Friday, 03 July 2020

06:01 PM

Goodbye, Pluto’s atmosphere [Nature - Issue - nature.com science feeds]

Nature, Published online: 03 July 2020; doi:10.1038/d41586-020-01985-3

The gases that envelop the distant dwarf planet might finally be freezing out and falling to the surface.

02:02 AM

Cross-Layer Deanonymization Methods in the Lightning Protocol. (arXiv:2007.00764v1 [cs.CR]) [cs.CR updates on arXiv.org]

Payment channel networks (PCNs) have emerged as a promising alternative to mitigate the scalability issues inherent to cryptocurrencies like Bitcoin and are often assumed to improve privacy, as payments are not stored on chain. However, a systematic analysis of possible deanonymization attacks is still missing. In this paper, we focus on the Bitcoin Lightning Network (LN), which is the most widespread implementation of PCNs to date. We present clustering heuristics that group Bitcoin addresses, based on their interaction with the LN, and LN nodes, based on shared naming and hosting information. We also present cross-layer linking heuristics that can, with our dataset, link 43.7% of all LN nodes to 26.3% Bitcoin addresses interacting with the LN. These cross-layer links allow us to attribute information (e.g., aliases, IP addresses) to 17% of the Bitcoin addresses contributing to their deanonymization. Further, we find the security and privacy of the LN are at the mercy of as few as five actors that control 34 nodes and over 44% of the total capacity. Overall, we present the first quantitative analysis of the security and privacy issues opened up by cross-layer interactions, demonstrating their impact and proposing suitable mitigation strategies.

Hunting for Re-Entrancy Attacks in Ethereum Smart Contracts via Static Analysis. (arXiv:2007.01029v1 [cs.CR]) [cs.CR updates on arXiv.org]

Ethereum smart contracts are programs that are deployed and executed in a consensus-based blockchain managed by a peer-to-peer network. Several re-entrancy attacks that aim to steal Ether, the cryptocurrency used in Ethereum, stored in deployed smart contracts have been found in the recent years. A countermeasure to such attacks is based on dynamic analysis that executes the smart contracts themselves, but it requires the spending of Ether and knowledge of attack patterns for analysis in advance. In this paper, we present a static analysis tool named \textit{RA (Re-entrancy Analyzer)}, a combination of symbolic execution and equivalence checking by a satisfiability modulo theories solver to analyze smart contract vulnerabilities to re-entrancy attacks. In contrast to existing tools, RA supports analysis of inter-contract behaviors by using only the Etherum Virtual Machine bytecodes of target smart contracts, i.e., even without prior knowledge of attack patterns and without spending Ether. Furthermore, RA can verify existence of vulnerabilities to re-entrancy attacks without execution of smart contracts and it does not provide false positives and false negatives. We also present an implementation of RA to evaluate its performance in analyzing the vulnerability of deployed smart contracts to re-entrancy attacks and show that RA can precisely determine which smart contracts are vulnerable.

Zooming Into Video Conferencing Privacy and Security Threats. (arXiv:2007.01059v1 [cs.CR]) [cs.CR updates on arXiv.org]

The COVID-19 pandemic outbreak, with its related social distancing and shelter-in-place measures, has dramatically affected ways in which people communicate with each other, forcing people to find new ways to collaborate, study, celebrate special occasions, and meet with family and friends. One of the most popular solutions that have emerged is the use of video conferencing applications to replace face-to-face meetings with virtual meetings. This resulted in unprecedented growth in the number of video conferencing users. In this study, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted on the Web. We used image processing, text recognition tools, as well as social network analysis to explore our web crawling curated dataset of over 15,700 collage images, and over 142,000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and sometimes even full names. This type of extracted data can vastly and easily jeopardize people's security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as young children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual.

