Tuesday, 27 October 2020

06:03 PM

Out of the Park—Encouragement Makes All the Difference [JAMA Current Issue]

In this narrative medicine essay, a surgical oncologist describes the erosion of her confidence when patients and colleagues question her abilities or ignore her contributions based on her diminutive stature and sex and suggests that colleagues offer encouragement and praise to help stay further damage.

Colchicine Reduces Cardiovascular Events in Chronic Coronary Disease [JAMA Current Issue]

The anti-inflammatory drug colchicine has been shown to reduce cardiovascular events after recent myocardial infarction. Now, a trial in the New England Journal of Medicine reports that daily low-dose colchicine also significantly reduces the cardiovascular event risk among patients with chronic coronary disease.

Vitamin D and Calcium Prevent Recurrent Vertigo [JAMA Current Issue]

Vitamin D and calcium supplements reduced vertigo recurrence after successful vestibular rehabilitation, especially among patients with subnormal vitamin D levels, a trial in Neurology reported.

Evaluating Non–Statistically Significant Results From Trials in Practice—Reply [JAMA Current Issue]

In Reply We submit that clinicians have a responsibility to evaluate major clinical trials and, at times, to implement their findings in advance of clinical guidelines being produced by scientific societies and government agencies. In our Viewpoint, we sought to provide a framework for them to do this in situations in which actionable findings might be hidden behind a non–statistically significant primary outcome. We agree with Dr Piovani and colleagues that when deciding whether to implement clinical trial findings, outcomes that matter to patients are the most important. However, because data pertaining to all potentially important outcomes are often not available, clinicians must make decisions about evidence under conditions of uncertainty. We consider that they should do so based on an assessment of the probability that a particular treatment will result in a net benefit for a patient, focusing first on outcomes that are likely to matter to patients.

Evaluating Non–Statistically Significant Results From Trials in Practice [JAMA Current Issue]

To the Editor Dr Young and colleagues suggested that there are occasions when clinicians may treat statistically nonsignificant results as clinically meaningful and incorporate them in important clinical decision-making, especially when considering the effect size and associated confidence intervals. This recommendation is problematic because it elevates underpowered studies in clinical decision-making. Even more problematic in this context is the assumption that inferences can be made directly from group-level data to the individual, even if the group-level results are ambiguous or nonsignificant.

Detect with PKAchu [EurekAlert! - Breaking News]

Researchers use genetically engineered mice that fluoresce during PKA activation -- PKAchu -- to observe its activation in retina cells. They found prolonged PKA activates in darkness, after a subsequent light-on mode. Moreover, the activation was seen only in rod cells. The group hopes the results will lead to a better understanding of how our eyes see at night.

02:07 AM

Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning. (arXiv:2010.12640v1 [cs.LG]) [cs.CR updates on arXiv.org]

More and more conventional electromechanical meters are being replaced with smart meters because of their substantial benefits such as providing faster bi-directional communication between utility services and end users, enabling direct load control for demand response, energy saving, and so on. However, the fine-grained usage data provided by smart meter brings additional vulnerabilities from users to companies. Occupancy detection is one such example which causes privacy violation of smart meter users. Detecting the occupancy of a home is straightforward with time of use information as there is a strong correlation between occupancy and electricity usage. In this work, our major contributions are twofold. First, we validate the viability of an occupancy detection attack based on a machine learning technique called Long Short Term Memory (LSTM) method and demonstrate improved results. In addition, we introduce an Adversarial Machine Learning Occupancy Detection Avoidance (AMLODA) framework as a counter attack in order to prevent abuse of energy consumption. Essentially, the proposed privacy-preserving framework is designed to mask real-time or near real-time electricity usage information using calculated optimum noise without compromising users' billing systems functionality. Our results show that the proposed privacy-aware billing technique upholds users' privacy strongly.

