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Objective: To demonstrate sexual dimorphism in serum leptin levels not only during puberty, but also in childhood in Japan. Design: Cross-sectional study. Setting: Hamamatsu-Hokuen study in Japan. Subjects: Body weight and height were measured in normal-weight Japanese children and adolescents (143 boys, 178 girls), and 161 boys and 129 girls whose percentage of overweight for the standard (%Wt) was more than+25%. Serum leptin levels were compared with %Wt. Subjects were divided into group 1 (6–10 y of age) and group 2 (11–15 y of age) according to their age. Results: In overweight subjects, leptin was more highly correlated with %Wt in boys of group 2 (r=0.67, P

Driven by a wide range of essential applications, significant achievements are made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf WiFi infrastructures to infer human activities without the need for subjects to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios.

In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle unoriented edges or latent assumptions violation suffered by conventional methods, researchers formulated a reinforcement learning (RL) procedure for causal discovery and equipped a REINFORCE algorithm to search for the best rewarded directed acyclic graph. The two keys to the overall performance of the procedure are the robustness of RL methods and the efficient encoding of variables. However, on the one hand, REINFORCE is prone to local convergence and unstable performance during training. Neither trust region policy optimization, being computationally expensive, nor proximal policy optimization (PPO), suffering from aggregate constraint deviation, is a decent alternative for combinatory optimization problems with considerable individual subactions. We propose a trust region-navigated clipping policy optimization method for causal discovery that guarantees both better search efficiency and steadiness in policy optimization, in comparison with REINFORCE, PPO, and our prioritized sampling-guided REINFORCE implementation. On the other hand, to boost the efficient encoding of variables, we propose a refined graph attention encoder called SDGAT that can grasp more feature information without priori neighborhood information. With these improvements, the proposed method outperforms the former RL method in both synthetic and benchmark datasets in terms of output results and optimization robustness.

To deal with a large amount of redundant data in the indirect category database and inefficient redundancy elimination of the existing methods, we proposed an indirect category data transfer learning algorithm based on regularization discrimination. First of all, we denoised indirect category data, calculated the objective function of distance between the source domain and the target domain, and established the transfer relationship between indirect category data. Second, we adopted the regularization discriminant technique to divide the transfer network structure of indirect category data into five modules, analyzed the effects and advantages of different modules, and constructed the transfer network structure of indirect category data. Finally, the indirect category data transfer was realized by the design of the indirect category data transfer learning algorithm. The results show that the proposed algorithm can effectively eliminate redundancy of indirect category data, the amplitude of fluctuation of indirect category data is small, the transfer time and energy consumption of the algorithm are low, and the accuracy is as high as about 90%, which indicates that the proposed algorithm is far superior to the traditional method and has high application value.

Objective To evaluate the benefits and harmful effects of conservative versus liberal oxygen therapy in patients admitted to the Intensive Care Unit (ICU). Design A systematic review and meta-analysis was carried out. Setting ICU. Participants Adult patients (aged 18 years or older) were randomized to either a lower oxygenation target strategy (conservative oxygen therapy) or a higher oxygenation target strategy (liberal oxygen therapy) in the ICU. Interventions Patients received different oxygenation target strategies. Results Ten studies involving 5429 adult patients admitted to the ICU were included in the meta-analysis. The pooled results showed no decreased all-cause mortality at 28 days (RR 0.90; 95%CI 0.75–1.09; p = 0.28), 90 days (RR 1.02; 95%CI 0.92–1.13; p = 0.71) or longest follow-up (RR 0.97; 95%CI 0.88–1.08; p = 0.63) among patients administered conservative oxygen therapy. Secondary outcomes were comparable between the two groups. The results of sensitivity analyses and subgroup analyses were consistent with the main analyses. Conclusion No beneficial or harmful effects of conservative oxygen therapy were found compared to liberal oxygen therapy in relation to all-cause mortality among adult patients in the ICU. Conservative oxygen therapy did not reduce all-cause mortality at 28 days, 90 days or longest follow-up. Other important clinical outcomes were also comparable between the two groups.

Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. As compared to lossless compressors, lossy compressors, such as SZ and ZFP, can reduce data volume more aggressively while maintaining the usefulness of the data. However, a reduction ratio of more than two orders of magnitude is almost impossible without seriously distorting the data. In deep learning, the autoencoder has shown potential for data compression. Whether the autoencoder can deliver similar performance on scientific data, however, is unknown. In this paper, we for the first time conduct a comprehensive study on the use of autoencoders to compress real-world scientific data and illustrate several key findings on using autoencoders for scientific data reduction. We implement an autoencoder-based compression prototype to reduce floating-point data. Our study shows that the out-of-the-box implementation needs to be further tuned in order to achieve high compression ratios and satisfactory error bounds. Our evaluation results show that, for most test datasets, the tuned autoencoder outperforms SZ by 2 to 4X, and ZFP by 10 to 50X in compression ratios, respectively. Our practices and lessons learned in this work can direct future optimizations for using autoencoders to compress scientific data.

Recent years have witnessed a tremendous increasing of first-person videos captured by wearable devices. Such videos record information from different perspectives than the traditional third-person view, and thus show a wide range of potential usages. However, techniques for analyzing videos from different views can be fundamentally different, not to mention co-analyzing on both views to explore the shared information. In this paper, we take the challenge of cross-view video co-analysis and deliver a novel learning-based method. At the core of our method is the notion of "joint attention", indicating the shared attention regions that link the corresponding views, and eventually guide the shared representation learning across views. To this end, we propose a multi-branch deep network, which extracts cross-view joint attention and shared representation from static frames with spatial constraints, in a self-supervised and simultaneous manner. In addition, by incorporating the temporal transition model of the joint attention, we obtain spatial-temporal joint attention that can robustly capture the essential information extending through time. Our method outperforms the state-of-the-art on the standard cross-view video matching tasks on public datasets. Furthermore, we demonstrate how the learnt joint information can benefit various applications through a set of qualitative and quantitative experiments.

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.

In this work, we consider transferring the structure information from large networks to compact ones for dense prediction tasks in computer vision. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pair-wise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection. Code is available at: https://git.io/StructKD, Comment: v1:10 pages cvpr2019 accepted; v2:15 pages for a journal version; Code is available at: https://github.com/irfanICMLL/structure_knowledge_distillation; fix typos

This pilot survey aimed to explore the feasibility of conducting a nationwide survey investigating the current practice and attitudes towards complementary and alternative medicine (CAM) in New Zealand registered physiotherapists. This was a cross-sectional, online study using a questionnaire developed from previous survey instruments. An electronic link to the questionnaire was distributed via email to members of the Otago Branch of Physiotherapy New Zealand (n = 344). Questions included current clinical practice and use of CAM as well as attitudes and opinions on its effectiveness. Feasibility outcomes of this survey included the response rate and completion rate. Data were analysed in Microsoft Excel®. The response rate was 10.5% (n = 36) and the completion rate was 86.1% (n = 31/36). A range of conditions treated with CAM were cited by respondents; the most frequently treated condition was “back and neck pain” (n = 10/36). Physiotherapists in this sample believed acupuncture and massage are the most effective CAM modalities used for “back and neck pain” treatment and were most likely to use acupuncture and massage as CAM modalities when treating these conditions. Acupuncture was found to be the most common CAM practiced by respondents, and an acupuncturist was the CAM practitioner to which patients are most commonly referred by respondents. This study demonstrated that conducting a nationwide survey is feasible, subject to an improved survey design and increased response rate.

This paper investigates the dynamic response and assesses the safety of a propylene tower subjected to blasting vibrations. A group of monitoring experiments on the response of the tower to blasting vibrations was carried out to this end. A model of a cantilever beam was used for the response along the horizontal direction and that of a strut for along the vertical direction. The theoretical and measured displacements were consistent in terms of time history curves and peak values. The design spectrum of the tower suitable for sustaining blasting vibrations was then proposed and verified, and its safety was assessed in combination with local requirements on seismic design. The results show that the first six modes of response should be considered under blasting vibrations in the horizontal direction, instead of the first three modes as in the case of an earthquake. The first three modes should be considered under blasting vibrations in the vertical direction. Owing to the importance higher modes in this scenario, the shear should be taken as the primary object of evaluation when assessing the safety of a tall propylene tower subjected to blasting vibrations.

With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.

Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which the performance of classifiers is seriously affected and becomes poor. Although many approaches, such as resampling, cost-sensitive, and ensemble learning methods, have been proposed to deal with the skewed data, they are constrained by high-dimensional data with noise and redundancy. In this study, we propose an adaptive subspace optimization ensemble method (ASOEM) for high-dimensional imbalanced data classification to overcome the above limitations. To construct accurate and diverse base classifiers, a novel adaptive subspace optimization (ASO) method based on adaptive subspace generation (ASG) process and rotated subspace optimization (RSO) process is designed to generate multiple robust and discriminative subspaces. Then a resampling scheme is applied on the optimized subspace to build a class-balanced data for each base classifier. To verify the effectiveness, our ASOEM is implemented based on different resampling strategies on 24 real-world high-dimensional imbalanced datasets. Experimental results demonstrate that our proposed methods outperform other mainstream imbalance learning approaches and classifier ensemble methods.

OBJECTIVE To clarify whether systemic lymph node dissection (LND) influences the safety of surgery and the survival of patients with locally advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemoradiotherapy (nCRT). SUMMARY BACKGROUND DATA Prognostic impact of systemic lymphadenectomy during surgery after nCRT for ESCC is still uncertain and requires clarification. METHODS This is a secondary analysis of NEOCRTEC5010 trial which compared nCRT followed by surgery versus surgery alone for locally advanced ESCC. Relationship between number of LND and perioperative, recurrence and survival outcomes were analyzed in the nCRT group. RESULTS Three-year overall survival was significantly better in the nCRT group than the S group (75.2% vs 61.5%; P=0.011). In the nCRT group, greater number of LND was associated with significantly better overall survival (HR, 0.358; P < 0.001) and disease-free survival (HR, 0.415; P=0.001), but without any negative impact on postoperative complications. Less LND (< 20 vs ≥ 20) was significantly associated with increased local recurrence (18.8% vs 5.2%, P=0.004) and total recurrence rates (41.2% vs 25.8%, P=0.027). Compared to patients with persistent nodal disease, significantly better survival was seen in patients with complete response and with LND ≥ 20, but not in those with LND < 20. CONCLUSIONS Systemic lymph node dissection does not increase surgical risks after nCRT in ESCC patients. And it is associated with better survival and local disease control. Therefore, systemic lymphadenectomy should still be considered as an integrated part of surgery after nCRT for ESCC.

We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-person distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mobility level, and recognizes the group structure by applying the multi-dimensional scaling technique on the matrix of distance between pairwise persons within the same group. We first validate the performance of between-person distance esti- mation via extensive experiments in a three-floor campus building and a shopping center, showing that WiDE system outperforms other machine learning based approaches for between-person distance estimation, with the average absolute error (AAE) of 0.69m and 1.14m for the campus building and shopping center, respectively, and the corridor identification accuracy for the campus building is over 99%. In addition, the experiments in the shopping center show that our approach can accurately detect groups, classify group mobility into fine-grained level and recognize the group structure.

Symmetric Searchable Encryption (SSE) schemes facilitate searching over encrypted data, and have been extensively explored to improve function, efficiency or security. There are, however, additional functions that we need to consider in a real-world setting. For example, forward and backward privacy are required to adequately secure newly added documents and deleted documents in Dynamic SSE (DSSE) schemes, and support boolean search (that allows users to search over encrypted data using basic boolean operations) to achieve improved efficiency and retrieval accuracy. Therefore, in this paper we first construct the Verifiable Boolean Search over encrypted data (VBS), and then improve VBS to achieve Forward and Backward privacy (VBS-FB). Finally, we formally prove the security of our proposed schemes, and evaluate their performance using real-world datasets.

In this article, a three-axis attitude manoeuvre spacecraft consisting of a central rigid body and a rotating solar array is studied. The rotating solar array is considered a disturbance to the spacecraft. In the design of the controller, the coupled terms and the rotating solar array are considered a disturbance. The improved extended state observer is proposed by combing the sliding mode observer with the originally extended state observer to estimate the disturbance. The sliding mode control method is adopted to adjust the attitude of the spacecraft. Numerical simulations are presented to demonstrate the outstanding performance of the present observer.

The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts on environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88%. We have released the source codes for public use.

