Wednesday, 17 July 2019

04:00 PM

Blockchain Mutability: Challenges and Proposed Solutions. (arXiv:1907.07099v1 [cs.CR]) [cs.CR updates on]

Blockchain's evolution during the past decade is astonishing: from bitcoin to over 2.000 altcoins, and from decentralised electronic payments to transactions programmable by smart contracts and complex tokens governed by decentralised organisations. While the new generation of blockchain applications is still evolving, blockchain's technical characteristics are also advancing. Yet, immutability, a hitherto indisputable property according to which blockchain data cannot be edited nor deleted, remains the cornerstone of blockchain's security. Nevertheless, blockchain's immutability is being called into question lately in the light of the new erasing requirements imposed by the GDPR's ``\textit{Right to be Forgotten (RtbF)}'' provision. As the RtbF obliges blockchain data to be editable in order restricted content redactions, modifications or deletions to be applied when requested, blockchains compliance with the regulation is indeed challenging, if not impracticable. Towards resolving this contradiction, various methods and techniques for mutable blockchains have been proposed in an effort to satisfy regulatory erasing requirements while preserving blockchains' security. To this end, this work aims to provide a comprehensive review on the state-of-the-art research approaches, technical workarounds and advanced cryptographic techniques that have been put forward to resolve this conflict and to discuss their potentials, constraints and limitations when applied in the wild to either permissioned or permissionless blockchains.

The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. (arXiv:1802.08232v3 [cs.LG] UPDATED) [cs.CR updates on]

This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model. Because such models are sometimes trained on sensitive data (e.g., the text of users' private messages), this methodology can benefit privacy by allowing deep-learning practitioners to select means of training that minimize such memorization.

In experiments, we show that unintended memorization is a persistent, hard-to-avoid issue that can have serious consequences. Specifically, for models trained without consideration of memorization, we describe new, efficient procedures that can extract unique, secret sequences, such as credit card numbers. We show that our testing strategy is a practical and easy-to-use first line of defense, e.g., by describing its application to quantitatively limit data exposure in Google's Smart Compose, a commercial text-completion neural network trained on millions of users' email messages.

The TGF{beta} type I receptor TGF{beta}RI functions as an inhibitor of BMP signaling in cartilage [Developmental Biology] [Early Edition]

The type I TGFβ receptor TGFβRI (encoded by Tgfbr1) was ablated in cartilage. The resulting Tgfbr1Col2 mice exhibited lethal chondrodysplasia. Similar defects were not seen in mice lacking the type II TGFβ receptor or SMADs 2 and 3, the intracellular mediators of canonical TGFβ signaling. However, we detected elevated BMP...

AI solves the Rubik’s cube [Nature - Issue - science feeds]

Nature, Published online: 16 July 2019; doi:10.1038/d41586-019-02195-2

An algorithm unerringly finds solutions for a mind-bending puzzle.

Apocarotenoids: Emerging Roles in Mammals [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 38, Issue 1, Page 153-172, August 2018.

Fibroblast Growth Factor 21: A Versatile Regulator of Metabolic Homeostasis [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 38, Issue 1, Page 173-196, August 2018.

An Overview of Attitudes Toward Genetically Engineered Food [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 38, Issue 1, Page 459-479, August 2018.

Growing Global Alcohol Use [JAMA Current Issue]

Increasing alcohol use worldwide threatens to derail the World Health Organization’s (WHO’s) goal of cutting alcohol use by 10% by 2025, according to a study in The Lancet.

Steroid Side Effects [JAMA Current Issue]

This JAMA Patient Page describes use of steroid medications and the side effects that can result.

Price Increases of Protected Class Drugs in Medicare Part D Relative to Inflation, 2012-2017 [JAMA Current Issue]

This study characterizes price increases exceeding inflation between 2012 and 2017 for protected-class drugs (antineoplastics, antiretrovirals, antidepressants, antipsychotics, anticonvulsants, and immunosuppressants for transplant patients) that would lead to their exclusion from Medicare Part D coverage based on excessive cost under a 2018 CMS rule intended to facilitate more effective price negotiations.