Towards Benchmark Datasets for Machine Learning Based Website Phishing Detection: An experimental study. (arXiv:2010.12847v1 [cs.CR]) [cs.CR updates on arXiv.org]

In this paper, we present a general scheme for building reproducible and extensible datasets for website phishing detection. The aim is to (1) enable comparison of systems using different features, (2) overtake the short-lived nature of phishing websites, and (3) keep track of the evolution of phishing tactics. For experimenting the proposed scheme, we start by adopting a refined classification of website phishing features and we systematically select a total of 87 commonly recognized ones, we classify them, and we made them subjects for relevance and runtime analysis. We use the collected set of features to build a dataset in light of the proposed scheme. Thereafter, we use a conceptual replication approach to check the genericity of former findings for the built dataset. Specifically, we evaluate the performance of classifiers on individual classes and on combinations of classes, we investigate different combinations of models, and we explore the effects of filter and wrapper methods on the selection of discriminative features. The results show that Random Forest is the most predictive classifier. Features gathered from external services are found the most discriminative where features extracted from web page contents are found less distinguishing. Besides external service based features, some web page content features are found time consuming and not suitable for runtime detection. The use of hybrid features provided the best accuracy score of 96.61%. By investigating different feature selection methods, filter-based ranking together with incremental removal of less important features improved the performance up to 96.83% better than wrapper methods.

Soft, skin-interfaced microfluidic systems with integrated immunoassays, fluorometric sensors, and impedance measurement capabilities [Engineering] [Early Edition]

Soft microfluidic systems that capture, store, and perform biomarker analysis of microliter volumes of sweat, in situ, as it emerges from the surface of the skin, represent an emerging class of wearable technology with powerful capabilities that complement those of traditional biophysical sensing devices. Recent work establishes applications in the...

A Modular Resonant DC–DC Converter With High Step-Down Ratio for Tapping Power From HVDC Systems [IEEE Transactions on Industrial Electronics - new TOC]

In this article, a new circuit topology of DC–DC converter for high step-down ratio is proposed. The proposed DC–DC converter can be used to tap power from high-voltage direct current (HVDC) system to feed the local loads that are located in the vicinity of HVDC transmission corridor. The proposed DC–DC converter consists of a large number of identical submodules each having one controllable switch, two diodes, and one capacitor. The operation of DC–DC converter is based on repeated charging and discharging of capacitors through a resonant circuit. Zero-current switching is achieved for all the switches used in the converter. In case of failure of one submodule, the failed submodule could be bypassed without affecting the normal operation of the HVDC tap. Equations governing the behavior of the proposed converter and the design of the components have been included in this article. The operational performance of the line commutated converter based HVDC system along with the HVDC tapping scheme with the proposed converter is investigated using a simulation model developed in PSCAD/EMTDC. The proposed circuit topology has also been verified with a down scaled experimental prototype.

Missing-Class-Robust Domain Adaptation by Unilateral Alignment [IEEE Transactions on Industrial Electronics - new TOC]

Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversarial methods have been particularly successful in alleviating the distribution shift between the source and the target domains. However, these methods assume an identical label space between the two domains. This assumption imposes a significant limitation for real applications since the target training set may not contain the complete set of classes. We demonstrate in this article that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training. To overcome this issue, we propose a two-stage unilateral alignment approach. The proposed methodology makes use of the interclass relationships of the source domain and aligns unilaterally the target to the source domain. The benefits of the proposed methodology are first evaluated on the modified national institute of standards and technology database (MNIST)$rightarrow$ MNIST-M adaptation task. The proposed methodology is also evaluated on a fault diagnosis task, where the problem of missing fault types in the target training dataset is common in practice. Both experiments demonstrate the effectiveness of the proposed methodology.

Predictive Position Control of Planar Motors Using Trajectory Gradient Soft Constraint With Attenuation Coefficients in the Weighting Matrix [IEEE Transactions on Industrial Electronics - new TOC]

This article proposes a predictive position control method of planar motors using trajectory gradient soft constraint with attenuation coefficients in the weighting matrix to achieve high-precision, time-varying, and long-stroke positioning. Based on a built dynamics model of a planar motor developed in the laboratory, a predictive model is established to predict the future positions of the motor. To improve the positioning precision, a soft constraint defined by a trajectory gradient difference between the gradients of the reference position and predictive position sequences is introduced to the cost function. Then, an explicitly analytical solution of the optimal control is obtained by minimizing the cost function. To highlight the stronger effects of the trajectory gradients closer to the current time, the attenuation coefficients are applied to the weighting matrix of the added soft constraint. The stability of the control system is proved employing the linear quadratic optimal control method and the Lyapunov stability theory. Moreover, the time complexity is discussed based on the analytical control action to show low computational burden of the proposed method. Finally, the given simulation and experimental results demonstrate the effectiveness of the proposed method to achieve high-precision time-varying positioning for planar motors.

