Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefit of Multimodality Image Acquisition in the Treatment Planning of Stereotactic Radiosurgery (SRS) of Arteriovenous Malformation (AVM)

Benefit of Multimodality Image Acquisition in the Treatment Planning of Stereotactic Radiosurgery (SRS) of Arteriovenous Malformation (AVM) Dan Ishihara, M.D, Ph.D1,*, Marco C Pinho, M.D.3, Xuejun Gu, Ph.D. 2, Strahinja Stojadinovic, Ph.D. 2, Hui Liu, M.S. 2, Robert Timmerman, MD2 1Norton Cancer Institute, Radiation Oncology, 676 S. Floyd St., First Floor, Louisville, KY, USA 40223 2University of Texas Southwestern Medical Center, Department of Radiation Oncology, 2280 Inwood Road, Dallas, Texas, USA 75390-9303 3Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA, 75235 Research Open Access Journal of Cancer Research and Therapeutic Oncology
Four Categories Vehicle Detection in Hsuehshan Tunnel via Single Shot Multibox Detector

Taiwan has many vehicles and as a result many traffic problems. In particular, during the Spring Festival and the holidays, Hsuehshan Tunnel (HST) between Yilan and Taipei is always a traffic jam. To solve this problem, intelligent transportation system (ITS) is necessary, and accurate vehicle detection (VD) is the first stage for ITS. In order to detect vehicles in HST, three training methods based on single shot multibox detector (SSD) are presented to detect four categories of vehicle in the Tunnel. The experimental results demonstrated that the presented three training methods, which only used 1000 training frames, can detect and categorize more vehicles than the pre-trained SSD model which used a large training dataset. Specifically, the SSD trained by our collected data set and data augmentation has the highest detection rates for sedan, van, bus, and truck – 93.6%, 90.9%, 100%, and 100%, respectively.
General First-Order Framework for Passive Detection with Two Sensor Arrays

In this paper we establish a general framework for deriving two-channel detectors for passively detecting sources of acoustic or electromagnetic radiation. The framework is based on a first-order model for multivariate normal measurements at two spatially separated arrays, each consisting of L sensors that record M snapshots. The question to be answered is whether or not these measurements contain a signal common to both sensor arrays, indicating the existence of a radiating source. Generalized likelihood ratios (GLRs) aim to maximize the output signal-to-noise ratio (SNR) of a two-channel receiver. Quite generally, the GLRs are maximum eigenvalues of variance-normalized covariance matrices constructed from spacetime measurements at the two arrays. So, while the underlying measurement model is a first-order model, the resulting GLR statistics are decidedly nonlinear functions of the measurements.
A New Real-Time Embedded Video Denoising Algorithm

Many embedded applications rely on video processing or on video visualization. Noisy video is thus a major issue for such applications. However, video denoising requires a lot of computational effort and most of the state-of-the-art algorithms cannot be run in real-time at camera framerate. This article introduces a new real-time video denoising algorithm for embedded platforms called RTE-VD. We first compare its denoising capabilities with other online and offline algorithms. We show that RTE-VD can achieve real-time performance (25 frames per second) for qHD video (960⨯540 pixels) on embedded CPUs and the output image quality is comparable to state-of-the-art algorithms. In order to reach real-time denoising, we applied several high-level transforms and optimizations (SIMDization, multi-core parallelization, operator fusion and pipelining). We study the relation between computation time and power consumption on several embedded CPUs and show that it is possible to determine different frequency and core configurations in order to minimize either the computation time or the energy.
Community Detection of Chinese Airport Delay Correlation Network