Objectives The study aimed to describe daily sleep characteristics for dementia care dyads in the context of adult day services (ADS) use and examine the associations with sleep quality and daytime functioning (fatigue, affect, and behavior problems). Methods Caregivers (CG; N = 173) reported daily bedtime, wake time, and sleep quality for themselves and the persons living with dementia (PLWD) across 8 consecutive days (N = 1359), where PLWD attended ADS at least 2 days of the week. On each day, caregivers also reported their own fatigue and affect and PLWD's daytime behavior problems and nighttime sleep problems. Considering the context of ADS use, we compared mean differences in bedtime, wake time, and total time in bed on nights before versus after ADS use. We estimated multilevel models to examine daily sleep-well-being associations. Results On nights before an upcoming ADS day, care dyads went to bed and woke up earlier, and spent less time in bed. Further, PLWD had better sleep quality the night before an upcoming ADS day. Using ADS during the day buffered the negative impact of PLWD's sleep problems in the previous night, reducing daytime negative affect for caregivers. For caregivers, using ADS yesterday attenuated the association between shorter than typical time in bed and daytime fatigue; it also attenuated the association between PLWD's nighttime sleep problems and lowered daytime positive affect. Conclusions Regular ADS use may promote earlier sleep timing and protect against the adverse impact of sleep disturbances on daytime functioning for dementia care dyads.

Objectives To monitor stimulant-involved overdose (SOD) trends, the Centers for Disease Control and Prevention (CDC) developed and evaluated the validity of a syndromic surveillance definition for suspected nonfatal, unintentional/undetermined intent stimulant-involved overdose (UUSOD). Methods We analyzed all emergency department (ED) visits in CDC’s surveillance system that met the UUSOD syndrome definition (January 2018–December 2019). We classified visits as true positive, possible, or not UUSODs after reviewing diagnosis codes and chief complaints. We first assessed whether visits were acute SODs, subsequently classifying acute SODs by intent. The percentage of true-positive UUSODs did not include intentional or possibly intentional visits. We considered all visits with UUSOD diagnosis codes to be acute SODs and reviewed them for intent. We manually reviewed and double-coded a 10% random sample of visits without UUSOD diagnosis codes using decision rules based on signs and symptoms. The overall percentage of true-positive UUSODs was a weighted average of the percentage of true-positive UUSODs based on diagnosis codes and the percentage of true-positive UUSODs determined by manually reviewing visits without codes. Results During 2018-2019, 40 045 ED visits met the syndrome definition for UUSOD. Approximately half (n = 18 793; 46.9%) of 40 045 visits had UUSOD diagnosis codes, indicating acute SOD; of these, 98.6% (n = 18 534) were true-positive UUSODs. Of 2125 manually reviewed visits without UUSOD diagnosis codes, 32.6% (n = 693) were true-positive UUSODs, 54.2% (n = 1151) were possible UUSODs, and 13.2% (n = 281) were not UUSODs. Overall, 63.6% of visits were true-positive UUSODs, 29.3% were possible UUSODs, and 7.1% were not UUSODs. Practice Implications CDC’s UUSOD definition may assist in surveillance efforts with further refinement to capture data on SOD clusters and trends.

Introduction Legume crops are an important source of protein and oil for human health and in fixing atmospheric N2 for soil enrichment. With an objective to accelerate much-needed genetic analyses and breeding applications, draft genome assemblies were generated in several legume crops; many of them are not high quality because they are mainly based on short reads. However, the superior quality of genome assembly is crucial for a detailed understanding of genomic architecture, genome evolution, and crop improvement. Objectives Present study was undertaken with an objective of developing improved chromosome-length genome assemblies in six different legumes followed by their systematic investigation to unravel different aspects of genome organization and legume evolution. Methods We employed in situ Hi-C data to improve the existing draft genomes and performed different evolutionary and comparative analyses using improved genome assemblies. Results We have developed chromosome-length genome assemblies in chickpea, pigeonpea, soybean, subterranean clover, and two wild progenitor species of cultivated groundnut (A. duranensis and A. ipaensis). A comprehensive comparative analysis of these genome assemblies offered improved insights into various evolutionary events that shaped the present-day legume species. We highlighted the expansion of gene families contributing to unique traits such as nodulation in legumes, gravitropism in groundnut, and oil biosynthesis in oilseed legume crops such as groundnut and soybean. As examples, we have demonstrated the utility of improved genome assemblies for enhancing the resolution of “QTL-hotspot” identification for drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea through genome-wide association study. Genomic resources developed in this study are publicly available through an online repository, ‘Legumepedia’. Conclusion This study reports chromosome-length genome assemblies of six legume species and demonstrates the utility of these assemblies in crop improvement. The genomic resources developed here will have significant role in accelerating genetic improvement applications of legume crops.