Microplastics in groundwater (and our drinking water) present unknown risk [EurekAlert! - Breaking News]

Microplastics (plastics <5mm) and their negative health impacts have been studied in oceans, rivers, and even soils, and scientists are beginning to grapple with the myriad human health impacts their presence might have. One understudied, but critical, link in the cycle is groundwater, which is often a source of drinking water.

Duke-NUS study uncovers why bats excel as viral reservoirs without getting sick [EurekAlert! - Breaking News]

Study confirms bats adopt multiple strategies to reduce pro-inflammatory responses, thus mitigating potential immune-mediated tissue damage and disease. Findings provide important insights for medical research on human diseases.

Phytoplasma effector proteins devastate host plants through molecular mimicry [EurekAlert! - Breaking News]

'Our group has been studying the proteins that are targeted by the phytoplasma effector proteins for almost 30 years,' said Günter Theißen, one of the scientists involved in the study. 'In our latest research, based on just few data and some simple assumptions, we predicted the structure of the respective effector protein (termed SAP54) about 5 years ago. With the new work, we tested our hypothesis experimentally, and found that our prediction was quite accurate.'

Monday, 26 October 2020

06:03 PM

Divide and conquer: a new formula to minimize 'mathemaphobia' [EurekAlert! - Breaking News]

Maths - it's the subject some kids love to hate, yet despite its lack of popularity, mathematics is critical for a STEM-capable workforce and vital for Australia's current and future productivity. New research finds that boosting student confidence in maths, is pivotal to greater engagement with the subject.

02:07 AM

Towards understanding flash loan and its applications in defi ecosystem. (arXiv:2010.12252v1 [cs.CR]) [cs.CR updates on arXiv.org]

Flash Loan, as an emerging service in the decentralized finance ecosystem, allows traders to request a non-collateral loan as long as the debt is repaid within the transaction. While providing convenience, it brings considerable challenges that Flash Loan allows speculative traders to leverage vulnerability of deployed protocols with vast capital and few risks and responsibilities. Most recently, attackers have gained over $15M profits from Eminence Finance via exploiting Flash Loans to repeatedly swap tokens (i.e., EMN and DAI). To be aware of foxy actions, we should understand what is the behavior running with the Flash Loan by traders. In this work, we propose ThunderStorm, a 3-phase transaction-based analysis framework, to systematically study Flash Loan on the Ethereum. Specifically, ThunderStorm first identifies Flash Loan transactions by applying observed transaction patterns, and then understands the semantics of the transactions based on primitive behaviors, and finally recovers the intentions of transactions according to advanced behaviors. To perform the evaluation, we apply ThunderStorm to existing transactions and investigate 11 well-known platforms. As the result, 22,244 transactions are determined to launch Flash Loan(s), and those Flash Loan transactions are further classified into 7 categories. Lastly, the measurement of financial behaviors based on Flash Loans is present to help further understand and explore the speculative usage of Flash Loan. The evaluation results demonstrate the capability of the proposed system.

Proof-of-Useful-Work as Dual-Purpose Mechanism for Blockchain and AI: Blockchain Consensus that Enables Privacy Preserving Data Mining. (arXiv:1907.08744v3 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, we propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models, a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.

Locality Sensitive Hashing with Extended Differential Privacy. (arXiv:2010.09393v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Extended differential privacy, which is a generalization of standard differential privacy (DP) using a general metric rather than the Hamming metric, has been widely studied to provide rigorous privacy guarantees while keeping high utility. However, existing works on extended DP focus on a specific metric such as the Euclidean metric and the Earth Mover's metric, and cannot be applied to other metrics. Consequently, existing extended DP mechanisms are limited to a small number of applications such as location-based services and document processing. In this paper, we propose two new extended DP mechanisms for privacy-preserving LSH. Our first mechanism is based on the multivariate Laplace mechanism and is designed for the Euclidean distance metric. Our second mechanism uses randomized response, and can be applied to a wide variety of metrics including the angular distance (or cosine) metric, Jaccard metric, Earth Mover's metric, and $l_p$ metric. Moreover, our mechanisms work well for personal data in a high-dimensional space. We theoretically analyze the privacy properties of our mechanisms, introducing new versions of concentrated and probabilistic extended DP to explain the guarantees provided. Finally, we apply our mechanisms to friend matching based on personal data in a high-dimensional space with an angular distance metric. We show through experiments that our mechanisms provides high utility, with our Laplace based mechanism performing well in lower dimensional spaces and the randomized response based mechanism in high dimensions. This makes possible friend matching with rigorous privacy guarantees and high utility.