Sorry, Shodan is not Enough! Assessing ICS Security via IXP Network Traffic Analysis. (arXiv:2007.01114v1 [cs.CR]) [cs.CR updates on arXiv.org]

Modern Industrial Control Systems (ICSs) allow remote communication through the Internet using industrial protocols that were not designed to work with external networks. To understand security issues related to this practice, prior work usually relies on active scans by researchers or services such as Shodan. While such scans can identify public open ports, they are not able to provide details on configurations of the system related to legitimate Industrial Traffic passing the Internet (e.g., source-based filtering in Network Address Translation or Firewalls). In this work, we complement Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling. This setup allows us to identify ICS endpoints actually exchanging Industrial Traffic over the Internet. Besides, we are able to detect scanning activities and what other type of traffic is exchanged by the systems (i.e., IT traffic). We find that Shodan only listed less than 2% of hosts that we identified as exchanging Industrial Traffic. Even with manually triggered scans, Shodan only identified 7% of them as ICS hosts. This demonstrates that active scanning-based analysis is insufficient to understand current security practices in ICS communications. We show that 75.6% of ICS hosts rely on unencrypted communications without integrity protection, leaving those critical systems vulnerable to malicious attacks.

A Method for Fast Computing the Algebraic Degree of Boolean Functions. (arXiv:2007.01116v1 [cs.CR]) [cs.CR updates on arXiv.org]

The algebraic degree of Boolean functions (or vectorial Boolean functions) is an important cryptographic parameter that should be computed by fast algorithms. They work in two main ways: (1) by computing the algebraic normal form and then searching the monomial of the highest degree in it, or (2) by examination the algebraic properties of the true table vector of a given function. We have already done four basic steps in the study of the first way, and the second one has been studied by other authors. Here we represent a method for fast computing (the fastest way we know) the algebraic degree of Boolean functions. It is a combination of the most efficient components of these two ways and the corresponding algorithms. The theoretical time complexities of the method are derived in each of the cases when the Boolean function is represented in a byte-wise or in a bitwise manner. They are of the same type $\Theta(n.2^n)$ for a Boolean function of $n$ variables, but they have big differences between the constants in $\Theta$-notation. The theoretical and experimental results shown here demonstrate the advantages of the bitwise approach in computing the algebraic degree - they are dozens of times faster than the byte-wise approaches.

[Comment] The Wakley–Wu Lien Teh Prize Essay 2020: Chinese health workers' experiences during the COVID-19 pandemic [The Lancet]

“I feel deeply the burden of the honour placed upon me in being chairman of this Medical Conference, which is unique in our history, powerful in its representation, and which gives China a strong position amongst nations seeking the welfare of the people”, wrote Wu Lien Teh in his first publication in The Lancet,1 on his inaugural address delivered at the International Plague Conference in Shenyang, China, in 1911. Wu was elected as the chair of the conference for his work in controlling the pneumonic plague epidemic outbreak in 1910–11 in northeastern China, which ultimately claimed about 60 000 lives.

[World Report] Collaborating on kidneys: Haiti's transplantation ambitions [The Lancet]

Haiti lacks health programmes and facilities for kidney disease. Jane Regan reports on an ambitious collaboration between Haitian and US doctors to make kidney transplantation widely available.

[Correspondence] Time for WHO to declare climate breakdown a PHEIC? [The Lancet]

At the opening plenary of the World Health Assembly in May, 2019, Richard Horton urged member states and the Secretariat of WHO to recognise climate change as a planetary emergency. A few days later, during a side event on air pollution, climate change, oceans, and health sponsored by the Government of Sweden, the Minister of Health for the Seychelles Jean Paul Adam argued that climate change has to be recognised as a public health emergency at the international level. Johan Giesecke1 once stressed that as public health emergencies of international concern (PHEICs) evolve into more complex forms, it becomes necessary to identify gaps in the alarm and response mechanism of the International Health Regulations (IHRs).

[Correspondence] Methodology in the GBD study of China [The Lancet]

Maigeng Zhou and colleagues1 claimed that they had implemented the same hierarchical model setup as Christopher Murray and colleagues had in their Global Burden of Disease Study (GBD),2 in which Taiwan and China were treated at the same level in the hierarchical model. However, according to figures 3 and 4 in the Article,1 Taiwan was placed at the level under the umbrella of China. The inconsistency between the methodology and results in these figures requires clarification.

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

Okell LC, Verity R, Watson OJ, et al. Have deaths from COVID-19 in Europe plateaued due to herd immunity? Lancet 2020; 395: e110–11—The appendix of this Correspondence has been corrected as of June 19, 2020.