In semi-supervised video object segmentation (VOS) task, temporal coherent object-level cues play a key role yet are hard to accurately model. To this end, this paper presents an object-aware global-local correspondence architecture, which enables to extract the inter-frame temporal coherent object-level features for accurate VOS. Specifically, we first generate a set of object masks by the ground-truth segmentation, and then we squeeze the current frame representation inside the object masks into a set of global object embeddings. Second, we compute the similarity between each embedding and the feature map, producing an object-aware weight for each pixel. The object-aware feature at each pixel is then constructed by summing the object embeddings weighted by their corresponding object-aware weights, which is able to capture rich object category information. Third, to establish the accurate correspondences between the inter-frame temporal coherent cues, we further design a novel global-local correspondence module to refine the temporal feature representations. Finally, we augment the object-aware features with the global-local aligned information to produce a strong spatio-temporal representation, which is essential to a more reliable pixel-wise segmentation prediction. Extensive evaluations are conducted on three popular VOS benchmarks containing Youtube-VOS, Davis2017 and Davis2016, demonstrating that the proposed method achieves favourable performance compared to the state-of-the-arts.

Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called Geometry-guided Dense Perspective Network (GDPnet), to achieve speaker-independent realistic 3D facial animation. The encoder is designed with dense connections to strengthen feature propagation and encourage the re-use of audio features, and the decoder is integrated with an attention mechanism to adaptively recalibrate point-wise feature responses by explicitly modeling interdependencies between different neuron units. We also introduce a non-linear face reconstruction representation as a guidance of latent space to obtain more accurate deformation, which helps solve the geometry-related deformation and is good for generalization across subjects. Huber and HSIC (Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the robustness of our model and to better exploit the non-linear and high-order correlations. Experimental results on the public dataset and real scanned dataset validate the superiority of our proposed GDPnet compared with state-of-the-art model. The code is available for research purposes at http://cic.tju.edu.cn/faculty/likun/projects/GDPnet.

Background Observational studies have shown promising therapeutic effects of long-pulsed neodymium-doped yttrium aluminum garnet (LP-Nd:YAG) laser on warts. Objective To evaluate whether LP-Nd:YAG laser was superior to cryotherapy for cutaneous warts. Methods In this study, 150 adult patients with warts were randomized equally to receive laser or cryotherapy every 3–4 weeks, a maximum of 4 sessions. The primary outcomes were cure rate at 16 weeks and 6 months; secondary outcomes included time to clearance of warts and treatment-related adverse effects. Results There was no difference in the cure rate for laser versus cryotherapy at 16 weeks (54.1% vs. 46.7%) and 6 months (59.5% vs. 57.3%). However, time to clearance of warts, up to 16 weeks and 6 months, tended to be shorter for laser versus cryotherapy ( P = .04 and .08, respectively). Post-hoc analyses showed a significantly higher cure rate for laser versus cryotherapy in 3 subgroups of HPV 2/27/57-induced recalcitrant warts, but not in their counterpart subgroups. Laser had more mild adverse effects. Limitations Single-center. Conclusions The overall therapeutic effects of LP-Nd:YAG laser were similar to cryotherapy, but laser may be more effective to relatively recalcitrant warts and may associate with shorter time to clearance of warts.

With increasing of data size and development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both SC and non-SC problems, while existing asynchronous algorithms (e.g., KroMagnon) only have convergence guarantees for SC problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++.

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This article thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ~10% in terms of TDR (91.19% versus 80.74%).