Friday, 23 October 2020

06:03 PM

Southpaw scientists need a helping (left) hand [Nature - Issue - nature.com science feeds]

Nature, Published online: 23 October 2020; doi:10.1038/d41586-020-03014-9

Lab-equipment manufacturers should consider left-handed researchers when they design products, says Parastoo Mashouri.

The volcanic debris that buried Pompeii wreaks further destruction [Nature - Issue - nature.com science feeds]

Nature, Published online: 23 October 2020; doi:10.1038/d41586-020-02955-5

Compounds in the detritus that entombed the ancient city might be degrading its murals.

COVID and the US election: will the rise of mail-in voting affect the result? [Nature - Issue - nature.com science feeds]

Nature, Published online: 23 October 2020; doi:10.1038/d41586-020-02979-x

With record numbers of postal ballots expected because of the pandemic, Nature dives into the data.

02:04 AM

[Editorial] Reimagining long-term care [The Lancet]

A dichotomous narrative surrounds ageing in the 21st century. On the one hand, there is increasing research and recognition of healthy ageing, and a recognition that age is not synonymous with ill health. On the other hand, there is the fear of looming economic costs and providing care for increasing numbers of older people with increasingly complex needs. These two narratives are hard to marry. Even the name long-term care (given to the latter narrative) is at odds with a short-term neoliberal political economy and, as a result, is generally low on government agendas.

[Comment] SARS-CoV-2 antibody seroprevalence in patients receiving dialysis in the USA [The Lancet]

Antibody serosurveillance is an essential tool for monitoring the COVID-19 pandemic, offering a more comprehensive picture of who has been infected than swab testing of symptomatic individuals alone. In recent months, several countries have done large-scale seroprevalence surveys, including the USA,1,2 China,3 Brazil,4 England,5 and Spain.6 These studies have confirmed that the world is still in the early stages of the COVID-19 pandemic, with the majority of the populations surveyed testing negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies.

[Comment] Offline: Science and politics in the era of COVID-19 [The Lancet]

“How can we follow the science when scientists haven't the foggiest.” “‘Big mouth’ scientists losing trust of Ministers.” Headlines such as these are increasingly common in UK newspapers. Scientists are no longer seen as providing impartial, independent advice to government. They are seen as being responsible for crashing economies, driving up unemployment, and ruining livelihoods. Chris Whitty, England's Chief Medical Officer, and Patrick Vallance, Chief Scientific Adviser, have received the sharpest criticism.

[Correspondence] Leonardo da Vinci has been misinterpreted [The Lancet]

About 1506–08, Leonardo da Vinci's studies of the brain culminated in filling the ventricles of an ox with melted wax, a pioneer accomplishment in medical science. An otherwise excellent review in The Lancet claimed that these drawings of the casted ventricles show the interventricular foramen of Monro and the paired lateral ventricles in a correct manner.1 This misconception has repeatedly been stated in the literature.2–4

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

Norman JE, Heazell AEP, Rodriguez A, et al. Awareness of fetal movements and care package to reduce fetal mortality (AFFIRM): a stepped wedge, cluster-randomised trial. Lancet 2018; 392: 1629–38—In table 2 of this Article, some data in the “Perinatal mortality, n (per 1000 births)” row have been amended in line with the definition for perinatal mortality given in the Outcomes section of the Methods, amends have been made to the legend to clarify the definition of stillbirths at ≥22 weeks' gestation, and the appendix has been corrected.

Wednesday, 21 October 2020

06:03 PM

Equilibrium between nascent and parental MCM proteins protects replicating genomes [Nature - Issue - nature.com science feeds]

Nature, Published online: 21 October 2020; doi:10.1038/s41586-020-2842-3

Mother cells recycle parental MCMs and simultaneously synthesize nascent MCMs, both of which are inherited by daughter cells, in which the former are preferentially used to form active replisomes and the latter adjust the pace of replisome movement to minimize errors during DNA replication.

02:03 AM

Daily briefing: A popular unproven COVID treatment is hindering drug trials [Nature - Issue - nature.com science feeds]

Nature, Published online: 20 October 2020; doi:10.1038/d41586-020-02969-z

Scientists are struggling to run clinical trials for ivermectin in Latin America. Plus: the unsung heroes of a Nobel-winning discovery and Nature has its first open-access agreement.