Pairwise Gaussian Loss for Convolutional Neural Networks [IEEE Transactions on Industrial Informatics - new TOC]

Convolutional neural networks (CNNs) have demonstrated great competence in feature representation, and then, achieved a good performance to many classification tasks. Cross-entropy loss, together with softmax, is arguably one of the most commonly used loss functions in CNNs (that is generally called softmax loss). However, the softmax loss can result in a weakly discriminative feature representation since it focuses on the interclass separability rather than the intraclass compactness. This article proposes a pairwise Gaussian loss (PGL) for CNNs that can well address the intraclass compactness through significantly penalizing those similar sample pairs with a relatively large distance. At the same time, PGL can still ensure a good interclass separability. Experiments show that PGL can guarantee that CNNs obtain a better classification performance compared to not only the softmax loss but also others often used in CNNs. Our experiments also show that PGL has a stable convergence for the stochastic gradient descent optimization method in CNNs and a good generalization ability for different structures of CNNs.

One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis [IEEE Transactions on Industrial Informatics - new TOC]

Vibration signals are generally utilized for machinery fault diagnosis to perform timely maintenance and then reduce losses. Thus, the feature extraction on one-dimensional vibration signals often determines accuracy of those fault diagnosis models. These typical deep neural networks (DNNs), e.g., convolutional neural networks (CNNs), perform well in feature learning and have been applied in machine fault diagnosis. However, the supervised learning of CNN often requires a large amount of labeled images and thus limits its wide applications. In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised-learning way. First, 1-D convolutional autoencoder is proposed in 1-DRCAE for feature extraction. Second, a deconvolution operation is developed as decoder of 1-DRCAE to reconstruct the filtered signals. Third, residual learning is employed in 1-DRCAE to perform feature learning on 1-D vibration signals. The results show that 1-DRCAE has good signal denoising and feature extraction performance on vibration signals. It performs better on feature extraction than the typical DNNs, e.g., deep belief network, stacked autoencoders, and 1-D CNN.

An FDM-Based Simultaneous Wireless Power and Data Transfer System Functioning With High-Rate Full-Duplex Communication [IEEE Transactions on Industrial Informatics - new TOC]

This article proposed a novel scheme integrating full-duplex communication into high-power wireless power transfer systems. The power and data are transferred through the same inductive link. A pair of coupling coils with taps are employed to omit the trap inductors that are widely employed in former simultaneous wireless power and data transfer (SWPDT) systems. The proposed scheme can reduce the system cost and size and improve the power transfer efficiency. The power and bidirectional data are transmitted using carriers of different frequencies. Frequency division multiplexing technique is utilized for full-duplex communication. The duplexer is carefully designed to separate the transmitted and received data signals. The circuit model of the system is built to analyze the power and data transfer performance. The crosstalk between the power and data transfer is also discussed. To improve the performance of data transfer, the method to determine the optimal coil tap position is proposed. A 300-W SWPDT prototype with a full-duplex data rate of 500 kb/s was built to demonstrate the feasibility of the proposed system.

Self-Evolving Neural Control for a Class of Nonlinear Discrete-Time Dynamic Systems With Unknown Dynamics and Unknown Disturbances [IEEE Transactions on Industrial Informatics - new TOC]

In this article, a novel self-evolving general regression neural network (SEGRNN) is designed for tracking control of a class of discrete-time dynamic systems with unknown dynamics and unknown external disturbances. The proposed controller starts from scratch and automatically adjusts its structure and parameters online to solve the tracking control problem. The proposed controller can add, prune, and replace nodes online according to the control task, external disturbance, and the design specifications. A robustifying control term is also added to SEGRNN's output to mitigate the effects of the external disturbance. The concept of a data reservoir is proposed where a record of the deleted nodes is stored for any future recall, if they are seen to be significant again. Unlike most of the previously proposed self-evolving systems, our controller offers user-friendly design parameters to suit a variety of real-world systems. Lyapunov stability analysis is utilized to study the stability of the suggested controller and to determine an appropriate learning rate for the SEGRNN weights. A continuous stirred-tank reactor simulation example is employed to verify the performance of the proposed controller. The performance of the proposed controller is also compared with a variety of controllers, including adaptive radial basis functional networks, adaptive feed-forward neural networks, adaptive fuzzy logic system, proportional integral derivative controller, sliding-mode controller, and iterative learning controller. Finally, a dc motor platform is used to experimentally validate the controller performance.

