Miracle-dz/UniFAR-FTUs huggingface dataset:
Skip Connection U-Net for White Matter Hyperintensities Segmentation From MRI

White matter hyperintensity (WMH) is associated with various aging and neurodegenerative diseases. In this paper, we proposed and validated a fully automatic system which integrates classical image processing and deep neural network for segmenting WHM from fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) images. In this system, a novel skip connection U-net (SC U-net) was proposed. In addition, an atlas-based method was introduced in the preprocessing stage to remove non-brain tissues (namely skull-stripping) and thus to improve the segmentation accuracy. Effectiveness of the proposed system was validated on a dataset of 60 paired images based on cross-scanner validation. Our experimental results revealed the effectiveness of the skull-stripping strategy. More importantly, compared to two existing state-of-the-art methods for segmenting WHM, including a U-net-like method and another deep learning method, the proposed SC U-net had a faster convergence, a lower loss and a higher segmentation accuracy. Both quantitative and qualitative analyses (via visual examinations) revealed the superior performance of our proposed SC U-net. The mean dice score of the proposed SC U-net was 78.36% which was much higher than those of a U-net-like method (74.99%) and an alternative deep learning method (74.80%). The software environment and model of the proposed system were made publicly accessible at Dockerhub.
Linear-Complexity H2-Based Direct Sparse Solver for Electromagnetic and Multiphysics Analysis

A fast nested direct matrix solution is developed for solving the sparse matrices resulting from electromagnetic and multiphysics analysis in linear complexity. In this solver, we accelerate all dense matrix computations underlying the state-of-the-art multifrontal direct solver by developing fast H2-based algorithms. Each computation is performed on small matrices of rank size, which is also the rank of the separator instead of the rank of the original 3-D problem. Direct solution of millions of unknowns arising from a finite-element based analysis is obtained in a few minutes on a single CPU core.
Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.
Improved Current Collapse in Recessed AlGaN/GaN MOS-HEMTs by Interface and Structure Engineering

Enhancement-mode (E-mode) GaN MOS-HEMTs using recess process typically face the challenge of precise thickness control, surface roughness issue, and interface traps, all of which could lead to the degradation of the device performance and cause reliability issues. In this article, we use a combined process of atomic layer etching (ALE) technique and atomic layer deposited (ALD) HfSiO dielectric. ALE is repeated oxidation and dry etching process with minimal surface damage, leading to a precisely controlled low-damage recess channel area. The fabricated AlGaN/GaN MOS-HEMTs exhibit E-mode operation with a positive threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula>) of +2.1 V with small <inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula> dispersion for different devices, an ultrahigh drain current ON– OFF ratio over 10<sup>10</sup> and a low ON-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$11.5~\Omega \cdot \text {mm}$ </tex-math></inline-formula> at gate-to-drain length (<inline-formula> <tex-math notation="LaTeX">${L}_{\text {GD}}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$25~\mu \text{m}$ </tex-math></inline-formula>. Source field plate (SFP) structure is employed to reduce the charge trapping process and improve the reliability at high voltages. The current collapse of the E-mode device with SFP structure can be effectively suppressed due to the optimized redistribution of the peak electric field in the gate-to-drain access region, where a significantly improved dynamic <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> of only 1.17 times increase from the static <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> after OFF-state <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula> stress of 600 V. Moreover, the enhanced vertical electric field with decreased AlGaN barrier thickness and the positive shift in threshold voltage for the E-mode device can effectively suppress the current collapse, which outperforms the depletion-mode counterpart. The breakdown voltage reaches a considerable value of 1560 V at an OFF-state current density of <inline-formula> <tex-math notation="LaTeX">${1}~\mu \text{A}$ </tex-math></inline-formula>/mm.
Broadband mmWave Splitters Based on Dielectric Waveguides

In this contribution, we are presenting mmWave signal splitters with equal and unequal splitting ratios. Based on a dielectric waveguide concept, the proposed components are cost effective, easy producible, and suitable for ultra broadband operation. Next to fundamental considerations, we are presenting 3D-EM simulations and measurement results of manufactured splitters that show excellent properties within the frequency range from 70 to 90 GHz.
Alphaherpesvirus infection of mice primes PNS neurons to an inflammatory state regulated by TLR2 and type I IFN signaling