Tuesday, 20 October 2020

02:03 AM

Ancient genomes reveal tropical bovid species in the Tibetan Plateau contributed to the prevalence of hunting game until the late Neolithic [Ecology] [Early Edition]

Local wild bovids have been determined to be important prey on the northeastern Tibetan Plateau (NETP), where hunting game was a major subsistence strategy until the late Neolithic, when farming lifestyles dominated in the neighboring Loess Plateau. However, the species affiliation and population ecology of these prehistoric wild bovids in...

Thursday, 15 October 2020

02:03 AM

Inner Workings: Crop researchers harness artificial intelligence to breed crops for the changing climate [Environmental Sciences] [Early Edition]

Until recently, the field of plant breeding looked a lot like it did in centuries past. A breeder might examine, for example, which tomato plants were most resistant to drought and then cross the most promising plants to produce the most drought-resistant offspring. This process would be repeated, plant generation...

Tuesday, 13 October 2020

02:03 AM

Sociopolitical stress and acute cardiovascular disease hospitalizations around the 2016 presidential election [Population Biology] [Early Edition]

Previous research suggests that stressors may trigger the onset of acute cardiovascular disease (CVD) events within hours to days, but there has been limited research around sociopolitical events such as presidential elections. Among adults ≥18 y of age in Kaiser Permanente Southern California, hospitalization rates for acute CVD were compared...

Friday, 09 October 2020

12:46 PM

Disarmed principals: institutional resilience and the non-enforcement of delegation [European Political Science Review]

Governments across the world increasingly rely on non-state agents for managing even the most sensitive tasks that range from running critical infrastructures to protecting citizens. While private agents frequently underperform, governments as principals tend nonetheless not to enforce delegation contracts. Why? We suggest the mechanism of institutional resilience. A preexisting set of rules shapes non-enforcement through the combination of (i) its structural misfit with the delegation contract and (ii) asymmetric interdependence that favors the agent over time. To demonstrate the plausibility of our argument, we trace the political process behind Europe’s largest military transport aircraft, the A400M. Governments delegated the development and production of this complex program to a private firm, Airbus. They layered a ‘commercial approach’ onto traditionally state-run defense industries. Yet, resilience caused these new formal rules to fail and eventually disarmed principals. Our mechanism constitutes an innovative approach by theorizing an alternative path toward dynamic continuity.

National identity, a blessing or a curse? The divergent links from national attachment, pride, and chauvinism to social and political trust [European Political Science Review]

Is it true that national identity increases trust, as liberal nationalists assume? Recent research has studied this side of the ‘national identity argument’ by focusing on conceptions of the content of national identity (often civic or ethnic) and their links to social, rather than political, trust. This paper argues that if we take social identity theory seriously, however, we need to complement this picture by asking how varying the strength – rather than the content – of a person’s sense of their national identity affects both their social and political trust. We break down the different dimensions of national identity, hypothesizing and empirically verifying that there are divergent links from national attachment, national pride, and national chauvinism to social and political trust. We do so with data from the US (General Social Survey) and the Netherlands (Longitudinal Internet Studies for the Social Sciences ), thus expanding current knowledge of national identity and trust to a highly relevant yet neglected European case.

Bicameralism and government formation: does bicameral incongruence affect bargaining delays? [European Political Science Review]

The effects of bicameral legislatures on government formation have attracted scholarly attention since Lijphart’s (1984) seminal contribution. Previous research found support for the ‘veto control hypothesis,’ showing that bicameralism affects coalition governments’ composition and duration. However, the effects of bicameralism on the duration of the bargaining process over government formation have yet to be explored. Our work contributes to this area of research by focusing on the impact of bicameralism on bargaining delays. We show that the duration of the bargaining process over government formation decreases at increasing levels of partisan incongruence of the two chambers, especially in those legislative assemblies in which the upper chamber plays a relevant role in the policy-making process. Such empirical evidence is in contrast with the conventional expectation according to which bicameralism should delay the government formation process, as it introduces an additional element of complexity in the bargaining environment. We test our hypothesis by using a novel data set about the partisan composition of upper and lower chambers in 12 Western and Eastern European democracies over the postwar period.