Network science has been a promising tool for characterizing and understanding complex systems. A challenging problem in network science is to uncover the community structure of the network. Community structure generally presents the partition of the nodes in the network into several groups based on various structural properties or dynamic behavior. In this paper, we analyze the community structure of Chinese airport network based on Stochastic Block Models (SBM). Different from exisiting studies, the Chinese Airport Delay Correlation Network (CADCN) is constructed with airports as nodes and the correlations between hourly delay time series of airport pairs as edges. To analyze the temporal patterns of community structures, we employ spectral clustering method and classify Chinese airports into 6 different communities. Airports within each community have closer relationships to each other on the delay propagation. A similar investigation to the traditional Chinese airport network (CAN) is carried out based on SBM as well. By comparing the results of two networks, we find that the CADCN has the advantage in revealing the implicit delay correlation than the directed flights connection between airports. Our findings have potential meanings to understand the spread of flight delays and to develop relevant management and control strategies.
Design of a Ka-Band Planar Magic-T Based on modified Gysel Structure

In this paper, a Ka-band magic-T based on the modified structure of Gysel power divider is proposed. Due to the symmetrical structure, two equal in-phase outputs are achieved. The signal applied to the difference port, is split by an additional half-wavelength branch into two equal out-of-phase signals. The planar magic-T has a bandwidth of 30.3% (28–38 GHz), when the difference port and the sum port are excited, the insertion loss of the magic-T is less than −3.26 dB. The isolation between the sum/difference ports and the two input ports is better than 40 and 20 dB. As for the in-phase and out-of-phase excitation, the phase characteristics are close to the ideal 180° and 0° with only ±1.4° deviation across the band from 28 GHz to 38 GHz, the amplitude imbalances are less than ±0.05 dB.
Offline Fault Detection in Gene Regulatory Networks using Next-Generation Sequencing Data

In a previous contribution, a method was proposed for on-line fault detection in Boolean gene regulatory networks based on the Boolean Kalman Filter and hypothesis testing on next-generation sequencing (NGS) data. In this paper, we present a related methodology, which differs in two main points: (1) it uses the Boolean Kalman Smoother on the entire data set stored off-line; (2) it employs a maximum-likelihood approach for simultaneous change detection and categorization. The efficacy of the proposed methodology is assessed using a p53-MDM2 negative feedback loop Boolean network with stuck-at faults that model molecular events commonly found in cancer.
High Throughput and QoE Fairness Algorithms for HD Video Transmission over IEEE802.11ac Networks

High capacity IEEE802.11 networks are increasingly supporting data intensive applications. Most of streaming applications are using high quality variable rate traffic which includes HD (High Definition) and UHD (Ultra High Definition) video streaming sources. Such services demand fair QoE for all end users which requires high network QoS as well as inter and intra service fairness. It is a challenging task to offer service fairness in a random access network due to nondeterministic resource allocation techniques. In this paper, we propose two video packet scheduling algorithms for the 802.11 MAC protocol which can offer high QoS as well as fairness among video streams transmitted on the downlink using the IEEE802.11ac MAC protocol. The first algorithm is the FRATXOP that allocates transmission resources for each flow based on experienced network congestion levels to improve throughput of HD and SD video connections. The second algorithm the EAS (Enhanced Access Scheduler) operates in conjunction with the FRA-TXOP to fairly allocate transmission resources to maintain fairness among all video streams. A ns-3 simulation model was developed to evaluate the performance of the above algorithms and compare with the standard IEEE802.11ac MAC protocol. Simulation results show that proposed algorithms can enhance the high network QoS as well as maintain high QoE fairness among all end video users.
Packet Routing Against Network Congestion: A Deep Multi-agent Reinforcement Learning Approach

The continuous growth of the network data would lead to the increased network congestion and the throughput decline. In this paper, we investigate the packet routing problem based on deep multi-agent reinforcement learning, where each router chooses the next hop router by itself intelligently. We design the modified deep Q-network in each router to evaluate the neighbor routers. The routers, each acting as an agent, choose the next hop router based on their local observation. Then they transfer the packets to the chosen routers and receive the reward and the observation of the next hop routers. Using their experience, the routers learn to improve the packet routing strategy by updating their Q-networks. We demonstrate that with proper reward set and training mechanism, the routers in the network can work in a distributed way to reduce the computational complexity compared with the single-agent reinforcement learning based algorithm. And the proposed algorithm can further reduce the congestion probability and improve the network performance.
Time-Ordered Bipartite Graph for Spatio-Temporal Social Network Analysis