Preserving Civility in Vaccine Policy Discourse [JAMA Current Issue]

This Viewpoint calls out the threat to vaccine policy posed by a very small number of antivaccine (“antivax”) advocates who disrupt attempts to engage vaccine-hesitant persons in public hearings about risks and benefits of vaccination and, acknowledging the concerns of a skeptical public, calls for a renewed commitment to civility in discussing public health policy.

The Academic Medical Center Value Debate—Are Teaching Hospitals Worth It? [JAMA Current Issue]

This Viewpoint reviews evidence about the outcomes and costs of health care at teaching vs nonteaching hospitals in an attempt to answer the question of whether the costs of academic medical centers are defensible and whether they deliver value for their price.

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.

Tuesday, 16 July 2019

04:00 PM

Anonymous and confidential file sharing over untrusted clouds. (arXiv:1907.06466v1 [cs.CR]) [cs.CR updates on]

Using public cloud services for storing and sharing confidential data requires end users to cryptographically protect both the data and the access to the data. In some cases, the identity of end users needs to remain confidential against the cloud provider and fellow users accessing the data. As such, the underlying cryptographic access control mechanism needs to ensure the anonymity of both data producers and consumers. We introduce A-SKY, a cryptographic access control extension capable of providing confidentiality and anonymity guarantees, all while efficiently scaling to large organizations. A-SKY leverages trusted execution environments (TEEs) to address the impracticality of anonymous broadcast encryption (ANOBE) schemes, achieving faster execution times and shorter ciphertexts. The innovative design of A-SKY limits the usage of the TEE to the narrow set of data producing operations, and thus optimizes the dominant data consumption actions by not requiring a TEE. Furthermore, we propose a scalable implementation for A-SKY leveraging micro-services that preserves strong security guarantees while being able to efficiently manage realistic large user bases. Results highlight that the A-SKY cryptographic scheme is 3 orders of magnitude better than state of the art ANOBE, and an end-to-end system encapsulating A-SKY can elastically scale to support groups of 10 000 users while maintaining processing costs below 1 second.

Time-Stamped Claim Logic. (arXiv:1907.06541v1 [cs.LO]) [cs.CR updates on]

The main objective of this paper is to define a logic for reasoning about distributed time-stamped claims. Such a logic is interesting for theoretical reasons, i.e., as a logic per se, but also because it has a number of practical applications, in particular when one needs to reason about a huge amount of pieces of evidence collected from different sources, where some of the pieces of evidence may be contradictory and some sources are considered to be more trustworthy than others. We introduce the Time-Stamped Claim Logic including a sound and complete sequent calculus that allows one to reduce the size of the collected set of evidence and removes inconsistencies, i.e., the logic ensures that the result is consistent with respect to the trust relations considered. In order to show how Time-Stamped Claim Logic can be used in practice, we consider a concrete cyber-attribution case study.

Estrela: Automated, Contextual Enforcement of Data Flow Policies across APIs. (arXiv:1811.08234v2 [cs.CR] UPDATED) [cs.CR updates on]

Existing solutions for tracking sensitive data and enforcing data usage policies have been intertwined with a specific host language---and multiple host languages in the case of database-backed applications. In this paper, we present an alternate, policy-agnostic approach that automatically enforces API-specific policies. We demonstrate that by associating policy enforcement with the API between the application and database, it is possible to automatically enforce rich and expressive policies across database-backed applications without depending on the application or database language. We present Estrela, a web framework that allows the specification of rich and expressive policies separately from the code and enforces the policies in a highly context-dependent manner. Estrela supports both query-level policies that are applied during data-access, and row-level policies that are more granular, complex and contextual. Estrela works with legacy applications without requiring any modification to the application code or the database for enforcing the policies. We build a prototype of Estrela and a language-agnostic version of Estrela in Python, on top of Django. We evaluate its performance and effectiveness by showing its application to a forum software, a social-networking site, a conference management system, and a company intranet. Estrela adds low overhead to existing applications and supports easy migration of existing applications for policy-compliance.

Correction for Saleem-Batcha et al., Enzymatic control of dioxygen binding and functionalization of the flavin cofactor [Corrections] [Early Edition]

BIOCHEMISTRY, BIOPHYSICS AND COMPUTATIONAL BIOLOGY Correction for “Enzymatic control of dioxygen binding and functionalization of the flavin cofactor,” by Raspudin Saleem-Batcha, Frederick Stull, Jacob N. Sanders, Bradley S. Moore, Bruce A. Palfey, K. N. Houk, and Robin Teufel, which was first published April 23, 2018; 10.1073/pnas.1801189115 (Proc. Natl. Acad. Sci....

