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The frequency domain dielectric response method has been one of the most effective approaches for condition assessment of an electrical insulation system. The low-frequency part is more effective for diagnosing the moisture content and aging status of oil-paper insulation. To facilitate the study of the low-frequency dielectric spectrum of oil-paper insulation, a model applicable to the low-frequency dielectric response of insulation oil is proposed in this study, which is based on the ionic motion of insulation oil. The creation of charge carriers under field test conditions mainly derives from the dissociation of ionic pairs. During testing, ionic motion of insulation oil is jointly affected by thermal diffusion and the electric field; moreover, dissociation and recombination of negative and positive ions also occur. Therefore, the above factors were all considered in the modelling process. The applicability of the model is verified against measured results. For further interpreting the proposed model, the characteristics of the ionic concentration distribution, the relation between ionic distribution and dielectric properties, and the application of the proposed model are analysed.

This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the proposed roll angle estimation algorithm takes fewer iterations to achieve the same precision as the previous method., Comment: 5 pages, six figures, 2019 EUSIPCO

Continuous blood pressure is measured from various extracranial body sites, with difference in amplitude and phase with intracranial blood pressure. Consequent influences on the accuracy of Windkessel model need further investigation. Between blood pressure and intracranial flow rate, a model with non-linear flow resistance (R-DT) was proposed and compared with the 3-element Windkessel (RCR) model. From the measured blood flow velocity in middle cerebral artery, the blood pressure was estimated by R-DT and RCR models respectively. The parameters in the models were optimized by genetic algorithm. The accuracies of R-DT and RCR models were compared based on their estimation errors to the measured blood pressure. The capacitance element in RCR model indicated limited ability to take the time shift into account. Compared with RCR model, R-DT model had less error (averaged relative error: 5.19% and 2.49% for RCR and RDT models). The non-linear flow resistance was applicable in simulating cerebral arteries.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p

Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points cannot always provide enough constraints. To address this problem, the existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing in surroundings is utilized to find correspondences. Two components are developed to ensure the extrinsic parameters to be found: a closed-form solver for initialization and an optimizer for refinement by minimizing a nonlinear cost function. Simulation and experimental results demonstrate the high accuracy of our calibration approach with the rotation and translation errors smaller than 0.05rad and 0.1m respectively., Comment: 6 pages, 8 figures, accepted by 2019 IEEE Intelligent Vehicles Symposium (IVS)

BACKGROUND: MRI-based modeling of tumor cell density (TCD) can significantly improve targeted treatment of Glioblastoma (GBM). Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a Transfer Learning (TL) method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient’s own histologic data. METHODS: We recruited primary GBM patients undergoing image-guided biopsies and preoperative imaging including contrast-enhanced MRI (CE-MRI), Dynamic-Susceptibility-Contrast (DSC)-MRI, and Diffusion Tensor Imaging (DTI). We calculated relative cerebral blood volume (rCBV) from DSC-MRI and mean diffusivity (MD) and fractional anisotropy (FA) from DTI. Following image coregistration, we assessed TCD for each biopsy and identified corresponding localized MRI measurements. We then explored a range of univariate and multivariate predictive models of TCD based on MRI measurements in a generalized one-model-fits-all (OMFA) approach. We then implemented both univariate and multivariate individualized TL predictive models, which harness the available population level data but allow for individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized TL versus generalized OMFA models. RESULTS: TCD significantly correlated with rCBV (r=0.33,p

This letter was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the letter, we propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website to provide the service based on LFRL: www.shared-robotics.com .

In recent years, computer binocular vision has been commonly utilized to provide depth information for autonomous vehicles. This paper presents an efficient binocular vision system implemented on an SoC TMS320C6678 DSP for real-time depth information extrapolation, where the search range propagates from the bottom of an image to its top. To further improve the stereo matching efficiency, the cost function is factorized into five independent parts. The value of each part is pre-calculated and stored in the DSP memory for direct data indexing. The experimental results illustrate that the proposed algorithm performs in real time, when processing the KITTI stereo datasets with eight cores in parallel.

