Friday, 07 August 2020

06:02 PM

Parents’ desire for one boy and one girl pushed trend in family patterns [Nature - Issue - nature.com science feeds]

Nature, Published online: 07 August 2020; doi:10.1038/d41586-020-02313-5

A change in the sex ratio of offspring in the United Kingdom might reflect shifting attitudes about gender.

NASA sounding rocket finds helium structures in sun's atmosphere [EurekAlert! - Breaking News]

Helium is the second most abundant element in the universe after hydrogen. But scientists aren't sure just how much there actually is in the Sun's atmosphere. NASA's HERSCHEL sounding rocket has taken the first global measurements of helium in the extended solar atmosphere - a key piece of information for understanding our space environment.

02:02 AM

Activity Detection from Encrypted Remote Desktop Protocol Traffic. (arXiv:2008.02685v1 [cs.CR]) [cs.CR updates on arXiv.org]

An increasing amount of Internet traffic has its content encrypted. We address the question of whether it is possible to predict the activities taking place over an encrypted channel, in particular Microsoft's Remote Desktop Protocol. We show that the presence of five typical activities can be detected with precision greater than 97\% and recall greater than 94\% in 30-second traces. We also show that the design of the protocol exposes fine-grained actions such as keystrokes and mouse movements which may be leveraged to reveal properties such as lengths of passwords.

Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection. (arXiv:1906.02325v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.

Security and Privacy in IoT Using Machine Learning and Blockchain: Threats & Countermeasures. (arXiv:2002.03488v4 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning. (arXiv:2004.08762v3 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life. Unfortunately, in most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. In this work, we propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them. Furthermore, RelSen is not application-specific as its implementation assumes a minimal prior knowledge of the process dynamics under monitoring. This significantly improves its generality and applicability in practice. In our experiments, we apply RelSen on an outdoor air pollution monitoring system and a condition monitoring system for a cement rotary kiln. Experimental results show that our framework can timely identify unreliable sensors and remove sensor measurement errors caused by three types of most commonly observed sensor faults.

[Editorial] Self-determination and Indigenous health [The Lancet]

Colonisation is a fundamental determinant of Indigenous peoples' health. Indigenous is a term defined by dislocation, and the effects of that displacement are felt by Indigenous peoples around the world. Aug 9, International Day of the World's Indigenous Peoples, is a chance to look at the continuing effects of territorial removal, the destruction of people, culture, and languages, and the lack of self-determination (the right to determine one's own social, cultural, and economic development), and their impact on Indigenous peoples' health.

[Comment] Political and institutional perils of Brazil's COVID-19 crisis [The Lancet]

Political scientists would presume that during a pandemic, political leaders will seek to use the situation to increase their power and electability. In the case of Brazil, however, President Jair Bolsonaro has not been able to achieve this, partly due to the government's poor policy response to COVID-19, which is shaped by Bolsonaro's political ideology. Yet Bolsonaro operates within a strong democratic institutional context that limits his policy authority. Brazil's Federal Supreme Court, for example, consistently upholds state physical distancing policies that the Bolsonaro administration opposes.

[Correspondence] Recurrence of breast cancer after anaesthesia – Author's reply [The Lancet]

I thank Masashi Ishikawa and colleagues, Karen Nielsen and colleagues, and Dimitrios Moris and Michael Kontos for their comments on the results of the randomised controlled trial of recurrance of breast cancer after regional or general anaesthesia.1

[Correspondence] COVID-19—a very visible pandemic [The Lancet]

I read with interest the Correspondence by Johan Giesecke.1 Applauding the Swedish model, Giesecke1 posits a “relaxed strategy” or the development of a herd immunity as the way forward in dealing with the COVID-19 pandemic. He recommends that the crucial task is not to stop the spread, “which is all but futile, but to concentrate on giving the unfortunate victims optimal care”. Given the drastic adverse economic consequences of the lockdown globally, the advocacy of herd immunity as a way out appears attractive.

[Articles] Mortality in adults with multidrug-resistant tuberculosis and HIV by antiretroviral therapy and tuberculosis drug use: an individual patient data meta-analysis [The Lancet]

Use of ART and more effective anti-tuberculosis drugs is associated with lower odds of death among HIV-positive patients with multidrug-resistant tuberculosis. Access to these therapies should be urgently pursued.

'Roaming reactions' study to shed new light on atmospheric molecules [EurekAlert! - Breaking News]

For the first time, a team of chemists has lifted the hood on the mechanics involved in the mysterious interplay between sunlight and molecules in the atmosphere known as 'roaming reactions', which could make atmospheric modelling more accurate.

