Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic Resonance Imaging haemodynamic modelling in chronic liver disease: development, validation and translation

Though haemodynamic changes underpin the pathophysiology of chronic liver disease, there are currently no robust non-invasive methods available for their assessment. I propose ‘caval subtraction’ phase contrast MRI (PCMRI) a novel method to measure total liver blood flow (TLBF) and hepatic arterial (HA) flow using PCMRI measurements of caval and portal venous (PV) flow. I validate this method at 9.4T and 3.0T to demonstrate: agreement between preclinical PCMRI and invasive transit-time ultrasound (TTUS) and fluorescent microsphere measurements of flow parameters; good consistency between clinical caval subtraction PCMRI and independent direct PCMRI measurements; encouraging correlations between PCMRI and invasive ICG clearance in patients; and good seven-day reproducibility of PCMRI derived haemodynamic parameters in normal volunteers. Using dynamic contrast enhanced (DCE) MRI on a 3.0T system, I demonstrate improved seven-day reproducibility using dual input single compartment pharmacokinetic modelling with a novel method for obtaining physiological vascular input function delays, correction of arterial input functions using PCMRI aortic flow and use of PCMRI estimations of TLBF to correct DCE MRI quantification. I also implement arterial spin labelling (ASL) at 9.4T and demonstrate a tendency for ASL to underestimate PCMRI hepatic parenchymal perfusion. Using bile-duct ligated (BDL) rats to study cirrhosis, I demonstrate that these have reduced TLBF and HA fraction at baseline, impaired HA regulation and buffer response, cirrhotic cardiomyopathy, and a failure to match hepatic circulatory demands with increased liver:body mass ratio. Acute-on-chronic liver failure (simulated using endotoxaemia) demonstrates reductions in TLBF, HA flow, absence of normal sepsis-induced hepatic hyperaemia and blunted cardiac systolic response. Studies in cirrhotic patients demonstrate increased TLBF and HA flow in higher risk portal hypertensive patients; that HA flow, HA fraction and cardiac output are important correlative parameters with hepatic venous pressure gradient and that caval subtraction PCMRI has potential in evaluating treatments for portal hypertension.
Comparison between 2 3D-Printed 2×2 Horn Antenna Arrays at 12.5GHz with Different Feeding Structures

This work introduces a comparison between H- and E-plane-network to feed antenna arrays. Due to the increasing demand for a low cost and a low weight antenna array for different communication applications. In addition to comparing between horn antennas and sub-arrays as aperture elements for both feeding networks, a 2×2 E-plane fed waveguide antenna array comprised of 2×2 aperture elements sub-arrays is designed to operate at 12.5 GHz. The proposed design is fabricated using 3D-printing technology from polymer and metalized in 2 different methods to demonstrate the advantages and challenges of each method. The fabricated design exhibits 16.2% fractional bandwidth with 18 dBi gain and around 16.2% efficiency at the operating frequency which showed good agreement with the simulated results.
Challenges and Chances for the Emerging Short Video Network

Short video has rapidly become one of the most popular online entertainment applications in the last two years. The new service pattern of short video network shows its unique characteristics, making it much more difficult to improve QoS by using traditional methods in content delivery network. In this paper, we deeply analyze the real traces of more than 28 million videos with 100 million accesses from 488 servers located in 33 cities, disclosing the following three unique characteristics/challenges of short video network compared to traditional Video on Demand (VoD) services: First, user access pattern shows less preferences on videos, making it's hard to find out the popular ones. Second, most videos have very few visits, which makes it difficult to learn and predict due to the lack of historical data. Third, the popularity changes much more quickly, which further makes it challenging to design efficient caching policy. To address these challenges, we're designing CoStore, a reinforcement learning-based caching policy which pre-fetches videos with the help of video correlations. This poster brings the short video network to light and we hope CoStore could also provide inspirations to related areas.
Poster Abstract: Jamming WLAN Data Frames and Acknowledgments using Commodity Hardware

Actively interfering wireless communication (also called jamming) to prevent a potential receiver to decode particular frames has recently attracted much interest in the research community. Reactive jammers extend this concept to allow on-demand selection whether to jam specific frames based on information included in the headers of those particular frames. Recently, several approaches were presented to allow reactive jamming by using commodity hardware, however, with certain limitations, e.g., a minimum necessary frame length. We go one step beyond and, to the best of our knowledge, present a first study on the feasibility of jamming even IEEE 802.11 control frames, such as unicast Acknowledgments (ACKs) following a reactive jamming approach. Our system has been implemented on commodity hardware. We further present analytical timing insights into the possibility of jamming control frames as well as regular data frames.
Intelligent Massive MIMO Antenna Selection Using Monte Carlo Tree Search