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

Objectives This study was to explore the potential relationship between the fibrinogen-to-albumin ratio (FAR) and the presence and severity of coronary artery disease (CAD) in stage 3–5 predialysis chronic kidney disease (CKD) patients. Design This study included 978 patients undergoing coronary angiography (CAG). CAD was defined as the presence of obstructive stenosis > 50% of the lumen diameter in any of the four main coronary arteries. Gensini scores (GSs), left main coronary artery (LMCA) and three-vessel coronary artery disease (TVD) were used to elevate the severity of CAD. Results The adjusted odds ratios of CAD were 3.059 (95% CI: 1.859–5.032) and 2.670 (95% CI: 1.605–4.441) in the third and fourth quartiles of FAR compared with the first quartile, respectively. Among 759 patients diagnosed with CAD, multivariate logistic regression analysis showed that FAR (at the 0.01 level) was significantly positively associated with the presence of LMCA (adjusted OR = 1.177, 95% CI 1.067–1.299, P = 0.001) or TVD (adjusted OR = 1.154, 95% CI 1.076–1.238, P Conclusions FAR levels were independently associated with the presence and severity of CAD in stage 3–5 predialysis CKD patients.

Power routing (PR) provides a good choice to improve the modular converter reliability by equalizing the lifetime among multi-cells. The existing PR strategies just equalize the lifetime of power semiconductor devices (PSDs) by routing lighter loads for aged cells. However, the neglect of capacitor may mislead the multi-cell lifetime convergence. Besides, the light load allocation of the aging cell does not always mean the lifetime extension considering topologies and operation principles. Hence, this paper proposed a capacitor lifetime-based PR for input-series-output-parallel (ISOP) connected dual active bridge (DAB) converter. The proposed strategy can achieve the same accuracy as existing PR strategies, greatly reducing computation time and the complexity of the hardware circuit. Moreover, the non-monotonic relationship between the damage growth rate and load of each cell is revealed and a damage growth rate constraint is developed to ensure the PR feasibility. Simulation is performed to verify the validity of the proposed strategy and constraints. Finally, a scaled down prototype has been developed to verify the PR feasibility and obtain the non-monotonic relationship, verifying the necessity of proposed constraints. In addition, the effect of PR on the thermal stress and cumulative damage of capacitors is verified by a temperature test.

Investigating the prognostic value of the Murray law-based quantitative flow ratio (μQFR) on the clinical outcome after treatment of in-stent restenosis (ISR) with a drug-coated balloon (DCB).Patients participating in a previous randomized clinical trial for DCB-ISR were post-hoc analyzed. The primary endpoint was vessel-oriented composite endpoint (VOCE), defined as cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization. μQFRs at baseline and after DCB angioplasty was calculated, and its prognostic value as a predictor of VOCE was explored in Cox regression.A total of 169 lesions in 169 patients were analyzed. At one-year follow-up, 20 VOCEs occurred in 20 patients. Receiver-operating characteristic curve analysis identified a post-procedural μQFR of ≤ 0.89 as the best cut-off to predict VOCE (area under curve [AUC]: 0.74; 95% confidence interval [CI]: 0.67-0.80; p0.001), superior to post-procedural in-stent percent diameter stenosis (DS), which reported an AUC of 0.61 (95% CI: 0.53-0.68; p = 0.18). Post-procedural μQFR was significantly lower in patients with VOCE compared with those without (0.88 [interquartile range: 0.79-0.94] vs. 0.96 [interquartile range: 0.91-0.98], respectively; p0.001). After correction for potential confounders, post-procedural μQFR ≤ 0.89 was associated with a 6-fold higher risk of VOCE than lesions with μQFR0.89 (hazard ratio: 5.94; 95% CI: 2.33-15.09; p0.001).Post-procedural μQFR may become a promising predictor of clinical outcome after treatment of DES-ISR lesions by DCB angioplasty.

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.

In our model, a venture seeks capital through an initial coin offering (ICO). The ICO enables the venture to collect demand information from decentralized investors. The venture makes a tradeoff between ensuring the project’s success and forecasting market demand through token size and token price. We find that the higher the demand uncertainty and production cost, the greater the venture’s incentive to learn from its investors. To identify when the venture has an incentive to raise funds through an ICO, we compare it with traditional bank financing and analyze the venture’s preference between the two financing options. The results show that when demand uncertainty is high, an ICO can provide both financing and information benefits, whereas when demand uncertainty is low, although the venture does not collect information through an ICO, it can still get financing benefit. Only when demand uncertainty is intermediate, the venture prefers bank financing. In addition, we find that although the revenue sharing effect of the ICO results in an underinvestment issue, it can also alleviate the loss caused by the increased cost of production. Therefore, with high information accuracy, as production cost goes up, ICOs become more attractive than bank financing for the venture.