Mapping with sonar sensor is highly interesting yet to be solved, due to the low-cost and high uncertainty of the sensor. A novel method for iterative probabilistic occupancy grid mapping using gradient ascent is introduced. The algorithm is based on the forward sensor model for sonar sensors. The experiments demonstrate that such method is capable of creating satisfying maps for a mobile robot in real time. It is rather robust to sensor noise and model errors and it can overcome the problem of sensor conflicts produced by some existing methods. We propose an efficient way of iterative mapping for mobile robots.

Software reliability growth modeling plays an important role in software reliability evaluation. To incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes has attracted widespread research attention recently. In modeling software correction processes, the assumption of fault correction time is relaxed from constant delay to random delay. However, stochastic distribution of fault correction time brings more difficulties in modeling and corresponding parameter estimation. In this paper, a framework of software reliability models containing both information from software fault detection process and correction process is studied. Different from previous extensions on software reliability growth modeling, the proposed approach is based on Markov model other than a nonhomogeneous Poisson process model. Also, parameter estimation is carried out with weighted least-square estimation method, which emphasizes the influence of later data on the prediction. Two data sets from practical software development projects are applied with the proposed framework, which shows satisfactory performance with the results. Copyright © 2016 John Wiley & Sons, Ltd.

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

Indoor localization for pedestrians has gained increasing popularity among the rich body of literature for the last decade. In this paper, a low-cost indoor mapping and localization solution is proposed using the opportunistic signals from ambient indoor environments with a smartphone. It is composed of GraphSLAM-based offline mapping and Bayesian filtering-based online localization using generated signal maps. The GraphSLAM front-end is constructed by motion constraints from pedestrian dead-reckoning (PDR), loop-closure constraints identified by magnetic sequence matching with WiFi signal similarity validation, and observation constraints from opportunistic magnetic headings after error rejection. Globally consistent trajectories are created by graph optimization, after which signal maps (e.g., WiFi, magnetic fields, lights) are generated by Gaussian Processes Regression (GPR) for later localization. We propose to use the pseudo-wall constraints from the GPR variance map of magnetic fields and the lights measurements as observations for particle filtering. The proposed method is evaluated on several datasets collected from both the in-compass office buildings and outside public areas. Real-time localization is demonstrated on a smartphone in an office building covering 2000 square meters with the 50- and 90-percentile accuracies being 2.30 m and 3.41 m, respectively.

Stereo vision technique has been widely used in robotic systems to acquire 3-D information. In recent years, many researchers have applied bilateral filtering in stereo vision to adaptively aggregate the matching costs. This has greatly improved the accuracy of the estimated disparity maps. However, the process of filtering the whole cost volume is very time consuming and therefore the researchers have to resort to some powerful hardware for the real-time purpose. This paper presents the implementation of fast bilateral stereo on a state-of-the-art GPU. By highly exploiting the parallel computing architecture of the GPU, the fast bilateral stereo performs in real time when processing the Middlebury stereo datasets., 8 pages, 7 figures, International Conference on Information and automation

Lane detection is very important for self-driving vehicles. In recent years, computer stereo vision has been prevalently used to enhance the accuracy of the lane detection systems. This paper mainly presents a multiple lane detection algorithm developed based on optimised dense disparity map estimation, where the disparity information obtained at time t_{n} is utilised to optimise the process of disparity estimation at time t_{n+1}. This is achieved by estimating the road model at time t_{n} and then controlling the search range for the disparity estimation at time t_{n+1}. The lanes are then detected using our previously published algorithm, where the vanishing point information is used to model the lanes. The experimental results illustrate that the runtime of the disparity estimation is reduced by around 37% and the accuracy of the lane detection is about 99%., Comment: 5 pages, 7 figures, IEEE International Conference on Imaging Systems and Techniques (IST) 2018

INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.

