Friday, 23 August 2019

04:00 PM

Secure Cloud Assisted Smart Cars Using Dynamic Groups and Attribute Based Access Control. (arXiv:1908.08112v1 [cs.CR]) [cs.CR updates on]

Future smart cities and intelligent world will have connected vehicles and smart cars as its indispensable and most essential components. The communication and interaction among such connected entities in this vehicular internet of things (IoT) domain, which also involves smart traffic infrastructure, road-side sensors, restaurant with beacons, autonomous emergency vehicles, etc., offer innumerable real-time user applications and provide safer and pleasant driving experience to consumers. Having more than 100 million lines of code and hundreds of sensors, these connected vehicles (CVs) expose a large attack surface, which can be remotely compromised and exploited by malicious attackers. Security and privacy are serious concerns that impede the adoption of smart connected cars, which if not properly addressed will have grave implications with risk to human life and limb. In this research, we present a formalized dynamic groups and attribute-based access control (ABAC) model (referred as \cvac) for smart cars ecosystem, where the proposed model not only considers system wide attributes-based security policies but also takes into account the individual user privacy preferences for allowing or denying service notifications, alerts and operations to on-board resources. Further, we introduce a novel notion of groups in vehicular IoT, which are dynamically assigned to moving entities like connected cars, based on their current GPS coordinates, speed or other attributes, to ensure relevance of location and time sensitive notification services to the consumers, to provide administrative benefits to manage large numbers of smart entities, and to enable attributes and alerts inheritance for fine-grained security authorization policies. We present proof of concept implementation of our model in AWS cloud platform demonstrating real-world uses cases along with performance metrics.

Flexible S-money token schemes. (arXiv:1908.08143v1 [quant-ph]) [cs.CR updates on]

S-money [Proc. R. Soc. A 475, 20190170 (2019)] schemes define virtual tokens designed for networks with relativistic or other trusted signalling constraints. The tokens allow near-instant verification and guarantee unforgeability without requiring quantum state storage. We present refined two stage S-money schemes. The first stage, which may involve quantum information exchange, generates private user token data. In the second stage, which need only involve classical communications, users determine the valid presentation point, without revealing it to the issuer. This refinement allows the user to determine the presentation point anywhere in the causal past of all valid presentation points. It also allows flexible transfer of tokens among users without compromising user privacy.

Blockchain based access control systems: State of the art and challenges. (arXiv:1908.08503v1 [cs.CR]) [cs.CR updates on]

Access to the system resources. The current access control systems face many problems, such as the presence of the third-party, inefficiency, and lack of privacy. These problems can be addressed by blockchain, the technology that received major attention in recent years and has many potentials. In this study, we overview the problems of the current access control systems, and then, we explain how blockchain can help to solve them. We also present an overview of access control studies and proposed platforms in different domains. This paper presents the state of the art and the challenges of blockchain-based access control systems.

DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling. (arXiv:1905.09136v3 [cs.CR] UPDATED) [cs.CR updates on]

With the number of new mobile malware instances increasing by over 50\% annually since 2012 [24], malware embedding in mobile apps is arguably one of the most serious security issues mobile platforms are exposed to. While obfuscation techniques are successfully used to protect the intellectual property of apps' developers, they are unfortunately also often used by cybercriminals to hide malicious content inside mobile apps and to deceive malware detection tools. As a consequence, most of mobile malware detection approaches fail in differentiating between benign and obfuscated malicious apps. We examine the graph features of mobile apps code by building weighted directed graphs of the API calls, and verify that malicious apps often share structural similarities that can be used to differentiate them from benign apps, even under a heavily "polluted" training set where a large majority of the apps are obfuscated. We present DaDiDroid an Android malware app detection tool that leverages features of the weighted directed graphs of API calls to detect the presence of malware code in (obfuscated) Android apps. We show that DaDiDroid significantly outperforms MaMaDroid [23], a recently proposed malware detection tool that has been proven very efficient in detecting malware in a clean non-obfuscated environment. We evaluate DaDiDroid's accuracy and robustness against several evasion techniques using various datasets for a total of 43,262 benign and 20,431 malware apps. We show that DaDiDroid correctly labels up to 96% of Android malware samples, while achieving an 91% accuracy with an exclusive use of a training set of obfuscated apps.

Differentially Private Summation with Multi-Message Shuffling. (arXiv:1906.09116v3 [cs.CR] UPDATED) [cs.CR updates on]

In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{\epsilon, \delta}(1)$ error and $\Theta(\epsilon\sqrt{n})$ one-bit messages per party. In contrast, every local model protocol for real summation must incur error $\Omega(1/\sqrt{n})$, and there exist protocols matching this lower bound which require just one bit of communication per party. Whether this gap in number of messages is necessary was left open by Cheu et al.