Pancreatic adenocarcinoma (PAAD) as one of the most aggressive and lethal malignant tumors is correlated with increased morbidity and mortality. Tumorigenesis, growth, and metastasis are affected by various cytokines. Among them, CC chemokines can modulate the infiltration of immune cells and recruit cancer-associated immune cells, which play an important role in the inhibition of tumor immunity and affect the clinical outcome of cancer patients. However, the therapeutic potential and prognostic value of CC chemokines in PAAD have not yet been elucidated. To do this, we comprehensively explore and analyze large amounts of data on the basis of ONCOMINE database, GEPIA, LinkedOmics, cBioPortal, GeneMANIA, UALCAN, jvenn, DAVID 6.8, TRRUST, TIMER, and TISIDB. We found the transcriptional levels of CCL5/7/13/15/18/19/20 in PAAD tissues were remarkably increased, whereas the transcriptional level of CCL17 was decreased. CCL20 expression had significantly been correlated with the tumor stage of PAAD patients. High expressions of CCL5, CCL7, CCL13, CCL18, and CCL20 were notably correlated with the prognosis of patients. Moreover, patients with CCL18 and CCL19 alterations showed a poor prognosis in both overall survival (OS) and disease-specific survival (DSS), and patients with CCL5 and CCL15 alterations also presented a poor prognosis in OS. The functions of the aberrantly expressed CC chemokines were mainly correlated with the cytokine-cytokine receptor interaction, chemokine signaling pathway, inflammatory response, and immune response. Our study shows that the key transcription factors for CC chemokines are RELA and NF-κB1. We also discovered significant associations between the expression levels of CC chemokines and six infiltrating immune cells including CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, and dendritic cells. Taken together, our study indicated the interaction between CC chemokines and PAAD and clarified the value of CC chemokines as potential therapeutic targets as well as prognostic markers for PAAD.

With the dawn of 5G network, a new set of requirements for site spectrum monitoring, cellular planning are emerging, all of which are relying on fine-grained signal map. Although with significant importance, the traditional signal map construction could be time-consuming and labor-intensive. The state-of-the arts usually employ crowdsourcing scheme and matrix completion algorithm to solve the dilemma. However, the crowdsourcing scheme usually suffers from uneven distributed and inadequate participants. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with smaller cost and propose a GAN-based active signal map reconstruction method (GSMAC). Our method is mainly innovative in two parts: GSMC, GAN-based signal map construction, and ACS, an active crowdsourcing scheme. Specifically, GSMC can effectively update the signal map with only a small number of observations while updating the signal map online. ACS consists of a reinforce learning-based active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction. The simulation results and real implemented data-driven experiments demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasets Geolife and Chengdu. Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed ~18% under evaluation metric perimeter and improve ~0.72% under evaluation metric ADTS.

Radiative cooling without energy consumption and environmental pollution holds great promise as the next-generation cooling technology. To date, daytime radiative cooling performance is still slightly low, especially in humid areas. In this work, we demonstrated that nanoporous polyethylene (Nano PE) film can improve solar reflectivity from 96% to 99%, thus boosting radiative cooling performance. Moreover, the experimental results in humid areas indicate that Nano PE films can improve radiative cooling performance by ∼76% in a clear day and 120% in a day with few clouds. Additionally, compared with ordinary PE films, thin Nano PE films have significantly higher weather fastness and mechanical strength. More importantly, nano PE films can scatter part of visible light, thus suppressing the generation of light pollution in practical applications. Lastly, the modeling results reveal that with Nano PE films, more than 95% of China's areas can achieve daytime cooling performance. Our work can boost the development of radiative cooling technology with a very low cost.

The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose a Water-filling Based Dynamic Task Scheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness.