AR-Net: Adaptive Attention and Residual Refinement Network for Copy-Move Forgery Detection [IEEE Transactions on Industrial Informatics - new TOC]

In copy-move forgery, the illumination and contrast of tampered and genuine regions are highly consistent, which poses a greater challenge in copy-move forgery detection. In this article, an end-to-end neural network is proposed based on adaptive attention and residual refinement network (AR-Net). Specifically, position and channel attention features are fused by the adaptive attention mechanism to fully capture context information and enrich the representation of features. Second, deep matching is adopted to compute the self-correlation between feature maps, and atrous spatial pyramid pooling fuses the scaled correlation maps to generate the coarse mask. Finally, the coarse mask is optimized through the residual refinement module, which retains the structure of object boundaries. Extensive experiments, evaluated on CASIAII, COVERAGE, and CoMoFoD datasets, demonstrate that the AR-Net has superior performance than state-of-the-art algorithms and can locate tampered and corresponding genuine regions at the pixel level. In addition, AR-Net has high robustness on postprocessing operations, such as noise, blur, and JPEG recompression.

Research reflects how AI sees through the looking glass [EurekAlert! - Breaking News]

Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell University researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards - findings with implications for training machine learning models and detecting faked images.

Abnormal proteins in the gut could contribute to the development of Alzheimer's Disease [EurekAlert! - Breaking News]

A new study published in The Journal of Physiology has shown that misfolded protein build-up in the gut could contribute to the development of Alzheimer's-like symptoms in mice. This could suggest a new treatment approach for Alzheimer's disease that would target the gut before symptoms of cognitive deficits appear in patients.

Thursday, 02 July 2020

06:01 PM

New Human Gene Therapy editorial: Concern following gene therapy adverse events [EurekAlert! - Breaking News]

Response to the recent report of the deaths of two children receiving high doses of a gene therapy vector (AAV8) in a Phase I trial for X-linked myotubular myopathy (MTM). The news "is a tragic reminder of how difficult it is to predict outcomes in first-in-human studies

New, more infectious strain of COVID-19 now dominates global cases of virus [EurekAlert! - Breaking News]

Researchers have shown that a variation in the viral genome of Covid-19 improved its ability to infect human cells and helped it become the dominant strain circulating around the world today.

10:02 AM

Mini-'Marsquakes' measured by InSight lander show effects of sun and wind [EurekAlert! - Breaking News]

Analysis of seismometer data from the InSight Martian lander revealed that different types and frequencies of ambient low-magnitude "microtremors" on Mars were associated with different sources, and some reflected daily variations in wind and solar irradiance, either in distant locations or near the lander. These findings will contribute to future projects seeking to model and monitor the Martian subsurface.

Wednesday, 01 July 2020

06:02 PM

Wapl repression by Pax5 promotes V gene recombination by Igh loop extrusion [Nature - Issue - nature.com science feeds]

Nature, Published online: 01 July 2020; doi:10.1038/s41586-020-2454-y

Pax5 regulates contraction of the immunoglobulin heavy chain (Igh) locus—an essential step in V(D)J recombination—by promoting chromatin loop extrusion via repression of Wapl expression.

Abrupt increase in harvested forest area over Europe after 2015 [Nature - Issue - nature.com science feeds]

Nature, Published online: 01 July 2020; doi:10.1038/s41586-020-2438-y

Fine-scale satellite data are used to quantify forest harvest rates in 26 European countries, finding an increase in harvested forest area of 49% and an increase in biomass loss of 69% between 2011–2015 and 2016–2018.

An antiviral response beyond immune cells [Nature - Issue - nature.com science feeds]

Nature, Published online: 01 July 2020; doi:10.1038/d41586-020-01916-2

Fibroblast, epithelial and endothelial cells are more than just the scaffold of an organ — it emerges that they communicate with immune cells and are primed to launch organ-specific gene-expression programs for antiviral defence.