Informational demand across the globe: toward a comparative understanding of information exchange [European Political Science Review]

This study examines the information demands of decision-makers from across the globe in their exchanges with interest organizations. It proposes two explanatory factors that drive these information demands: democracy and development. We argue that decision-makers’ information demands vary depending on whether they hail from developed countries or developing countries, as well as the extent to which their political systems are democratically accountable. We test our expectations based on interviews with 297 decision-makers from 107 different countries who were active during transnational trade and climate change negotiations. Our findings demonstrate that decision-makers from less developed countries exhibit a higher preference for interactions with organizations that provide them with technical information. Decision-makers from democratically accountable countries, by contrast, tend to place relatively greater value on political information provided by interest groups.

02:03 AM

VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder [IEEE Transactions on Information Forensics and Security - new TOC]

In recent years, linguistic steganography based on text auto-generation technology has been greatly developed, which is considered to be a very promising but also a very challenging research topic. Previous works mainly focus on optimizing the language model and conditional probability coding methods, aiming at generating steganographic sentences with better quality. In this paper, we first report some of our latest experimental findings, which seem to indicate that the quality of the generated steganographic text cannot fully guarantee its steganographic security, and even has a prominent perceptual-imperceptibility and statistical-imperceptibility conflict effect (Psic Effect). To further improve the imperceptibility and security of generated steganographic texts, in this paper, we propose a new linguistic steganography based on Variational Auto-Encoder (VAE), which can be called VAE-Stega. We use the encoder in VAE-Stega to learn the overall statistical distribution characteristics of a large number of normal texts, and then use the decoder in VAE-Stega to generate steganographic sentences which conform to both of the statistical language model as well as the overall statistical distribution of normal sentences, so as to guarantee both the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic texts at the same time. We design several experiments to test the proposed method. Experimental results show that the proposed model can greatly improve the imperceptibility of the generated steganographic sentences and thus achieves the state of the art performance.

Thursday, 08 October 2020

Thursday, 01 October 2020

02:02 AM

Estimating Postmatch Fatigue in Soccer: The Effect of Individualization of Speed Thresholds on Perceived Recovery [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 9
Pages: 1216-1222

Differences in Lower Limb Strength and Structure After 12 Weeks of Resistance, Endurance, and Concurrent Training [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 9
Pages: 1223-1230

Effects of Active and Passive Recovery on Muscle Oxygenation and Swimming Performance [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 9
Pages: 1289-1296

Altitude and Heat Training in Preparation for Competitions in the Heat: A Case Study [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 9
Pages: 1344-1348

Skin Temperature, Training Load, and Subjective Muscle Soreness in Junior Endurance Athletes: A Case Study [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 9
Pages: 1349-1352

Friday, 25 September 2020

02:02 AM

Robust Face Super-Resolution via Position Relation Model Based on Global Face Context [IEEE Transactions on Image Processing - new TOC]

Because Face Super-Resolution (FSR) tends to infer High-Resolution (HR) face image by breaking the given Low-Resolution (LR) image into individual patches and inferring the HR correspondence one patch by one separately, Super-Resolution (SR) of face images with serious degradation, especially with occlusion, is still a challenging problem of the computer vision field. To address this problem, we propose a patch-level face model for FSR, which we called the position relation model. This model consists of the mapping relationships in every face position to the rest of the face positions based on similarity. In other words, we build a constraint for each patch position via the relationship in this model from the global range of face. Once an individual input LR image patch is seriously deteriorated, the substitute patch in whole face range can be sought according to the relationship of the model at this position as the provider of the LR information. In this way, the lost facial structures can be compensated by knowledge located in remote pixels or structure information which leads to better high-resolution face images. The LR images with degradations, not only the serious low-quality degradation, e.g. noise, blur, but also the occlusions, can be effectively hallucinated into HR ones. Quantitative and qualitative evaluations on the public datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.

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

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

Thursday, 24 September 2020

02:03 AM

An Accidental Nutritionist [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 1-23, September 2020.

Nutrient Control of mRNA Translation [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 51-75, September 2020.

Nutritional Requirements for Sustaining Health and Performance During Exposure to Extreme Environments [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 221-245, September 2020.

Short Bowel Syndrome: A Paradigm for Intestinal Adaptation to Nutrition? [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 299-321, September 2020.

Drinking Water in the United States: Implications of Water Safety, Access, and Consumption [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 345-373, September 2020.

