Tuesday, 25 February 2020

04:00 PM

Blockchain-based Proof of Location. (arXiv:1607.00174v3 [cs.DC] UPDATED) [cs.CR updates on arXiv.org]

Location-Based Services (LBSs) build upon geographic information to provide users with location-dependent functionalities. In such a context, it is particularly important that geographic locations claimed by users are trustworthy. Centralized verification approaches proposed in the last few years are not satisfactory, as they entail a high risk to the privacy of users. In this paper, we present and evaluate a novel decentralized, infrastructure-independent proof-of-location scheme based on blockchain technology. Our scheme guarantees both location trustworthiness and user privacy preservation.

Trace-Relating Compiler Correctness and Secure Compilation. (arXiv:1907.05320v4 [cs.PL] UPDATED) [cs.CR updates on arXiv.org]

Compiler correctness is, in its simplest form, defined as the inclusion of the set of traces of the compiled program into the set of traces of the original program, which is equivalent to the preservation of all trace properties. Here traces collect, for instance, the externally observable events of each execution. This definition requires, however, the set of traces of the source and target languages to be exactly the same, which is not the case when the languages are far apart or when observations are fine-grained. To overcome this issue, we study a generalized compiler correctness definition, which uses source and target traces drawn from potentially different sets and connected by an arbitrary relation. We set out to understand what guarantees this generalized compiler correctness definition gives us when instantiated with a non-trivial relation on traces. When this trace relation is not equality, it is no longer possible to preserve the trace properties of the source program unchanged. Instead, we provide a generic characterization of the target trace property ensured by correctly compiling a program that satisfies a given source property, and dually, of the source trace property one is required to show in order to obtain a certain target property for the compiled code. We show that this view on compiler correctness can naturally account for undefined behavior, resource exhaustion, different source and target values, side-channels, and various abstraction mismatches. Finally, we show that the same generalization also applies to many secure compilation definitions, which characterize the protection of a compiled program against linked adversarial code.

Providing Input-Discriminative Protection for Local Differential Privacy. (arXiv:1911.01402v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Local Differential Privacy (LDP) provides provable privacy protection for data collection without the assumption of the trusted data server. In the real-world scenario, different data have different privacy requirements due to the distinct sensitivity levels. However, LDP provides the same protection for all data. In this paper, we tackle the challenge of providing input-discriminative protection to reflect the distinct privacy requirements of different inputs. We first present the Input-Discriminative LDP (ID-LDP) privacy notion and focus on a specific version termed MinID-LDP, which is shown to be a fine-grained version of LDP. Then, we focus on the application of frequency estimation and develop the IDUE mechanism based on Unary Encoding for single-item input and the extended mechanism IDUE-PS (with Padding-and-Sampling protocol) for item-set input. The results on both synthetic and real-world datasets validate the correctness of our theoretical analysis and show that the proposed mechanisms satisfying MinID-LDP have better utility than the state-of-the-art mechanisms satisfying LDP due to the input-discriminative protection.

Biometric and Physical Identifiers with Correlated Noise for Controllable Private Authentication. (arXiv:2001.00847v2 [cs.IT] UPDATED) [cs.CR updates on arXiv.org]

The problem of secret-key based authentication under privacy and storage constraints on the source sequence is considered. The identifier measurement channels during authentication are assumed to be controllable via a cost-constrained action sequence. Single-letter inner and outer bounds for the key-leakage-storage-cost regions are derived for a generalization of a classic two-terminal key agreement model with an eavesdropper that observes a sequence that is correlated with the sequences observed by the legitimate terminals. The additions to the model are that the encoder observes a noisy version of a remote source, and the noisy output and the remote source output together with an action sequence are given as inputs to the measurement channel at the decoder. Thus, correlation is introduced between the noise components on the encoder and decoder measurements. The model with a secret-key generated by an encoder is extended to the randomized models, where a secret-key is embedded to the encoder. The results are relevant for several user and device authentication scenarios including physical and biometric identifiers with multiple measurements that provide diversity and multiplexing gains. To illustrate the behavior of the rate region, achievable (secret-key rate, storage-rate, cost) tuples are given for binary identifiers and measurement channels that can be represented as a set of binary symmetric subchannels. The gains from using an action sequence such as a large secret-key rate at a significantly small hardware cost, are illustrated to motivate the use of low-complexity transform-coding algorithms with cost-constrained actions.