Antenna selection is a promising technology to achieve a good balance between high transmission capacity and low hardware complexity for massive multiple-input multiple-output (MIMO) systems. However, the design of a near-optimal antenna selection algorithm with low searching complexity is still a challenge. In this paper, we describe a self-supervised learning based Monte Carlo Tree Search (MCTS) method to solve the antenna selection problem for a massive MIMO system. The search process for selecting antennas with maximal channel capacity is converted to a decision-making based problem. Based on the system model of antenna selection, we map the components of a MIMO system to the basic elements of MCTS such as action, tree state, and reward. To improve the search efficiency of the MCTS, we employ a linear regression module to extract the channel features from the channel state information (CSI) and output the prediction to MCTS as prior probability. Since the data and label are generated by the MCTS process itself, the entire process can be considered as a self-supervised learning process. According to the simulation results, the proposed self-supervised learning MCTS-based antenna search method exhibits a high searching efficiency with near-optimal performance, which archives 40% and 15% outage capacity compared with random selection and greedy search selection, respectively. The bit error rate (BER) performance of the proposed method has about 1-dB gain compared to the greedy search selection method.
A Medical-History-Based Potential Disease Prediction Algorithm

As an important application of medical informatization, healthcare big data analysis has been extensively researched in the fields of intelligent consultation, disease diagnosis, intelligent question-answering doctors, and medical assistant decision support, and have made many achievements. In order to improve the comprehensiveness and pertinence of the medical examination, this paper intends to use healthcare big data analysis combined with deep learning technology to provide patients with potential diseases which is usually neglected for lacking of professional knowledge, so that patients can do targeted medical examinations to prevent health condition from getting worse. Inspired by the existing recommendation methods, this paper proposes a novel deep-learning-based hybrid recommendation algorithm, which is called medical-history-based potential disease prediction algorithm. The algorithm predicts the patient’s possible disease based on the patient’s medical history, providing a reference to patients and doctors to reduce the problem of delaying treatment due to unclear description of the symptom or limited professional knowledge. The experimental results show that our approach improves the accuracy of the potential diseases prediction.
Optimizing Degree Distributions of LT-based Codes with Deep Reinforcement Learning

Fountain codes are a modern class of error-correcting codes with a reasonably low encoding/decoding complexity and an ability that constitutes their rateless property to generate encoded symbols on demand. Fountain codes have found various applications, most notably in the areas of network communications, data storage systems, delay sensitive transmissions and continuous data delivery. Furthermore, fountain codes form the core part of alternative communications protocols, also known as a digital fountain communication protocol and its modifications, for future networks. Luby Transform (LT) codes are the first practical realization of fountain codes for binary and packet erasure channels. LT codes and the fountain codes that originate from LT codes (LT-based codes), such as Raptor or Zigzag Decodable fountain codes, utilize monomial distributions for degree selection, also known as degree distributions. Degree distributions have a significant impact on the overall efficiency of the fountain codes; therefore, we propose a novel technique that uses deep reinforcement learning to approximate/learn an optimal degree distribution (ODD). We obtain an approximation of an ODD, which we call a learned degree distribution (LDD), and evaluate the fountain codes with the LDD and other previously proposed degree distributions and observe improvements in their performance whenever the codes use the LDD.
A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

This paper presents a decoupled trajectory planning framework based on the integration of lattice searching and convex optimization for autonomous driving in structured environments. For a 3D trajectory planning problem with timestamps information, due to the presence of multiple kinds of constraints, the feasible domain is non-convex, so it is easy to fall into local optimum for trajectory planning. And the solution space of this problem is so enormous that it is difficult to identify an optimal solution in polynomial time. To address this non-convex problem, and to improve the convergence speed of an optimization process, the approach based on lattice searching is adopted in consideration of the ability to discretize driving environments and reduce the solution space. And the resulting path generated by lattice searching typically lies in the neighborhood of the global optimum. But this solution is neither spatiotemporally smooth nor globally optimal, so it is generally called the rough solution. For this reason, a subsequent nonlinear optimization process is introduced to refine the rough trajectory (combined by path and speed). The proposed framework is implemented and evaluated through simulations in various challenging scenarios in this paper. The simulation results verify that the trajectory planner can generate high-quality trajectories, and the execution time is also acceptable.
Demo Abstract: Turning OpenStack into a Fog Orchestrator