Background gamma radiation and soil activity measurements in the northern Marshall Islands [Environmental Sciences] [Early Edition]

We report on measurements of external gamma radiation on 9 islands in 4 atolls in the northern Marshall Islands, all of which were affected by the US nuclear testing program from 1946 to 1958 (Enjebi, Ikuren, and Japtan in Enewetak Atoll; Bikini and Enyu in Bikini Atoll; Naen in Rongelap...

Xmark: Dynamic Software Watermarking Using Collatz Conjecture [IEEE Transactions on Information Forensics and Security - new TOC]

Dynamic software watermarking is one of the major countermeasures against software licensing violations. However, conventional dynamic watermarking approaches have exhibited a number of weaknesses including exploitable payload semantics, exploitable embedding/recognition procedures, and weak correlation between payload and subject software. This paper presents a novel dynamic watermarking method, Xmark, which leverages a well-known unsolved mathematical problem referred to as the Collatz conjecture. Our method works by transforming selected conditional constructs (which originally belonged to the software to be watermarked) with a control flow obfuscation technique based on Collatz conjecture. These obfuscation routines are built in a particular way such that they are able to express a watermark in the form of iteratively executed branching activities occurred during computing the aforementioned conjecture. Exploiting the one-to-one correspondence between natural numbers and their orbits computed by the conjecture (also known as the “Hailstone sequences”), Xmark's watermark-related activities are designed to be insignificant without the pre-defined secret input. Meanwhile, being integrated with obfuscation techniques, our method is able to resist attacks based on various reverse engineering techniques on both syntax and semantic levels. Analyses and simulations indicated that Xmark could evade detections via pattern matching and model checking, and meanwhile effectively prohibit dynamic symbolic execution. We have also shown that our method could remain robust even if a watermarked software is compromised via re-obfuscation using approaches like control flow flattening.

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.

Maternal secrets of our earliest ancestors unlocked [EurekAlert! - Breaking News]

New research brings to light for the first time the evolution of maternal roles and parenting responsibilities in one of our oldest evolutionary ancestors. Australopithecus africanus mothers breastfed their infants for the first 12 months after birth, and continued to supplement their diets with breastmilk during periods of food shortage. Tooth chemistry analyses enable scientists to 'read' more than two-million-year-old teeth. Finding demonstrates why early human ancestors had fewer offspring and extended parenting role.

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, 14 July 2019

10:12 AM

Versatile virus hops between three primate species — including humans [Nature - Issue - science feeds]

Nature, Published online: 12 July 2019; doi:10.1038/d41586-019-02122-5

No other emerging pathogen is known to have jumped so frequently from species to species.

Friday, 12 July 2019

04:00 PM

Cell communication in the blink of an eye [Nature - Issue - science feeds]

Nature, Published online: 10 July 2019; doi:10.1038/d41586-019-02069-7

The bodies of unicellular organisms called protists can contract extremely fast. Analysis reveals that the flow of surrounding fluid during contraction triggers a chain reaction of contraction of neighbouring protists.

Structure and assembly of the mitochondrial membrane remodelling GTPase Mgm1 [Nature - Issue - science feeds]

Nature, Published online: 10 July 2019; doi:10.1038/s41586-019-1372-3

Crystal and electron cryo-tomography structures of Mgm1 from Chaetomium thermophilum reveal that Mgm1 forms bent tetramers, which further assemble into helical filaments on both positively and negatively curved membranes.

[Comment] Polio endgame options: will we have the vaccines needed? [The Lancet]

The Global Polio Eradication Initiative (GPEI), which launched in 1988 with a mandate to eradicate polio by 2000,1 has maintained its mission to stop and prevent the transmission of all three serotypes (1, 2, and 3) of wild poliovirus (WPV). Ultimately, ending all cases of poliomyelitis requires successful cessation of the use of all live-attenuated oral poliovirus vaccines (OPV) after certification of WPV eradication. Global transmission of serotype-2 WPV ended before 2000; in September, 2015, the world certified serotype-2 WPV eradication;2 and in late April and early May, 2016, the GPEI globally coordinated the cessation of all use of serotype -2-containing OPV (OPV2), except for in emergency uses to respond to serotype-2 outbreaks.