Compact Merkle Multiproofs. (arXiv:2002.07648v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

The compact Merkle multiproof is a new and significantly more memory-efficient way to generate and verify sparse Merkle multiproofs. A standard sparse Merkle multiproof requires to store an index for every non-leaf hash in the multiproof. The compact Merkle multiproof on the other hand requires only $k$ leaf indices, where $k$ is the number of elements used for creating a multiproof. This significantly reduces the size of multirpoofs, especially for larger Merke trees.

Daily briefing: Retreating glacier reveals uncharted island in Antarctica [Nature - Issue - nature.com science feeds]

Nature, Published online: 24 February 2020; doi:10.1038/d41586-020-00542-2

Island hidden by ice, top tips for new PIs and how to prepare for a coronavirus pandemic.

How to build a genome [Nature - Issue - nature.com science feeds]

Nature, Published online: 24 February 2020; doi:10.1038/d41586-020-00511-9

A powerful set of molecular tools helps synthetic biologists to assemble DNA of different sizes, from the gene to the chromosome scale.

Swarming robots avoid collisions, traffic jams [EurekAlert! - Breaking News]

Researchers have developed the first decentralized algorithm with a collision-free, deadlock-free guarantee and validated it on a swarm of 100 autonomous robots in the lab.

UBC researchers develop strategy to protect wine grapes from smoke-taint [EurekAlert! - Breaking News]

It's a problem plaguing grape-growers worldwide -- in an ever-changing climate, how can they protect their crops from the undesirable effects of wildfire smoke exposure.A recent study by a team of UBC Okanagan researchers has led to the development of a preventative strategy for protecting grapes from volatile phenols -- flavored compounds present in smoke that may be absorbed into ripening grapes and subsequently impact wine flavor.

Monday, 24 February 2020

04:00 PM

China set to clamp down permanently on wildlife trade in wake of coronavirus [Nature - Issue - nature.com science feeds]

Nature, Published online: 21 February 2020; doi:10.1038/d41586-020-00499-2

Wild-animal markets are the suspected origin of the current outbreak and the 2002 SARS outbreak.

Daily briefing: True random-number generator powered by crystals [Nature - Issue - nature.com science feeds]

Nature, Published online: 19 February 2020; doi:10.1038/d41586-020-00485-8

Really random numbers from crystal growth, caution greets signs that the coronavirus epidemic has peaked and the next chapter for African genomics is being written in Nigeria.

[Comment] The voices of children in the global health debate [The Lancet]

In the face of imminent threats arising from climate change, commercial marketing of harmful products, and pervasive inequities, the new WHO–UNICEF–Lancet Commission1 makes a compelling ethical and economic case for investing in the world's children. The Commission advocates for children to be at the centre of the Sustainable Development Goals (SDGs) and for the protection of their health and rights. This agenda is essential and urgent to avoid mistakes that could cost a generation the chance to grow up safely, happily, and with abundant resources.

[Perspectives] Picturing health: speak up, do more—the first World NTD Day [The Lancet]

Neglected tropical diseases (NTDs) occur in some 150 countries and typically affect the poorest and most marginalised communities in Africa, Asia, and the Americas that do not have adequate access to clean water and sanitation and live in close contact with vectors of disease. About 1·6 billion people worldwide, including more than 500 million children, are at risk of one or more NTDs and NTDs cost affected economies billions of dollars. For many people living with NTDs, stigma and discrimination remain unchallenged.