Today, human beings are surrounded by heterogeneous networking environments consisting of growing numbers of portable computation and communication devices. As most devices are carried by people, this creates communication networks between them that are highly influenced by human mobility. This implies that the presence of possible patterns in human movements can be exploited by network applications in order to extract valuable information that can be used to develop new services. Extracting this information does not concern only the spatial or temporal dimensions, but also the social aspects of the people involved. To evaluate this information, and capture the important and relevant features regarding both spatial and temporal dimensions, we have developed a model that is sufficiently expressive and easily tunable. Using complex network theory to analyze user mobility patterns in spatio-temporal social networks has produced a number of significant results in previous studies. However, these studies have often neglected time properties in spatio-temporal social networks and used static models to represent them. However, in real-world scenarios, the time effects play a pivotal role. In this study, we propose a new model to analyze spatio-temporal social networks represented by bipartite graphs, and extend the static centrality to temporal centrality, with a focus on closeness and betweenness. Our results show that the time-ordered bipartite graph provides a dynamic and comprehensive analysis of real spatio-temporal social networks, and that temporal centrality better captures the effects of time on the topology of networks over static models.
Overlapping-Group-Lasso-Based ISAR Imagery via Alternating Direction Method of Multipliers

In this paper, we propose a novel method called overlapping group Lasso to solve inverse synthetic aperture radar (ISAR) imaging problem. Unlike the traditional least absolute shrinkage and selection operator (Lasso) model, overlapping group Lasso is based on the $\ell_{1}/\ell_{2}$ mixed-norm and take advantage of the prior knowledge of the continuity structures of the scatters. Besides, we present a generic optimization approach, the alternating direction method of multipliers (ADMM) method, for dealing with overlapping group Lasso that including structured-sparsity penalties and the predefined weight for group. ADMM is a simple but powerful algorithm that blending the benefits of augmented Lagrangian and dual decomposition method. Therefore, it makes the proposed algorithm faster and more robust. Experimental results of simulated data and Yak-42 real data verify the feasibility of ADMM achieves sparse and structural feature enhancement via the overlapping group Lasso. The comparison of the results of overlapping group Lasso and Lasso shows: the new developed model has the good ability of denoising and structural feature enhancement.
A Generic Construction for All Parameters in Minimum Storage Regenerating Codes

Minimum-Storage Regenerating (MSR) codes have become superior alternatives to traditional erasure codes as they can provide optimal repair bandwidth while the reliability and the storage overhead are still optimal. So far, the state-of-the-art MSR codes mainly focus on connecting to all the remaining nodes to repair a single failure. Since the recovery latency may be bottlenecked by the time taken to retrieve the slowest or straggling block, it is however impractical to have the highest connectivity in MSR codes. In this paper, we introduce a generic construction for all parameters in MSR codes, which allows for bandwidth-efficient repair of a single data node failure with an arbitrary (but fixed) number of accessed nodes d. Our method provides explicit and generic encoding and repairing processes, and such codes were not previously known to exist. When d = n - 1, we show that our codes are not inferior to the other MSR codes and retain the same recovery optimality. Furthermore, the arbitrary number of d allows our codes to adapt to the late binding strategy to avoid stragglers (overloaded sites) for efficient recovery performance. As a result, our codes outperform both traditional erasure codes and the state-of-the-art MSR codes on the aspect of the response time when the system is subjected to an imbalanced load.
On advantages of data driven traffic classification for dynamic routing in optical networks