Suppression of proteolipid protein rescues Pelizaeus-Merzbacher disease [Nature - Issue - nature.com science feeds]

Nature, Published online: 01 July 2020; doi:10.1038/s41586-020-2494-3

Suppression of proteolipid protein rescues Pelizaeus-Merzbacher disease

Tuesday, 30 June 2020

02:02 AM

JNK-mediated disruption of bile acid homeostasis promotes intrahepatic cholangiocarcinoma [Medical Sciences] [Early Edition]

Metabolic stress causes activation of the cJun NH2-terminal kinase (JNK) signal transduction pathway. It is established that one consequence of JNK activation is the development of insulin resistance and hepatic steatosis through inhibition of the transcription factor PPARα. Indeed, JNK1/2 deficiency in hepatocytes protects against the development of steatosis, suggesting...

A lipocalin mediates unidirectional heme biomineralization in malaria parasites [Microbiology] [Early Edition]

During blood-stage development, malaria parasites are challenged with the detoxification of enormous amounts of heme released during the proteolytic catabolism of erythrocytic hemoglobin. They tackle this problem by sequestering heme into bioinert crystals known as hemozoin. The mechanisms underlying this biomineralization process remain enigmatic. Here, we demonstrate that both rodent...

The capillary Kir channel as sensor and amplifier of neuronal signals: Modeling insights on K+-mediated neurovascular communication [Physiology] [Early Edition]

Neuronal activity leads to an increase in local cerebral blood flow (CBF) to allow adequate supply of oxygen and nutrients to active neurons, a process termed neurovascular coupling (NVC). We have previously shown that capillary endothelial cell (cEC) inwardly rectifying K+ (Kir) channels can sense neuronally evoked increases in interstitial...

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

Provides a listing of current committee members and society officers.

Friday, 26 June 2020

02:02 AM

Discrete Space Vector Modulation-Based Model Predictive Torque Control With No Suboptimization [IEEE Transactions on Industrial Electronics - new TOC]

This article presents a simplified discrete space vector modulation (DSVM)-based predictive torque control (PTC) scheme in order to improve the performance of a two-level inverter-fed induction motor drive. DSVM technique creates a number of virtual vectors which are evaluated in the conventional all vector-based discrete space vector modulation-based model predictive torque control (DSVM-MPTC) method. The high number of admissible vectors increases the computational burden of DSVM-MPTC, significantly. In this article, an efficient optimal voltage vector selection method is proposed to reduce the computational load of DSVM-MPTC from 37 to 13 enumerations. The vector selected from the reduced set of admissible voltage vectors produces the same cost function value as that of all vector-based DSVM-MPTC in the entire range of operation of induction motor (IM) drives. The proposed method reduces the computational burden effectively without causing any suboptimization issues in both transients and steady states. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the switching-table-based DSVM-MPTC and the classic finite-control-set model-predictive-control which only utilizes the real voltage vectors.

Acoustic Source Localization From Multirotor UAVs [IEEE Transactions on Industrial Electronics - new TOC]

In this article, we address the problem of acoustic source localization using a microphone array mounted on multirotor unmanned aerial vehicles (UAVs). Conventional localization beamforming techniques are especially challenging in these specific conditions, due to the nature and intensity of the disturbances affecting the recorded acoustic signals. The principal disturbances are related to the high-frequency, narrowband noise originated by the electrical engines, and to the broadband aerodynamic noise induced by the propellers. A solution to this problem is proposed, which adopts an efficient beamforming technique for the direction of arrival estimation of an acoustic source and a circular array detached from the multirotor vehicle body in order to reduce the effects of noise generated by the propellers. The approach used to localize the source relies on a diagonal unloading beamforming with a novel norm transform frequency fusion. The proposed algorithm is tested on a multirotor UAV equipped with a compact uniform circular array of eight microphones, placed on the bottom of the drone to localize the target acoustic source placed on the ground while the quadcopter is hovering at different altitudes. The experimental results conducted in outdoor hovering conditions are illustrated, and the localization performances are reported under various recording conditions and source characteristics.

