Miracle-dz/UniFAR-FTUs huggingface dataset:
TRINITY: Coordinated Performance, Energy and Temperature Management in 3D Processor-Memory Stacks

The consistent demand for better performance has lead to innovations at hardware and microarchitectural levels. 3D stacking of memory and logic dies delivers an order of magnitude improvement in available memory bandwidth. The price paid however is, tight thermal constraints. In this paper, we study the complex multiphysics interactions between performance, energy and temperature. Using a cache coherent multicore processor cycle level simulator coupled with power and thermal estimation tools, we investigate the interactions between (a) thermal behaviors (b) compute and memory microarchitecture and (c) application workloads. The key insights from this exploration reveal the need to manage performance, energy and temperature in a coordinated fashion. Furthermore, we identify the concept of "effective heat capacity" i.e. the heat generated beyond which no further gains in performance is observed with increases in voltage-frequency of the compute logic. Subsequently, a real-time, numerical optimization based, application agnostic controller (TRINITY) is developed which intelligently manages the three parameters of interest. We observe up to $30\%$ improvement in Energy Delay$^2$ Product and up to $8$ Kelvin lower core temperatures as compared to fixed frequencies. Compared to the \texttt{ondemand} Linux CPU DVFS governor, for similar energy efficiency, TRINITY keeps the cores cooler by $6$ Kelvin which increases the lifetime reliability by up to 59\%.
An Attention-Gated Convolutional Neural Network for Sentence Classification

The classification of sentences is very challenging, since sentences contain the limited contextual information. In this paper, we proposed an Attention-Gated Convolutional Neural Network (AGCNN) for sentence classification, which generates attention weights from the feature's context windows of different sizes by using specialized convolution encoders. It makes full use of limited contextual information to extract and enhance the influence of important features in predicting the sentence's category. Experimental results demonstrated that our model can achieve up to 3.1% higher accuracy than standard CNN models, and gain competitive results over the baselines on four out of the six tasks. Besides, we designed an activation function, namely, Natural Logarithm rescaled Rectified Linear Unit (NLReLU). Experiments showed that NLReLU can outperform ReLU and is comparable to other well-known activation functions on AGCNN.
A multi-objective optimization design tool for Smart Converters in photovoltaic applications

During last decade new scenarios are emerging in the energetic context. Photovoltaic systems role has expanded since they are not only employed in residential/industrial plants, but they also appear promising solutions in DC micro-grids and Net Zero Energy Buildings. In fact, Smart Converters can improve photovoltaic generators productivity and they can also contribute to energy and data flows control. The mentioned scenarios require performant Smart Converters able to assure high efficiency and reliability levels. The design of such converters is a challenging task since many and conflicting requirements have to be considered. A dedicated tool can represent a useful design support for optima Smart Converters realization. The proposed software is developed by a modular architecture on the base of the specific energetic scenario considered and the relative specifications and constraints. This paper focuses on the implementation concepts and fundamental phases of the developed tool depicting the applied approach. A Graphical User Interface allows the selection of Smart Converter topologies and input/output operating data depending on the specific applicative context. An evolutionary optimization algorithm identifies feasible electronic device in commercial databases and suitable subroutines run adequate circuital equations to evaluate reliability indices and thermal stresses which the converter devices are subjected in real operating conditions. The optimization process provides a set of nondominated performant solutions able to satisfy all the design requirements. The developed tool is realized by means of MathWorks MATLAB environment able to manage the issue complexity and to generate stand-alone software.
High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor

High precision position control is essential in the process of parts manufacturing and assembling, where eddy current displacement sensors (ECDSs) are widely used owing to the advantages of non-contact sensing, compact volume, and resistance to harsh conditions. To solve the nonlinear characteristics of the sensors, a high-accuracy calibration method based on linearity adjustment is proposed for ECDSs in this paper, which markedly improves the calibration accuracy and then the measurement accuracy. After matching the displacement value and the output voltage of the sensors, firstly, the sensitivity is adjusted according to the specified output range. Then, the weighted support vector adjustment models with the optimal weight of the zero-scale, mid-scale and full-scale are established respectively to cyclically adjust the linearity of the output characteristic curve. Finally, the final linearity adjustment model is obtained, and both the calibration accuracy and precision are verified by the established calibration system. Experimental results show that the linearity of the output characteristic curve of ECDS adjusted by the calibration method reaches over 99.9%, increasing by 1.9–5.0% more than the one of the original. In addition, the measurement accuracy improves from 11–25 μm to 1–10 μm in the range of 6mm, which provides a reliable guarantee for high accuracy displacement measurement.
Image Super-resolution Reconstruction via Multi-Frame Defocused Images