5G networks are expected to enable revolutionary services to be established, such as tactile Internet and online augmented reality applications. These services require a dynamically programmable back-haul network topology in order to serve large amount of network traffic and to guarantee near real-time response times. For high flexibility of service capabilities service functions will be deployed in virtualized environments instead of the currently used special purpose hardware. The most widely used Virtual Infrastructure Manager (VIM) is OpenStack, which is responsible for managing compute, storage and virtual network resources. As the current scheduler of OpenStack does not take into account the underlying physical network characteristics, in order to deploy network services (NS) in a geographically distributed infrastructure, multiple VIMs and an orchestrator on top of them, are necessary for resource management. In contrast to today's setups we show a novel solution that merges these functionalities under one common OpenStack domain: our solution is capable of i) measuring the bandwidth and delay characteristics of the underlying physical network among compute nodes, ii) creating a topology model that contains both compute-, and network-related features, iii) mapping the incoming service requests, and re-mapping already deployed services to the underlying resources with our novel orchestration algorithm, iv) deploying and migrating services via OpenStack API calls.
Validation of Thermal Resistance Extracted From Measurements on Stripe Geometry SiGe HBTs

In this article, we present a straightforward methodology to validate the consistency of thermal resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\text {TH}}$ </tex-math></inline-formula>) measurements for a set of stripe geometry silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs). The proposed approach is based on the behavior of frequency-dependent thermal impedance (<inline-formula> <tex-math notation="LaTeX">${Z}_{\text {TH}}$ </tex-math></inline-formula>) of HBTs. The key advantage of this method is its simplicity and ease of applicability because it requires no additional measurements than the conventional approaches to extract the electrothermal parameters. First, we provide a physics-based formulation to extract <inline-formula> <tex-math notation="LaTeX">${Z}_{\text {TH}}$ </tex-math></inline-formula> as a function of <inline-formula> <tex-math notation="LaTeX">${R}_{\text {TH}}$ </tex-math></inline-formula>. As a next step, we propose different normalization methods for <inline-formula> <tex-math notation="LaTeX">${Z}_{\text {TH}}$ </tex-math></inline-formula> in stripe emitter SiGe HBTs to validate the <inline-formula> <tex-math notation="LaTeX">${R}_{\text {TH}}$ </tex-math></inline-formula> used in our <inline-formula> <tex-math notation="LaTeX">${Z}_{\text {TH}}$ </tex-math></inline-formula> formulation. Finally, we substantiate our validation technique across stripe emitter SiGe HBTs having different emitter dimensions corresponding to STMicroelectronics B55 technology.
Sapphire Concave Microlens Arrays for High-Fluence Pulsed Laser Homogenization

Microlens array has been demonstrated to be an effective method for laser beam homogenization. However, the homogenization of high-fluence laser beam is still a great challenge due to the serious damage to the microlens-array-based homogenizer. Here, an ultra-smooth sapphire concave microlens-array-based homogenizer, which is fabricated by dry-etching-assisted femtosecond laser machining, is proposed for shaping high-fluence pulsed laser beam to a flat-top profile intensity distribution. By taking advantages of high transmittance (>70%) and low surface roughness (1.1 nm), high-fluence (5 J/cm2) and wide-band (266 nm, 532 nm and 808 nm) laser beams homogenization is successfully realized by the sapphire concave microlens arrays.
Multi-step temperature prediction model based on surrounding cities and long-term memory neural networks

In this paper, a multi-step temperature prediction model based on geographic location and long-term memory neural network is proposed. In order to verify the validity of the data of surrounding cities, two sets of comparative experiments are carried out which using 80-dimensional data of weather data added to surrounding cities and not joining the surrounding area. Urban weather data 8D data, 10D data added to surrounding city temperature data, and 1D data without temperature of surrounding cities. The test results show that the model of adding weather data of surrounding cities in most cases has better prediction effect than the model of weather data without surrounding cities. The results show that the model of weather data added to surrounding cities has better prediction effect.
Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