Swarm robot systems are widely applied in the warehouse logistics, which effectively improve logistics efficiency. Task allocation is the core problem for swarm robot systems. Therefore, evaluation of the task allocation strategy has very important practical significance. However, current simulation platforms, such as GAZEBO, require a lot of setup work before testifying the task allocation strategy for various applications. Regarding warehousing logistics, a few special factors need to be considered: man-robot coexistence in working environment, the energy consumption of robots and collision avoidance among robots. Therefore, we propose a novel swarm robot simulation platform, called MultiBots, based on multi-agent pathfinding (MAPF) method and collision avoidance strategy, which can correctly evaluate the effectiveness of task allocation strategy. Moreover, we design the charging process to supplement the energy consumption of robots. The experimental results show that the proposed MultiBots satisfies the requirements of task allocation strategy evaluation in warehouse logistics scenarios. The MultiBots can be applied in testifying the efficiency of task allocation strategy for logistics systems.

Computer software is widely applied in safety-critical systems. The ever-increasing complexity of software systems makes it extremely difficult to ensure software reliability, and this problem has drawn considerable attention from both industry and academia. Most software reliability models are built on a common assumption that the detected faults are immediately corrected; thus, the fault detection and correction processes can be regarded as the same process. In this article, a comprehensive study is conducted to analyze the time dependencies between the fault detection and correction processes. The model parameters are estimated using the Maximum Likelihood Estimation (MLE) method, which is based on an explicit likelihood function combining both the fault detection and correction processes. Numerical case studies are conducted under the proposed modeling framework. The obtained results demonstrate that the proposed MLE method can be applied to more general situations and provide more accurate re...

Nanosized Sb doped SnO2 (ATO) particles were successfully synthesized. Electrically conducting rubbery composites based on epichlorohydrin rubber (ECO) and two kinds of conductive fillers (SnO2 and ATO) were prepared by a conventional blending method. The morphology, structure and electrical conductivity of ECO/SnO2 and ECO/ATO composites were investigated. Results showed that SnO2 and ATO had great influence on the structure of ECO composite. Besides, the addition of oxide particles enhanced its electrical conductivity significantly. The electrical conductivity of ECO/ATO composite appeared an obvious threshold value with the variation of the loading amount of ATO fillers, whereas the resistivity of ECO filled with SnO2 decreased monotonically with the increasing SnO2. The temperature dependence of resistivity indicated that the ECO/SnO2 and ECO/ATO composites exhibited a negative temperature coefficient (NTC) effect. Scanning electron microscopy (SEM) was used to investigate the morphological states of oxide fillers in ECO matrix, and specifically continuous networks were observed. POLYM. COMPOS., 2015. © 2015 Society of Plastics Engineers

Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios.

Turbo encoding and decoding system plays a key role in improving the reliability of digital communication, and it has been widely used. Based on in-depth analysis of the Turbo code encoding and decoding principle, a comprehensive analysis of the performance of Turbo code system, a simulation scheme and result of Turbo system are given, and based on practical application and simulation results to optimize the performance of Turbo code system.

Most family does not implement intelligent management of the most commonly used medicine. Little research teams designed the family medicine chest intelligent management. This article is based on this background, committed to the development of smart phone App managing medicine and cloud management platform software system. The mobile terminal will also develop Android and Apple’s dual-platform software. The medicine information is obtained by scanning the bar code. The backstage medicine information database maintenance and pharmaceutical science information releasing based on cloud-based platform architecture with more prominent security and scalability. The results show that the software can automatically remind medication and warn expired medicines. It can also well extend family health science knowledge.

Intelligent human robot interaction are becoming popular in both industry and academia. However, amongst current techniques, speech recognition is a challenging topic, including real-time translation with high accuracy, amicability and the support for recognizing minor languages or sophisticated dialects. In this paper, we propose a human-friendly prototype deployed on NAO robots in a real-life scenario through daily speech commands and NAO would act accordingly. We primarily adopt HMM-GMM, the combination of HMMs (Hidden Markov Models) and GMMs (Gaussian Mixtures Models). The experimental results show that the proposed prototype achieves high accuracy and well-received by experiment subjects.

Point-cloud is a widely used representation for objects and scenes. It generally consists of a large amount of 3D coordinates of points describing reflective surfaces. A subtle problem is that the number of points is usually so large that real-time transmission and efficient storage is not feasible. In this work, we propose to use 3D Discrete Cosine Transform (3D-DCT) to compress these two typical categories of data, namely point-cloud data extracted from objects and environments (i.e. 3D maps). Experimental results show that the proposed method leads to high compression ratio and flexible reconstruction behaviors comparing with other related methods.