Brain noise contains unique signature of dream sleep [EurekAlert! - Breaking News]

Dream or REM sleep is distinguished by rapid eye movement and absence of muscle tone, but electroencephalogram (EEG) recordings are indistinguishable from those of an awake brain. UC Berkeley neuroscientists have now found an EEG signature of REM sleep, allowing scientists for the first time to distinguish dreaming from wakefulness through brain activity alone. This could help in determining the prognosis for coma patients, and allow study of the impact of anesthesia on dreaming.

Thursday, 06 August 2020

06:02 PM

Daily briefing: Satellites spot 11 more emperor-penguin colonies [Nature - Issue - nature.com science feeds]

Nature, Published online: 05 August 2020; doi:10.1038/d41586-020-02317-1

View from space boosts known penguin numbers by an estimated 5–10% — but they’re on unstable ice. Plus: why two decades of pandemic planning couldn’t predict the chaos in the United States and how scientists can help free the world of nuclear weapons.

Researchers take the ultimate Earth selfie [EurekAlert! - Breaking News]

In a new study, a team of scientists set out to achieve something new in planetary photography: The group used the Hubble Space Telescope to try to view Earth as if it were an exoplanet.

ASH releases new clinical practice guidelines on acute myeloid leukemia in older adults [EurekAlert! - Breaking News]

Today, ASH published new guidelines to help older adults with acute myeloid leukemia (AML) and their health care providers make critical care decisions, including if and how to proceed with cancer treatment and the need for blood transfusions for those in hospice care.

02:02 AM

DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining. (arXiv:2008.01855v1 [cs.CR]) [cs.CR updates on arXiv.org]

Numerous metamorphic and polymorphic malicious variants are generated automatically on a daily basis by mutation engines that transform the code of a malicious program while retaining its functionality, in order to evade signature-based detection. These automatic processes have greatly increased the number of malware variants, deeming their fully-manual analysis impossible. Malware classification is the task of determining to which family a new malicious variant belongs. Variants of the same malware family show similar behavioral patterns. Thus, classifying newly discovered variants helps assess the risks they pose and determine which of them should undergo manual analysis by a security expert. This motivated intense research in recent years of how to devise high-accuracy automatic tools for malware classification. In this paper, we present DAEMON - a novel dataset-agnostic and even platform-agnostic malware classifier. We've optimized DAEMON using a large-scale dataset of x86 binaries, belonging to a mix of several malware families targeting computers running Windows. We then applied it, without any algorithmic change, features re-engineering or parameter tuning, to two other large-scale datasets of malicious Android applications of numerous malware families. DAEMON obtained top-notch classification results on all datasets, making it the first provably dataset-agnostic malware classifier to date. An important byproduct of the type of features used by DAEMON and the manner in which they are mined is that its classification results are explainable.

Direct label-free imaging of nanodomains in biomimetic and biological membranes by cryogenic electron microscopy [Biophysics and Computational Biology] [Early Edition]

The nanoscale organization of biological membranes into structurally and compositionally distinct lateral domains is believed to be central to membrane function. The nature of this organization has remained elusive due to a lack of methods to directly probe nanoscopic membrane features. We show here that cryogenic electron microscopy (cryo-EM) can...

Wednesday, 05 August 2020

06:02 PM

The tuatara genome reveals ancient features of amniote evolution [Nature - Issue - nature.com science feeds]

Nature, Published online: 05 August 2020; doi:10.1038/s41586-020-2561-9

The approximately 5-Gb tuatara (Sphenodon punctatus) genome assembly provides a resource for analysing amniote evolution, and highlights the imperative for meaningful cultural engagement with Indigenous communities in genome-sequencing endeavours.

Tuesday, 04 August 2020

06:02 PM

Intravenous Interferon β-1a for Severe ARDS—Reply [JAMA Current Issue]

In Reply Drs Zhou and Tang comment that the survival curves crossed each other in our study, and thus they questioned whether the proportionality assumption was met. Although the 1-year survival curves in the Kaplan-Meier plot overlapped at some points, the lines were generally distinct and there was no actual line crossing except at the very end, when there were few patients left in follow-up. In a diagnostic graph of log(−log[survival]) vs log of survival time, there was visible crossing of lines only in the very beginning of the log-time axis. Thus, overall, the time proportionality assumption seemed to hold well. In addition, in a Cox regression model including the evaluation of proportional hazards using the ASSESS statement (SAS proc phreg), no evidence of proportional hazards assumption violation was observed (P > .05). Further testing applying a Cox regression model including evaluation of proportional hazards using an interaction test with log(time) did not reveal a significant interaction (P > .05). In all, no significant deviation from the proportional hazards assumption was noted in visual or statistical evaluation of survival curves or resulting hazards.