[Correspondence] IADR and AADR applaud the Lancet Oral Health Series – Authors' reply [The Lancet]

On behalf of all coauthors of the Lancet Oral Health Series1,2 and the accompanying Comment,3 we welcome the Correspondence from the leadership of the International Association for Dental Research (IADR) and the American Association for Dental Research (AADR). We are all proud IADR members and appreciate that the leadership of these organisations highly values the advancement of dental public health research. The new policies of the IADR and AADR, including divestment of sugar-sweetened beverage companies from their investment portfolios, are indeed important models for other institutions and associations.

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

Burtness B, Harrington KJ, Greil R, et al. Pembrolizumab alone or with chemotherapy versus cetuximab with chemotherapy for recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-048): a randomised, open-label, phase 3 study. Lancet 2019; 394: 1915–28—In figure 2 of this Article, the data curves in panel G were mistakenly copied from panel F. The milestone percentages, numbers at risk, and the interpretation of the figure remain unchanged. In table 1, the number of female participants in the cetuximab with chemotherapy group was corrected from “93 (17%)” to “36 (13%)”.

[Articles] Impact of HPV vaccination and cervical screening on cervical cancer elimination: a comparative modelling analysis in 78 low-income and lower-middle-income countries [The Lancet]

Predictions were consistent across our three models and suggest that high HPV vaccination coverage of girls can lead to cervical cancer elimination in most LMICs by the end of the century. Screening with high uptake will expedite reductions and will be necessary to eliminate cervical cancer in countries with the highest burden.

Drug cocktail holds promise for spinal injuries [EurekAlert! - Breaking News]

Scientists have discovered a combination of two commonly available drugs that could help the body heal spinal fractures.

Alcohol-induced deaths in US [EurekAlert! - Breaking News]

National vital statistics data from 2000 to 2016 were used to examine how rates of alcohol-induced deaths (defined as those deaths due to alcohol consumption that could be avoided if alcohol weren't involved) have changed in the US and to compare the results by demographic groups including sex, race/ethnicity, age, socioeconomic status and geographic location. The study is accompanied by two commentaries.

Lower dose of newer clot-buster may be appropriate for some stroke patients [EurekAlert! - Breaking News]

In a comparison of 0.25mg/kg and 0.40mg/kg doses of the newer and more convenient clot-busting medication tenecteplase, there was no advantage in increasing the dose above 0.25mg/kg in stroke patients who planned to have mechanical clot retrieval.In addition, administering tenecteplase may decrease the need for mechanical clot removal.

Wednesday, 19 February 2020

04:00 PM

The role of critical micellization concentration in efficacy and toxicity of supramolecular polymers [Engineering] [Early Edition]

The inception and development of supramolecular chemistry have provided a vast library of supramolecular structures and materials for improved practice of medicine. In the context of therapeutic delivery, while supramolecular nanostructures offer a wide variety of morphologies as drug carriers for optimized targeting and controlled release, concerns are often raised...

The evolution of early symbolic behavior in Homo sapiens [Psychological and Cognitive Sciences] [Early Edition]

How did human symbolic behavior evolve? Dating up to about 100,000 y ago, the engraved ochre and ostrich eggshell fragments from the South African Blombos Cave and Diepkloof Rock Shelter provide a unique window into presumed early symbolic traditions of Homo sapiens and how they evolved over a period of...

Zoonotic Virus Linked to Severe Encephalitis in Southern Germany [JAMA Current Issue]

A recent study suggested that Borna disease virus 1 (BoDV-1), which is carried by wild shrews in Germany, Austria, Switzerland, and Liechtenstein, could be a more common cause of severe or fatal encephalitis among people in endemic areas than previously realized.

He Who Keeps Going [JAMA Current Issue]

for Peter You sail into the garage on your bike, report on the miles wheeled in the woods, on the serpent that crossed your path, a turtle bobbing its head amid puddles, and all the creatures bounding away from your human scent.