[Perspectives] Escaping the scarcity loop [The Lancet]

We belong to a group of medical educators who share a concern about pressures in our work that threaten our capacity for reflection and creativity. We meet regularly to discuss papers that might enhance our practice and recently came across one by Arthur Frank in which he outlines sociological influences on medical practice. Frank argues that sociologists have a duty to uncover the unexamined assumptions that underlie the discourses of today's society and to question these when needed. We were intrigued by Frank's use of the term “scarcity loop” to describe an assumption that he believes dominates health care.

[Correspondence] Global mental health [The Lancet]

As UK trainee psychiatrists sharing an interest in global mental health, we welcome the Lancet Commission on global mental health and sustainable development1 with enthusiasm. We concur that task sharing, as a central factor to scaling up global mental health care, requires engagement from multidisciplinary mental health specialists. We also agree that in addition to optimising clinical care at a distance, digital technologies can enhance the training and supervision of non-specialist health workers.

[Department of Error] Department of Error [The Lancet]

Imel EA, Glorieux FH, Whyte MP, et al. Burosumab versus conventional therapy in children with X-linked hypophosphataemia: a randomised, active-controlled, open-label, phase 3 trial. Lancet 2019; 393: 2416–27—In this Article, the error bars in figure 2B have been updated. In table 1, “Mean serum TmP/GFR concentration (mmol/L)” has been changed to “Mean TMP/GFR ratio (mmol/L)”. The appendix has also been corrected. These corrections have been made to the online version as of July 11, 2019.

[Articles] The safety and immunogenicity of two novel live attenuated monovalent (serotype 2) oral poliovirus vaccines in healthy adults: a double-blind, single-centre phase 1 study [The Lancet]

We found that the novel OPV2 candidates were safe and immunogenic in IPV-immunised adults, and our data support the further development of these vaccines to potentially be used for maintaining global eradication of neurovirulent type-2 polioviruses.

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

Provides a listing of current committee members and society officers.

Wednesday, 10 July 2019

04:00 PM

Characteristic disruptions of an excitable carbon cycle [Applied Mathematics] [Early Edition]

The history of the carbon cycle is punctuated by enigmatic transient changes in the ocean’s store of carbon. Mass extinction is always accompanied by such a disruption, but most disruptions are relatively benign. The less calamitous group exhibits a characteristic rate of change whereas greater surges accompany mass extinctions. To...

Moon on the mind: two millennia of lunar literature [Nature - Issue - science feeds]

Nature, Published online: 09 July 2019; doi:10.1038/d41586-019-02090-w

Fireworks, wild swans and super-cannons were propelling people mentally Moonwards long before 1969, reveals David Seed.

Monday, 08 July 2019

12:00 AM

Stochastic geometry sensing and polarization in a lipid kinase-phosphatase competitive reaction [Biophysics and Computational Biology] [Early Edition]

Phosphorylation reactions, driven by competing kinases and phosphatases, are central elements of cellular signal transduction. We reconstituted a native eukaryotic lipid kinase–phosphatase reaction that drives the interconversion of phosphatidylinositol-4-phosphate [PI(4)P] and phosphatidylinositol-4,5-phosphate [PI(4,5)P2] on membrane surfaces. This system exhibited bistability and formed spatial composition patterns on supported membranes. In smaller...

Friday, 05 July 2019

04:00 PM

Global and Local Consistent Wavelet-Domain Age Synthesis [IEEE Transactions on Information Forensics and Security - new TOC]

Age synthesis is a challenging task due to the complicated and non-linear transformation in the human aging process. Aging information is usually reflected in local facial parts, such as wrinkles at the eye corners. However, these local facial parts contribute less in previous GAN-based methods for age synthesis. To address this issue, we propose a wavelet-domain global and local consistent age generative adversarial network (WaveletGLCA-GAN), in which one global specific network and three local specific networks are integrated together to capture both global topology information and local texture details of human faces. Different from the most existing methods that modeling age synthesis in image domain, we adopt wavelet transform to depict the textual information in frequency domain. Moreover, five types of losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2) identity preserving loss aims to better preserve identity information; 3) age preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise loss aims to preserve the background information of the input face; and 5) the total variation regularization aims to remove ghosting artifacts. Our method is evaluated on three face aging datasets, including CACD2000, Morph, and FG-NET. Qualitative and quantitative experiments show the superiority of the proposed method over other state-of-the-arts.