In this note we show a protocol with $O(1/\epsilon)$ error and $O(\log(n/\delta))$ messages of size $O(\log(n))$ per party. This protocol is based on the work of Ishai et al.\ (FOCS 2006) showing how to implement distributed summation from secure shuffling, and the observation that this allows simulating the Laplace mechanism in the shuffle model.

CRISPR cuts turn gels into biological watchdogs [Nature - Issue - science feeds]

Nature, Published online: 22 August 2019; doi:10.1038/d41586-019-02542-3

Wunderkind gene-editing tool used to trigger smart materials that can deliver drugs and sense biological signals.

[Comment] Blood pressure control: a challenge to global health systems [The Lancet]

Raised blood pressure is the most important risk factor in the global burden of disease.1 Although there is robust evidence to show that lowering blood pressure can substantially reduce cardiovascular morbidity and mortality,2 the global burden of hypertension is increasing.3,4 To achieve a reduction in the burden of disease related to hypertension, health systems must ensure that high blood pressure treatment and control rates are achieved. The status of controlled blood pressure is being promoted as a measure of universal health coverage, especially in the context of non-communicable diseases.

[Obituary] Marc Mitchell [The Lancet]

Paediatrician who used digital technologies to improve global health. Born on Sept 1, 1948, in Newton, MA, USA, he died while hiking in Alaska, USA, on June 25, 2019, aged 70 years.

[Correspondence] High-quality evidence to inform clinical practice [The Lancet]

One of the basic tenets of evidence-based medicine is that randomisation is crucial to understanding treatment effects. Observational studies are subject to confounding and selection bias. Researchers can adjust for measured differences between treatment groups, but unmeasured or unmeasurable differences might exist between groups that obscure true treatment effects and cannot be accounted for by any statistical method.1 The published medical literature is filled with examples of associations between treatment and outcome identified in observational studies that were subsequently disproven by well conducted randomised controlled trials (RCTs).

[Correspondence] The importance of randomised vs non-randomised trials – Authors' reply [The Lancet]

We thank the correspondents for their responses to our Comment.1

[Correspondence] Management of rheumatic mitral stenosis [The Lancet]

Mariam Chekhchar and colleagues1 discuss branch retinal artery occlusion in a young woman, probably due to occult cardioembolus from rheumatic mitral stenosis. Despite decreasing incidence in developed nations, rheumatic heart disease remains a major source of preventable morbidity and mortality worldwide2 and we commend the authors for bringing attention to this important clinical entity. However, given this valvulopathy's highly thrombogenic nature, therapeutic anticoagulation should be considered.

JCS-Net: Joint Classification and Super-Resolution Network for Small-Scale Pedestrian Detection in Surveillance Images [IEEE Transactions on Information Forensics and Security - new TOC]

While convolutional neural network (CNN)-based pedestrian detection methods have proven to be successful in various applications, detecting small-scale pedestrians from surveillance images is still challenging. The major reason is that the small-scale pedestrians lack much detailed information compared to the large-scale pedestrians. To solve this problem, we propose to utilize the relationship between the large-scale pedestrians and the corresponding small-scale pedestrians to help recover the detailed information of the small-scale pedestrians, thus improving the performance of detecting small-scale pedestrians. Specifically, a unified network (called JCS-Net) is proposed for small-scale pedestrian detection, which integrates the classification task and the super-resolution task in a unified framework. As a result, the super-resolution and classification are fully engaged, and the super-resolution sub-network can recover some useful detailed information for the subsequent classification. Based on HOG+LUV and JCS-Net, multi-layer channel features (MCF) are constructed to train the detector. The experimental results on the Caltech pedestrian dataset and the KITTI benchmark demonstrate the effectiveness of the proposed method. To further enhance the detection, multi-scale MCF based on JCS-Net for pedestrian detection is also proposed, which achieves the state-of-the-art performance.

Self-Guiding Multimodal LSTM—When We Do Not Have a Perfect Training Dataset for Image Captioning [IEEE Transactions on Image Processing - new TOC]

In this paper, a self-guiding multimodal LSTM (sgLSTM) image captioning model is proposed to handle an uncontrolled imbalanced real-world image-sentence dataset. We collect a FlickrNYC dataset from Flickr as our testbed with 306,165 images and the original text descriptions uploaded by the users are utilized as the ground truth for training. Descriptions in the FlickrNYC dataset vary dramatically ranging from short term-descriptions to long paragraph-descriptions and can describe any visual aspects, or even refer to objects that are not depicted. To deal with the imbalanced and noisy situation and to fully explore the dataset itself, we propose a novel guiding textual feature extracted utilizing a multimodal LSTM (mLSTM) model. Training of mLSTM is based on the portion of data in which the image content and the corresponding descriptions are strongly bonded. Afterward, during the training of sgLSTM on the rest training data, this guiding information serves as additional input to the network along with the image representations and the ground-truth descriptions. By integrating these input components into a multimodal block, we aim to form a training scheme with the textual information tightly coupled with the image content. The experimental results demonstrate that the proposed sgLSTM model outperforms the traditional state-of-the-art multimodal RNN captioning framework in successfully describing the key components of the input images.

