Miracle-dz/UniFAR-FTUs huggingface dataset:
Indirect Targeting of Subthalamic Deep Brain Stimulation Guided by Stereotactic Computed Tomography and Microelectrode Recordings in Patients With Parkinson’s Disease

Objective: Magnetic resonance imaging fusion techniques guided by frame-based stereotactic computed tomography and microelectrode recordings are widely used to target the subthalamic nucleus. However, MRI is not always available. The aim of this study was to determine whether the indirect targeting of the subthalamic nucleus for deep brain stimulation using frame-based stereotactic computed tomography and microelectrode recording guidance in patients with advanced idiopathic Parkinson’s disease was an effective and safe treatment and to determine the factors that contributed to outcome. Methods: Thirty-four consecutive patients with Parkinson’s disease who were treated from 2010 to 2012 were enrolled in this retrospective cohort study. The patients were assessed with the Unified Parkinson’s Disease Rating Scale-part III (UPDRS-III) and other clinical profiles peri- and post-operatively. The horizontal and vertical distances between the midpoint of the head frame and the brain midline at the septum pellucidum level and the upper edge of the bilateral lens, respectively, on a thin-section brain computed tomography scan were defined as the horizontal and vertical deviations, respectively. Results: After the deep brain stimulation surgery, the patients’ UPDRS-III scores improved 48 ± 2.8% (range, 20–81%) compared to the patients’ baseline off-levodopa scores. No surgery-associated complications were found. The mean recorded length difference of the subthalamic nucleus between the initial and final single microelectrode recording trajectories was 5.37 ± 0.16 mm (range, 3.99–7.50). Multiple linear regression analyses revealed that the increased lengths of the vertical (regression coefficient [B]: -0.0626; 95% confidence interval [CI]: -0.113 to -0.013) and horizontal deviations (B: -0.0497; 95% CI: -0.083 to -0.017) were associated with less improvement in the patients’ UPDRS scores. Conclusion: These results showed that the indirect targeting of the subthalamic nucleus for deep brain stimulation using frame-based stereotactic computed tomography and microelectrode recording guidance in patients with advanced idiopathic Parkinson’s disease was effective and safe. Greater symmetry of the head frame fixation resulted in better outcomes of the deep brain stimulation of the subthalamic nucleus in patients with Parkinson’s disease, especially when the horizontal deviation was 2 mm or less and the vertical deviation was 1 mm or less.
An Adaptive Least Angle Regression Method for Uncertainty Quantification in FDTD Computation

The nonintrusive polynomial chaos expansion method is used to quantify the uncertainty of a stochastic system. It potentially reduces the number of numerical simulations in modeling process, thus improving efficiency while ensuring accuracy. However, the number of polynomial bases grows substantially with the increase of random parameters, which may render the technique ineffective due to the excessive computational resources. To address such problems, methods based on the sparse strategy such as the least angle regression (LARS) method with hyperbolic index sets can be used. This paper presents the first work to improve the accuracy of the original LARS method for uncertainty quantification. We propose an adaptive LARS method in order to quantify the uncertainty of the results from the numerical simulations with higher accuracy than the original LARS method. The proposed method outperforms the original LARS method in terms of accuracy and stability. The L2 regularization scheme further reduces the number of input samples while maintaining the accuracy of the LARS method.
General Analytical Model for the Thermal Resistance of Windings Made of Solid or Litz Wire

In this paper, an analytical method to model the thermal resistance of windings made either of solid or litz wire is presented and validated by measurements. In addition, an extended numerical approach for litz wire windings, which also considers the twist pitch, is shown. With the presented model, it is possible to describe the thermal resistance of arbitrary wire arrangements. This approach can be used in straightforward thermal designs of magnetic devices, or it can be integrated in optimization procedures to improve the thermal designs of magnetic components. The analytical models are verified by measurements with nonpotted and epoxy-potted solid wire test setups and test setups for litz wire, which all show a good matching between the calculated and measured values. Besides the models for the thermal resistance between the winding layers, a method to simplify the thermal network of multilayer windings is presented. Finally, a thermal equivalent circuit of a test transformer has been calculated, which shows a good match between the measured and the predicted temperature distribution.
Does A Body Image Tell Age?