Interdependent Strategic Security Risk Management With Bounded Rationality in the Internet of Things [IEEE Transactions on Information Forensics and Security - new TOC]

With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks that he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network of nodes to respond to. Based on this simplified cognitive network representation, each user then determines his security management policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent and thus should be addressed in a holistic manner. We establish a games-in-games framework and propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents and quantify their risk of bounded perception due to the limited attention. In addition, we design a proximal-based iterative algorithm to compute the GNE. With case studies of smart communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security management.

Privacy-Preserving Deep Learning via Weight Transmission [IEEE Transactions on Information Forensics and Security - new TOC]

This paper considers the scenario that multiple data owners wish to apply a machine learning method over the combined dataset of all owners to obtain the best possible learning output but do not want to share the local datasets owing to privacy concerns. We design systems for the scenario that the stochastic gradient descent (SGD) algorithm is used as the machine learning method, because SGD (or its variants) is at the heart of recent deep learning techniques over neural networks. Our systems differ from the existing systems in the following features: 1) any activation function can be used, meaning that no privacy-preserving-friendly approximation is required; 2) gradients computed by SGD are not shared but the weight parameters are shared instead; and 3) robustness against colluding parties even in the extreme case that only one honest party exists. One of our systems requires a shared symmetric key among the data owners (trainers) to ensure the secrecy of the weight parameters against a central server. We prove that our systems, while privacy preserving, achieve the same learning accuracy as SGD and, hence, retain the merit of deep learning with respect to accuracy. Finally, we conduct several experiments using benchmark datasets and show that our systems outperform the previous system in terms of learning accuracies.

Secrecy Amplification of Distributed Encrypted Sources With Correlated Keys Using Post-Encryption-Compression [IEEE Transactions on Information Forensics and Security - new TOC]

In this paper, we are interested in the problem of strengthening the secrecy of distributed encryption in a particular case where the encryption keys are correlated to each other. We show that the post-encryption-compression (PEC) paradigm provides a suitable solution for the problem without requiring any additional secret randomness. More precisely, we show that in the case of one-time-pad encryption, we can use affine encoders with specific properties to strengthen the secrecy by using them to compress the ciphertexts before sending them to public communication channels. We show how to derive those affine encoders using universal code construction paradigm. The universal code construction gives us advantages as follows. 1) We can construct good encoders based on the transmission rates only, without knowing the value of the distribution of sources or keys. 2) Reliability and secrecy are achievable by construction even in particular cases such as follows: 1) only the range of correlation between keys is known but the exact amount is unknown or 2) the correlation between keys are changing time to time within a certain range. We also derive explicit lower bounds on the achievable reliability and secrecy exponents, i.e., the exponential rates of decay of the probability of decoding error and of the information leakage as the block length becomes large.

Sensor-Network-Based Navigation of a Mobile Robot for Extremum Seeking Using a Topology Map [IEEE Transactions on Industrial Informatics - new TOC]

A navigation algorithm for source seeking in a sensor network environment is presented in this paper. The solution consists of a gradient-free approach and maximum likelihood topology maps of sensor networks. The robot is navigated using an angular velocity limited by maximum and minimum constants, and by sensor measurements gathered by sensors that are close to robot's current location. The location of the robot is calculated using sensor topology coordinates, which is an alternative to the physical coordinate system and does not depend on physical distance measurement techniques such as received signal strength. However, actual physical distances are hidden in topology maps due to nonlinear distortions compared to physical distance between nodes. Thus, the proposed control law does not depend on any distance-based information. The performance of the algorithm is evaluated using computer simulations and experiments with a real mobile robot.

