Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.
Noncontact Sensing of Contagion

The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.
Evaluation of Event-Based Corner Detectors

Bio-inspired Event-Based (EB) cameras are a promising new technology that outperforms standard frame-based cameras in extreme lighted and fast moving scenes. Already, a number of EB corner detection techniques have been developed; however, the performance of these EB corner detectors has only been evaluated based on a few author-selected criteria rather than on a unified common basis, as proposed here. Moreover, their experimental conditions are mainly limited to less interesting operational regions of the EB camera (on which frame-based cameras can also operate), and some of the criteria, by definition, could not distinguish if the detector had any systematic bias. In this paper, we evaluate five of the seven existing EB corner detectors on a public dataset including extreme illumination conditions that have not been investigated before. Moreover, this evaluation is the first of its kind in terms of analysing not only such a high number of detectors, but also applying a unified procedure for all. Contrary to previous assessments, we employed both the intensity and trajectory information within the public dataset rather than only one of them. We show that a rigorous comparison among EB detectors can be performed without tedious manual labelling and even with challenging acquisition conditions. This study thus proposes the first standard unified EB corner evaluation procedure, which will enable better understanding of the underlying mechanisms of EB cameras and can therefore lead to more efficient EB corner detection techniques.
Multi-objective Portfolio Selection Based on Skew-Normal Uncertainty Distribution and Asymmetric Entropy

Empirical studies illustrate that in numerous cases the returns of securities are not normally distributed. In this paper, skew-normal uncertainty distribution is proposed to capture skewness in the portfolio selection problem. Furthermore, the concept of asymmetric entropy for uncertain variables as the quantiﬁer of diversiﬁcation is presented and its mathematical properties such as translation invariance and positive linearity are studied. To examine the effect of asymmetric entropy parameter on portfolio diversiﬁcation, a mean-CVaR-entropy portfolio selection problem is presented based on asymmetric entropy with different parameter values and logarithm entropy. A non-dominated sorting genetic algorithm II (NSGA-II) is implemented in MATLAB to solve the corresponding problem. Numerical results show that asymmetric entropy for a speciﬁc parameter value will outperform logarithm entropy in portfolio diversiﬁcation.
Production of Solid Lipid Nanoparticles (SLN) and Nanostructured Lipid Carriers (NLC) Containing Antitumoral Drug (Saquinavir)

The blood-brain barrier (BBB) is a limiting barrier to the passage of a large number of drugs, particularly antitumoral drugs. This is mainly due to the tight junctions between the endothelial cells lining the blood vessels of the brain. Inevitably, this barrier makes these drugs ineffective on the treatment of brain tumors, since they cannot diffuse freely across this barrier. Glioblastoma (GBM) is the most common and most malignant form of primary brain tumors, with relatively short survival rate (9-12 months). There have been several attempts to overcome the BBB and nanotechnology emerges as leading solution. By developing colloidal delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs), new boundaries have been opened to improve the delivery of drugs. These nanoparticles are nontoxic, biodegradable, with high loading capacity of lipophilic drugs and increased bioavailability in target cells. The main goal of this Master's thesis project was to develop, characterize and optimize SLNs and NLCs containing saquinavir (SQV-SLNs and SQV-NLCs). SQV is a lipophilic and poorly water soluble HIV protease inhibitor, also used as a potent antitumoral agent efficient against numerous tumor cell lines in vitro and in vivo. The nanoparticles were produced by hot high-pressure homogenization and ultrasonication techniques with different lipid (5% and 10%), surfactant (1%, 2% and 3%) and drug (0.05% and 0.1%) concentrations and physically characterized in order to assess which ones are the most efficient and that best meet the desired requirements. The size, polydispersity, zeta potential and stability (LD, DLS and ELS), encapsulation efficiency (HPLC) and thermodynamic behavior (DSC) of SLNs and NLCs were also tested. The formulations with lipid 5%, polysorbate 2% and drug 0.05%, produced by ultrasonication, were the most promising. The final diameter and zeta potential of the tested SQV-SLNs and SQV-NLCs were ~ 200 nm and ~ -24 mV, respectively. Encapsulation efficiency greater than 90% was obtained for the two types of nanoparticles. The drug was solubilized in the lipid both in SQV-SLNs and the SQV-NLCs, because there were not any drug fusion events as showed by the DSC technique. In order to assess the cell response to a drug and the vehicle carrying it, cell viability and cytotoxicity assays were performed. NLC showed better behavior, with a low cytotoxicity compared with SLN. If on one hand the SLNs showed a good stability of the suspension while keeping the same size throughout the investigation period, the NLCs showed good stability of matrix with high encapsulation efficiency and fewer losses of the drug over time. Furthermore, cytotoxicity and viability studies also showed the most promising results for this type of lipid nanoparticles.
Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization

Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vù algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (ℓ1-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real prospective experiments.
Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.
Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.
Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques

In the article, the authors study the possibility of detecting some fungal diseases of rice using visual computing and machine learning techniques. Leaf blast and brown spot diseases are considered. Modern computer vision methods based on convolutional neural networks are used to identify a particular disease on an image. The authors compare the four most successful and compact convolutional neural network architectures: GoogleNet, ResNet-18, SqueezeNet-1.0, and DenseNet-121. The authors show that in the dataset used for the analysis, the disease can be detected with an accuracy of at least 95%. Testing the algorithm on real data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution. Data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution.
Millimeter Wave Path Loss Modeling for 5G Communications Using Deep Learning With Dilated Convolution and Attention

An accurate and efficient path loss modeling method for millimeter wave communications plays a significant role in the large-scale deployment of a fifth-generation (5G) mobile communication system. Conventional path loss modeling methods such as deterministic methods, empirical methods, and machine learning-based methods, in practice, cannot achieve the desired level of performance in terms of accuracy. This paper proposes a novel path loss model for 5G communications in suburban scenarios using deep learning with dilated convolution and attention. Dilated convolution is used to alleviate the locality of feature extraction and capture the global information on input images. Attention rendered by global context blocks helps the attention-enhanced convolutional neural network (AE-CNN) model utilize the global information on inputs to extract essential features of propagation environments. A distance-embedded local area multi-scanning algorithm which generates input images that can improve learning the latent features with dependency on distance is proposed. The experimental results indicate that the AE-CNN model can outperform state-of-the-art deterministic and empirical methods in terms of root mean square error in test scenarios.
Reducing Arm Fatigue in Virtual Reality by Introducing 3D-Spatial Offset

Arm fatigue is an important factor affecting user experience in Virtual Reality (VR). In this work, we have proposed ProxyHand and StickHand, virtual hand techniques to address this issue. Using ProxyHand or StickHand, users can flexibly adjust the 3D-spatial offset between the physical hand and its virtual representation. This will allow users to keep their arms in a comfortable posture (vertically down) even when they have to manipulate objects in locations that require lifting of arms using the default interaction method. Proposed ProxyHand and StickHand have a similar Underlying concept that is to introduce a 3D-spatial offset between the physical hand and its virtual representation in VR. However, they respond differently to the user’s hand movements because of different working mechanisms. Question arises whether the 3D-spatial offset will negatively impact the hand control ability as the directness of interaction is being violated. To investigate this, we conducted user studies where users were asked to perform object translation, rotation and hybrid tasks. ProxyHand and StickHand are used in combination in some scenarios to maximize positive impact on the user experience in VR. This raises the question to find the best possible combination of these virtual hands to reduce arm fatigue. Firstly, for this purpose, we combined both virtual hands by manually allowing users to switch between ProxyHand and StickHand. Secondly, we used machine learning to automatically switch between both the virtual hands. Results showed that introduction of a 3D-spatial offset largely reduced the arm fatigue while offering equal performance to the default interaction method for all these tasks; translation, rotation and hybrid task. Users preferred using ProxyHand and StickHand to interact in the VR environment for longer periods of time.
Gene Expression Based Tumor Purity Estimation and Individual-Specific Survival Tool in Skin Cutaneous Melanoma

Skin Cutaneous Melanoma (SKCM) is a type of cancer that arises from the occurrence of genetic mutations in melanocytes and is the most aggressive and fatal type of cancer affecting mostly the Caucasian population with increasing incidences in Asia. Tumors and lesions are highly heterogeneous comprised of cancerous and non-cancerous cells, and the admixture is thought to have an important role in tumor growth and progression of the disease. This study features a system capable of estimating tumor purity from RNA-Seq gene expression data using Gradient Boosting Machines and providing individual-specific survival prediction (death or progression of the disease) using a set of clinical features and the tumor purity estimate from the trained model. The performance of the models for tumor purity using the entire set of gene expression and selected features by importance scores were compared. The survival models have shown that the tumor purity estimate from the trained model provided additional prognostic information over established clinical features including age, tumor stage, and sex. Survival models using Cox Proportional Hazards are provided to allow users to evaluate and probe the models for further in-sights, whether with past historical cases, current or hypothetical patients. Future model improvements and prospective replication will be necessary to demonstrate true clinical utility.
Research on Key Technologies of Railway Electric and Electronic Systems Engineering Construction Management based on BIM