A Work Efficient Parallel Algorithm for Exact Euclidean Distance Transform [IEEE Transactions on Image Processing - new TOC]

A fully-parallelized work-time optimal algorithm is presented for computing the exact Euclidean Distance Transform (EDT) of a 2D binary image with the size of $ntimes n$ . Unlike existing PRAM (Parallel Random Access Machine) and other algorithms, this algorithm is suitable for implementation on modern SIMD (Single Instruction Multiple Data) architectures such as GPUs. As a fundamental operation of 2D EDT, 1D EDT is efficiently parallelized first. Specifically, the GPU algorithm for the 1D EDT, which uses CUDA (Compute Unified Device Architecture) binary functions, such as ballot(), ffs(), clz(), and shfl(), runs in $O(log_{32}n)$ time and performs $O(n)$ work. Using the 1D EDT as a fundamental operation, the fully-parallelized work-time optimal 2D EDT algorithm is designed. This algorithm consists of three steps. Step 1 of the algorithm runs in $O(log_{32}n)$ time and performs $O(N)$ ( $N = n^{2}$ ) of total work on GPU. Step 2 performs $O(N)$ of total work and has an expected time complexity of $O(logn)$ on GPU. Step 3 runs in $O(log_{32}n)$ time and performs $O(N)$ of total work on GPU. As far as we know, this algorithm is the first fully-parallelized and realized work-time optimal algorithm for GPUs. The experimental results show that this algorit- m outperforms the prior state-of-the-art GPU algorithms.

Reference-Free Quality Assessment of Sonar Images via Contour Degradation Measurement [IEEE Transactions on Image Processing - new TOC]

Sonar imagery plays a significant role in oceanic applications since there is little natural light underwater, and light is irrelevant to sonar imaging. Sonar images are very likely to be affected by various distortions during the process of transmission via the underwater acoustic channel for further analysis. At the receiving end, the reference image is unavailable due to the complex and changing underwater environment and our unfamiliarity with it. To the best of our knowledge, one of the important usages of sonar images is target recognition on the basis of contour information. The contour degradation degree for a sonar image is relevant to the distortions contained in it. To this end, we developed a new no-reference contour degradation measurement for perceiving the quality of sonar images. The sparsities of a series of transform coefficient matrices, which are descriptive of contour information, are first extracted as features from the frequency and spatial domains. The contour degradation degree for a sonar image is then measured by calculating the ratios of extracted features before and after filtering this sonar image. Finally, a bootstrap aggregating (bagging)-based support vector regression module is learned to capture the relationship between the contour degradation degree and the sonar image quality. The results of experiments validate that the proposed metric is competitive with the state-of-the-art reference-based quality metrics and outperforms the latest reference-free competitors.

Learning to Find Unpaired Cross-Spectral Correspondences [IEEE Transactions on Image Processing - new TOC]

We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation and feature extraction modules to be learned in a joint and boosting manner. Concretely, the image translation module is learned only with the unpaired cross-spectral data, and the feature extraction module is learned with an input image and its translated image. By learning two modules simultaneously, the image translation module generates the translated image that preserves not only the domain-specific attributes with separate latent spaces but also the domain-agnostic contents with feature consistency constraint. In an inference phase, the cross-spectral feature similarity is augmented by intra-spectral similarities between the features extracted from the translated images. Experimental results show that this model outperforms the state-of-the-art unpaired image translation methods and cross-spectral feature descriptors on various visible and infrared benchmarks.

Weakly Supervised Salient Object Detection by Learning A Classifier-Driven Map Generator [IEEE Transactions on Image Processing - new TOC]

Top-down saliency detection aims to highlight the regions of a specific object category, and typically relies on pixel-wise annotated training data. In this paper, we address the high cost of collecting such training data by a weakly supervised approach to object saliency detection, where only image-level labels, indicating the presence or absence of a target object in an image, are available. The proposed framework is composed of two collaborative CNN modules, an image-level classifier and a pixel-level map generator. While the former distinguishes images with objects of interest from the rest, the latter is learned to generate saliency maps by which the images masked by the maps can be better predicted by the former. In addition to the top-down guidance from class labels, the map generator is derived by also exploring other cues, including the background prior, superpixel- and object proposal-based evidence. The background prior is introduced to reduce false positives. Evidence from superpixels helps preserve sharp object boundaries. The clue from object proposals improves the integrity of highlighted objects. These different types of cues greatly regularize the training process and reduces the risk of overfitting, which happens frequently when learning CNN models with few training data. Experiments show that our method achieves superior results, even outperforming fully supervised methods.