Driver drowsiness is receiving a lot of deliberation as it is a major cause of traffic accidents. This paper proposes a method which utilizes the fuzzy common spatial pattern optimized differential phase synchrony representations to inspect electroencephalogram (EEG) synchronization changes from the alert state to the drowsy state. EEG-based reaction time prediction and drowsiness detection are formulated as primary and ancillary problems in the context of multi-task learning. Statistical analysis results suggest that our method can be used to distinguish between alert and drowsy state of mind. The proposed Multi-Task DeepNet (MTDNN) performs superior to the baseline regression schemes, like support vector regression (SVR), least absolute shrinkage and selection operator, ridge regression, K-nearest neighbors, and adaptive neuro fuzzy inference scheme (ANFIS), in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) metrics. In particular, the best performing multi-task network $\text{MTDNN}_5$ recorded a 15.49% smaller RMSE, a 27.15% smaller MAPE, and a 10.13% larger CC value than SVR.
Antenna beam tilting and gain enhancement using novel metamaterial structure at 28 GHz

This paper introduces an antenna beam tilting in azimuth-plane at a 5th generation (5G) candidate band, 28 GHz. The proposed antenna is dipole antenna and the beam tilting is achieved using array of metamaterial (MTM) unit cells. The new square range resonator (SRR) unit cell is composed of a one range included into square shape. Integration of SRR array in the dipole antenna allows to create different refractive index and consequently tilt the main beam of the original antenna in both positive (+24 degrees) and negative (−25 degrees) directions. Furthermore, there is a gain enhancement through the deflection process by 1.87 dB (positive tilt) and 1.84 dB (positive tilt) compared to the antenna with no SRR unit cells. In addition, the return loss of the original antenna corresponds well with that of the antenna loading by SRR array.
Advances in Cross-Spectral Iris Recognition Using Integrated Gradientface-Based Normalization

Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfaces-based normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.
An Integrated Approach to Human-in-the-Loop Systems and Online Social Sensing

As the number of smart devices that surround us increases, so do the possibilities of leveraging them to create intelligent, socially-aware systems. However, apart from e-health systems, few applications consider humans as active system players. In fact, most applications/systems have the sole objective of providing some service to humans without considering their intents, actions or emotions. In this respect, they can be looked at as open loop, rather than integrating humans in a feedback loop, with potential to react to and change the environment. Systems that do consider human feedback are called Human-in-the-Loop Cyber-Physical-Systems (HiTLCPS). In this paper we propose an approach that implements the HITLCPS concept. The developed platform, called ISABELA, explores both online social networks (OSN) data as well as a variety of other data, such as environmental data and personal mobile devices data, thus combining IoT, social sensors and mobile sensing. In addition to presenting the system architecture, the paper provides results obtained during field trials.
Power Transferring and Analogue Communication Approach for Implantable Devices

In this paper, we introduce a new approach of power transferring and communication for implantable devices to retrieve information from an implanted sensor. This approach originates in Radio Frequency Identification (RFID), and uses an analogue approach to communicate in order to decrease the complexity of implanted part and influence of attenuation, which requires coupling of coils. The design of power transferring mechanism consists of two parts, i.e., one reader and one label, which are similar to but actually different from those of RFID. To be specific, RFID uses electromagnetic fields to automatically identify and track the label attached to the object. In this work, the label can be implanted into a human body, and the reader is used to retrieve information from the label. The label is passive and the reader is active. When the reader transmits interrogator signals to the label, it will feedback the reader with two voltages, i.e., one is obtained with sensor and one without. The voltages are then used to locate the corresponding resistance of the sensor in a table. Experiment results of this work show that it is possible to retrieve information from a sensor placed on a passive label using load modulation approach in analogue manner. In addition, by computing the attenuation of human tissues to electromagnetic waves of different frequencies, we draw the conclusion that low frequencies are adaptive for the environment of human body.
Classification of diabetic retinopathy based on DSIRNet

The rapid development of deep learning in recent years has achieved great success in the fields of speech recognition, image recognition and natural language processing. At present, the automatic diagnosis of medical images is the focus of attention from all walks of life, and deep learning technology has shown good application prospects in this respect. In response to the screening of diabetic retinopathy, this paper proposes a deep supervision of the Inception-Residual network(DSIRNet) to classify DR images end-to-end. The network combines the advantages of Inception module and residual module, which can not only learn multi-scale features, but also ensure the transmission of feature information between network layers. At the same time, this paper also uses deep monitoring method to assist the training network, which can improve thermal classification effect of the network to a certain extent. In terms of data sets, data noise and sample distribution imbalances are also addressed. The effectiveness of the proposed model and method is verified by comparative experiments.