.
Intuitive Editing of Visual Appearance from Real-World Datasets

Computer-generated imagery is ubiquitous, spanning ﬁelds such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the ﬁeld towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems. First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human-friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unﬁt to editing operations, which limits the creative process for scientists, engineers, artists, and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the ﬂexibility to edit such data. The current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. To deﬁne intuitive and predictable editing spaces, algorithms, and workﬂows, we must investigate at the theoretical, algorithmic and application levels, putting the user at the core, learning key relevant appearance features in terms humans understand.
Changes in Energy Expenditure, Dietary Intake, and Energy Availability Across an Entire Collegiate Women's Basketball Season.

Zanders, BR, Currier, BS, Harty, PS, Zabriskie, HA, Smith, CR, Stecker, RA, Richmond, SR, Jagim, AR, and Kerksick, CM. Changes in energy expenditure, dietary intake, and energy availability across an entire collegiate women's basketball season. J Strength Cond Res XX(X): 000-000, 2018-The purpose of this study was to identify changes in energy expenditure and dietary intake across an entire women's basketball season. On 5 different occasions across the competitive season, female collegiate basketball players (19.8 ± 1.3 years, 173.9 ± 13.6 cm, 74.6 ± 9.1 kg, 27.1 ± 3.2% fat, 53.9 ± 6.4 ml·kg·min, n = 13) were outfitted with heart rate and activity monitors over 4 consecutive days and completed 4-day food and fluid records to assess changes in energy expenditure and dietary status. Dual-energy x-ray absorptiometry was used to assess baseline body composition and resting energy expenditure (REE) was measured before and after the season. Data were analyzed using 1-factor repeated-measures analysis of variance. Total daily energy expenditure (TDEE, p = 0.059) and physical activity levels (TDEE/REE, p = 0.060) both tended to decrease throughout the season. Energy balance was negative at all time points throughout the season. Absolute and normalized daily protein intake at the end of the season was significantly (p < 0.05) lower than at the beginning of the season. Carbohydrate (3.7 ± 0.4 g·kg·d) and protein (1.17 ± 0.16 g·kg·d) intakes were lower than commonly recommended values based on previously published guidelines. These findings suggest that greater education and interventions for collegiate athletes and coaches regarding dietary intake and energy expenditure are warranted.
Sirt1 protects neural stem cells from apoptosis by decreasing acetylation of histone 3K9

Objective To explore the role and mechanism of Sirt1 in protecting neural stem cells (NSCs) from apoptosis. Materials and methods Transfection was used to overexpress Sirt1 in rat NSCs. The effect of Sirt1 overexpression on camptothecin-induced apoptosis of NSCs was evaluated. Western blotting was used to examine the expression of Sirt1, cleaved caspase-3, and acetylated histone 3K9. Results Overexpression of Sirt1 in NSCs decreased the cleavage of caspase-3 and acetylation of histone 3K9. Conclusion Sirt1 may protect NSCs from apoptosis by decreasing the acetylation of histone 3 on K9.
Truthful unsplittable flow for large capacity networks

The unsplittable flow problem is one of the most extensively studied optimization problems in the field of networking. An instance of it consists of an edge capacitated graph and a set of connection requests, each of which is associated with source and target vertices, a demand, and a value. The objective is to route a maximum value subset of requests subject to the edge capacities. It is a well known fact that as the capacities of the edges are larger with respect to the maximal demand among the requests, the problem can be approximated better. In particular, it is known that for sufficiently large capacities, the integrality gap of the corresponding integer linear program becomes 1 + &epsis;, which can be matched by an algorithm that utilizes the randomized rounding technique. In this article, we focus our attention on the large capacities unsplittable flow problem in a game theoretic setting. In this setting, there are selfish agents, which control some of the requests characteristics, and may be dishonest about them. It is worth noting that in game theoretic settings many standard techniques, such as randomized rounding, violate certain monotonicity properties, which are imperative for truthfulness, and therefore cannot be employed. In light of this state of affairs, we design a monotone deterministic algorithm, which is based on a primal-dual machinery, which attains an approximation ratio of e/e−1, up to a disparity of &epsis; away. This implies an improvement on the current best truthful mechanism, as well as an improvement on the current best combinatorial algorithm for the problem under consideration. Surprisingly, we demonstrate that any algorithm in the family of reasonable iterative path minimizing algorithms, cannot yield a better approximation ratio. Consequently, it follows that in order to achieve a monotone PTAS, if that exists, one would have to exert different techniques. We also consider the large capacities single-minded multi-unit combinatorial auction problem. This problem is closely related to the unsplittable flow problem since one can formulate it as a special case of the integer linear program of the unsplittable flow problem. Accordingly, we obtain a comparable performance guarantee by refining the algorithm suggested for the unsplittable flow problem.
Robust Active Damping in LCL-Filter-Based Medium-Voltage Parallel Grid Inverters for Wind Turbines