Thursday, 22 August 2019

04:00 PM

DEAD-box ATPases are global regulators of phase-separated organelles [Nature - Issue - science feeds]

Nature, Published online: 21 August 2019; doi:10.1038/s41586-019-1502-y

RNA-dependent DEAD-box ATPases (DDXs) regulate the dynamics of phase-separated organelles, with ATP-bound DDXs promoting phase separation, and ATP hydrolysis inducing compartment disassembly and RNA release.

Droplet motion electrically controlled [Nature - Issue - science feeds]

Nature, Published online: 21 August 2019; doi:10.1038/d41586-019-02451-5

The movement of small droplets on a substrate is governed by surface-tension forces. A technique that can tune the surface tension of robust oxide substrates for droplet manipulation could open up many applications.

FADS1 and FADS2 Polymorphisms Modulate Fatty Acid Metabolism and Dietary Impact on Health [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 21-44, August 2019.

Dietary Fuels in Athletic Performance [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 45-73, August 2019.

The Benefits and Risks of Iron Supplementation in Pregnancy and Childhood [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 121-146, August 2019.

Mitochondrial DNA Mutation, Diseases, and Nutrient-Regulated Mitophagy [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 201-226, August 2019.

Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 291-315, August 2019.

Effect of Continuing Olanzapine vs Placebo on Relapse Among Patients With Psychotic Depression in Remission [JAMA Current Issue]

This randomized clinical trial compares the effect on relapse of continuing olanzapine vs placebo among patients with psychotic depression who achieved remission of psychosis and depressive symptoms while taking olanzapine and sertraline.

Treating Hypercholesterolemia in Older Adults [JAMA Current Issue]

To the Editor In his Viewpoint, Dr Skolnik discussed the 2018 American College of Cardiology (ACC)/American Heart Association (AHA) guideline on the management of blood cholesterol and its implications for older adults. We would like to highlight relevant features of the guidelines that merit greater recognition.

Averting Alert Fatigue to Prevent Adverse Drug Reactions [JAMA Current Issue]

Although various electronic health records (EHRs) have different features, nearly all seem to have alerts for potential problems with drug prescribing. It’s one thing that many believe that EHRs do very well. However, a recent study warns that when it comes to opioids and benzodiazepines, we shouldn’t always assume that such alerts work as intended.

Weighing the Risks and Rewards of Peanut Oral Immunotherapy [JAMA Current Issue]

This Medical News article discusses a recent meta-analysis of oral immunotherapy trials for people with peanut allergies.

Electronic Fetal Monitoring to Prevent Fetal Brain Injury—Ubiquitous But Flawed [JAMA Current Issue]

This Viewpoint argues that the near-universal adoption of electronic fetal monitoring (EFM) in labor and delivery units has occurred without evidence that it has reduced adverse neurological events and has contributed to an increase in US cesarean delivery rates, and calls for the education of physicians and the public about EFM’s demonstrated reliability and value.

Tuesday, 20 August 2019

04:00 PM

Survival: the first 3.8 billion years [Nature - Issue - science feeds]

Nature, Published online: 20 August 2019; doi:10.1038/d41586-019-02475-x

Lisa Feldman Barrett ponders Joseph LeDoux’s study on how conscious brains evolved.

Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users [IEEE Transactions on Information Forensics and Security - new TOC]

The Internet of Things (IoT) is increasingly empowering people with an interconnected world of physical objects ranging from smart buildings to portable smart devices, such as wearables. With recent advances in mobile sensing, wearables have become a rich collection of portable sensors and are able to provide various types of services, including tracking of health and fitness, making financial transactions, and unlocking smart locks and vehicles. Most of these services are delivered based on users' confidential and personal data, which are stored on these wearables. Existing explicit authentication approaches (i.e., PINs or pattern locks) for wearables suffer from several limitations, including small or no displays, risk of shoulder surfing, and users' recall burden. Oftentimes, users completely disable security features out of convenience. Therefore, there is a need for a burden-free (implicit) authentication mechanism for wearable device users based on easily obtainable biometric data. In this paper, we present an implicit wearable device user authentication mechanism using combinations of three types of coarse-grain minute-level biometrics: behavioral (step counts), physiological (heart rate), and hybrid (calorie burn and metabolic equivalent of task). From our analysis of over 400 Fitbit users from a 17-month long health study, we are able to authenticate subjects with average accuracy values of around .93 (sedentary) and .90 (non-sedentary) with equal error rates of .05 using binary SVM classifiers. Our findings also show that the hybrid biometrics perform better than other biometrics and behavioral biometrics do not have a significant impact, even during non-sedentary periods.