Intravenous Interferon β-1a for Severe ARDS [JAMA Current Issue]

To the Editor Dr Ranieri and colleagues found that intravenous interferon (IFN) β-1a administered for 6 days, compared with placebo, did not result in significant improvement in a composite score that included death and ventilator-free days over a total of 28 days in patients with acute respiratory distress syndrome (ARDS). Some important issues remain to be discussed.

Immunotherapy Is Now First-line Therapy for Some Colorectal Cancers [JAMA Current Issue]

Pembrolizumab is the first immunotherapy drug approved as a first-line treatment for patients with certain types of colorectal cancer that can be administered without initial chemotherapy.

First-in-Class HIV Drug Is Approved [JAMA Current Issue]

A new medication has been approved for patients with HIV infection who, after receiving multiple treatments, have exhausted other options because of drug resistance, intolerance, or safety issues.

Maintaining Quality of Editorial Evaluation and Peer Review [JAMA Current Issue]

Concerns have been raised about how journals maintain their standards during the current coronavirus disease 2019 (COVID-19) pandemic, given the rapid pace and unprecedented volume of research being conducted in such a short time, and the large number of reports submitted to journals. For example, at JAMA, from January 1 to June 1, 2020, more than 11 000 manuscripts were submitted, compared with approximately 4000 manuscripts submitted during the same period in 2019. Virtually the entire increase has been related to manuscripts focused on COVID-19, with about one-third representing original research (full-length manuscripts, brief reports, and research letters) and two-thirds representing opinion (Viewpoints, A Piece of My Mind) and reviews.

02:02 AM

Poverty and the Labor Market: Today and Yesterday [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 107-134, August 2020.

How Distortions Alter the Impacts of International Trade in Developing Countries [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 213-238, August 2020.

Cities in the Developing World [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 273-297, August 2020.

Social Identity and Economic Policy [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 355-389, August 2020.

Peer Effects in Networks: A Survey [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 603-629, August 2020.

Sparse Domain Gaussianization for Multi-Variate Statistical Modeling of Retinal OCT Images [IEEE Transactions on Image Processing - new TOC]

In this paper, a multivariate statistical model that is suitable for describing Optical Coherence Tomography (OCT) images is introduced. The proposed model is comprised of a multivariate Gaussianization function in sparse domain. Such an approach has two advantages, i.e. 1) finding a function that can effectively transform the input - which is often not Gaussian - into normally distributed samples enables the reliable application of methods that assume Gaussianity, 2) although multivariate Gaussianization in spatial domain is a complicated task and rarely results in closed-form analytical model, by transferring data to sparse domain, our approach facilitates multivariate statistical modeling of OCT images. To this end, a proper multivariate probability density function (pdf) which considers all three properties of OCT images in sparse domains (i.e. compression, clustering, and persistence properties) is designed and the proposed sparse domain Gaussianization framework is established. Using this multivariate model, we show that the OCT images often follow a 2-component multivariate Laplace mixture model in the sparse domain. To evaluate the performance of the proposed model, it is employed for OCT image denoising in a Bayesian framework. Visual and numerical comparison with previous prominent methods reveals that our method improves the overall contrast of the image, preserves edges, suppresses background noise to a desirable amount, but is less capable of maintaining tissue texture. As a result, this method is suitable for applications where edge preservation is crucial, and a clean noiseless image is desired.

Weighted Smallest Deformation Similarity for NN-Based Template Matching [IEEE Transactions on Industrial Informatics - new TOC]

This article deals with the template matching problem, and a weighted smallest deformation similarity measure, which is robust to occlusions, background outliers, and complex deformations. The appearance-based nearest neighbor (NN) matching of points is constructed and the smallest location distance between each point in the template and its matching points is employed to penalize the deformation explicitly. Then, the weights are added to points in the template relied on their likelihood of belonging to the background through NN matching with the points around the target window. Experiments show that the proposed method improves the state-of-the-art performance on real-world scenario benchmarks and can be applied in rough positioning of surface mount technology components.