Risk of Offspring Birth Defects in Women After Bariatric Surgery—Reply [JAMA Current Issue]

In Reply We agree with Dr Auger and colleagues that there are several effects of gastric bypass surgery that may influence the risk of birth defects, including positive effects from weight loss and improved glucose control, as well as negative effects such as nutrient deficiencies and increased risk of substance abuse.

Unmeasured Confounding in Observational Studies of Management of Cerebellar Intracranial Hemorrhage [JAMA Current Issue]

To the Editor In a propensity score–matched cohort of 578 patients from 4 observational cohort studies, Dr Kuramatsu and colleagues showed that evacuation of medium-sized intracerebellar hematomas (approximate volume, 20 cm3) was not associated with better functional outcome. Assessing treatment effectiveness in observational data is challenging because treatment decisions are based on patient characteristics that also are typically predictive of outcome, causing confounding by indication. Although the authors addressed this potential bias with propensity scores, we would like to emphasize the possibility of residual confounding.

Ethical and Legal Aspects of Ambient Intelligence in Hospitals [JAMA Current Issue]

This Viewpoint reviews the ethical and legal implications of using ambient intelligence, the use of artificial intelligence–based technologies to monitor health care and quality measures like handwashing and patient falls in health care setting.

Tuesday, 18 February 2020

04:00 PM

Set a global target for ecosystems [Nature - Issue - nature.com science feeds]

Nature, Published online: 18 February 2020; doi:10.1038/d41586-020-00446-1

The conservation community must be able to track countries’ progress in protecting wetlands, reefs, forests and more, argue James Watson and colleagues.

SDN-Based Quality of Service Networking for Wide Area Measurement System [IEEE Transactions on Industrial Informatics - new TOC]

Wide area measurement system (WAMS) infrastructure is one of the most significant elements of the smart grid that measures, collects, and analyzes data in the power system. Fundamental components of the WAMS are phasor measurement units (PMUs), phasor data concentrators (PDCs), and its relevant applications. The PDC collects data from several different PMUs, integrates data and sends it to the control center. In order to achieve a stable implementation of WAMS, the communication structure should be reliable and meets the quality of service (QoS) requirements for various applications in the network. In this paper, we propose a novel WAMS communication infrastructure by utilizing the software-defined networking technology, which can enhance the reliability of the corresponding networks. In the proposed model, in order to meet the applications QoS needs, the traffic flows are categorized into different class of services, and then by applying a QoS mechanism, along with a content-aware queuing algorithm, the maximum capacity for the critical traffic is obtained. This provides a low latency for critical WAMS applications. The proposed model has been implemented in the Mininet environment using the Ryu controller. The implementation results indicate that the proposed infrastructure reduces end-to-end delay and packets loss and utilizes the network resources optimally.

A Telemanipulation-Based Human–Robot Collaboration Method to Teach Aerospace Masking Skills [IEEE Transactions on Industrial Informatics - new TOC]

Traditional offline programming or teach pendant-based methods limit the collaboration capabilities of users and robots to cope with complex and changing industrial tasks. To address efficient robot manipulation in high-mix and low-volume tasks, especially for skillful tasks involving both trajectory and force control requirements, fast robot teaching and skill transferability are critical. Compared to manually dragging a heavy robot, or programming a trajectory by complex calculations, we believe that robots can efficiently learn skills from direct teaching through telemanipulation, and improve the skills based on optimization with sensory feedback. In aerospace engine, maintenance, repair, and operations, the surfaces of aerospace components are required to be masked by tapes. We propose a fast and intuitive telemanipulation-based method to teach a robot these masking skills and compare the performance of the proposed method with teach pendant–based methods among several users. This study aims to prove the efficiency and intuitiveness of the telemanipulation-based method proposed herein for enabling a robot to learn skillful and complex manipulation tasks.

Robust WiFi Localization by Fusing Derivative Fingerprints of RSS and Multiple Classifiers [IEEE Transactions on Industrial Informatics - new TOC]

It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.

DeSVig: Decentralized Swift Vigilance Against Adversarial Attacks in Industrial Artificial Intelligence Systems [IEEE Transactions on Industrial Informatics - new TOC]

Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.