In this paper, we concentrate on solving the dynamic routing problem with traffic classification using the Software Defined Elastic Optical Networks framework. We first define three categories of traffic based on the CISCO Traffic Reports. Then, we propose two algorithms to solve the Routing, Modulation and Spectrum Assignment problem with the support of traffic class recognition - the Split Spectrum and the Buffer. We evaluate our approach using representative network topologies and a wide range of simulations. The results clearly show that the Butter algorithm provisions efficiently the incoming traffic in the network. In addition, we show the reduction of request blocking for the highest priority traffic that cannot be interrupted.
Robust Elliptic Localization Using Worst-Case Formulation and Convex Approximation

Recent years have seen a rapid growth in research on elliptic localization, due to its widespread usage in systems such as multiple-input multiple-output radar and multistatic sonar. However, most of the algorithms are devised under line-of-sight propagation, while a number of those existing non-line-of-sight (NLOS) mitigation schemes for elliptic localization are highly case-dependent. This paper addresses the problem of elliptic localization in adverse environments without assumptions about distribution/statistics of errors and NLOS status. To achieve robustness towards NLOS bias, we resort to the worst-case least squares formulation which requires only a bound on the errors. We then apply certain approximations to the resultant intractable constrained minimax optimization problem, and finally relax it into a readily solvable convex optimization problem. Simulation results show that our method can outperform several existing non-robust approaches in terms of positioning accuracy, and achieve comparable performance to a robust estimator with a lower computational cost.
Alternating Gradient Descent Ascent for Nonconvex Min-Max Problems in Robust Learning and GANs

We study a class of nonconvex-strongly-concave min-max optimization problems. A most commonly used algorithm for such problems in machine learning applications is the class of first-order algorithms where gradient descent and ascent steps are performed simultaneously or alternatively in each step. Despite its great success in practice, its theoretical properties are far from being understood. In fact, not much has been said about its convergence once the convex-concave assumption is absent. This is considerably different from minimization problems where many techniques are available to analyze nonconvex problems. It is not clear that if these techniques can be applied to min-max optimization. Despite the simplicity of this type of first-order methods, its properties are extremely difficult to analyze due to the nonlinear and nonconvex coupling between the maximization and minimization steps. (p)(/p)In this paper, we take a step toward this direction by examining a special class of nonconvex-strongly-concave min-max problems. We show that, with a proper stepsize choice, a simple alternating gradient descent/ascent (AGDA) algorithm would, in fact, converge to a stationary solution with a sublinear rate $\mathcal{O}\left( {1/t} \right)$, where t is the iteration number. We hope our analysis sheds light on future studies on the theoretical properties of relevant machine learning problems.
Critical Success Factors of Analysing User Emotions to Improve the Usability of Systems

Emotions of a person depend on several factors like personality, relationships, mental health, and among them, context also plays a major role. The acceptability and usability of a system can be enhanced when the user emotions are considered based on the context. Most of the past research has been carried out related to the detection of emotions using facial expressions, gaze direction, heart rate analysis, Electroencephalogram (EEG) signals, text analytics, and keystroke dynamics along with mouse movements. This paper is a literature review which summarizes the previous research work in order to identify the critical success factors of the existing approaches when detecting user emotions through facial expressions and text data along with the context. Finally, as a conclusion, considering the advantages and drawbacks of the existing applications, the novel approaches that need to be followed to analyse user emotions to improve the usability of systems are proposed.
Modeling the Magnitude and Phase of Multipath UWB Signals for the Use in Passive Localization