An Output Capacitorless Low-Dropout Regulator With a Low-VDD Inverting Buffer for the Mobile Application [IEEE Transactions on Industrial Electronics - new TOC]

To provide power to the latest mobile applications that use functions with heavy loads, in this letter, we present a capacitorless low-dropout regulator (LDO) that supplies a large load current up to 600 mA. The proposed buffer and the feedforward paths are used to provide a stable operation and fast response along with a large load current. Owing to these schemes, the proposed LDO has a high unity gain frequency of 2.85 MHz at 100 mA with a total compensation capacitance of 5.1 pF. In addition, the LDO operates under a wide input voltage range of 1.5–5.0 V owing to the low-VDD structure. Also, a power supply rejection ratio was –52 dB at 100 kHz. The chip was implemented with a small size of 0.082 mm2 using the I/O devices of a 0.18 μm CMOS process with a minimum length of 0.5 μm.

Predictive Control Based DC Microgrid Stabilization With the Dual Active Bridge Converter [IEEE Transactions on Industrial Electronics - new TOC]

Dual-active-bridge (DAB) enabled dc microgrids stabilization is investigated in this article. DAB has two control objectives: load current regulation and the dc-bus voltage stabilization. In multiobjective control applications, the conventional proportional integrator (PI)-based controllers face challenges in the control loop coordination. The saturation of the loops largely deteriorate the control performance. Moreover, the system impedance has to be measured before designing the active damping control. In this article, a moving discretized control set—model predictive control (MDCS-MPC) is proposed for DAB. The proposed MDCS-MPC is inherently a good choice for multiobjective control. It provides several advantages, such as a good tradeoff between two control objectives and adaptive performance on system impedance. The evaluation and comparison of the proposed MDCS-MPC and PI are carried out. Experiments on a 270–270V, 20 kHz, 1 kW DAB converter are conducted to verify the theoretical claims.

Thursday, 25 June 2020

02:01 AM

PPAR{alpha} exacerbates necroptosis, leading to increased mortality in postinfluenza bacterial superinfection [Immunology and Inflammation] [Early Edition]

Patients infected with influenza are at high risk of secondary bacterial infection, which is a major proximate cause of morbidity and mortality. We have shown that in mice, prior infection with influenza results in increased inflammation and mortality upon Staphylococcus aureus infection, recapitulating the human disease. Lipidomic profiling of the...

Tuesday, 23 June 2020

06:02 PM

Subunit composition of the mammalian serine-palmitoyltransferase defines the spectrum of straight and methyl-branched long-chain bases [Biochemistry] [Early Edition]

Sphingolipids (SLs) are chemically diverse lipids that have important structural and signaling functions within mammalian cells. SLs are commonly defined by the presence of a long-chain base (LCB) that is normally formed by the conjugation of l-serine and palmitoyl-CoA. This pyridoxal 5-phosphate (PLP)-dependent reaction is mediated by the enzyme serine-palmitoyltransferase...

Effect of Intraoperative Dexamethasone on Major Complications and Mortality Among Infants Undergoing Cardiac Surgery [JAMA Current Issue]

This randomized trial compares the effect of intravenous dexamethasone vs saline on death, need for ECMO or CPR, and other complications among infants younger than 12 months undergoing cardiac surgery with cardiopulmonary bypass.

Vegetable Consumption and Progression of Prostate Cancer [JAMA Current Issue]

To the Editor In the Men’s Eating and Living (MEAL) randomized clinical trial, men with early-stage prostate cancer were randomized to receive counseling to increase vegetable consumption to 7 servings per day or more vs the control group receiving written dietary information. We would caution that the generalizability of the findings may be limited to men with the baseline dietary habits of those recruited to the study. At baseline, the mean total vegetable intake was 3.38 servings per day, an amount twice the mean intake of US men (1.5 servings per day). Hence, most men in the US are likely to have lower vegetable intake than the amounts consumed at baseline by participants in this trial.

Government Not Doing All It Could to Recruit and Retain Scientists [JAMA Current Issue]

The US Department of Health and Human Services (HHS) hasn’t yet used the authority it was given more than 3 years ago to recruit and retain biomedical research scientists, according to a recent Government Accountability Office (GAO) report.

Payment Reforms to Incentivize Innovations in Home-Based Care [JAMA Current Issue]

This Viewpoint discusses the need for new payment models to incentivize innovations in personalized home care and facilitate transitions already occurring in response to the COVID-19 pandemic, and to develop lower-cost higher-quality approaches to home-based management of chronic illnesses such as heart failure and Parkinson disease.