Individual Identification Based on Code-Modulated Visual-Evoked Potentials [IEEE Transactions on Information Forensics and Security - new TOC]

The electroencephalography (EEG) method has recently attracted increasing attention in the study of brain activity-based biometric systems because of its simplicity, portability, noninvasiveness, and relatively low cost. However, due to the low signal-to-noise ratio of EEG, most of the existing EEG-based biometric systems require a long duration of signals to achieve high accuracy in individual identification. Besides, the feasibility and stability of these systems have not yet been conclusively reported, since most studies did not perform longitudinal evaluation. In this paper, we proposed a novel EEG-based individual identification method using code-modulated visualevoked potentials (c-VEPs). Specifically, this paper quantitatively compared eight code-modulated stimulation patterns, including six 63-bit (1.05 s at 60-Hz refresh rate) m-sequences (M1-M6) and two spatially combined sequence groups (M×4: M1-M4 and M× 6: M1-M6) in recording the c-VEPs from a group of 25 subjects for individual identification. To further evaluate the influence of inter-session variability, we recorded two data sessions for each individual on different days to measure intra-session and cross-session identification performance. State-of-the-art VEP detection algorithms in brain-computer interfaces (BCIs) were employed to construct a template-matching-based identification framework. For intra-session identification, we achieved a 100% correct recognition rate (CRR) using 5.25-s EEG data (average of five trials for M5). For cross-session identification, 99.43% CRR was attained using 10.5-s EEG signals (average of ten trials for M5). These results suggest that the proposed c-VEP based individual identification method is promising for real-world applications.

Monday, 19 August 2019

04:00 PM

Two-dimensional polymer knits together and unravels in an electric field [Nature - Issue - science feeds]

Nature, Published online: 19 August 2019; doi:10.1038/d41586-019-02452-4

The tip of a scanning tunnelling microscope has been used to convert a molecular assembly into a 2D polymer and back, at room temperature — revealing how extreme environmental conditions can alter the progress of reactions.

Tuesday, 13 August 2019

11:00 PM

Upregulation of reduced folate carrier by vitamin D enhances brain folate uptake in mice lacking folate receptor alpha [Pharmacology] [Early Edition]

Folates are critical for central nervous system function. Folate transport is mediated by 3 major pathways, reduced folate carrier (RFC), proton-coupled folate transporter (PCFT), and folate receptor alpha (FRα/Folr1), known to be regulated by ligand-activated nuclear receptors. Cerebral folate delivery primarily occurs at the choroid plexus through FRα and PCFT;...

Phosphorylation-guarded light-harvesting complex II contributes to broad-spectrum blast resistance in rice [Plant Biology] [Early Edition]

Environmental conditions are key factors in the progression of plant disease epidemics. Light affects the outbreak of plant diseases, but the underlying molecular mechanisms are not well understood. Here, we report that the light-harvesting complex II protein, LHCB5, from rice is subject to light-induced phosphorylation during infection by the rice...

Learning active sensing strategies using a sensory brain-machine interface [Neuroscience] [Early Edition]

Diverse organisms, from insects to humans, actively seek out sensory information that best informs goal-directed actions. Efficient active sensing requires congruity between sensor properties and motor strategies, as typically honed through evolution. However, it has been difficult to study whether active sensing strategies are also modified with experience. Here, we...

Monday, 12 August 2019

11:00 PM

ARF6 and AMAP1 are major targets of KRAS and TP53 mutations to promote invasion, PD-L1 dynamics, and immune evasion of pancreatic cancer [Medical Sciences] [Early Edition]

Although KRAS and TP53 mutations are major drivers of pancreatic ductal adenocarcinoma (PDAC), the incurable nature of this cancer still remains largely elusive. ARF6 and its effector AMAP1 are often overexpressed in different cancers and regulate the intracellular dynamics of integrins and E-cadherin, thus promoting tumor invasion and metastasis when...

Predictive Location Aware Online Admission and Selection Control in Participatory Sensing [IEEE Transactions on Industrial Informatics - new TOC]

Participatory sensing is a crowdsourcing-based framework, where the platform executes the sensing requests with the help of many common peoples’ handheld devices (typically smartphones). In this paper, we mainly address the online sensing request admission and smartphone selection problem to maximize the profit of the platform, taking into account the queue backlog, and the location of sensing requests and smartphones. First, we formulate this problem as a discrete time model and design a location aware online admission and selection control algorithm (LAAS) based on the Lyapunov optimization technique. The LAAS algorithm only depends on the currently available information and makes all the control decisions independently and simultaneously. Next, we utilize the recent advancement of the accurate prediction of smartphones’ mobility and sensing request arrival information in the next few time slots and develop a predictive location aware admission and selection control algorithm (PLAAS). We further design a greedy predictive location aware admission and selection control algorithm (GPLAAS) to achieve the online implementation of PLAAS approximately and iteratively. Theoretical analysis shows that under any control parameter V > 0, both LAAS and PLAAS algorithm can achieve O(1/V)-optimal average profit, while the sensing request backlog is bounded by O(V). Extensive numerical results based on both synthetic and real trace show that LAAS outperforms the Greedy algorithm and Random algorithm and GPLAAS improves the profit-backlog tradeoff over LAAS.