Defocused images are often neglected due to visional blur. While, they also contain valuable target information. Reconstructing high resolution (HR) image from the defocused images is a challenge especially when the point spread function (PSF) is unknown. Aiming at the problem, an image super-resolution reconstruction method based on PSF estimation is proposed. According to the image degraded model, we establish the cost function of PSF and utilize the particle swarm optimization (PSO) algorithm to estimate it. In the image reconstruction process, the method of image block reconstruction based on compressive sensing is adopted and a blurring matrix without edge effect is constructed which solved the problem caused by large image size. Experimental results show that the algorithm can accurately estimate image PSF and the reconstructed HR image has high peak signal to noise ratio (PSNR) and structural similarity (SSIM).
Surface Soil Moisture Retrieval Using Optical/Thermal Infrared Remote Sensing Data

Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3 were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies.
Removing out-of-focus blur from a single image

Reproducing an all-in-focus image from an image with defocus regions is of practical value in many applications, eg, digital photography, and robotics. Using the output of some existing defocus map estimator, existing approaches first segment a de-focused image into multiple regions blurred by Gaussian kernels with different variance each, and then de-blur each region using the corresponding Gaussian kernel. In this paper, we proposed a blind deconvolution method specifically designed for removing defocus blurring from an image, by providing effective solutions to two critical problems: 1) suppressing the artifacts caused by segmentation error by introducing an additional variable regularized by weighted $\ell_0$-norm; and 2) more accurate defocus kernel estimation using non-parametric symmetry and low-rank based constraints on the kernel. The experiments on real datasets showed the advantages of the proposed method over existing ones, thanks to the effective treatments of the two important issues mentioned above during deconvolution.
Convergence of detection probability, computational gains, and asymptotic analysis of an algorithm for physical‐layer intrusion detection system

Asymptotic analysis of an algorithm is crucial for practical engineering design. It provides important information about the performance of an algorithm in terms of time complexity, space complexity, and input scale. Based on these performance metrics, engineers can utilize available resources for efficient implementation of the algorithm. In this paper, asymptotic analysis is performed for an algorithm designed for a physical‐layer intrusion detection system (PLIDS). The algorithm first solves Fredholm integral equation of the first kind using Tikhonov regularization. Then, the solution of this equation is used to design a criterion for detection of smart jamming attacks at physical‐layer of the TCP/IP protocol stack. The proposed detection criterion works in constant time. The asymptotic performance metric derived for PLIDS gives insights about its algorithmic behavior. Convergence of detection probability and computational gain of using PLIDS, over higher‐layer intrusion detection systems, are also explained. Finally, apart from asymptotic performance, the detection performance metrics of the PLIDS are also discussed.
A tale of two cities: The genetic mechanisms governing calvarial bone development

The skull bones must grow in a coordinated, three‐dimensional manner to coalesce and form the head and face. Mammalian skull bones have a dual embryonic origin from cranial neural crest cells (CNCC) and paraxial mesoderm (PM) and ossify through intramembranous ossification. The calvarial bones, the bones of the cranium which cover the brain, are derived from the supraorbital arch (SOA) region mesenchyme. The SOA is the site of frontal and parietal bone morphogenesis and primary center of ossification. The objective of this review is to frame our current in vivo understanding of the morphogenesis of the calvarial bones and the gene networks regulating calvarial bone initiation in the SOA mesenchyme.
Ride through fault on the rectifier controller of an offshore wind system aided by VRFB

This paper is a simulation study on the improving of the ability of an offshore wind system to recover from a fault in the rectifier converter by shielding the DC link of the converter assisted by a redox vanadium flow battery. The wind system is a variable speed wind turbine with a permanent magnet synchronous generator and a five-level multiple point diode clamped converter. Contributions are given for the study of: (i) the fault influence on the DC link capacitor bank; (ii) the drivetrain modeling of the floating platform, including marine waves modelling; (iii) the vanadium flow battery integration in the wind system. A case study is offered to conclude about the ability of riding through the fault.
Geographic Trends in the Orthopedic Surgery Residency Match.