Distributed Electric Vehicles Charging Management With Social Contribution Concept [IEEE Transactions on Industrial Informatics - new TOC]

This article proposes a charging management of electric vehicles (EVs) with a newly presented EV social contribution. The EV charging problem is represented by a generalized Nash equilibrium game where each individual EV tries to minimize its charging cost while satisfying its own charging requirements and respecting the charging facility constraints. The individual EV features a social behavior to potentially contribute in shifting its charging schedule from specific intervals that have insufficient charging power. This shift in the EV schedule will allow more charging power to other EVs that admit stricter charging requirements, i.e., intervals and demands. In this way, the contributed EVs socially help others in reducing their charging costs, which is particularly important during the overload cases in the system. The proposed solution is reached iteratively in a distributed way utilizing the consensus network and found based on the receding horizon optimization framework. Both simulation and experimental results demonstrate the effectiveness and correctness of the proposed social contribution in the charging management for reducing the charging cost of EVs.

Friday, 14 February 2020

04:00 PM

QnAs with Bin Yu [QnAs] [Early Edition]

The explosion of available data in the past decades has birthed a myriad of statistical and machine-learning tools. These tools have allowed scientists from fields as disparate as genomics and cosmology to model and interpret data, draw conclusions, and move science forward. Building on computational advances and increased data availability,...

Modeling and Control of Low Switching Frequency High-Performance Induction Motor Drives [IEEE Transactions on Industrial Electronics - new TOC]

Complex state variables are used to study the dynamic behavior of induction motors considering the propagation in space of the distributed magnetic field inside the machine. The objective of this paper is to improve the dynamics of pulsewidth modulation inverters in medium-voltage drive systems. To keep the dynamic losses of the power devices at a tolerable level, the switching frequency must be below 1 kHz. The sampling rate of the digital signal processing system is then low which introduces considerable signal delay. The delay has an adverse influence on the dynamic behavior of the current control system. It introduces undesired cross coupling between the current components $i_{d}$ and $i_{q}$. The degree of cross coupling is described by a cross-frequency transfer function. It is shown that the mechanism of cross coupling is different and more adverse than the conventional theory discloses. A current controller structure having poles and zeroes of the single-complex type is synthesized. Cross coupling is completely eliminated at any low switching frequency. Experimental results demonstrate that high dynamic performance and zero cross coupling is achieved even at very low switching frequency.

Research on the Dynamic Characteristics and Regulation Method of the Energy Stored Quasi-Z-Source Inverter System [IEEE Transactions on Industrial Electronics - new TOC]

Energy storage units (such as battery or ultracapacitor) can help to reduce the power fluctuation in the impedance source network converter applications such as the photovoltaic (PV) and fuel cells systems. Due to the unknown dynamic characteristics of the energy stored impedance network, the unexpected oscillation and inrush currents in dynamic process might threaten the storage device safety. In this article, an energy stored quasi-Z-source converter with the battery is modeled and analyzed including the transient capacitor current. The impact of the network internal resonance on the battery current control loop is mathematically explored. The resulted conflict between the zero steady-state error and bandwidth requirement is revealed. That yields the sacrifice and limitation of the closed-loop dynamic performance in the conventional methods. An active damping technique is then proposed to ensure the extended bandwidth and better dynamic performances under different state of charge (SOC) occasions. The theoretical analysis and performance are both verified by the simulations and experiments.

Pole Balancing and Thermal Management in Multiterminal HVdc Grids Using Single H-Bridge-Based Current Flow Controllers [IEEE Transactions on Industrial Electronics - new TOC]

Current flow controllers (CFCs) are power electronics-based devices that may remove some technical barriers preventing multiterminal high-voltage direct current (MTdc) grid deployment. In this paper, an interline CFC topology is investigated. The single H-bridge CFC (1B-CFC) alters the grid power flow by transferring power between neighboring dc lines. The operation and control of a 1B-CFC under a single modulation scheme is presented. A control strategy has been proposed to provide pole balancing support during imbalance conditions. Small-scale prototypes have been developed to demonstrate the functionality and operational range of the device. To this end, an experimental MTdc grid test-rig has been employed. It is shown that a 1B-CFC could be used to limit the dc line current and, additionally, it can be employed to enable asymmetrical tapping of dc lines. For completeness, the performance of the device has been experimentally validated under line overloading, pole imbalance conditions, and a pole-to-pole dc fault.