In this study, we aimed to validate the association of 8 reported significant loci at 5q35.3, 7p14.3, 13q14.1 and 16p12.3 with urolithiasis in Chinese Han population. We performed case-control association analysis using 624 patients with nephrolithiasis and 1008 control subjects. We selected single-nucleotide polymorphism (SNPs) including rs12654812 and rs11746443 from 5q32.3; rs12669187 and rs1000597 from 7q14.3; rs7981733, rs4142110 and rs17646069 from 13q14.1 and rs4293393 from 16p12.3 which were previously reported to be associated with nephrolithiasis. We found none of these eight reported SNPs were significant associated with urolithiasis risk in Chinese Han population, which suggested that differences could exist in the mechanisms of calcium urolithiasis between Chinese and Japanese Ethnics. The A allele of rs12669187 was significantly correlated with increased level of serum magnesium. The C allele of rs1000597 was associated with higher levels of serum creatinine, uric acid, calcium and lower urine pH level. The T allele of rs4142110 was correlated with higher levels of serum magnesium, phosphorus, and lower AKP level. The G alleles of rs4293393 was associated with higher serum CO2 level. The risk alleles of these SNPs were proved to be associated with the electrolytes metabolism that may result in the formation of urolithiasis.

Extramammary Paget’s disease (EMPD) is a rare malignancy, and little was known about its prognostic factors and optimal treatment. In the current study, we aimed to discuss clinical and pathological features of scrotal EMPD and determine the prognostic factors for cancer-specific survival and local recurrence. A total of 206 patients with scrotal EMPD lesions surgically treated at our institute were studied. All clinical and pathological data were reviewed. Immunohistochemical staining of TP53 and Ki67 was examined as well. At the last follow-up, 175 patients (84.95%) were alive. Twelve patients (5.83%) had died of the disease due to distant metastases. Fifteen patients (7.28%) developed local recurrences of scrotal EMPD. Ki67 expression was significantly elevated in patients with wide horizontal invasion (P = 0.003). In univariate analysis, high invasion level, presence of nodule, presence of lymphovascular invasion, adnexa invasion, lymph node metastasis and high p53 expression were significant factors for poor cancer-specific survival. In multivariate analysis, high p53 expression was significantly correlated with poor cancer-specific survival. Wide horizontal invasion was independently correlated with local recurrence-free survival of scrotal EMPD. In conclusion, wide horizontal invasion is an independent risk factor for local recurrence-free survival in the patients with scrotal EMPD.

High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency., Comment: For ICRA2018

This study was to evaluate the risk factors of infectious complications after percutaneous nephrolithotomy (PCNL) and build a prediction tool for postoperative complications based on the risk factors. A total of 110 male (67.1%) and 54 female (32.9%) patients who underwent PCNL for renal stones between 2010 and 2014 in our institute were included. A detailed clinical information and laboratory results were obtained from patients. Systemic inflammatory response syndrome (SIRS) and postoperative fever were recorded after PCNL surgery. In all, 45 cases (27.4%) developed SIRS and fever was observed in 20 cases (12.2%). In multivariate analysis, stone size (odds ratio, OR = 1.471,p=0.009) and urine white blood cell (WBC) (OR = 1.001,p=0.007) were related to the development of SIRS. Stone size (OR = 1.644,p=0.024), urine WBC (OR = 1.001,p=0.002) and serum albumin (OR = 0.807,p=0.021) were associated with postoperative fever. We concluded that patients with larger stone size and preoperative urinary tract infection might have a higher risk of developing SIRS and fever after operation, while a high-normal level of serum albumin might be the protective factor for postoperative fever.