In view of the problems of short construction period, insufficient design depth, multi-disciplinary cross and frequent construction quality problem in the railway electric/electronic systems engineering construction, introducing BIM(Building Information Modeling) to support construction management is able to detect and solve problems in advance, avoid reprocessing and material waste, enhance the quality and economic performance. This paper discusses technology feature of BIM, analyses status of BIM adoption, proposes BIM systematic application solution in railway electric/electronic systems engineering construction period, including building BIM application organization, integration and deployment of software/hardware, BIM application standards. This paper studies BIM-based professional applications including parametric modeling approach of overhead contact line, pre-calculation of catenary wrist arm, mistake/omission /conflict/missing optimization, engineering material statistics, cables layout optimization. Also studies BIM-based integrated management applications including information management of catenary mast, progress management and inventory management. By adopting BIM technology, the application can intuitively reflect the construction process, enhance data sharing and timely detect the errors and omissions among various majors, leading to reduce re-work, improve building efficiency and quality. This study provides valuable experience for BIM application in railway electric/electronic systems engineering construction.
Power/Performance/Area Evaluations for Next-Generation HPC Processors using the A64FX Chip

Future HPC systems, including post-exascale supercomputers, will face severe problems such as the slowing-down of Moore's law and the limitation of power supply. To achieve desired system performance improvement while counteracting these issues, the hardware design optimization is a key factor. In this paper, we investigate the future directions of SIMD-based processor architectures by using the A64FX chip and a customized version of power/performance/area simulators, i.e., Gem5 and McPAT. More specifically, based on the A64FX chip, we firstly customize various energy parameters in the simulators, and then evaluate the power and area reductions by scaling the technology node down to 3nm. Moreover, we investigate also the achievable FLOPS improvement at 3nm by scaling the number of cores, SIMD width, and FP pipeline width under power/area constraints. The evaluation result indicates that no further SIMD/pipeline width scaling will help with improving FLOPS due to the memory system bottleneck, especially on L1 data caches and FP register files. Based on the observation, we discuss the future directions of SIMD-based HPC processors.
Enhanced Machine Learning-based Inter Coding for VVC

In this paper, we propose an enhanced machine learning-based inter coding algorithm for VVC. Conceptually, the reference pictures from the decoded picture butter are processed using a recurrent neural network to generate an artificial reference picture at the time instance of the currently coded picture. The network is trained using a SATD cost function to minimize the bit rate cost for the prediction error rather than the pixel-wise difference. By this we achieved average weighted BD-rate gains of 0.94%. The coding time increased about 5% for the encoder and 300% for the decoder due to the use of a neural network.
Terahertz Slot Dielectric Waveguide Implemented on the Silicon-BCB-Quartz Platform

A novel terahertz slot dielectric waveguide based on the Silicon-BCB-Quartz platform is proposed, and the design example for the 500 to 570 GHz frequency range is presented. Moreover, the proposed waveguide is fabricated and measured, and its low loss characteristics are verified experimentally. The proposed waveguide provides a highly confined electric field (<inline-formula><tex-math notation="LaTeX">$\frac{\lambda _{0}}{\text{6}}$</tex-math></inline-formula> × <inline-formula><tex-math notation="LaTeX">$\frac{\lambda _{0}}{\text{28}}$</tex-math></inline-formula> at 530 GHz), which can be extremely advantageous for sensing and nonlinear applications in the terahertz frequency band.
A Robust Control Approach to Event-Triggered Networked Control Systems With Time-Varying Delays

A robust control approach is proposed to solve the asymptotic stabilization problem of networked control systems, where the data transmission is mediated by a triggering function, and is subject to time-varying transmission delays. We show that the triggering mechanism and the delay effects could be regarded as uncertain components interconnected with a stable linear time-invariant system. We then use two operators to describe the uncertainties, and find the corresponding integral quadratic constraints of the operators. The stability of the networked control system is analyzed from a robust control point of view. Beyond the stability analysis, a co-design algorithm is devised to design the control gain and detection parameters jointly to maximize the inter-event interval as well as the maximum allowable time-delay. Theoretically, the proposed scheme guarantees that out-of-order packet delivery does not happen. The robust control framework allows the algorithm developed for time-varying delays to be applied to the constant delay and the delay free cases. Finally, the theoretical results are demonstrated and verified by numerical simulations.