Demand-Side Energy Management Considering Price Oscillations for Residential Building Heating and Ventilation Systems [IEEE Transactions on Industrial Informatics - new TOC]

This paper presents an energy management method to optimally control the energy supply and the temperature settings of distributed heating and ventilation systems for residential buildings. The control model attempts to schedule the supply and demand simultaneously with the purpose of minimizing the total costs. Moreover, the Predicted Percentage of Dissatisfied (PPD) model is introduced into the consumers’ cost functions and the quadratic fitting method is applied to simplify the PPD model. An energy management algorithm is developed to seek the optimal temperature settings, the energy supply, and the price. Furthermore, due to the ubiquity of price oscillations in electricity markets, we analyze and examine the effects of price oscillations on the performance of the proposed algorithm. Finally, the theoretical analysis and simulation results both demonstrate that the proposed energy management algorithm with price oscillations can converge to a region around the optimal solution.

Preheating Quantification for Smart Hybrid Heat Pumps Considering Uncertainty [IEEE Transactions on Industrial Informatics - new TOC]

The deployment of smart hybrid heat pumps (SHHPs) can introduce considerable benefits to electricity systems via smart switching between electricity and gas while minimizing the total heating cost for each individual customer. In particular, the fully optimized control technology can provide flexible heat that redistributes the heat demand across time for improving the utilization of low-carbon generation and enhancing the overall energy efficiency of the heating system. To this end, an accurate quantification of the preheating is of great importance to characterize the flexible heat. This paper proposes a novel data-driven preheating quantification method to estimate the capability of the heat pump demand shifting and isolate the effect of interventions. Varieties of fine-grained data from a real-world trial are exploited to estimate the baseline heat demand using Bayesian deep learning while jointly considering epistemic and aleatoric uncertainties. A comprehensive range of case studies are carried out to demonstrate the superior performance of the proposed quantification method, and then, the estimated demand shift is used as an input into the whole-system model to investigate the system implications and quantify the range of benefits of rolling out the SHHPs developed by PassivSystems to the future GB electricity systems.

A Comparison Study on Stochastic Modeling Methods for Home Energy Management Systems [IEEE Transactions on Industrial Informatics - new TOC]

Obtaining an appropriate model is very crucial to develop an efficient energy management system for the smart home, including photovoltaic (PV) array, plug-in electric vehicle (PEV), home loads, and heat pump (HP). Stochastic modeling methods of smart homes explain random parameters and uncertainties of the aforementioned components. In this paper, a concise yet comprehensive analysis and comparison are presented for these techniques. First, modeling methods are implemented to find appropriate and precise forecasting models for PV, PEV, HP, and home load demand. Then, the accuracy of each model is validated by the real measured data. Finally, the pros and cons of each method are discussed and reviewed. The obtained results show the conditions under which the methods can provide a reliable and accurate description of smart home dynamics.

IEEE Industrial Electronics Society [IEEE Transactions on Industrial Informatics - new TOC]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Thursday, 08 August 2019

04:00 PM

Machine Learning for Sociology [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 27-45, July 2019.

The Role of Space in the Formation of Social Ties [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 111-132, July 2019.

The Social Structure of Time: Emerging Trends and New Directions [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 301-320, July 2019.

Retail Sector Concentration, Local Economic Structure, and Community Well-Being [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 321-343, July 2019.

Well-Being at the End of Life [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 515-534, July 2019.