Smoking Cessation—Progress, Barriers, and New Opportunities [JAMA Current Issue]

This Viewpoint summarizes the 2020 report from the US Surgeon General, summarizing updated scientific evidence on the benefits of smoking cessation and individual, health systems, and population strategies that can facilitate smoking cessation.

Friday, 12 June 2020

06:02 PM

Individualized Training Based on Force–Velocity Profiling During Jumping in Ballet Dancers [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 6
Pages: 788-794

Validity of a Smartphone Application and Chest Strap for Recording RR Intervals at Rest in Athletes [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 6
Pages: 896-899

Affective Feelings and Perceived Exertion During a 10-km Time Trial and Head-to-Head Running Race [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 6
Pages: 903-906

Retraction: Barbalho et al (2020) [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 6
Pages: 914-914

Wednesday, 10 June 2020

Tuesday, 09 June 2020

02:01 AM

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

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

Monday, 08 June 2020

Friday, 29 May 2020

Thursday, 21 May 2020

Tuesday, 12 May 2020

02:02 AM

Partisan Gerrymandering and Political Science [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 171-185, May 2020.

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

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

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

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

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

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

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

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

Monday, 13 April 2020

02:00 PM

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

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

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

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

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

Annual Review of Nutrition, Volume 39, Issue 1, Page 175-200, August 2019.

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

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

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

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

Tuesday, 11 February 2020

03:00 PM

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

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

Cardiac Fibroblast Diversity [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 63-78, 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.

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

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

Monday, 03 February 2020

03:00 PM

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.

Impact of Outdated CSI on the Secrecy Performance of Wireless-Powered Untrusted Relay Networks [IEEE Transactions on Information Forensics and Security - new TOC]

We investigate the effect of outdated channel state information (CSI) on the secrecy performance of wireless-powered untrusted relay networks, in which the relay is a potential eavesdropper. To keep the information secret from this untrusted relay, the destination sends a jamming signal to the relay when the source transmits an information signal. At the same time, the relay harvests energy from the radio-frequency power, and forwards the received signals to the destination using this harvested energy. To determine the proportions of energy harvesting and information processing, the relay makes use of relaying based on power splitting or time switching policy. Although the destination tries to remove the jamming signal from the relaying signal, it cannot cancel out the jamming signal perfectly due to imperfect channel reciprocity caused by the outdated CSI; this residual jamming signal therefore has a negative impact on secrecy performance. In this scenario, we derive the closed-form expressions for the outage probability and average secrecy rate, and find the jamming power ratio, power splitting ratio, and time switching ratio to optimize these secrecy performance metrics. The numerical results demonstrate the accuracy of our analysis, and show that the proposed secure relaying protocols achieve a near-optimal secrecy performance, as well as outperforming the conventional scheme without the jamming power control.

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

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

Wednesday, 29 January 2020

03:00 PM

Image Representations With Spatial Object-to-Object Relations for RGB-D Scene Recognition [IEEE Transactions on Image Processing - new TOC]

Scene recognition is challenging due to the intra-class diversity and inter-class similarity. Previous works recognize scenes either with global representations or with the intermediate representations of objects. In contrast, we investigate more discriminative image representations of object-to-object relations for scene recognition, which are based on the triplets of <;object, relation, object> obtained with detection techniques. Particularly, two types of representations, including co-occurring frequency of object-to-object relation (denoted as COOR) and sequential representation of object-to-object relation (denoted as SOOR), are proposed to describe objects and their relative relations in different forms. COOR is represented as the intermediate representation of co-occurring frequency of objects and their relations, with a three order tensor that can be fed to scene classifier without further embedding. SOOR is represented in a more explicit and freer form that sequentially describe image contents with local captions. And a sequence encoding model (e.g., recurrent neural network (RNN)) is implemented to encode SOOR to the features for feeding the classifiers. In order to better capture the spatial information, the proposed COOR and SOOR are adapted to RGB-D data, where a RGB-D proposal fusion method is proposed for RGB-D object detection. With the proposed approaches COOR and SOOR, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.

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.

Monday, 26 August 2019

02:00 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.

History, Microdata, and Endogenous Growth [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 11, Issue 1, Page 615-633, 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

02:00 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.

Technology, Work, and Family: Digital Cultural Capital and Boundary Management [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 425-447, July 2019.

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