Smooth Transition in Communication for Swarm Control With Formation Change [IEEE Transactions on Industrial Informatics - new TOC]

In this article, we propose a smooth transition in communication for swarm control with formation change. The communication topology in a swarm is automatically constructed by the relation-invariable persistent formation, which is a method of a self-organized system. Based on the communication topology, intelligent units in swarm systems can solve difficult tasks which would be hardly completed by a single intelligent unit. Intelligent units in the swarm are controlled to realize swarm behaviors, such as aggregation, dispersion, and switching formation when the swarm moves. The switching function in the traditional swarm control with formation change is step function and the transient switching may result in communication failure, transient velocity change, energy waste, and actuator damage. To overcome transient switching in traditional methods, we design smooth transition in communication. The proposed method guarantees the intelligent units could be connected in the communication, which can ensure the communication security of swarm system during the process of communication topology changing. Introduction of smooth switching function in swam control makes it difficult to design the controller. The swarm control with smooth transition in communication, which is based on sliding mode control, is designed and analyzed. Finally, a simulation is carried out to verify the effectiveness of the proposed approaches.

Multiobjective Deployment of Data Analysis Operations in Heterogeneous IoT Infrastructure [IEEE Transactions on Industrial Informatics - new TOC]

The growth of Internet of Things (IoT) technology brings many new opportunities for applications in areas including smart healthcare, smart buildings, and smart agriculture. These applications must normally distribute the computations, required for extracting value from sensor data, over the IoT infrastructure platforms (e.g., sensors, phones, field-gateways, and clouds). This can be very challenging for IoT application developers due to the heterogeneity of the aforementioned platforms, potentially conflicting nonfunctional requirements (e.g., battery power, latency, and cost), and related deployment criteria, which is impossible to resolve manually. To address the above challenges, we have developed the PATH2iot framework that decomposes a complex IoT application into self-contained micro-operations. Based on the deployment criteria, PATH2iot automatically distributes the set of micro-operations across IoT infrastructure platforms, while respecting their run-time data and control flow dependencies. In our previous work, we have shown how to use the PATH2iot to optimize the battery life of a healthcare wearable. In this article, we describe a new research that significantly extends PATH2iot, which introduces a heuristic model capable of making optimal deployment decisions based on multiple conflicting nonfunctional requirements and selection criteria (user preferences). It does so by leveraging a well-known multicriteria decision-making method called the analytic hierarchical processes (AHP). The applicability of the deployment model is validated based on a real-world digital healthcare analytics use case. The results show that our model is able to find the optimal deployment solution for different user preferences.

Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition [IEEE Transactions on Industrial Informatics - new TOC]

With the advances of sensing technology and deep learning, deep learning based human activity recognition from sensor signal data has been actively studied. While deep neural networks can automatically extract features appropriate for the target task and focus on increasing the recognition performance, they cannot select important input sensor signals, which leads to the lack of interpretability. Since not all signals from wearable sensors are important for the target task, sensor signal importance will be insightful information for practitioners. In this article, we propose an interpretable and accurate convolutional neural network capable of select important sensor signals. This is enabled by spatially sparse convolutional filters whose sparsity is imposed by spatial group lasso. While there is a tradeoff between accuracy and interpretability in a model, experimental results on the opportunity activity recognition dataset show that the proposed model can help improve recognition performance and select important sensor signals, providing interpretability.

Stochastic Modeling Based Nonlinear Bayesian Filtering for Photoplethysmography Denoising in Wearable Devices [IEEE Transactions on Industrial Informatics - new TOC]

Photoplethysmography (PPG) has shown its great potential for noninvasive health monitoring, but its application in wearable devices is largely impeded due to its extreme vulnerability to motion artifacts. In this article, we proposed a new stochastic modeling based nonlinear Bayesian filtering framework for the recovery of corrupted PPG waveform under strenuous physical exercise in wearable health-monitoring devices. A deep recurrent neural network was first recruited for accurate cardiac-period segmentation of corrupted PPG signals. Then, a stochastic model was applied to extract waveform details from clean PPG pulses, and was further derived into a system-state space. Following this was an extended Kalman filter using the state-space structured by modeling. The covariance of measurement noise was estimated by motion-related information to adjust it into the real physical environment adaptively. Comparison results with state-of-the-art methods on a wearable-device-based 48-subject data set showed the outstanding performance of the proposed denoising framework, with period-segmentation sensitivity and precision higher than 99.1%, instantaneous heart rate (HR) error lower than 2 beats/min, average HR error down to 1.14 beats/min, and recovery accuracy of waveform details significantly improved (p < 0.05). This framework is the first PPG denoising strategy that introduces waveform-modeling methods to ensure detail recovery, and a great example of algorithm fusion between stochastic signal processing and emerging deep learning methods for time-sequential biomedical signal processing.

Friday, 31 July 2020

02:02 AM

Of Modernity and Public Sociology: Reflections on a Career So Far [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 19-35, July 2020.

The Comparative Politics of Collective Memory [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 175-194, July 2020.