We study the problem of autonomous service migration in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. As the origins of the mobile accesses frequently change over time, this problem is particularly important for time-bounded services to achieve enhanced QoS and cost effectiveness. Moving the service closer to its client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, the migration comes at costs of bulk-data transfer and service disruption, as a result, increasing the overall service costs. To gain the benefits of service migration while minimizing the service costs, we propose an efficient search-based algorithm Dmig the service migration in an autonomous way. Compared with existing algorithms, the proposed algorithm is fully distributed, symmetric, and characterized by the effective use of historical access information to perform virtual migration that overcomes the limitation of traditional local search in cost reduction. To evaluate the algorithm, we compared it with some existing algorithms, and show that the proposed algorithm exhibits better performance by adapting to the changes of mobile access patterns in a cost effective way.

BACKGROUND An important challenge in radiomics research is reproducibility. Images are collected on different image scanners and protocols, which introduces significant variability even for the same type of image across institutions. In the present proof-of-concept study, we address the reproducibility issue by using domain adaptation – an algorithm that transforms the radiomic features of each new patient to align with the distribution of features formed by the patient samples in a training set. METHOD Our dataset included 18 patients in training with a total of 82 biopsy sample. The pathological tumor cell density was available for each sample. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. A Gaussian Process (GP) classifier was built to predict tumor cell density using radiomic features. Another 6 patients were used to test the training model. These patients had a total of 31 biopsy samples. The images of each test patient were purposely normalized using a different approach, i.e., using the CSF instead of the whole brain as the reference. This was to mimic the practical scenario of image source discrepancy between different patients. Domain adaptation was applied to each test patient. RESULTS Among the 18 training patients, the leave-one-patient-out cross validation accuracy is 0.81 AUC, 0.78 sensitivity, and 0.83 specificity. When the trained model was applied to the 6 test patients (purposely normalized using a different approach than that of the training data), the accuracy dramatically reduced to 0.39 AUC, 0.08 sensitivity, and 0.61 specificity. After using domain adaption, the accuracy improved to 0.68 AUC, 0.62 sensitivity, and 0.72 specificity. CONCLUSION We provide candidate enabling tools to address reproducibility in radiomics models by using domain adaption algorithms to account for discrepancy of the images between different patients.

Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.

Objectives To (i) investigate alterations in homotopic functional connectivity (hfc) in concussed patients relative to healthy controls (HC) and to (ii) interrogate whether hfc in concussed patients normalized during the recovery process. The relationship between symptom recovery and change in hfc was assessed using post-hoc analyses. Methods This study included 15 concussed patients (mean age = 39.1, SD = 10.1; sex: 13 females, 2 males) and 15 HC (mean age = 39.1, SD = 11.7; sex: 13 females, 2 males). Hfc patterns were interrogated using resting-state magnetic resonance imaging (rs-MRI) for 29 a priori selected pain-processing regions. Concussed patients underwent imaging at two time-points; at 1-month post-concussion (mean time following concussion: 28 days, SD = 9.5) and again at 5-months post-concussion (mean time following concussion: 121 days, SD = 13). At both time-points, symptoms associated with concussion were assessed using the Sports Concussion Assessment Tool (SCAT-3). Results Concussed patients had significantly weaker hfc in the following six regions 1-month post-concussion compared to HC: middle cingulate, posterior insula, middle occipital, spinal trigeminal nucleus, precentral and the pulvinar. There were no regions of significantly stronger hfc in concussed patients relative to HC. Longitudinally, patients showed significant symptom recovery 5-months post-concussion and had significant strengthening of hfc patterns in seven homotopic ROIs: middle cingulate, posterior insula, middle occipital, secondary somatosensory area, spinal trigeminal nucleus, precentral, and the pulvinar. Post-hoc analyses indicated a significant negative correlation between somatosensory functional connectivity strengthening and symptom severity. Conclusion At 1-month post-concussion, patients had significantly weaker hfc in a number of pain-processing regions relative to HC. However, over a period of 5-months, region-pair connectivity showed significant recovery and normalization. Those patients with more successful symptom recovery at 5-months post-concussion had more functional somatosensory strengthening, suggesting an association between functional strengthening and post-concussion symptom recovery.