Age estimation is an important task in computer vision and is widely used in applications. However, such a technology is largely affected by the resolution of face, and it would be a challenge if one has to estimate the age of a person at a distance. While body image of a person is often captured more clearly, when and how to use body-based visual cues for age estimation are largely under studied. In this work, we argue that body-based visual cues are better for estimating the age group and can assist the estimation of exact age value. For this purpose, we develop a Body-based Age Net (BAN) that unifies selective local convolution features and contextual convolution features. The network is designed based on two assumptions: 1) a person's wearing is closely related to his/her age group property; 2) some selective local parts of a body are more discriminative for age group estimation. We have contributed a large-scale and publicly available Body Age (BAG) dataset. We have quantitatively evaluated the proposed model on BAG.
Synchronization of Passifiable Lurie Systems Via Limited-Capacity Communication Channel

Output-feedback controlled synchronization problems for a class of nonlinear unstable systems under information constraints imposed by limited capacity of the communication channel are analyzed. A binary time-varying coder-decoder scheme is described, and a theoretical analysis for multidimensional master-slave systems represented in Lurie form (linear part plus nonlinearity depending only on measurable outputs) is provided. An output-feedback control law is proposed based on the passification theorem. It is shown that the synchronization error exponentially tends to zero for sufficiently high transmission rate (channel capacity). The results obtained for the synchronization problem can be extended to tracking problems in a straightforward manner if the reference signal is described by an external (exogenous) state space model. The results are illustrated by the controlled synchronization of two chaotic Chua systems via a communication channel with limited capacity.
Stability Analysis on the Networked Multi-Agent System

In this paper, a new stability analysis method on the networked multi-agent system is proposed. The main idea is to model the networked control system as a multi-output-multi-input transfer function and to apply the Nyquist criterion for the closed-loop stability. This paper provides two application examples: formation control of first- and second-order agents. The stability conditions are derived with the proposed method, showing the effect of network connectivity with multi-hop communication. The numerical simulations are conducted to verify the analysis. This work is applicable for general agent dynamics, controllers, and communication characteristics.
An Efficient Image Matching Algorithm Based on Adaptive Threshold and RANSAC

The education plays a more and more important role in disseminating knowledge because of the explosive growth of knowledge. As one kind of carrier delivering knowledge, image also presents an explosive growth trend and plays an increasingly important role in education, medical, advertising, entertainment, and so on. Aiming at the long time of massive image feature extraction in the construction of smart campus, the traditional Harris corner has problems, such as low detection efficiency and many non-maximal pseudocorner points. This paper proposes a Harris image matching method that combines adaptive threshold and random sample consensus (RANSAC). First, the Harris feature points are selected based on the adaptive threshold and the Forstner algorithm in this method. On the one hand, candidate points are filtered based on the adaptive threshold. On the other hand, the Forstner algorithm is used to further select the corner points. Second, the normalized cross correlation matching and the RANSAC are applied to precisely match the detected Harris corners. The experimental results show that compared with the existing algorithms, the proposed method not only obtains a matching accuracy higher than 20% of Cui’s algorithm but also saves more than 30% detection time of corner detection and image matching. Furthermore, the proposed method obtains a matching accuracy higher than 50% of the Cui’s algorithm and saves more than 50% detection time of corner detection and image matching.
Environment Descriptor for the Visually Impaired

The ability to provide a system for automatically describing images to the users, especially the blind is a matter which can improve their lives significantly, if made possible to a user a friendly way. We present, a way of achieving the task of producing natural language descriptions of images through the use of recent deep architectures in neural networks, specifically based on recurrent and convolution architectures. A generative model for this is created and the accuracy for the MSCOCO Captioning dataset is presented along with a software architecture of how this can be extended as a standalone application for the blind.
Soft but Strong: Software-Based Innovation and Product Differentiation in the IT Hardware Industry

It has been argued in recent years that software is a significant value-creating factor within the manufacturing sector, leading to an increased interest in understanding the mechanisms by which firms may benefit from software-based innovation. Empirical work in evaluating these mechanisms, however, remains underdeveloped. In this paper, we examine one such process through which software-based innovation may provide value for information technology hardware firms in the U.S. Specifically, we examine the influence of software patents on the extent to which firms are able to benefit from product differentiation in their product markets. Using data on over 380,000 patent grants for innovations filed by 730 public IT hardware firms over the time period 1996-2015, we find that greater levels of software-based innovation within the firm are associated with higher levels of product differentiation in product markets: increases in the intensity of software patents within the firm’s patent stock, which we refer to as software intensity, are associated with lower total similarity of product offerings, relative to those offered by rivals, as well as fewer effective competitors in the market. Furthermore, using a different dataset consisting of 23,000 new IT hardware product announcements, we show that firms with greater software intensity subsequently launch a higher number of new products. Moreover, these firms are more likely to launch products that are not only differentiated from those of rivals but also distinct from their own product lines. Our research contributes to the emerging literature on software-based innovation by substantiating an important mechanism through which software helps transform industry sectors, specifically through product differentiation.
Improved Phosphorus Emitter Passivation using Chemically Grown SiO2 Layer for Industrial-sized Selective Emitter PERC