Pseudorabies virus (PRV), an alphaherpesvirus closely related to Varicella-Zoster virus (VZV) and Herpes simplex type 1 (HSV1) infects mucosa epithelia and the peripheral nervous system (PNS) of its host. We previously demonstrated that PRV infection induces a specific and lethal inflammatory response, contributing to severe neuropathy in mice. So far, the mechanisms that initiate this neuroinflammation remain unknown. Using a mouse footpad inoculation model, we found that PRV infection rapidly and simultaneously induces high G-CSF and IL-6 levels in several mouse tissues, including the footpad, PNS and central nervous system (CNS) tissues. Interestingly, this global increase occurred before PRV had replicated in dorsal root ganglia (DRGs) neurons and also was independent of systemic inflammation. These high G-CSF and IL-6 levels were not caused by neutrophil infiltration in PRV infected tissues, as we did not detect any neutrophils. Efficient PRV replication and spread in the footpad was sufficient to activate DRGs to produce cytokines. Finally, by using knockout mice, we demonstrated that TLR2 and IFN type I play crucial roles in modulating the early neuroinflammatory response and clinical outcome of PRV infection in mice. Overall, these results give new insights into the initiation of virus-induced neuroinflammation during herpesvirus infections.
Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions

There has been an increasing interest in reducing the computational cost to develop efficient deep convolutional neural networks (DCNN) for real-time semantic segmentation. In this paper, we introduce an efficient convolution method, Spatio-Channel dilated convolution (SCDC) which is composed of structured sparse kernels based on the principle of split-transform-merge. Specifically, it employs the kernels whose shapes are dilated, not only in spatial domain, but also in channel domain, using a channel sampling approach. Based on SCDC, we propose an efficient convolutional module named Efficient Spatio-Channel dilated convolution (ESC). With ESC modules, we further propose ESCNet based on ESPNet architecture which is one of the state-of-the-art real-time semantic segmentation network that can be easily deployed on edge devices. We evaluated our ESCNet on the Cityscapes dataset and obtained competitive results, with a good trade-off between accuracy and computational cost. The proposed ESCNet achieves 61.5 % mean intersection over union (IoU) with only 196 K network parameters, and processes high resolution images at a rate of 164 frames per second (FPS) on a standard Titan Xp GPU. Various experimental results show that our method is reasonably accurate, light, and fast.
Cell Injection Microrobot Development and Evaluation in Microfluidic Chip

We propose an innovative design of microrobot, which can achieve donor cell suction, delivery and injection in a mammalian oocyte on microfluidic chip. The microrobot body contains a hollow space that produces suction and ejection forces for injection of cell nuclei using a nozzle at the tip of the robot. Specifically, a controller changes the hollow volume by balancing the magnetic and elastic forces of the membrane, and along with motion of stages in the XY plane. A glass capillary attached at the tip of the robot contains the nozzle is able to absorb and inject cell nuclei. The microrobot provides three degrees of freedom and generates micronewton forces. We demonstrate the effectiveness of the proposed microrobot through an experiment of absorption and ejection of 20 $\mu$ m particles from the nozzle using magnetic control in a microfluidic chip.
4×4 Broadband Butler Matrix and Its Application in Antenna Arrays

This paper introduces the design method of broadband 4×4 Butler matrix feeding network and applies it to broadband antenna array to realize broadband multi-beam antenna. The proposed Butler matrix switches to different forms of Butler matrices when operating in different frequency ranges to achieve broadband performance. The broadband 4×4 Butler matrix consists of three kinds of directional couplers: a conventional 3dB directional coupler, a broadband 3dB/90° directional coupler, and a three-section directional coupler. The antenna array is designed by stacking antenna patch and coupling between antennas. The Butler matrix feeding network and patch antenna array designed in this paper operate from 2 to 4 GHz. Four beams are obtained in one frequency octave operating frequency range (2 GHz-4 GHz), and the simulation result achieves the desired result.
A variational approach to uniqueness of ground states for certain quasilinear PDEs

Abstract We used a variational method based on optimal transport arguments to prove uniqueness of radial ground states for certain quasilinear elliptic equations, and we give the explicit expressions of the solutions. Our variational approach relies on a correspondence between the ground states of these equations and the equilibrium solutions of Fokker–Planck type equations. Our method also allows to identify all the optimal functions of many geometric inequalities, such as the Sobolev inequalities, the logarithmic Sobolev inequalities, and certain Gagliardo–Nirenberg inequalities.
Hybrid Precoding for Wideband Multi-user MIMO Millimeter Wave System