Tumor markers Ki67, TP53, and TP63 are common labels in the diagnosis of bladder cancer (BCa) around the world. The combination of those biomarkers may have advantages in predicting BCa prognosis and non-muscle-invasive bladder cancer (NMIBC) postoperative recurrence. We investigated the immunohistochemical profiles of 313 bladder cancer samples classified under the WHO/ISUP (2004) grading scale and the UICC-TNM (2002) classification. Then we investigated their predictive value in the tumor recurrence of 270 NMIBC patients after TURBT. Expression of Ki67 correlates with grade, stage, tumor size, and tumor numbers. Semiquantitative evaluation of TP53 correlates with grade and invasive conditions. The positive expression rate of TP63 correlated with tumor grade and stage. The combined effect of TP53 and Ki67 revealed a predictive value in NMIBC recurrence. However, the positive TP63 expression did not show any protective effect in NMIBC recurrence. The expression of TP53 and Ki67 could be used to predict the risk of NMIBC recurrence postoperatively.

Our previous study found that the activity of PCa-MSCs, which could stimulate the cell proliferation of RM-1, was significantly different compared to BMMSCs. Our results indicated that it could be mediated in part by growth factors/chemokines, which were involved in the different activity between two kinds of MSCs (PCa-MSCs and BMMSCs). Normal MSCs (BMMSCs) were isolated from the femur, tibia of the normal mice; prostate tumor MSCs (PCa-MSCs) were obtained from the mice implanted with prostate tumor. Analysis of the expression of SDF-1, CXCR4, VEGF, bFGF and vWF of two kinds of MSCs were examined by ELISA, Realtime-PCR and Western blotting. The expressions of SDF-1 and CXCR4 in PCa-MSCs were higher compared to BMMSCs. Expressions of bFGF and vWF were higher in PCa-MSCs yet the difference did not reach statistical significance. The expression of VEGF was significantly higher in PCa-MSCs. Our data showed that activity of PCa-MSCs was significantly improved compared with BMMSCs, which seemed to have an intrin- sic, cell-specific capacity localized to PCa. It could be induced by some factors or chemokines such as SDF-1, CXCR4, and VEGF. The possible role of PCa-MSCs in the process of PCa development needed further clarification.

In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.

The frequency domain dielectric response method has been one of the most effective approaches for the condition assessment of transformers. The low frequency part is more effective for diagnosing the moisture content and aging status of oil-paper insulation, which reflects the individual properties of the oil and the cellulose material. To facilitate the study of the low frequency dielectric spectrum of oil-paper insulation, a model applicable to the low frequency dielectric response of oil-paper insulation is proposed in this paper, which is on the basis of the electrode polarization of oil-paper insulation and XY model. The applicability of the model is verified against measured results.

With the surging demand for network, Apps and clouds from both mobile devices and broadband end-users, the way of resource management and allocation is changing greatly. Smart pricing can tackle the issue of data traffic and services for network monetization and growth sustainability. Therefore, to design optimal pricing policies is the most fundamental work for both the profits of cloud providers and Quality of Services (QoS) of end-users. This paper investigates the pricing models and approaches to resource allocation and service management problems in cloud resource and service markets. We propose a taxonomy of pricing problems in the cloud resource and service markets, which shows how these problems can be formulated as optimization problems. We demonstrate how relevant theory from game theory, control theory and optimization can be leveraged to address smart pricing problems for resource and service allocation of the cloud markets.

Smoke recognition is one of the research directions in the field of digital image processing, but common algorithms are mostly based on the video sequence of images. A combination of infrared and visible images is presented in this paper, by extracting the analyte infrared image outer contour, and complete comparison of the extent of the visible outline of the image in the same area. Then according to the measured object within the outer contour of the two bands contain the number of pixels ratio, determine the impact of smoggy on the visible image. Experiments show that the algorithm needs to be analyzed only for the infrared and visible band single still image. You can draw judgment of smoggy environment, and it can provide the basis for a fire alarm.

Many software reliability methods have been proposed for estimating and predicting reliability, and the method based on Bayesian framework is a popular one. However, the present Bayesian approaches are all based on the impractical assumption that the detected faults are corrected immediately with no debugging time delay. In this paper, we derive effective parameter estimation algorithms based on Bayesian framework not only for fault detection process, but also for combined detection and correction processes. To have a better understanding of the estimation performance, a simulation study and a practical example are conducted to investigate the performance of the proposed Bayesian approach.