What Do Platforms Do? Understanding the Gig Economy [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 273-294, July 2020.

Transnational Professionals [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 399-417, July 2020.

Detecting Hardware-Assisted Virtualization With Inconspicuous Features [IEEE Transactions on Information Forensics and Security - new TOC]

Recent years have witnessed the proliferation of the deployment of virtualization techniques. Virtualization is designed to be transparent, that is, unprivileged users should not be able to detect whether a system is virtualized. Such detection can result in serious security threats such as evading virtual machine (VM)-based malware dynamic analysis and exploiting vulnerabilities for cross-VM attacks. The traditional software-based virtualization leaves numerous artifacts/fingerprints, which can be exploited without much effort to detect the virtualization. In contrast, current mainstream hardware-assisted virtualization significantly enhances the virtualization transparency, making itself more transparent and difficult to be detected. Nonetheless, we showcase three new identified low-level inconspicuous features, which can be leveraged by an unprivileged adversary to effectively and stealthily detect the hardware-assisted virtualization. All three features come from the chipset fingerprints, rather than the traces of software-based virtualization implementations (e.g., Xen or KVM). The identified features include i) Translation-Lookaside Buffer (TLB) stores an extra layer of address translations; ii) Last-Level Cache (LLC) caches one more layer of page-table entries; and iii) Level-1 Data (L1D) Cache is unstable. Based on the above features, we develop three corresponding virtualization detection techniques, which are then comprehensively evaluated on three native environments and three popular cloud providers: i) Amazon Elastic Compute Cloud, ii) Google Compute Engine and iii) Microsoft Azure. Experimental results validate that these three adversarial detection techniques are effective (with no false positive) and stealthy (without triggering suspicious system events, e.g., VM-exit) in detecting the above commodity virtualized environments.

Evaluation of the Time Stability and Uniqueness in PPG-Based Biometric System [IEEE Transactions on Information Forensics and Security - new TOC]

In this work, we demonstrates the feasibility of employing the biometric photoplethysmography (PPG) signal for human verification applications. The PPG signal has dominance in terms of accessibility and portability which makes its usage in many applications such as user access control very appealing. Therefore, we developed robust time-stable features using signal analysis and deep learning models to increase the robustness and performance of the verification system with the PPG signal. The proposed system focuses on utilizing different stretching mechanisms namely Dynamic Time Warping, zero padding and interpolation with Fourier transform, and fuses them at the data level to be then deployed with different deep learning models. The designed deep models consist of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) which are considered to build a user specific model for the verification task. We collected a dataset consisting of 100 participants and recorded at two different time sessions using Plux pulse sensor. This dataset along with another two public databases are deployed to evaluate the performance of the proposed verification system in terms of uniqueness and time stability. The final result demonstrates the superiority of our proposed system tested on the built dataset and compared with other two public databases. The best performance achieved from our collected two-sessions database in terms of accuracy is 98% for the single-session and 87.1% for the two-sessions scenarios.

On the Security of Reversible Data Hiding in Encrypted Images by MSB Prediction [IEEE Transactions on Information Forensics and Security - new TOC]

The reversible data hiding in encrypted images by MSB prediction of P. Puteaux and W. Puesch not only provides high embedding bit-rates, but also entails a very low mathematical complexity. This correspondence investigates its security and shows flaws in embedding imperceptibility, unauthorized detection/removal of embedded data and unauthorized access to image content. Secure solutions are discussed.

Optimizing Inner Product Masking Scheme by a Coding Theory Approach [IEEE Transactions on Information Forensics and Security - new TOC]

Masking is one of the most popular countermeasures to protect cryptographic implementations against side-channel analysis since it is provably secure and can be deployed at the algorithm level. To strengthen the original Boolean masking scheme, several works have suggested using schemes with high algebraic complexity. The Inner Product Masking (IPM) is one of those. In this paper, we propose a unified framework to quantitatively assess the side-channel security of the IPM in a coding-theoretic approach. Specifically, starting from the expression of IPM in a coded form, we use two defining parameters of the code to characterize its side-channel resistance. In order to validate the framework, we then connect it to two leakage metrics (namely signal-to-noise ratio and mutual information, from an information-theoretic aspect) and one typical attack metric (success rate, from a practical aspect) to build a firm foundation for our framework. As an application, our results provide ultimate explanations on the observations made by Balasch et al. at EUROCRYPT’15 and at ASIACRYPT’17, Wang et al. at CARDIS’16 and Poussier et al. at CARDIS’17 regarding the parameter effects in IPM, like higher security order in bounded moment model. Furthermore, we show how to systematically choose optimal codes (in the sense of a concrete security level) to optimize IPM by using this framework. Eventually, we present a simple but effective algorithm for choosing optimal codes for IPM, which is of special interest for designers when selecting optimal parameters for IPM.

PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously [IEEE Transactions on Information Forensics and Security - new TOC]

Today’s blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: “polynomially coded sharding” scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system. We provide simulation results that numerically demonstrate the performance improvement over state of the arts, and the scalability of the PolyShard system. Finally, we discuss potential enhancements, and highlight practical considerations in building such a system.

Thursday, 30 July 2020

06:02 PM

This super ‘sponge’ releases steam when the Sun shines [Nature - Issue - nature.com science feeds]

Nature, Published online: 30 July 2020; doi:10.1038/d41586-020-02255-y

A curious gel can soak up water vapour from the air and swell to enormous size without bursting.

Violence in Latin America: An Overview of Research and Issues [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 693-706, July 2020.

02:02 AM

LRCH1 deficiency enhances LAT signalosome formation and CD8+ T cell responses against tumors and pathogens [Immunology and Inflammation] [Early Edition]

CD8+ T cells play pivotal roles in eradicating pathogens and tumor cells. T cell receptor (TCR) signaling is vital for the optimal activation of CD8+ T cells. Upon TCR engagement, the transmembrane adapter protein LAT (linker for activation of T cells) recruits other key signaling molecules and forms the “LAT...

Wednesday, 29 July 2020

06:02 PM

Bacteria-eating viruses could provide a route to stability in cystic fibrosis [Nature - Issue - nature.com science feeds]

Nature, Published online: 29 July 2020; doi:10.1038/d41586-020-02109-7

Phage therapy is broadening the treatment landscape for people with drug-resistant infections.

Tuesday, 28 July 2020

06:02 PM

Algorithms as discrimination detectors [Computer Sciences] [Early Edition]

Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity...

02:03 AM

Bacterial synthesis of C3-C5 diols via extending amino acid catabolism [Applied Biological Sciences] [Early Edition]

Amino acids are naturally occurring and structurally diverse metabolites in biological system, whose potentials for chemical expansion, however, have not been fully explored. Here, we devise a metabolic platform capable of producing industrially important C3-C5 diols from amino acids. The presented platform combines the natural catabolism of charged amino acids...

Friday, 24 July 2020

02:02 AM

A structural framework for unidirectional transport by a bacterial ABC exporter [Biophysics and Computational Biology] [Early Edition]

The ATP-binding cassette (ABC) transporter of mitochondria (Atm1) mediates iron homeostasis in eukaryotes, while the prokaryotic homolog from Novosphingobium aromaticivorans (NaAtm1) can export glutathione derivatives and confer protection against heavy-metal toxicity. To establish the structural framework underlying the NaAtm1 transport mechanism, we determined eight structures by X-ray crystallography and single-particle...

Thursday, 23 July 2020

Tuesday, 21 July 2020

02:02 AM

An Unsaturated Inductance Reconstruction Based Universal Sensorless Starting Control Scheme for SRM Drives [IEEE Transactions on Industrial Electronics - new TOC]

In this article, an universal sensorless starting scheme considering magnetic saturation effects is proposed to control the switched reluctance motor (SRM) drives. In this approach, the full-cycle unsaturated inductance can be reconstructed by converting the saturated incremental inductance into the unsaturated inductance with a simple mathematical model in the conduction region. Based on the reconstructed three-phase full-cycle unsaturated inductance, the inductance vector coordinate transformation method and the linear region inductance model based method can be applied for estimating the rotor position even under high load starting case. To verify the validity of the proposed methods, experiments have been implemented in a 1-kW three-phase 12/8 structure SRM prototype. The experimental results verify that the unsaturated inductance reconstruction scheme can transfer the nonlinear magnetic saturation problem into an unsaturation problem, and thus the rotor position estimation at the sensorless starting state can be simplified. The method can realize the initial position estimation and reliable sensorless starting control even under load conditions with only very simple initial inductance data acquisition, position partition logics design, and mathematic modeling. In addition, as the back-electromotive force is eliminated indirectly in the inductance calculation process, the adaptable speed range can also be extended.