Joint Intensity Transformer Network for Gait Recognition Robust Against Clothing and Carrying Status [IEEE Transactions on Information Forensics and Security - new TOC]

Clothing and carrying status variations are the two key factors that affect the performance of gait recognition because people usually wear various clothes and carry all kinds of objects, while walking in their daily life. These covariates substantially affect the intensities within conventional gait representations such as gait energy images. Hence, to properly compare a pair of input gait features, an appropriate metric for joint intensity is needed in addition to the conventional spatial metric. We therefore propose a unified joint intensity transformer network for gait recognition that is robust against various clothing and carrying statuses. Specifically, the joint intensity transformer network is a unified deep learning-based architecture containing three parts: a joint intensity metric estimation net, a joint intensity transformer, and a discrimination network. First, the joint intensity metric estimation net uses a well-designed encoder-decoder network to estimate a sample-dependent joint intensity metric for a pair of input gait energy images. Subsequently, a joint intensity transformer module outputs the spatial dissimilarity of two gait energy images using the metric learned by the joint intensity metric estimation net. Third, the discrimination network is a generic convolution neural network for gait recognition. In addition, the joint intensity transformer network is designed with different loss functions depending on the gait recognition task (i.e., a contrastive loss function for the verification task and a triplet loss function for the identification task). The experiments on the world’s largest datasets containing various clothing and carrying statuses demonstrate the state-of-the-art performance of the proposed method.

A Hand-Based Multi-Biometrics via Deep Hashing Network and Biometric Graph Matching [IEEE Transactions on Information Forensics and Security - new TOC]

At present, the fusion of different unimodal biometrics has attracted increasing attention from researchers, who are dedicated to the practical application of biometrics. In this paper, we explored a multi-biometric algorithm that integrates palmprints and dorsal hand veins (DHV). Palmprint recognition has a rather high accuracy and reliability, and the most significant advantage of DHV recognition is the biopsy (Liveness detection). In order to combine the advantages of both and implement the fusion method, deep learning and graph matching were, respectively, introduced to identify palmprint and DHV. Upon using the deep hashing network (DHN), biometric images can be encoded as 128-bit codes. Then, the Hamming distances were used to represent the similarity of two codes. Biometric graph matching (BGM) can obtain three discriminative features for classification. In order to improve the accuracy of open-set recognition, in multi-modal fusion, the score-level fusion of DHN and BGM was performed and authentication was provided by support vector machine (SVM). Furthermore, based on DHN, all four levels of fusion strategies were used for multi-modal recognition of palmprint and DHV. Evaluation experiments and comprehensive comparisons were conducted on various commonly used datasets, and the promising results were obtained in this case where the equal error rates (EERs) of both palmprint recognition and multi-biometrics equal 0, demonstrating the great superiority of DHN in biometric verification.

Sensorless Low Switching Frequency Explicit Model Predictive Control of Induction Machines Fed by Neutral Point Clamped Inverter [IEEE Transactions on Industrial Electronics - new TOC]

This paper proposes an explicit predictive current control scheme implemented with a low carrier frequency pulsewidth modulation (PWM) on an induction machine fed by a three-level neutral point clamped inverter. The PWM carrier and the main current sampling frequency are both set to 1 kHz, resulting in a 500 Hz average switching frequency per device, which is very suitable for large drive applications. The explicit predictive control is introduced to optimize the available bandwidth provided by such a low sampling frequency, maximizing the dynamic performance. The strategy has been tested in a 2.2-kW induction motor experimental prototype.

Peak Current Control-Based Power Ripple Decoupling of AC–DC Multichannel LED Driver [IEEE Transactions on Industrial Electronics - new TOC]

AC–DC light-emitting diode (LED) drivers suffer from short lifetime because of the low-lifetime electrolytic capacitors used for dc bus decoupling. In this paper, a primary-side peak current control method applied for driving a two-stage multichannel LED driver is proposed. The LED driver consists of an ac–dc boost power factor correction stage and an isolated dc–dc nonresonant stage. A long-lifetime and small film capacitor is used for implementing the intermediate dc bus. The proposed method, which controls the peak value of the primary-side current of the transformers, is applied to the dc–dc stage to ensure constant dc current output of LEDs in spite of the widely varying dc bus voltage due to low bus capacitance. The proposed method compensates the effect of the large dc bus voltage ripple by varying the switching frequency of the primary-side switches. Detailed design procedure, theoretical analysis, and experimental results of the LED driver operating at 180 W with the proposed method are provided. The LED driver with the proposed control method is proved to have high overall efficiency.

Data-Efficient Reinforcement Learning for Energy Optimization of Power-Assisted Wheelchairs [IEEE Transactions on Industrial Electronics - new TOC]

The objective of this paper is to develop a method for assisting users to push power-assisted wheelchairs (PAWs) in such a way that the electrical energy consumption over a predefined distance-to-go is optimal, while at the same time bringing users to a desired fatigue level. This assistive task is formulated as an optimal control problem and solved by Feng et al. using the model-free approach gradient of partially observable Markov decision processes. To increase the data efficiency of the model-free framework, we here propose to use policy learning by weighting exploration with the returns (PoWER) with 25 control parameters. Moreover, we provide a new near-optimality analysis of the finite-horizon fuzzy Q-iteration, which derives a model-based baseline solution to verify numerically the near-optimality of the presented model-free approaches. Simulation results show that the PoWER algorithm with the new parameterization converges to a near-optimal solution within 200 trials and possesses the adaptability to cope with changes of the human fatigue dynamics. Finally, 24 experimental trials are carried out on the PAW system, with fatigue feedback provided by the user via a joystick. The performance tends to increase gradually after learning. The results obtained demonstrate the effectiveness and the feasibility of PoWER in our application.