Sequential Fusion Estimation for Networked Multisensor Nonlinear Systems [IEEE Transactions on Industrial Electronics - new TOC]

This article presents a sequential fusion approach for state estimation of networked multisensor nonlinear systems, where sensors and estimators are allowed to work asynchronously. Both sequential measurement fusion (SMF) and sequential state fusion (SSF) estimators are designed, where the unscented filtering method is used in the design of the local SMF estimator, and the matrix weighting method is applied in the design of the SSF estimator. It is shown that the proposed SSF estimators provide a satisfactory estimation precision that is close to the centralized batch state fusion (BSF) estimator, while requiring smaller computation burden as compared with the BSF estimator. Both simulations and experiments of a moving target tracking system are presented to show the effectiveness of the proposed sequential fusion methods.

Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability [IEEE Transactions on Industrial Electronics - new TOC]

Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to counteracting or eliminating faults in industrial processes. Many conventional data-driven fault diagnosis methods ignore the fault tendency of abnormal samples, and they need a complete retraining process to include the newly collected abnormal samples or fault classes. In this article, a broad convolutional neural network (BCNN) is designed with incremental learning capability for solving the aforementioned issues. The proposed method combines several consecutive samples as a data matrix, and it then extracts both fault tendency and nonlinear structure from the obtained data matrix by using convolutional operation. After that, the weights in fully connected layers can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of the BCNN model can be improved by adding newly generated additional features. Finally, the incremental learning capability of the proposed method is also designed, so that the BCNN model can update itself to include new coming abnormal samples and fault classes. The proposed method is applied both to a simulated process and a real industrial process. Experimental results illustrate that it can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples, and fault classes.

A Context-Aware Locality Measure for Inlier Pool Enrichment in Stepwise Image Registration [IEEE Transactions on Image Processing - new TOC]

We present a feature-based image registration method, the stepwise image registration (SIR), with a closed-form solution. Our SIR creates an inlier pool and a candidate pool as the initialization, and then gradually enriches the inlier pool and refines the transformation. In each step, the enriched correspondence exclusively tunes the transformation coefficient within the confirmed inlier pairs, instead of updating the mapping using the complete putative set. In turn, the refined transformation prunes inconsistent mismatches to alleviate the incoming matching ambiguity. The context-aware locality measure (CALM) is designed for dissimilarity measure. The capability of the CALM can be enhanced by the progressive inlier pool enrichment. Finally, a retrieval process is performed based on the finest CALM and alignment, by which the inlier pool is maximized. Extensive experiments of enrichment evaluation, feature matching, image registration, and image retrieval demonstrate the favorable performance of our SIR against state-of-the-art methods. The code and datasets are available at https://github.com/sucv/SIR.

Wednesday, 12 February 2020

04:00 PM

Nucleolar localization of RAG1 modulates V(D)J recombination activity [Immunology and Inflammation] [Early Edition]

V(D)J recombination assembles and diversifies Ig and T cell receptor genes in developing B and T lymphocytes. The reaction is initiated by the RAG1-RAG2 protein complex which binds and cleaves at discrete gene segments in the antigen receptor loci. To identify mechanisms that regulate V(D)J recombination, we used proximity-dependent biotin...

Early Last Interglacial ocean warming drove substantial ice mass loss from Antarctica [Earth, Atmospheric, and Planetary Sciences] [Early Edition]

The future response of the Antarctic ice sheet to rising temperatures remains highly uncertain. A useful period for assessing the sensitivity of Antarctica to warming is the Last Interglacial (LIG) (129 to 116 ky), which experienced warmer polar temperatures and higher global mean sea level (GMSL) (+6 to 9 m)...