Extending the Linear Modulation Range to Full Base Speed Independent of Load Power Factor for a Multilevel Inverter Fed IM Drive [IEEE Transactions on Industrial Electronics - new TOC]

In this article, a method is proposed for the first time, to increase the linear modulation range till full base speed irrespective of load power factor for a multilevel inverter fed induction motor drive. A five-level inverter with a single dc source is used for the proposed scheme. Using this method, linear pulsewidth modulation (PWM) operation of the inverter can be achieved till peak phase fundamental voltage of 0.637 $V_{text{dc}}$ without exceeding machine phase voltage rating. This method eliminates all the lower order harmonics from machine phase voltage till the full speed range of operation. The proposed five-level inverter scheme is realized by cascading a two-level inverter with a capacitor fed H-bridge, feeding open-end induction motor drive from one end and a capacitor fed two-level inverter from the other end. The proposed scheme uses space vector redundancy to balance all the capacitors during its operation. Operation of the proposed drive is analyzed extensively for normal five-level operation as well as during the extended modulation range. Experimental results for steady state and transient state operation are presented to validate the proposed scheme

1-<roman>k</roman>V Input 1-MH<roman>z</roman> G<roman>a</roman>N Stacked Bridge <inline-formula><tex-math notation="LaTeX">$textit{LLC}$</tex-math></inline-formula> Converters [IEEE Transactions on Industrial Electronics - new TOC]

In this article, a stacked bridge LLC converter with the split resonant tanks suitable for high input voltage is proposed to reduce the voltage stress of primary-side devices by half. The 650-V enhancement-mode gallium nitride high-electron mobility transistors (eGaN HEMTs) with much lower $R_{ds{rm{(on)}}}$ and $Q_{g}$ are applied instead of 1700-V silicon carbide (SiC) mosfets at 1-kV input to produce significantly reduced conduction loss and switching loss at 1 MHz. With eGaN HEMTs switching speed of 2.5 ns at 1-kV input, high dv/dt of 200 kV/μs causes large displacement current via the interwinding capacitance of the planar transformers, which distorts the resonant current seriously and causes zero voltage switching (ZVS) lost inducing additional switching loss and root mean square loss. The split resonant tanks solution is proposed to reduce the resonant current distortion to guarantee ZVS and voltage balance over the split capacitance. To reduce the interwinding capacitance, the noninterleaving type windings are proposed to minimize the displacement current from the primary side to secondary side of the matrix transformer. A practical engineering practice of a 1-MHz prototype with 1-kV input and 32-V/3-kW output is built. It achieves the peak efficiency of 96.2% at full load and power density of 107 W/in3, a size reduction of 69% compared to the 300-kHz SiC counterpart.

Research on Power Equalization of Three-Phase Cascaded H-Bridge Photovoltaic Inverter Based on the Combination of Hybrid Modulation Strategy and Zero-Sequence Injection Methods [IEEE Transactions on Industrial Electronics - new TOC]

Due to the nonuniform solar irradiance, unequal ambient temperatures, or inconsistent degradation of photovoltaic (PV) modules in three-phase cascaded H-bridge (CHB) PV inverter, the unbalanced output power among PV modules will lead to the imbalanced power between phases and bridges, resulting in unbalanced or even distorted grid current between three phases, which cannot meet the requirements of grid codes. Concerning this issue, this article proposes a novel power equalization method based on the combination of hybrid modulation strategy and zero-sequence injection methods, which deals with interbridge power imbalance by hybrid modulation and interphase power imbalance by zero-sequence injection methods. By utilizing the proposed method, three-phase-balanced grid currents with low total harmonic distortion are able to be achieved even when the interbridge and the interphase power are seriously unbalanced. In addition, each H-bridge cell in three-phase CHB inverter uses square-wave modulation, which maximizes the dc-side voltage utilization of each H-bridge cell, thus extending its power balance region. Experimental results achieved by a laboratory prototype of a three-phase seven-level CHB inverter demonstrate both feasibility and validity of the proposed method.

Salient Object Detection by Spatiotemporal and Semantic Features in Real-Time Video Processing Systems [IEEE Transactions on Industrial Electronics - new TOC]

Object detection is significant for event analysis in various intelligent multimedia processing systems. Although there have been many studies conducting research in this area, effective and efficient object detection methods for video sequences are still much desired. In this article, we investigate salient object detection in real-time multimedia processing systems. Considering the intrinsic relationship between top-down and bottom-up saliency features, we present a new effective method for video salient object detection based on deep semantic and spatiotemporal cues. After extracting top-down semantic features for object perception by a 2-D convolutional network, we concatenate them with bottom-up spatiotemporal cues for motion perception extracted by a 3-D convolutional network. In order to combine these features effectively, we feed them into a 3-D deconvolutional network for feature-sharing learning between semantic features and spatiotemporal cues for the final saliency prediction. Additionally, we propose a novel Gaussian-like loss function with an $L_{2}$-norm regularization term for parameter learning. Experimental results show that the proposed salient object detection approach performs better in terms of both effectiveness and efficiency for video sequences compared with the state-of-the-art models.