Optimal Configuration of Interdependence Between Communication Network and Power Grid [IEEE Transactions on Industrial Informatics - new TOC]

Smart grid is a cyber-physical system with interdependent power grid and communication network. Such interdependence make the smart grid fragile against cascading failures, where an initial failure in communication network may lead to further failures in power grid, and vice versa. We prevent such inter-network cascading failure by stopping an initial failure from propagating. This is achieved by providing a sufficient number of power-disjoint communication routes between power nodes and control center. The number of power-disjoint routes is a robustness metric. We aim to maximize robustness by configuring the interdependence relationship between power grid and communication networks. Such relationship indicates which power node supplies energy to which communication node, and which communication route is used to connect which power node to the control center. Following Menger's Theorem, we transform such configuration problem into a Maxflow problem and solve it using the simplex algorithm. We have evaluated the proposed method through extensive simulations. The results confirm the optimality and computational efficiency of the proposed method. The number of power-disjoint routes increases with an increase in the network size, but can never exceed the number of node-disjoint routes for a same network.

Optimal Power Scheduling of Cooperative Microgrids in Electricity Market Environment [IEEE Transactions on Industrial Informatics - new TOC]

This paper proposes an optimal power scheduling strategy for cooperative operation of multiple-coupled microgrids, where the microgrid coalition makes day-ahead energy exchange commitments with the grid. Deviations from their commitments are limited by penalizing the microgrids. Various system intermittencies are captured via scenarios. The framework aims at maximizing the expected profit of each microgrid, while mitigating the distribution power losses by enabling local power trades between all microgrids within a coalition. The scheduling problem only utilizes the power exchange requests as the control signals between participating microgrids. Extensive numerical results are presented to corroborate the efficacy of the proposed approach.

Multilayer Data-Driven Cyber-Attack Detection System for Industrial Control Systems Based on Network, System, and Process Data [IEEE Transactions on Industrial Informatics - new TOC]

The growing number of attacks against cyber-physical systems in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes, and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (bagging), and random forest (RF), to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, bagging, and RF have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber attacks. Cyber attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression model is studied to strengthen early attack detection. The result shows that this approach de- ects physically impactful cyber attacks before significant consequences occur. The proposed multiple-layer data-driven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.

Thursday, 04 July 2019

Tuesday, 02 July 2019

04:00 PM

Expanded Space Vector Modulation of Direct Matrix Converters Including Hidden Rotating Pairs [IEEE Transactions on Industrial Electronics - new TOC]

A novel version of the traditional space vector modulation (SVM) technique, with the integration of the rotating vectors, hereinafter referred to as expanded SVM, is introduced in this paper. Although different ways to benefit from the rotating vectors’ configuration have already been proposed in the literature, the generalization of the SVM theory to include them has not been proposed so far. The rotating vectors are grouped in rotating pairs, which are demonstrated to behave, by all practical aspects, as any other stationary vector. As a consequence, a circular modulation style is developed, which comprises increased flexibility over the traditional approach, thus improving the direct matrix converter capabilities. The new technique is used to achieve safe commutation, even though it might have been also applied to improve the switching losses as well. Besides the theoretical analysis, simulation and experimental results are shown to support the feasibility of the approach.

Weather-Classification-MARS-Based Photovoltaic Power Forecasting for Energy Imbalance Market [IEEE Transactions on Industrial Electronics - new TOC]

Energy imbalance market (EIM) provides an opportunity that allows larger shares of variable renewable energy sources in the grid. Under highly volatile weather conditions, an accurate forecasting of photovoltaic (PV) power is necessary for grid stability and market operation. Most of existing forecasting methods strongly rely on the accuracy of measurements, and the adaptability of these methods to complex weather conditions is rarely discussed. In this paper, a weather classification multivariate adaptive regression spline (MARS) forecasting model is introduced for complex weather conditions in all seasons. It can be updated incrementally and its high computational efficiency satisfies EIM operations. A data set that consists of the historical power and meteorological parameters produced by a small-scale PV platform is classified and used to train MARS models with forecast horizons ranging from 15 min to 24 h in different seasons. The tests and analyses results indicate higher accuracy, adaptability, and efficiency of the novel model.

Uncertainty and Disturbance Estimator Based Controller Equipped With a Multiple-Time-Delayed Filter to Improve the Voltage Quality of Inverters [IEEE Transactions on Industrial Electronics - new TOC]

In this paper, a two-degrees-of-freedom control algorithm based on uncertainty and disturbance estimator (UDE), aimed to minimize the total harmonic distortion of inverter output voltage is proposed, possessing enhanced robustness to fundamental frequency variations. A multiple-time-delay action is combined with a commonly utilized low-pass UDE filter to increase the range of output impedance magnitude minimization around odd multiples of fundamental frequency for enhanced rejection of typical single-phase nonlinear loads harmonics. Marginal robustness improvement achieved by increasing the number of time delays is quantified analytically and revealed to be independent of delay order. The performance of the proposed control approach and its superiority over two recently proposed methods is validated successfully by experimental results.