We demonstrate the improvement of phosphorus emitter passivation for an industrial-sized passivated emitter and rear cell (PERC) with selective emitter (SE) structure using a method of nitric acid oxidation of silicon (NAOS) to form ultrathin SiO2layer. The results clearly show a significant impact of the presence of NAOS-SiO2 above emitters on all I-V parameters of PERCsWe observed an increase in the opencircuit voltage (Voc), and theshort-circuit current density(Jsc) along with strong improvements in the internal quantum efficiency (IQE) for short-wavelength photons. These improvements can be ascribed to the high level of chemical passivation, as clearly shown by a reduced interface trap density (Dit), and it is due to the less Shockley-Read-Hall recombination. The results presented here suggest that NAOS surface pretreatment is a very promising technology to improve the level of chemical passivation and thereby enhancing the industrialsized PERC performance.
Anthocyanins in the Management of Metabolic Syndrome: A Pharmacological and Biopharmaceutical Review

The term “metabolic syndrome” (MetS) refers to a combination of diabetes, high blood pressure, and obesity. The origin of MetS includes a combination of multiple factors, such as sedentary lifestyle, unhealthy diet choice, and genetic factors. MetS is highly prevalent and adversely affects the general population by elevating risk of cardiovascular complications, organ failure, and much other pathology associated with late-stage diabetes. Anthocyanins (ANTs) are health-promoting bioactive compounds belonging to the flavonoids subclass of polyphenols. Numerous studies have reported the potential therapeutic benefits on MetS syndrome and diabetes from fruits rich in ANTs. This review summarizes the role of several dietary ANTs on preventing and managing MetS as well as the pharmacological mechanisms and biopharmaceutical features of their action. We also discuss potential nanoformulation and encapsulation approaches that may enhance the bioefficacy of ANTs in MetS. Experiments have demonstrated that ANTs may attenuate the symptoms of MetS via improving insulin resistance, impaired glucose tolerance, dyslipidaemia, cholesterol levels, hypertension, blood glucose, protecting β cells, and preventing free radical production. In brief, the intake of ANT-rich supplements should be considered due to their plausible ability for prevention and management of MetS. Additionally, randomized double-blind clinical trials are obligatory for evaluating the bioefficacy and pharmacological mechanisms of ANTs and their pharmaceutical formulations in patients with MetS.
Conceptualization and Measurement of Individual-Level Transparency in Enterprise System Implementations

Prior research on transparency through enterprise systems (ES) primarily focused on the organizational context and the observer’s perspective. Contrastingly, this paper presents a conceptualization of an individual’s perception of being transparent that is argued to have important consequences. We develop according measures following a ten-step procedure and include a substantial body of qualitative data in the process. Specifically, we draw from three cases encompassing three subsidiaries, 14 sites, and 52 interviews to develop our conceptualization of perceived transparency as a second order construct. In addition to insights from an extensive qualitative study, we present a longitudinal survey research design. We expect our findings to contribute to existing literature on transparency and large-scale ES implementation in providing clear measurement scales and integrating findings from research on transparency and privacy.
2D to 3D Label Propagation For Object Detection In Point Cloud

Object detection and classification from LiDAR point cloud is increasingly important in robotic system. For training a classifier, huge datasets with object labeling is needed. However, manually labeled data from point cloud is time-consuming and costly. We present a framework, which propagates image annotation to point cloud for making a training data. Each object point cloud is projected on the corresponding image and searches for the overlapping area within the 2D bounding box. If the occurred area is matched, the label propagates to the point cloud object. While previous works trained their classifier with 3D ground truth and tested by combining point cloud and RGB image. Our system trained and tested using only point cloud to identify objects. The comparison between using manual labeling and label propagation training data demonstrate that the label propagation can be used to train the classifier without manually ground truth with 80.22% mean average precision.
Revised Spatial Transformer Network towards Improved Image Super-resolutions