Author(s): Si, Bing; Dumkrieger, Gina; Wu, Teresa; Zafonte, Ross; Valadka, Alex B; Okonkwo, David O; Manley, Geoffrey T; Wang, Lujia; Dodick, David W; Schwedt, Todd J; Li, Jing | Abstract: BackgroundThe current classification of traumatic brain injury (TBI) into "mild", "moderate", or "severe" does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify "sub-groups" of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI.MethodsData from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI.ResultsAmongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment.ConclusionsSub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient's sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI.

Software reliability is one of the most significant attributes in various safety-critical systems. One popular approach to estimate the software reliability is based on Bayesian statistics. However, there are few attempts on Bayesian approaches considering fault correction process. In this paper, we present parameter estimation method based on Bayesian statistics for combined fault detection and correction processes. A practical example is devoted to investigating the fitting performance of the proposed Bayesian approach.

Radical cystectomy (RC) is the gold standard treatment for patients with high-risk non-muscle-invasive bladder cancer and muscle-invasive bladder cancer (MIBC) . However, despite aggressive treatment, mortality rates of patients are still high. Therefore, it is crucial to indentify some practical prognostic factors for cancer-specific survival in those patients to guide our therapeutic strategies. We included 103 patients treated with RC for urothelial bladder cancer (UBC) between July 2007 and June 2014 in our institution and analyzed their clinicopathological parameters. The mean follow-up was 22 months (range 2–89 months). Advanced tumor T stage, N stage, ABO blood type, a history of DM and positive p63 expression were independent factors for worse survival in all patients who underwent RC. Moreover, in female gender, high Ki67 expression was significant risk factor for cancer-specific survival (CSS) in MIBC patients. Our study demonstrated that immunohistochemical expressions of Ki67 and p63 are potential prognostic factors for UBC patients who underwent RC.

Cloud robotics is currently driving interests in both academia and industry, especially for systems with limited computation capability. Resource allocation is the fundamental and dominant problem for resource sharing among agents in the cloud robotics system. This chapter introduces a novel resource allocation framework for cloud robotics and proposes a Stackelberg game model and the corresponding task oriented pricing mechanism for resource allocation. Simulation investigates the parameter selection and time cost of the proposed mechanism. Experimental results of co-localization task demonstrate that the proposed mechanism achieve an optimal performance in resource allocation.

We aimed to exploit novel compounds with high selectivity to clear cell renal cell carcinoma (ccRCC) with common mutations. Using the GDSC databases, we searched for compounds with high selectivity for ccRCC with VHL and/or SETD2 mutations. Clinical impact and gene interactions were analysed using TCGA database. In vitro and in vivo studies were performed to validate the inhibitory effects of the compound. We identified the selective PI3Kβ inhibitor TGX221 as a selective inhibitor for ccRCC with both VHL and SETD2 mutations. TGX221 also targeted cancer cells with CDKN2A and PTEN mutations. Changes in PTEN and CDKN2A gene sets were associated with worsened prognosis of ccRCC. TGX221 substantially and selectively inhibited the down stream products of VHL, SETD2 and PTEN in ccRCC cells with VHL and SETD2 mutations. TGX221 also exhibited significant selectivity in inhibiting cell motility and tumourigenesis of ccRCC cells with VHL and SETD2 mutations. TGX221 is a novel inhibitor with high selectivity for ccRCC with VHL and SETD2 mutations. It also targeted PTEN and CDKN2A mutations. How those genes were associated with PI3Kβ warranted further investigations.

To overcome the defects of involute single spiral transmitter, which has a long time weighing and beyond the permissible error easily, we designed a double helix differential quantitative transmission system as the main structure of the up-stream equipment for automatic weighing and pushing of materials, the equipment used the spiral data acquisition-control device that the main core is Σ-ΔADC (Sigma-Delta Analog-to-Digital Converter) and multi-component extraction model weighing algorithm, having a digital filter and characteristics analysis for the weight value, filter out the impact component from the weight information and add estimated aloft value to compensate aloft component. Shortening the time of weighing and increasing the weighing accuracy extremely through control precision grade of millisecond, prolonged failure-free operation of automatic weighing equipment, meanwhile, satisfied the requirements of all kinds of rubber tire production process.