Tuesday, 22 September 2020

02:02 AM

Active Suspension System Control With Decentralized Event-Triggered Scheme [IEEE Transactions on Industrial Electronics - new TOC]

This article concerns the problem of improving ride comfort of a full-vehicle active suspension system through an observer-based decentralized event-triggered H controller. The observer-based controller is adopted to cope with the situation that states information of vehicles are not available. To reduce the observer-based controller computing frequency, a decentralized event-triggered data transmission scheme is provided, and lower bounds on the minimum interevent time are guaranteed. Based on the proposed controller, the H performance of an active full-vehicle suspension system can be obtained. Finally, simulations in the bump and random road conditions are shown to demonstrate the effectiveness of the derived algorithm.

An Efficient Peer-to-Peer Energy-Sharing Framework for Numerous Community Prosumers [IEEE Transactions on Industrial Informatics - new TOC]

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

Intermediate Observer-Based Robust Distributed Fault Estimation for Nonlinear Multiagent Systems With Directed Graphs [IEEE Transactions on Industrial Informatics - new TOC]

This article focuses on the problem of robust distributed fault estimation for nonlinear multiagent systems with actuator faults and sensor faults. The communication topology of the multiagent systems is assumed to be directed. A novel intermediate observer design method is proposed to estimate the system states, actuator faults, and sensor faults. For the observer constructed in one agent, the output estimation errors of itself and its neighbors are considered, simultaneously. The observer matching condition is not needed in the observer design process. Based on Schur decomposition, the observer parameter calculation method is presented in terms of solution to one linear matrix inequality, which is with the same order as it is for the single agent system. Thus, the calculated amount remains unchanged even when the number of agents increases, since the inequality dimension is independent of the agent number. At last, simulation results are provided to illustrate the effectiveness of the proposed technique.

Maritime Search and Rescue Based on Group Mobile Computing for Unmanned Aerial Vehicles and Unmanned Surface Vehicles [IEEE Transactions on Industrial Informatics - new TOC]

Accidents often occur at sea, so effective maritime search and rescue is essential. In the current process of sea search and rescue, the operation efficiency of large search and rescue equipment is low and it cannot provide stable communication link. In this article, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are used to form a cognitive mobile computing network for co-operative search and rescue, and reinforcement learning (RL) is used to plan search path and improve communication throughput. Based on the scene of marine search and rescue, the grid method is used to model the search and rescue area. Meanwhile, an intragroup communication architecture based on UAVs and USVs is designed to assist intragroup communication by recognizing the link channel state between UAVs. Search and rescue path planning is carried out through the strategy iteration of Markov decision process (MDP). Furthermore, distributed RL is used to recognize the channel state and perform mobile computing, so as to optimize the data throughput in the communication group. The simulation results show that we have successfully completed the path planning task. Compared with conventional methods, RL based on different reward functions has better throughput performance under the same number of UAVs auxiliary communications.

Hierarchical Two-Stream Growing Self-Organizing Maps With Transience for Human Activity Recognition [IEEE Transactions on Industrial Informatics - new TOC]

The rapid growth in autonomous industrial environments has increased the need for intelligent video surveillance. As a predominant element of video surveillance, recognition of complex human movements is important in a wide range of surveillance applications. However, the current state-of-the-art video surveillance techniques use supervised deep learning pipelines for human activity recognition (HAR). A key shortcoming of such techniques is the inability to learn from unlabeled video streams. To operate effectively in natural environments, video surveillance techniques have to be able to handle huge volumes of unlabeled video data, monitor and generate alerts and insights derived from multiple characteristics such as spatial structure, motion flow, color distribution, etc. Furthermore, most conventional learning systems lack memory persistence capability which can reduce the influence of outdated information in memory-guided decision-making resulting in limiting plasticity and overfitting based on specific past events. In this article, we propose a new adaptation of the Growing Self-Organizing Map (GSOM) to address these shortcomings by 1) adopting two proven concepts of traditional deep learning, hierarchical, and multistream learning, applied into GSOM self-structuring architecture to accommodate learning from unlabeled video data and their diverse characteristics, 2) address overfitting and the influence of outdated information on neural architecture by implementing a transience property in the algorithm. We demonstrate the proposed model using three benchmark video datasets and the results confirm its validity and usability for HAR.