Radio-frequency (RF)-based device-free localization (DFL) systems measure RF parameters such as the received signal strength or channel state information to detect and track objects within a certain area. However, the change of the RF signal caused by the object is superimposed with various changes of the RF signal due to multipath propagation, especially in indoor environments. In this paper, we develop a model for ultra-wideband (UWB) channel impulse response (CIR) measurements for application in DFL systems. The model predicts received signal parameters in a setup with a transmitter and a receiver node, a person and multipath propagation. Different from other approaches, the RF hardware, and the model provides both magnitude and phase information for individual multipath components. We evaluate the new model with real measurements that have been conducted with a Decawave DW1000 radio chip. For the magnitudes, we achieved a correlation factor from 0.78 to 0.87 and maximum mean and standard deviation errors of 1.7 dB and 2.2 dB respectively. For the phase, we achieved correlation factor from 0.6 to 0.81 and maximum mean and standard deviation errors of 0.32 dB and 0.47 dB respectively, showing that the prediction of our proposed model for the magnitude and phase fits well to our measurements.
Unsupervised Classification of PolSAR Images Based on CFAR Detection of Cluster Centers

For unsupervised classification of polarimetric synthetic aperture radar (PolSAR) images, it is a challenge task to determine an appropriate number of categories. Recently, a method called clustering by fast search and find of density peaks (CFSFDP) has provided a good solution. However, it requires to select the cluster centers by visual observation, which is not only less automated but also may be difficult to implement for some data sets. Focusing on this problem, this paper proposes a method of detecting cluster centers with the constant false alarm rate (CFAR) criterion, and also applies it to the unsupervised classification of PolSAR images. In our method, a variable called center-likelihood for each data point is constructed by multiplying its local density and distance that defined in the CFSFDP algorithm. Then, after estimating the probability density of the center-likelihood, the threshold to detect cluster centers is obtained with the CFAR criterion. The effectiveness of the proposed method is verified by the experimental results of two actual PolSAR images.
Optimal Stochastic Media Storage in Federated Cloud Environments

With the emergence of cloud computing, users are increasingly using storage services of cloud providers to satisfy their storage needs. However, these cloud providers charge at different rates and are not 100% reliable, resulting in occasional service outages. Thus the costs on end users are two folds: (1) cost of storage service, and (2) cost of information loss in cloud service outages. End users require a solution that minimizes the overall cost to balance the storage cost and the probability of successfully retrieving stored objects in cases of provider failures. In this paper, based on the concept of federated cloud agent proposed in our previous work [1], we study the cloud storage management strategy which allows end users to simultaneously deploy storage objects to multiple cloud service providers, based on their unit storage costs and failure behaviors. We propose to use random network coding and a decision model to solve this storage management problem. The random network coding over information objects to be stored in clouds helps simplify the cloud storage management and the decision model forms the foundation of solving the problem in a dynamic environment. We solve the decision model analytically and also propose two practical solutions based on efficient local search algorithms which can achieve near-optimal performance and lower computational complexity. In the experiments, we evaluate the proposed cloud storage strategies against a few reference schemes and find our schemes can achieve a good balance between the information success recovery rate and storage cost.
Cloud-Aided Max-Link Relay Selection for Two-Way Cooperative Multi-Antenna Systems

In this paper, we investigate a relay-selection technique for two-way cooperative multiple-antenna systems that are aided by a cloud central node, where two terminals simultaneously transmit to each other with the help of relays. In particular, we present a novel two-way relay selection technique based on the selection of the best link, exploiting a cloud buffer and PLNC, that is named Two-Way Cloud-Aided Max-Link (TWC-Max-Link). We compare the proposed TWC-Max-Link to existing approaches, in terms of pairwise error probability and bit error rate. Simulations are then employed to evaluate the performance of the proposed and existing techniques.
A Fundamental Analysis of Conventional Laser Triangulation Technique for the Development of Revamped 3D Scanning System

With the proliferating intricacy of equipment being manufactured in this era of fourth industrial revolution; a lot of expertise, cost and time are entailed to design a 3D model for rapid prototyping and reverse engineering. The solution for the stated affair lies in 3D scanning technology. The research is based on the scrupulous and methodical study of the already existing 3D scanning techniques indicatively discussing laser triangulation technique, analyzing its results and proposing a revamped technique that caters for its flaws. The scheme is then scrutinized to substantiate the effectiveness of the proposed technique.