Background Residency program location may be an important factor for orthopedic surgery residency applicants. More than half of residents locate their practice near the site of their training, and surgical specialties (eg, otolaryngology, plastic surgery, and general surgery) have shown geographic patterns in their residency matches. Objective We analyzed geographic trends in the orthopedic surgery Match. Methods Hometown, undergraduate institution, and medical school "preresidency locations" of all allopathic, nonmilitary, orthopedic surgery residents were recorded from program websites for the 2015-2016 academic year. Program and preresidency locations were coded according to state and US census division. Statistical analysis was used to investigate associations between residency program locations and preresidency locations. Results Of 2662 residents in the study, 1220 of 2614 (47%), 536 of 1329 (40%), and 308 of 744 (41%) matched into the same division as their medical school, undergraduate institution, and hometown, respectively. There were significant differences among divisions (P < .001). Also, 817 of 2662 (31%), 319 of 1329 (24%), and 200 of 770 (26%) residents matched in the same state as their medical school, undergraduate institution, and hometown, respectively, with significant differences between states for medical school (P < .0001) and undergraduate institution (P < .0001), but not hometown (P = .22). Overall, 21% of residents (538 of 2612) matched at the program affiliated with their medical school. Conclusions There is an association among hometown, undergraduate institution, and medical school for the training program location in which orthopedic surgery residents match, with variability in locations matched at state and census division levels.
Illumination-based and Device-independent Imaging Model and Spectral Response Functions

This work provides a theoretical framework for the colorimetric scene reproduction under different illuminants. First, based on the principle of CIE color matching experiment as well as image production pipeline in digital still cameras, we analyze and conclude that the Color Matching Function (CMF) of the color space determined by a display is the best Spectral Response Function (SRF) for scene reproduction on the certain conditions. Then, we wonder what the best SRFs for other illuminants beyond D65 are. In order to do this, we propose a new imaging formula in which the SRF is changing with illumination variation. Different from previous imaging formulae suitable for RAW format for D65 illuminants, our imaging formulae is a theoretical one that can achieve color constancy for an arbitrary camera under any illuminants and is suitable for final image formats. We also propose two methods to solve the optimal SRF, including the pseudo-inverse solution and Brandford method. Finally, we compare the proposed method with previous common-used method, and the experimental results can validate the performance of the proposed method.
Bi-Directional LSTM Recurrent Neural Network for Lumbar Vertebrae Identification in X-Ray Images

Duo to the capability of providing online patient pose, mobile C-arm X-ray images play a key role in image-guided minimally invasive spine surgery. However, automatic lumbar vertebrae identification is still a challenge task because of the inherent limitation of mobile C-arm. In order to solve these problems, a novel automatic lumbar vertebrae identification method is proposed, which based on bidirectional long short-term memory (LSTM) recurrent neural network (RNN). First, in order to solve the problem of lumbar vertebrae texture overlapping in X-ray images, the curvature features of 3D lumbar vertebrae model, which are common to the 2D X-ray images, are taken as the input of the model. Second, in order to simulate the multi-view imaging of intraoperative C-arm, the bi-directional recurrent neural network is exploited to learn the correlation of lumbar curvature features at different imaging angles. Finally, in order to avoid of gradient vanishing and error blowing up, the LSTM neuron is applied to replace the notes of bi-directional RNN. Experiment results show that our method identified lumbar vertebrae more accurately than another two methods.
Acetabular reconstruction using a cage in complex primary total hip replacement in a developing country: An assessment of early functional outcome