Thursday, 16 July 2020

Thursday, 09 July 2020

06:02 PM

Effect of External Counterpulsation on Running Performance and Perceived Recovery [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 7
Pages: 920-926

Effect of Sildenafil Citrate on Exercise Capacity in Athletes With Spinal Cord Injury [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 7
Pages: 971-975

Acute and Short-Term Response to Different Loading Conditions During Resisted Sprint Training [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 7
Pages: 997-1004

Ultraendurance Exercise in a Renal Transplant Recipient: A Case Study [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 7
Pages: 1039-1042

Thursday, 02 July 2020

Tuesday, 09 June 2020

02:01 AM

Deblurring Face Images Using Uncertainty Guided Multi-Stream Semantic Networks [IEEE Transactions on Image Processing - new TOC]

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards the challenging regions of the human face such as the eyes and nose. The entire network is trained in an end-to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at: https://github.com/rajeevyasarla/UMSN-Face-Deblurring.

Tuesday, 12 May 2020

02:02 AM

Understanding Multilateral Institutions in Easy and Hard Times [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 1-18, May 2020.

The Fluidity of Racial Classifications [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 221-240, May 2020.

Economic Development and Democracy: Predispositions and Triggers [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 241-257, May 2020.

Clientelism's Red Herrings: Dead Ends and New Directions in the Study of Nonprogrammatic Politics [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 277-294, May 2020.

Survey Experiments in International Political Economy: What We (Don't) Know About the Backlash Against Globalization [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 333-356, May 2020.

Monday, 13 April 2020

02:00 PM

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.

Copper Transport and Disease: What Can We Learn from Organoids? [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 75-94, August 2019.

Bile Acids as Metabolic Regulators and Nutrient Sensors [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 175-200, 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.

The Economics of Taxes on Sugar-Sweetened Beverages: A Review of the Effects on Prices, Sales, Cross-Border Shopping, and Consumption [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 317-338, August 2019.

Tuesday, 11 February 2020

03:00 PM

New Approaches to Target Inflammation in Heart Failure: Harnessing Insights from Studies of Immune Cell Diversity [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 1-20, February 2020.

Neuronal Mechanisms that Drive Organismal Aging Through the Lens of Perception [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 227-249, February 2020.

Diurnal Regulation of Renal Electrolyte Excretion: The Role of Paracrine Factors [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 343-363, February 2020.

Marrow Adipocytes: Origin, Structure, and Function [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 461-484, February 2020.

The Osteocyte: New Insights [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 485-506, February 2020.

Monday, 03 February 2020

03:00 PM

Reverse Attention-Based Residual Network for Salient Object Detection [IEEE Transactions on Image Processing - new TOC]

Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel top-down reverse attention block to guide the above side-output residual learning. Specifically, the current predicted salient regions are used to erase its side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in more complete detection and high accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art approaches, and shows advantages in simplicity, compactness and efficiency.

Wednesday, 29 January 2020

03:00 PM

50 FPS Object-Level Saliency Detection via Maximally Stable Region [IEEE Transactions on Image Processing - new TOC]

The human visual system tends to consider saliency of an object as a whole. Some object-level saliency detection methods have been proposed by leveraging object proposals in bounding boxes, and regarding the entire bounding box as one candidate salient region. However, the bounding boxes can not provide exact object position and a lot of pixels in bounding boxes belong to the background. Consequently, background pixels in bounding box also show high saliency. Besides, acquiring object proposals needs high time cost. In order to compute object-level saliency, we consider region growing from some seed superpixels, to find one surrounding region which probably represents the whole object. The desired surrounding region has similar appearance inside and obvious difference with the outside, which is proposed as maximally stable region (MSR) in this paper. In addition, one effective seed superpixel selection strategy is presented to improve speed. MSR based saliency detection is more robust than pixel or superpixel level methods and object proposal based methods. The proposed method significantly outperforms the state-of-the-art unsupervised methods at 50 FPS. Compared with deep learning based methods, we show worse performance, but with about 1200-1600 times faster, which means better trade-off between performance and speed.

LCSCNet: Linear Compressing-Based Skip-Connecting Network for Image Super-Resolution [IEEE Transactions on Image Processing - new TOC]

In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parameter-economic form of ResNet. Moreover, to better exploit hierarchical information from both low and high levels of various receptive fields in deep models, inspired by gate units in LSTM, we also propose an adaptive element-wise fusion strategy with multi-supervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.

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