Integration of Solar PV With Low-Voltage Weak Grid System: Using Maximize-M Kalman Filter and Self-Tuned P&O Algorithm [IEEE Transactions on Industrial Electronics - new TOC]

This paper introduces a novel self-tuned perturb and observe (SPO) algorithm for quick maximum power point tracking (MPPT) and a novel maximize-M Kalman filter (MMKF)-based control technique for optimal operation of grid-integrated solar photovoltaic (PV) energy conversion system, where linear/nonlinear loads are attached at point of common coupling (PCC). The proposed SPO is the improved form of perturb and observe (P&O) algorithm, where inherent problems of traditional P&O such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. Therefore, SPO tracks maximum power peak (MPP) very rapidly, and it very accurately extracts maximum power from the PV array. The extracted power is used to meet the active power requirement of loads, and after meeting the load demand, the excess power is supplied to the grid. During power feeding, the power quality and power management are maintained by the MMKF-based control technique. In control strategy, the MMKF is used for fundamental harmonic component extraction from the grid voltage and load current, even when the grid voltage is characterized by adverse situations, such as sag, swell, harmonic distortion, dc offset, etc. Here, the SPO MPPT algorithm and MMKF-based control techniques are tested on a developed prototype. The efficient and reliable performances of SPO MPPT algorithm and MMKF-based control algorithm, in dynamic as well as in steady-state condition, are demonstrated under insolation variation conditions, nonlinear loading, as well as in different grid disturbances such as overvoltage, undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc.

Full-State Feedback Control of Back-to-Back Converters Based on Differential and Common Power Concepts [IEEE Transactions on Industrial Electronics - new TOC]

The back-to-back (BTB) converter is one of the most popular converter topologies for the control of electrical machines, power transmission systems, and power quality applications. The conventional cascade control structure is commonly used due to its simple design procedure and reliable operation. However, the bandwidth of the dc-link voltage controller has to be limited in order to avoid instability issues and be restrictive in high-performance applications. This paper presents a differential- and common-current (power)-based state-feedback control for BTB converters. This controller features a fast control of active and reactive powers, and a stiff regulation of the dc-link voltage. A theoretical analysis of the proposed controller along with a strategy for current limiting is presented. Its robustness was tested against variations of the filter parameters, the dc capacitance, the grid inductance, and the grid voltage values. The controller was experimentally validated under nominal operation, voltage sags, and connected to a weak grid by using a 15-kVA BTB converter in a laboratory test network.

Color Image and Multispectral Image Denoising Using Block Diagonal Representation [IEEE Transactions on Image Processing - new TOC]

Filtering images of more than one channel are challenging in terms of both efficiency and effectiveness. By grouping similar patches to utilize the self-similarity and sparse linear approximation of natural images, recent nonlocal and transform-domain methods have been widely used in color and multispectral image (MSI) denoising. Many related methods focus on the modeling of group level correlation to enhance sparsity, which often resorts to a recursive strategy with a large number of similar patches. The importance of the patch level representation is understated. In this paper, we mainly investigate the influence and potential of representation at patch level by considering a general formulation with a block diagonal matrix. We further show that by training a proper global patch basis, along with a local principal component analysis transform in the grouping dimension, a simple transform-threshold-inverse method could produce very competitive results. Fast implementation is also developed to reduce the computational complexity. The extensive experiments on both the simulated and real datasets demonstrate its robustness, effectiveness, and efficiency.

Structural Similarity-Based Nonlocal Variational Models for Image Restoration [IEEE Transactions on Image Processing - new TOC]

In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models.