Background: Acetabular reconstruction following bone loss is a major challenge facing the arthroplasty surgeon. Traditionally, the armamentarium for the treatment of large bone defects (Paprosky Type 2C or 3) included antiprotrusio cages (APC). The aim of this study is to determine the pattern of presentation and assess the early functional outcome of patients who had undergone acetabular reconstruction using APC in complex primary total hip replacement (THR). Patients and Method: Between November 2008 and November 2015, 38 THR were carried out in 35 patients who required acetabular reconstruction, at Davidson and Judith Consultants Clinics Enugu, Nigeria. Results: There were 25 males and 10 females, with a ratio of 2.5:1. The average age of the patients was 61.33 ± 6.92 with a range of 56 to 72. The mean pre-op Harris score was 49.02 ± 2.3. The mean post-op Harris hip score (HSS) was 88.75 ± 10 (P < 0.001) at one year and 92.25 ± 13 (P < 0.001) at 5 years. The etiology showed that most (52.63%) of our patients had primary osteoarthritis with Type 3 acetabular defect. Two (5.26%) patients had the following complications: dislocation (2.63%) and screw breakage (2.63%). The minimum follow-up period was 5 years. Discussion: After follow-up at 1 year and 5 years, the hips showed significant improvement concerning pain, gait, and mobility based on HHS and were able to return to their various professions. Conclusion: APC provides a satisfactory solution for patients who present with acetabular deficiency in complex primary THR.
Robust 3D Localization of Underwater Optical Wireless Sensor Networks via Low Rank Matrix Completion

Location is one of the basic information required for underwater optical wireless sensor networks (UOWSNs) for three main purposes: 1) Relating the sensing measurements with precise sensor positions, 2) Enabling efficient routing techniques by exploiting the sensor locations, and 3) Sustaining link connectivity and performance via pointing and alignment mechanisms between the nodes. Even though various two-dimensional UOWSNs localization methods have been proposed in the past, the directivity of optical wireless communications and three dimensional (3D) deployment of sensors require to develop 3D underwater localization methods. Therefore, we propose a robust 3D localization method for partially connected UOWSNs with a limited number of anchors. The proposed method achieves an accurate 3D localization of all the nodes in the network by using a novel low-rank matrix approximation and outliers removal method. The performance of the proposed method is compared with the well known iterative majorization approach. The numerical results indicate that the proposed method outperforms the iterative majorization method substantially.
Vehicle Tracking Based Traffic State Estimation Method

to reveal the real and hidden information of urban traffic system, this paper proposed a vehicle-tracking based traffic state estimation method. First, some terms are introduced to facilitate the following algorithm. Then, the A * heuristic search algorithm is calibrated to give the optimal path between two neighboring GPS points. Next, a vehicle-tracking based algorithm is proposed to calculate the spatiotemporal average velocity and estimate the traffic states. Finally, a real probe car dataset in Beijing is used to evaluate the effectiveness of the proposed method. In the validation experiments, the ground truth was determined by manually recording the passing time of the same vehicle and the experiment results proves the effectiveness of the proposed method to estimate the traffic states in large-scale urban road network.
Assessment of condition perception of educational -research environment and academic self-efficacy

INTRODUCTION: Academic self-efficacy is the learners’ belief in their abilities in academic process. Perception of the educational-research environment is one of the effective factors on academic self-efficacy. The aim of this study was to examine the perception of educational-research environment and academic self-efficacy in students of the University of Tehran in 2016. METHODS: This cross-sectional study was done on 385 students of Tehran University of Medical Sciences. Samples were selected through stratified sampling method. Data were collected through questionnaires of perception of educational-research environment scale 24 and of academic self-efficacy scale 32 that reliability and validity of that were confirmed. Data analysis was performed using Pearson's correlation coefficient. RESULTS: The mean age of participants was 25.31 (standard deviation [SD] = 3.94), and 56.1% of them were female and the rest were male. The mean perception of educational-research environment was 65.79 (SD = 13.13), and the mean academic self-efficacy was 107.44 (15.92). The Pearson's correlation test results showed that there was a statistically significant relationship between perception of educational-research environment with academic self-efficacy (P = 0.001, r = 0.180). CONCLUSION: There was a significant statistical relationship between perception of educational-research environment and academic self-efficacy. Students who have high perception of educational-research environment are high academic self-efficacy. Academic self-efficacy can be improved by improving perception of the educational-research environment.