In this paper, we propose two approaches to generate a high resolution (HR) image from a low resolution (LR) version. It is commonly referred as single-image super-resolution(SISR). Our approaches are inspired by the spatial transformer (ST) module and Very Deep Convolutional Network (VDSR). The spatial transformer module is the neural network originally used for geometric transformations of images, while the VDSR is for image super-resolution. In the first approach, we propose to add the ST module with the VDSR to generate HR images. The use of the spatial transform with VDSR makes the network more robust to the geometric transformations. We propose the second approach to replace the convolutional neural network (CNN) used in the ST module with VDSR network. The replacement of CNN leads to improvements in the performance of ST module. The simulation results confirm that the feasibility of combing ST module and VDSR for super-resolution reconstruction, where the performance of the combination of ST module and VDSR is comparable to the VDSR alone in terms of Peak signal-to-noise ratio (PSNR) and structural Similarity Index Measurement (SSIM). Hence, the revised spatial transformer network can be used in the future for simultaneous geometric transformation and image super-resolution, which solve the practical applications of image super-resolution in real life.
Use of Design of Experiments (DoE) predictive models as a method of comparison of enameled wires used in low voltage inverter-fed motors

The electrical insulation system of low voltage motors is exposed to new hazards since the introduction of power electronic power supplies. Fast changing voltage fed by long connecting cable between the frequency inverter and the motor can result in high overvoltage at machine's terminals, which can lead to partial discharge (PD) activity. Primarily the insulation of the magnet wire, a very thin enamel layer, is particularly endangered by this phenomenon, as it can resist PD activity only for very short time. This will lead to premature breakdown of a machine. This paper proposes a methodology allowing to compare magnet wires basing only on relatively quick aging tests but providing reliable predictions concerning much lower stress levels. This method is based on the Design of Experiments and applies Weibull statistics. It also allows to take into consideration the dispersion of the results. Model predictions are experimentally verified in order to prove the efficiency on this method.
Analog Codebook Design for mmWave Communications with An Auxiliary Phase Shifter Group

Multi-resolution codebook based hierarchical beam training is an attractive candidate solution to beam alignments for millimeter-wave (mmWave) and sub-terahertz (sub-THz) systems dueto its low training overhead and the channel sparsity. However, conventional analog codebook methods suffer from the considerable fluctuation in main lobe and the lost of partial array gain. To address these issues, an analog multi-resolution codebook design with a single radio-frequency $(\mathrm{R}\Gamma)$ chain is proposed, in which wide beams are obtained by jointly exploiting virtual-subarray technique and array-reuse approach with an auxiliary phase-shifter group (PSG). With the PSG, the whole array gain is achievable owing to non-idle antennas. Further- more, the beam fluctuation is significantly alleviated by tackling mutual coupling between subarrays elegantly. Simulation results demonstrate the effectiveness of the proposed scheme.
A Convolutional Neural Network for Pixelwise Illuminant Recovery in Colour and Spectral Images

Here, we present a pixelwise illuminant recovery method for both, trichromatic and multi or hyperspectral images which employs a convolutional neural nettwork. The network used here is based upon the simple, yet effective architecture employed by the CIFARIO-quick net[1]. The network is trained using a loss function which employs the angular difference between the target illuminant and the estimated one as the data term. The loss used here also includes a regularisation term which encourages smoothness in the spectral domain. Moreover, the network takes, at input, a tensor which is constructed making use of an image patch at different scales. This allows the network to predict the illuminant per-pixel using locally supported multiscale information. We illustrate the utility of our method for both, colour and hyperspectal illuminant recovery and compare our results against other techniques elsewhere in literature.
Microbial Community Structure in a Malaysian Tropical Peat Swamp Forest: The Influence of Tree Species and Depth

Tropical peat swamp forests sequester globally significant stores of carbon in deep layers of waterlogged, anoxic, acidic and nutrient-depleted peat. The roles of microbes in supporting these forests through the formation of peat, carbon sequestration and nutrient cycling are virtually unknown. This study investigated physicochemical peat properties and microbial diversity between three dominant tree species: Shorea uliginosa (Dipterocarpaceae), Koompassia malaccensis (legumes associated with nitrogen-fixing bacteria), Eleiodoxa conferta (palm) and depths (surface, 45 and 90 cm) using microbial 16S rRNA gene amplicon sequencing. Water pH, oxygen, nitrogen, phosphorus, total phenolic contents and C/N ratio differed significantly between depths, but not tree species. Depth also strongly influenced microbial diversity and composition, while both depth and tree species exhibited significant impact on the archaeal communities. Microbial diversity was highest at the surface, where fresh leaf litter accumulates, and nutrient supply is guaranteed. Nitrogen was the core parameter correlating to microbial communities, but the interactive effects from various environmental variables displayed significant correlation to relative abundance of major microbial groups. Proteobacteria was the dominant phylum and the most abundant genus, Rhodoplanes, might be involved in nitrogen fixation. The most abundant methanogens and methanotrophs affiliated, respectively, to families Methanomassiliicoccaceae and Methylocystaceae. Our results demonstrated diverse microbial communities and provide valuable insights on microbial ecology in these extreme ecosystems.