Joint Image-Text Hashing for Fast Large-Scale Cross-Media Retrieval Using Self-Supervised Deep Learning [IEEE Transactions on Industrial Electronics - new TOC]

Recent years have witnessed the promising future of hashing in the industrial applications for fast similarity retrieval. In this paper, we propose a novel supervised hashing method for large-scale cross-media search, termed self-supervised deep multimodal hashing (SSDMH), which learns unified hash codes as well as deep hash functions for different modalities in a self-supervised manner. With the proposed regularized binary latent model, unified binary codes can be solved directly without relaxation strategy while retaining the neighborhood structures by the graph regularization term. Moreover, we propose a new discrete optimization solution, termed as binary gradient descent, which aims at improving the optimization efficiency toward real-time operation. Extensive experiments on three benchmark data sets demonstrate the superiority of SSDMH over state-of-the-art cross-media hashing approaches.

IEEE Transactions on Industrial Electronics Information for Authors [IEEE Transactions on Industrial Electronics - new TOC]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

Monday, 29 July 2019

Wednesday, 17 July 2019

04:00 PM

Proposed gene therapy for a heart arrhythmia, based on models made from patient cells [EurekAlert! - Breaking News]

Researchers at Boston Children's Hospital report creating the first human tissue model of an inherited heart arrhythmia, replicating two patients' abnormal heart rhythms in a dish, and then suppressing the arrhythmia with gene therapy in a mouse model.

Women's stronger immune response to flu vaccination diminishes with age [EurekAlert! - Breaking News]

Women tend to have a greater immune response to a flu vaccination compared to men, but their advantage largely disappears as they age and their estrogen levels decline, suggests a study from researchers at the Johns Hopkins Bloomberg School of Public Health.

Tuesday, 16 July 2019

04:00 PM

UMN researcher identifies differences in genes that impact response to cryptococcus infection [EurekAlert! - Breaking News]

Cryptococcus neoformans is a fungal pathogen that infects people with weakened immune systems, particularly those with advanced HIV/AIDS. New University of Minnesota Medical Research could mean a better understanding of this infection and potentially better treatments for patients.

Save your money: Vast majority of dietary supplements don't improve heart health or put off death [EurekAlert! - Breaking News]

In a massive new analysis of findings from 277 clinical trials using 24 different interventions, Johns Hopkins Medicine researchers say they have found that almost all vitamin, mineral and other nutrient supplements or diets cannot be linked to longer life or protection from heart disease.

Stripping down bacterial armor: A new way to fight anthrax [EurekAlert! - Breaking News]

A new study led by Dr. Antonella Fioravanti in the lab of Prof. Han Remaut (VIB-VUB Center for Structural Biology) has shown that removing the armor of the bacterium that causes anthrax slows its growth and negatively affects its ability to cause disease. This work will be published in the prestigious journal Nature Microbiology can lead the way to new, effective ways of fighting anthrax and various other diseases.

Sunday, 09 June 2019

07:32 PM

The Economics and Politics of Preferential Trade Agreements [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 75-92, May 2019.

The Politics of Housing [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 165-185, May 2019.

Bias and Judging [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 241-259, May 2019.

Climate Change and Conflict [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 343-360, May 2019.

Firms in Trade and Trade Politics [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 399-417, May 2019.

Tuesday, 12 February 2019

04:00 PM

Cysteine-Based Redox Sensing and Its Role in Signaling by Cyclic Nucleotide–Dependent Kinases in the Cardiovascular System [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 63-87, February 2019.

Biomarkers of Acute and Chronic Kidney Disease [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 309-333, February 2019.

Cellular Metabolism in Lung Health and Disease [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 403-428, February 2019.

Innate Lymphoid Cells of the Lung [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 429-452, February 2019.

Regulation of Blood and Lymphatic Vessels by Immune Cells in Tumors and Metastasis [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 535-560, February 2019.

Thursday, 09 August 2018

04:00 PM

Sorting in the Labor Market [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 10, Issue 1, Page 1-29, August 2018.

Radical Decentralization: Does Community-Driven Development Work? [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 10, Issue 1, Page 139-163, August 2018.

The Development of the African System of Cities [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 10, Issue 1, Page 287-314, August 2018.

Idea Flows and Economic Growth [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 10, Issue 1, Page 315-345, August 2018.

Progress and Perspectives in the Study of Political Selection [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 10, Issue 1, Page 541-575, August 2018.


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