Objective The purpose of the study was to assess the clinical efficacy and safety of a combined perfusion suction platform with pressure feedback control function and an ureteroscopic suction sheath that can measure the ureteropelvic pressure in implementing lithotripsies. Methods Fifty-two patients who underwent lithotripsy under intelligent monitoring of ureteral intraluminal pressure from June 2016 to January 2018 were retrospectively recruited. The inclusion standard was stone diameter >1.5 cm but Results Forty-seven patients underwent successful primary sheath placement and lithotripsy with an average operative time of 34.5 ± 18.3 min. Retrograde stone migration did not occur. There were eight patients with hematuria postoperatively. Serious complication was 1.9% with one case of ureteral perforation. Stone clearance was 95.7% at 1–3 days postoperatively, and 100% at 30 days postoperatively. Conclusion Ureteroscopic lithotripsy with intelligent pressure control using our device improved the efficiency of the lithotripsy and rate of stone clearance. The safety of the operation can be ensured. It is worth popularization and application in clinical practice.

The security of our data stores is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB's padding strategies ensure that the access pattern to the database is obfuscated all the time. Additionally, ShieldDB provides other advanced features, including forward privacy, re-encryption, and flushing, to further improve its security and efficiency. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud., Comment: Accepted version of our work published in IEEE Transactions on Knowledge and Data Engineering (TKDE, 2021)

Geothermal energy with abundance and large quantity can partially cover building heating/cooling loads and promote the carbon-neutrality transitions. Shallow geothermal ventilation (SGV) system, with a little initial investment cost, is one of promising technologies to partly replace the conventional air-conditioning system for air pre-cooling/pre-heating. This paper reviews applications of SGV system for improving thermal performance over latest two decades, which mainly includes the reclassification of SGV system, coupling with other advanced energy-saving technologies, application potentials for building cooling/heating under various weather conditions. Heat transfer mechanism and mathematical modelling techniques have been reviewed, together with in-depth analysis on current research trends, existing limitations, and recommendations of SGV system. Phase change materials, with considerable latent energy density, can stabilize the thermal performance with high reliability. The review identifies that optimization designs and advanced approaches need to be investigated to address the existing urgent issues of SGV system (e.g., large land occupation, difficulty in centralized collection of condensate water timely for horizontal buried pipe, bacteria growth, polluted supply air, and high construction cost for vertical buried pipe). A plenty of studies show that the SGV system could greatly expand the application scope and improve system energy efficiency by combining with other energy-saving technologies. This paper will provide some guidelines for the scientific researchers and engineers to keep track on recent advancements and research trends of SGV system for the building thermal performance enhancement and pave path for future research works.

The appearance of Mobile Edge Computing (MEC) successfully solves the bottlenecks of traditional Cloud based networks. Since mobile edges, e.g. base stations, and mobile devices have certain data processing capabilities, it is not necessary to offload all the tasks to the cloud for handling. Therefore, it is quite important to decide the optimal task assignment in MEC systems, and a series of algorithms have been proposed. However, the existing algorithms ignored the data distribution during task assignment, so that their applied ranges are quite limit. Considering the data sharing is quite important in a MEC system, this paper studies task assignment algorithms in Data Shared Mobile Edge Computing Systems in detail. Specifically, three algorithms are proposed to deal with the unordered and ordered holistic tasks respectively. Meanwhile, the situation that the tasks are divisible is also considered, and two algorithms for rearranging the divisible tasks are proposed for different optimization goals. The hardness of the problem, the correctness, complexities, and ratio bounds of the proposed algorithms are analyzed theoretically. Finally, extensive experimental results are carried out. Both theoretical analysis and experiment results show that all the proposed algorithms have high performance in terms of latency, satisfied rate, and energy consumption.

An application of geometric reliability techniques on various pile types based on 67 load–displacement curves obtained from pertinent literature is presented in this paper. These static loading tests were performed at local scale (even building-specific sites) under essentially identical geotechnical conditions. A power-law function with two parameters was used to fit the measured load–settlement curves. For each site, the means and coefficients of variation for the power-law parameters were obtained. Since the number of tests conducted at each site is usually small, it is extremely difficult to identify a certain distribution type for these regression parameters. Thus, for simplicity, a bivariate normal distribution was assumed to represent the set of regression pairs. This joint distribution was incorporated into a geometric reliability method, which offers an estimation of the bearing capacity of piles at the serviceability limit state. Examples are provided to illustrate the application of the proposed reliability method to interpret the visually determined reliability index of the bearing capacity of piles. These case studies showed that the proposed method is a useful and comprehensive tool for capturing the load–displacement response and for evaluating the bearing capacity of piles by considering uncertainties in their load–displacement behaviour.