Tuesday, 11 February 2020

04:00 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.

Evidence Collection and Evaluation for the Development of Dietary Guidelines and Public Policy on Nutrition [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 227-247, 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.

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

04:00 PM

REMIND: Risk Estimation Mechanism for Images in Network Distribution [IEEE Transactions on Information Forensics and Security - new TOC]

People constantly share their photographs with others through various social media sites. With the aid of the privacy settings provided by social media sites, image owners can designate scope of sharing, e.g., close friends and acquaintances. However, even if the owner of a photograph carefully sets the privacy setting to exclude a given individual who is not supposed to see the photograph, the photograph may still eventually reach a wider audience, including those clearly undesired through unanticipated channels of disclosure, causing a privacy breach. Moreover, it is often the case that a given image involves multiple stakeholders who are also depicted in the photograph. Due to various personalities, it is even more challenging to reach agreement on the privacy settings for these multi-owner photographs. In this paper, we propose a privacy risk reminder system, called REMIND, which estimates the probability that a shared photograph may be seen by unwanted people-through the social graph-who are not included in the original sharing list. We tackle this problem from a novel angle by digging into the big data regarding image sharing history. Specifically, the social media providers possess a huge amount of image sharing information (e.g., what photographs are shared with whom) of their users. By analyzing and modeling such rich information, we build a sophisticated probability model that efficiently aggregates the image disclosure probabilities along different possible image propagation chains and loops. If the computed disclosure probability indicates high risks of privacy breach, a reminder is issued to the image owner to help revise the privacy settings (or, at least, inform the user about this accidental disclosure risk). The proposed REMIND system also has a nice feature of policy harmonization that helps resolve privacy differences in multi-owner photographs. We have carried out a user study to validate the rationale of our proposed solutions and also conducte- experimental studies to evaluate the efficiency of the proposed REMIND system.

Electromagnetic Side Channel Information Leakage Created by Execution of Series of Instructions in a Computer Processor [IEEE Transactions on Information Forensics and Security - new TOC]

The side-channel leakage is a consequence of program execution in a computer processor, and understanding relationship between code execution and information leakage is a necessary step in estimating information leakage and its capacity limits. This paper proposes a methodology to relate program execution to electromagnetic side-channel emanations and estimates side-channel information capacity created by execution of series of instructions (e.g., a function, a procedure, or a program) in a processor. To model dependence among program instructions in a code, we propose to use Markov source model, which includes the dependencies among sequence of instructions as well as dependencies among instructions as they pass through a pipeline of the processor. The emitted electromagnetic (EM) signals during instruction executions are natural choice for the inputs into the model. To obtain the channel inputs for the proposed model, we derive a mathematical relationship between the emanated instruction signal power (ESP) and total emanated signal power while running a program. Then, we derive the leakage capacity of EM side channels created by execution of series of instructions in a processor. Finally, we provide experimental results to demonstrate that leakages could be severe and that a dedicated attacker could obtain important information.

Information Theoretical Analysis of Unfair Rating Attacks Under Subjectivity [IEEE Transactions on Information Forensics and Security - new TOC]

Ratings provided by advisors can help an advisee to make decisions, e.g., which seller to select in e-commerce. Unfair rating attacks-where dishonest ratings are provided to mislead the advisee-impact the accuracy of decision making. Current literature focuses on specific classes of unfair rating attacks, which does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behavior that is possible within a given system. We propose a probabilistic modeling of rating behavior, and apply information theory to quantitatively measure the impact of attacks. In particular, we can identify the attack with the worst impact. In the simple case, honest advisors report the truth straightforwardly, and attackers rate strategically. In real systems, the truth (or an advisor's view on it) may be subjective, making even honest ratings inaccurate. Although there exist methods to deal with subjective ratings, whether subjectivity influences the effect of unfair rating attacks was an open question. We discover that subjectivity decreases the robustness against attacks.