Hybrid precoding design for a wideband multiuser multiple input multiple output (MIMO) millimeter wave (mmWave) system is more challenging than the narrowband design. This is due to the fact that the radio frequency (RF) precoder and combiner are frequency non-selective and they should be optimized with respect to all scheduled users on all allocated subcarriers. In this work, we adopt the separate stage hybrid precoding strategy and propose two algorithms with good performance and complexity trade-off. Based on the proposed strategies of minimal equivalent channel gain maximization or average wideband sum rate maximization, the non-convex RF beamforming can be solved first by a semi-definite relaxation and codebook projection. In the second stage, conventional sum rate maximization digital precoder/combiner can be obtained based on smaller dimension equivalent channel feedback. Simulations show that the proposed algorithms outperform the state of art algorithms in multiple receive antennas setting.
FETI‐DP: a dual–primal unified FETI method—part I: A faster alternative to the two‐level FETI method

The FETI method and its two‐level extension (FETI‐2) are two numerically scalable domain decomposition methods with Lagrange multipliers for the iterative solution of second‐order solid mechanics and fourth‐order beam, plate and shell structural problems, respectively.The FETI‐2 method distinguishes itself from the basic or one‐level FETI method by a second set of Lagrange multipliers that are introduced at the subdomain cross‐points to enforce at each iteration the exact continuity of a subset of the displacement field at these specific locations. In this paper, we present a dual–primal formulation of the FETI‐2 concept that eliminates the need for that second set of Lagrange multipliers, and unifies all previously developed one‐level and two‐level FETI algorithms into a single dual–primal FETI‐DP method. We show that this new FETI‐DP method is numerically scalable for both second‐order and fourth‐order problems. We also show that it is more robust and more computationally efficient than existing FETI solvers, particularly when the number of subdomains and/or processors is very large. Copyright © 2001 John Wiley & Sons, Ltd.
Explorer Numerical Simulation of Spring Reverberation

Virtual analog modeling of spring reverberation presents a challenging problem to the algorithm designer, regardless of the particular strategy employed. The difficulties lie in the behaviour of the helical spring, which, due to its inherent curvature, shows characteristics of both coherent and dispersive wave propagation. Though it is possible to emulate such effects in an efficient manner using audio signal processing constructs such as delay lines (for coherent wave propagation) and chains of allpass filters (for dispersive wave propagation), another approach is to make use of direct numerical simulation techniques, such as the finite difference time domain method (FDTD) in order to solve the equations of motion directly. Such an approach, though more computationally intensive, allows a closer link with the underlying model system— and yet, there are severe numerical difficulties associated with such designs, and in particular anomalous numerical dispersion, requiring some care at the design stage. In this paper, a complete model of helical spring vibration is presented; dispersion analysis from an audio perspective allows for model simplification. A detailed description of novel FDTD designs follows, with special attention is paid to issues such as numerical stability, loss modeling, numerical boundary conditions, and computational complexity. Simulation results are presented.
Millimeter-Wave SIW Cavity-Backed Dual-Band Self-Complementary Log-Periodic Antenna

In this is paper, a millimeter-wave dual-band self-complementary log-periodic SIW Cavity Backed Antenna for 5G applications is proposed. The antenna is designed on a substrate integrated waveguide cavity with a 2-arms log-periodic toothed bow tie shaped slot. This antenna is designed to operate for the next generation fifth generation wireless communication systems. The proposed antenna is printed on a cavity backed planar substrate integrated waveguide technology. The proposed antenna shows a good performance in terms of return loss and efficiency.
Investigating a Dual MOSCAP Variant of Line-TFET With Improved Vertical Tunneling Incorporating FIQC Effect

In this article, we investigate a variant of the line-tunnel FET employing dual MOS–capacitor (MOSCAP) extensions incorporating field-induced quantum confinement (FIQC). Unlike Gate-over-Source (GoSo) TFET having an all-lateral design, dual-MOS (D-MOS) TFET has raised channel/drain regions exhibiting better electrostatics at the 2-D source boundary. At similar dimensions, 2-D-calibrated simulations reveal that under No-FIQC condition, D-MOS exhibits <inline-formula> <tex-math notation="LaTeX">$2.1\times $ </tex-math></inline-formula> better <inline-formula> <tex-math notation="LaTeX">${I} _{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> along with much improved parasitic leakage in the OFF-state (<inline-formula> <tex-math notation="LaTeX">${V} _{ \mathrm{GS}}=\textsf {0}$ </tex-math></inline-formula> V, <inline-formula> <tex-math notation="LaTeX">${V} _{\textsf {DS}}=\textsf {1}$ </tex-math></inline-formula> V). Energy quantization due to FIQC in the conduction band (CB) near the gate dielectric is captured along with the reshaped carrier density distribution. The delay in the onset of vertical band-to-band tunneling (BTBT), <inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\textsf {BTBT}}$ </tex-math></inline-formula> shift, and deterioration in <inline-formula> <tex-math notation="LaTeX">${I} _{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> are also calculated. We later observe that the use of a dual-metal-gate (DMG) and an unequal lateral/vertical oxide thickness as structural improvements further eliminates the parasitic leakage. In addition, with the gate–drain underlap (<inline-formula> <tex-math notation="LaTeX">${L} _{\textsf {GD}}$ </tex-math></inline-formula>) up to 10 nm, a reduction in intrinsic delay is also observed.
Sustained effects on attachment security in middle childhood: Results from a randomized clinical trial of the Attachment and Biobehavioral Catch-up (ABC) intervention.