Ad-hoc networks, being infrastructure-less, have much scarcer amounts of bandwidth. Most multi-robotic ap- plications adopt decentralised approaches in order to avoid single points-of-failure. A decentralised approach would require robots to continuously update each other to coordinate their ac- tions. For successful coordination, hard real-time requirements must be met. Thus, a resource allocation strategy is required to fulfil these requirements. This paper presents a novel resource allocation strategy that uses auction-based methods to allocate the network resource among nodes in a robotic ad-hoc network. The strategy's aim is two-fold: 1) to allocate the network fairly among all nodes when unmanned; 2) to allocate more of the network resource to a node that the user may favour over the other nodes. It has been tested in two different indoor environments to see how environmental factors affect the performance of the system. The experimental results show that the proposed resource allocation strategy improves the use of bandwidth and enhances transmission. I. INTRODUCTION In recent years, researchers in robotics have been focusing on task allocation problems, which is a subset of the field of resource allocation problems. The Contract Net Protocol (1) features algorithms that allow many nodes to negotiate and agree on 'contracts' to complete composite tasks among each other, based on each node's current capabilities and workloads. It successfully introduced a general framework of using auction-based mechanisms in cooperative robotics. MURDOCH (2) was the first implementation on top of the CNP framework in robotics to introduce single-round, first- price auctions. The quality of its produced allocations is considered to be 3-competitive for on-line task problems (tasks that arrive at random). Computational costs are very low and network resources used are linearly related to the number of robots. TraderBots (3) is probably the most repre- sentative implementation of early auction-based techniques for multirobot coordination. The active use of a market economy helps to take into account changes in the global environment in a decentralised way. While the CNP can be applied to any kind of multi-agent system, MURDOCH and TraderBots are more specific to task allocation for mobile robots. However, the validation of these systems do not consider applications that require real-time data transmission.

—In order to share information in the cloud formulti-robot systems, efcient data transmission is essentialfor real-time operations such as coordinated robotic missions.As a limited resource, bandwidth is ubiquitously required byapplications among physical multi-robot systems. In this paper,we proposed a hierarchical auction-based mechanism, namelyLQM (Link Quality Matrix)-auction. It consists of multipleprocedures, such as hierarchical auction, proxy scheduling. Notethat the proposed method is designed for real-time resourceretrieval for physical multi-robot systems, instead of simulatedvirtual agents.We validate the proposed mechanism through real-timeexperiments. The results show that LQM-auction is suitable forscheduling a group of robots, leading to optimized performancefor resource retrieval. I. I NTRODUCTION Nowadays, mobile robotics evolves rapidly along with thesocial development. They provide heterogeneous servicesfor humans. Not surprisingly, robotic services are morecomplicated than ever before. Despite the diversity of roboticservices, it is impossible to develop a universal robot whichis capable of all expected services due to various limitations,such as power consumption, payload, sensory and kinematicconstraints. As for a typical robotic system, a mobile robotequipped with various sensors, is usually expensive andpower consuming. Beneting from the highly developednetwork, all primary information can be stored and retrievedfrom online data centers such as a cloud, by which therequirements on local infrastructures are alleviated.Nevertheless, there are still drawbacks and challenges tobe further addressed. For instance, limited network band-width and communication range in robotic system are theprimary hold-backs, since most of the robotic applicationsneed real-time data transmission, e.g. navigation and lo-calization generally require a large bandwidth to transmitthe raw sensor measurements. Moreover, some applicationsinvolving cooperative control of a robot team have hardreal-time communication requirements. Recently, the real-time wireless multi-hop protocol (RT-WMP) [1] has beenproposed on top of IEEE 802.11, which provides real-timecommunication among multiple robots. RT-WMP is capableof managing the message priority and mobility for bothoutdoor and indoor environments. Adopting this facility, we