Automatically Dismantling Online Dating Fraud [IEEE Transactions on Information Forensics and Security - new TOC]

Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.

Neural Network Architecture and Transient Evoked Otoacoustic Emission (TEOAE) Biometrics for Identification and Verification [IEEE Transactions on Information Forensics and Security - new TOC]

This study presents a deep neural network architecture that achieves state of the art multi-session verification and identification performance for Transient Evoked Otoacoustic Emission (TEOAE) biometric system. TEOAE is a 20ms long response generated by the ear that is naturally strong against falsification, and replay attacks. It can be measured using a device with a speaker and multiple microphones. Previous TEAOE authentication methods focused on single-session or mixed-session performance. Our method focuses on multi-session authentication performance. We train a neural network model that generates a TEOAE embedding that is separable in Euclidean space by using the triplet loss function. These embeddings are used to create identity templates which are used to authenticate the user. We achieved identification accuracy of 99.3 ± 1.04%, and achieved an EER(Equal Error Rate) of 0.187 ± 0.146% for verification scenarios. Our method has achieved 7.56% performance increase for identification scenarios and 13.3% performance increase for verification scenarios over previous methods when averaged across all tests.

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.

Thursday, 30 January 2020

Wednesday, 29 January 2020

04:00 PM

Robust Seismic Image Interpolation With Mathematical Morphological Constraint [IEEE Transactions on Image Processing - new TOC]

Seismic image interpolation is a currently popular research subject in modern reflection seismology. The interpolation problem is generally treated as a process of inversion. Under the compressed sensing framework, various sparse transformations and low-rank constraints based methods have great performances in recovering irregularly missing traces. However, in the case of regularly missing traces, their applications are limited because of the strong spatial aliasing energies. In addition, the erratic noise always poses a serious impact on the interpolation results obtained by the sparse transformations and low-rank constraints-based methods,. This is because the erratic noise is far from satisfying the statistical assumption behind these methods. In this study, we propose a mathematical morphology-based interpolation technique, which constrains the morphological scale of the model in the inversion process. The inversion problem is solved by the shaping regularization approach. The mathematical morphological constraint (MMC)-based interpolation technique has a satisfactory robustness to the spatial aliasing and erratic energies. We provide a detailed algorithmic framework and discuss the extension from 2D to higher dimensional version and the back operator in the shaping inversion. A group of numerical examples demonstrates the successful performance of the proposed technique.

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.

Thursday, 23 January 2020

Friday, 17 January 2020

Monday, 26 August 2019

04:00 PM

Transitional Dynamics in Aggregate Models of Innovative Investment [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 11, Issue 1, Page 273-301, August 2019.

Echo Chambers and Their Effects on Economic and Political Outcomes [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 11, Issue 1, Page 303-328, August 2019.

Auction Market Design: Recent Innovations [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 11, Issue 1, Page 383-405, August 2019.

Weak Instruments in Instrumental Variables Regression: Theory and Practice [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 11, Issue 1, Page 727-753, August 2019.

Has Dynamic Programming Improved Decision Making? [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 11, Issue 1, Page 833-858, August 2019.

Thursday, 08 August 2019

04:00 PM

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

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

Moral Cultures, Reputation Work, and the Politics of Scandal [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 247-264, July 2019.

Divergent Destinies: Children of Immigrants Growing Up in the United States [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 383-399, July 2019.

Examining Public Opinion About LGBTQ-Related Issues in the United States and Across Multiple Nations [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 401-423, July 2019.

Family Instability in the Lives of American Children [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 493-513, July 2019.

Sunday, 09 June 2019

07:32 PM

A Conversation with Theda Skocpol [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 1-16, May 2019.

The Return of the Single-Country Study [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 187-203, 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.

Integrating the Civil–Military Relations Subfield [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 379-398, May 2019.

Not So Civic: Is There a Difference Between Ethnic and Civic Nationalism? [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 419-434, May 2019.


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