High ISO JPEG Image Denoising by Deep Fusion of Collaborative and Convolutional Filtering [IEEE Transactions on Image Processing - new TOC]

Capturing images at high ISO modes will introduce much realistic noise, which is difficult to be removed by traditional denoising methods. In this paper, we propose a novel denoising method for high ISO JPEG images via deep fusion of collaborative and convolutional filtering. Collaborative filtering explores the non-local similarity of natural images, while convolutional filtering takes advantage of the large capacity of convolutional neural networks (CNNs) to infer noise from noisy images. We observe that the noise variance map of a high ISO JPEG image is spatial-dependent and has a Bayer-like pattern. Therefore, we introduce the Bayer pattern prior in our noise estimation and collaborative filtering stages. Since collaborative filtering is good at recovering repeatable structures and convolutional filtering is good at recovering irregular patterns and removing noise in flat regions, we propose to fuse the strengths of the two methods via deep CNN. The experimental results demonstrate that our method outperforms the state-of-the-art realistic noise removal methods for a wide variety of testing images in both subjective and objective measurements. In addition, we construct a dataset with noisy and clean image pairs for high ISO JPEG images to facilitate research on this topic.

Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes [IEEE Transactions on Image Processing - new TOC]

Semantic segmentation, a pixel-level vision task, is rapidly developed by using convolutional neural networks (CNNs). Training CNNs requires a large amount of labeled data, but manually annotating data is difficult. For emancipating manpower, in recent years, some synthetic datasets are released. However, they are still different from real scenes, which causes that training a model on the synthetic data (source domain) cannot achieve a good performance on real urban scenes (target domain). In this paper, we propose a weakly supervised adversarial domain adaptation to improve the segmentation performance from synthetic data to real scenes, which consists of three deep neural networks. A detection and segmentation (DS) model focuses on detecting objects and predicting segmentation map; a pixel-level domain classifier (PDC) tries to distinguish the image features from which domains; and an object-level domain classifier (ODC) discriminates the objects from which domains and predicts object classes. PDC and ODC are treated as the discriminators, and DS is considered as the generator. By the adversarial learning, DS is supposed to learn domain-invariant features. In experiments, our proposed method yields the new record of mIoU metric in the same problem.

Combining Bottom-Up and Top-Down Visual Mechanisms for Color Constancy Under Varying Illumination [IEEE Transactions on Image Processing - new TOC]

Multi-illuminant-based color constancy (MCC) is quite a challenging task. In this paper, we proposed a novel model motivated by the bottom-up and top-down mechanisms of human visual system (HVS) to estimate the spatially varying illumination in a scene. The motivation for bottom-up based estimation is from our finding that the bright and dark parts in a scene play different roles in encoding illuminants. However, handling the color shift of large colorful objects is difficult using pure bottom-up processing. Thus, we further introduce a top-down constraint inspired by the findings in visual psychophysics, in which high-level information (e.g., the prior of light source colors) plays a key role in visual color constancy. In order to implement the top-down hypothesis, we simply learn a color mapping between the illuminant distribution estimated by bottom-up processing and the ground truth maps provided by the dataset. We evaluated our model on four datasets and the results show that our method obtains very competitive performance compared with the state-of-the-art MCC algorithms. Moreover, the robustness of our model is more tangible considering that our results were obtained using the same parameters for all the datasets or the parameters of our model were learned from the inputs, that is, mimicking how HVS operates. We also show the color correction results on some real-world images taken from the web.

Thursday, 20 June 2019

04:00 PM

Introduction [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 38, Issue 1, Page v-v, August 2018.

Nutritional Influences on One-Carbon Metabolism: Effects on Arsenic Methylation and Toxicity [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 38, Issue 1, Page 401-429, August 2018.

Thursday, 13 June 2019

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.

Political Responses to Economic Shocks [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 277-295, 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.

Epithelial-Stromal Interactions in Pancreatic Cancer [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 211-233, 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.

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.

Steps in Mechanotransduction Pathways that Control Cell Morphology [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 585-605, 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.

On Becoming a Mathematical Demographer—And the Career in Problem-Focused Inquiry that Followed [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 44, Issue 1, Page 1-17, July 2018.

Environmental Inequality: The Social Causes and Consequences of Lead Exposure [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 44, Issue 1, Page 263-282, July 2018.

Poverty in America: New Directions and Debates [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 44, Issue 1, Page 305-318, July 2018.

The Reversal of the Gender Gap in Education and Its Consequences for Family Life [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 44, Issue 1, Page 341-360, July 2018.

Latin America, a Continent in Movement but Where To? A Review of Social Movements' Studies in the Region [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 44, Issue 1, Page 535-551, July 2018.


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