LCL-filter-based grid-tie inverters require damping for the current-loop stability. There are only software modifications in active damping, whereas resistors are added in passive damping. Although passive damping incurs in additional losses, it is widely used because of its simplicity. This paper considers the active damping in medium-voltage parallel inverters for wind turbines. Due to cost reasons, only minimal software changes are allowed and no extra sensors can be used. The procedure must be robust against line-inductance variations in weak grids. Double-update mode is needed so the resonance frequency is under the Nyquist limit. The bandwidth reduction when using active damping is also required to be known beforehand. Moreover, the design procedure should be simple without requiring numerous trial-and-error iterations. In spite of the abundant literature, the options are limited under these circumstances. Filter-based solutions are appropriate and a new procedure for tuning the notch filter is proposed. However, this procedure requires that the resistance of the inductors is known and a novel filter-based solution is proposed that uses lag filters. The lag filters displace the phase angle at the resonance frequency so that the Nyquist stability criterion is fulfilled. Simulations and experiments with a 100-kVA prototype validate the analysis.
Expression levels of IL-15 and IL-17 in synovial fluid of rheumatoid arthritis animal model

The aim of the present study was to investigate the expression levels of interleukin-15 (IL-15) and interleukin-17 (IL-17) in synovial fluid of rheumatoid arthritis (RA) animal model, and to investigate their correlations with RA. A total of 100 Wistar rats were selected, among which 60 rats were used to establish the collagen II-induced arthritis (CIA) model as the model observation group, and the remaining 40 rats were used as blank control group. The levels of IL-15 and IL-17 in synovial fluid were detected via enzyme-linked immunosorbent assay (ELISA) at 1, 7, 14, 21 and 28 days after successful modeling. RA was evaluated by using arthritis index (AI) and pedal swelling volume. The expression levels of IL-15 and IL-17 in synovial fluid of rats in model observation group were higher than those in blank control group (P<0.05), and the levels of IL-15 and IL-17 in model observation group were gradually increased over time. In model observation group at 7 days after modeling, AI and pedal swelling volume began to be increased gradually reaching a peak at 28 days. The pedal swelling volume of CIA model rats was significantly higher than that of the blank control group (P<0.05). The increased expression levels of IL-15 and IL-17 in synovial fluid of rats in the CIA model observation group are correlated with the activity of disease, which can be used as reference indexes for the activity of RA.
Duty-Ratio Feedforward Controller Design to Minimize the Capacitor Effect of the Flyback Inverter Under the Light-Load Condition

We propose a new duty-ratio feedforward controller for the flyback inverter with a synchronous rectifier (SR) circuit. For grid-connected photovoltaic power systems, the power factor (PF) of the output current must be high under most of the load conditions. To improve the PF of the flyback inverter even under light load, we propose a compensation method for the output capacitor current. The proposed method improves the displacement factor as well as the distortion factor, so the flyback inverter with the SR circuit has high power quality. By means of the SR circuit, the flyback inverter can operate in reverse when the negative current is required. The derivation of the proposed method is presented based on time-domain analysis. We also provide the boundary of the region where the proposed scheme should be adopted. The effectiveness of the proposed method is demonstrated in experiments with a 120-W rated power prototype.
Rear-Approaching Vehicle Detection using Frame Similarity base on Faster R-CNN

In this paper, we propose a new algorithm to detect rear-approaching vehicle using frame structure similarity based on deep learning algorithm for use in agricultural machinery systems. The commonly used deep learning models well detect various types of vehicles and detect the shapes of vehicles from various camera angles. However, since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear, when a general deep learning model is used, a false positive is generated by a vehicle running on the opposite side (passing vehicle). In this paper, first, we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. Second, we proposed an algorithm that uses the structural similarity and the root mean square comparison method for the region of interest(vehicles area) which is detected by Faster R-CNN between the coming vehicle and the passing vehicle. Experimental results show that the proposed method has a detection rate of 98.2% and reduced the false positive values, which is superior to general deep learning method.
Approximate Nash Region of the Gaussian Interference Channel with Noisy Output Feedback

In this paper, an achievable $\eta$-Nash equilibrium $(\eta-$NE) region for the two-user Gaussian interference channel with noisy channel-output feedback is presented for all $\eta \geq 1$. This result is obtained in the scenario in which each transmitter-receiver pair chooses its own transmit-receive configuration in order to maximize its own individual information transmission rate. At an $\eta-\mathrm{N}\mathrm{E}$, any unilateral deviation by either of the pairs does not increase the corresponding individual rate by more than $\eta$ bits per channel use.