BACKGROUND Interventions have been developed to promote the development of secure and organized attachments during early childhood among children who have experienced early adversity, yet little is known about whether the effects of these early interventions are sustained beyond 12 months postintervention. The current study examined whether receiving the Attachment and Biobehavioral Catch-Up (ABC) intervention during infancy led to improvements in perceived attachment security in middle childhood among 100 Child Protective Services (CPS)-referred children. METHODS Children and parents were randomized to receive ABC or a control intervention during infancy. Children completed the Kerns Security Scale at age nine (Mage = 9.46, SD = 0.36). (Trial Registry Name: Intervening Early with Neglected Children; Registry ID: NCT02093052; URL for registry: https://clinicaltrials.gov/ct2/show/NCT02093052? term=dozier&rank=1). RESULTS Children whose parents received ABC reported higher levels of attachment security on the Kerns Security Scale at age nine than children whose parents had received the control intervention, t(98) = 2.31, p = . 023, d = 0.49. CONCLUSIONS Findings underscore the long-term benefits of intervening early to promote caregiving quality among at-risk families and demonstrate the efficacy of a brief 10-session intervention in promoting attachment security over the span of eight years in a sample of CPS-referred children.
The Measurement of Destination Image: An Empirical Assessment

The purpose of this article is to carefully examine the concept of destination image with the goal of designing more appropriate and rigorous techniques for its measurement. A framework is presented which suggests that to completely measure destination image, several components must be captured. These include attribute-based images, holistic impressions, and functional, psychological, unique and common characteristics. It is proposed that a combination of structured and unstructured methodologies is necessary to measure destination image as envisaged in the conceptual framework. A series of openended questions and scale items are developed and are shown to successfully capture all of the components of destination image.
Estimating Functional Connectivity Networks via Low-Rank Tensor Approximation With Applications to MCI Identification

Functional connectivity network (FCN) has become an increasingly important approach to gain a better understanding of the brain, as well as discover informative biomarkers for diagnosis of neurodegenerative diseases. Due to its importance, many FCN estimation methods have been developed in the past decades, including methods based on the classical Pearson's correlation, (regularized) partial correlation, and some higher-order variants. However, most of the existing methods estimate one FCN at a time, thus ignoring the possibly shared structure among FCNs from different subjects. Recently, researchers introduce group constraints (or population priors) into FCN estimation by assuming that FCNs are topologically identical across subjects. Obviously, such a constraint/prior is too strong to be satisfied in practice, especially when both patients and healthy subjects are involved simultaneously in the group. To address this problem, we propose a novel FCN estimation approach based on an assumption that the involved FCNs have similar but not necessarily identical topology. More specifically, we implement this idea under a two-step learning framework. First, we independently estimate FCNs based on traditional methods, such as Pearson's correltion and sparse representation, making sure that each FCN captures the specific properties of the corresponding subject. Then, we stack the estimated FCNs (in fact, their adjacency matrices) into a tensor, and refine the stacked FCNs via low-rank tensor approximation. Finally, we apply the improved FCNs to identify subjects with mild cognitive impairment (MCI) from healthy controls, and achieve a higher classification accuracy.
Clustering of Students Based on Principal Factor Iteration

There are few documents about the mining of the latent abilities of students. Factor analysis can be used to the mining of the latent factors. However, the factor analysis method has the shortcoming that its load matrix is not easy to be explained, which will affect the subsequent clustering results. Therefore, a new method is proposed for mining students' latent abilities and clustering the students. The method relies mainly on the iteration of principal factors, thus overcoming the shortcoming of factor analysis method. It consists of the iterative principal factor algorithm, the least square algorithm and the k-means algorithm. Through these algorithms, students' latent abilities can be recognized, and students can precisely be divided into different clusters. Compared with k-means algorithm and the clustering algorithm based on factor analysis, experiments show that the proposed method is effective.