Tuesday, 15 September 2020

02:02 AM

CANTO - Covert AutheNtication With Timing Channels Over Optimized Traffic Flows for CAN [IEEE Transactions on Information Forensics and Security - new TOC]

Previous research works have endorsed the use of delays and clock skews for detecting intrusions or fingerprinting controllers that communicate on the CAN bus. Recently, timing characteristics of CAN frames have been also used for establishing a covert channel for cryptographic authentication, in this way cleverly removing the need for cryptographic material inside the short payload of data frames. However, the main drawback of this approach is the limited security level that can be achieved over existing CAN bus traffic. In this work we significantly improve on this by relying on optimization algorithms for scheduling CAN frames and deploy the covert channel on optimized CAN traffic. Under practical bus allocations, we are able to extract 3–5 bits of authentication data from each frame which leads to an efficient intrusion detection and authentication mechanism. By accumulating covert channel data over several consecutive frames, we can achieve higher security levels that are in line with current real-world demands. To prove the correctness of our approach, we present experiments on automotive-grade controllers, i.e., Infineon Aurix, and bus measurements with the use of industry standard tools, i.e., CANoe.

Tuesday, 08 September 2020

02:03 AM

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

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

Friday, 04 September 2020

02:03 AM

<italic>TransPCFG</italic>: Transferring the Grammars From Short Passwords to Guess Long Passwords Effectively [IEEE Transactions on Information Forensics and Security - new TOC]

Long passwords are gaining popularity in password policy recommendations; however, data-driven guessing studies are woefully inadequate in adapting to long passwords, lacking in both guessing efficiency and their composition guidelines. For state-of-the-art data-driven password guessing methods such as PCFGs (Probabilistic Context-free Grammars), their guessing efficiency is limited by the presence of a large scale training data, or the lack thereof. Given that long passwords leaked in the real world are typically scarce, coupled with the fact that the data-driven methods’ performance depends on training data, obtaining good performance on long passwords has become a key challenge. To overcome the dataset limitation, we propose a framework TransPCFG, that transfers the knowledge, (i.e., grammars in PCFGs), from short passwords to facilitate long password guessing. We further perform an empirical evaluation based on three real-world datasets and the results demonstrate superior performance over the state-of-the-art data-driven guessing methods under ${10}^{14}$ offline guesses. For passwords with 16 characters, TransPCFG can compromise an average of 23.30% of the passwords, outperforming PCFG_v4.1 by 56.10%. Additionally,for better password-composition guidelines, we find that long password-composition policies requiring more segments are more resistant to guessing attacks. For the segment, the password 12zxcvbnword1997 has four segments since it follows the template ${Digit}_{2}{Keyboard}_{6}{Letter}_{4}{Year}_{4}$ . We thus recommend users to create long passwords with four or more segments instead of the widely recommended more character classes for security.

Tuesday, 25 August 2020

02:03 AM

Reduced-Order Thermal Observer for Power Modules Temperature Estimation [IEEE Transactions on Industrial Electronics - new TOC]

In this article, a reduced-order thermal observer with disturbance estimation is applied for temperature monitoring in a power electronics module. Although the accurate thermal models of power electronics assemblies are widely available based on computational fluid dynamics solvers, their computational complexity hinders the application in real-time temperature monitoring applications. This article proposes a reduced-order state-space observer to provide a real-time estimation of temperature in power electronics modules. The observer is coupled with a disturbance estimator to minimize the error caused by uncertainties in the model and unknown operating conditions.

Tuesday, 11 August 2020

02:02 AM

End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network [IEEE Transactions on Image Processing - new TOC]

The deep convolutional neural network (CNN) has achieved great success in image recognition. Many image quality assessment (IQA) methods directly use recognition-oriented CNN for quality prediction. However, the properties of IQA task is different from image recognition task. Image recognition should be sensitive to visual content and robust to distortion, while IQA should be sensitive to both distortion and visual content. In this paper, an IQA-oriented CNN method is developed for blind IQA (BIQA), which can efficiently represent the quality degradation. CNN is large-data driven, while the sizes of existing IQA databases are too small for CNN optimization. Thus, a large IQA dataset is firstly established, which includes more than one million distorted images (each image is assigned with a quality score as its substitute of Mean Opinion Score (MOS), abbreviated as pseudo-MOS). Next, inspired by the hierarchical perception mechanism (from local structure to global semantics) in human visual system, a novel IQA-orientated CNN method is designed, in which the hierarchical degradation is considered. Finally, by jointly optimizing the multilevel feature extraction, hierarchical degradation concatenation (HDC) and quality prediction in an end-to-end framework, the Cascaded CNN with HDC (named as CaHDC) is introduced. Experiments on the benchmark IQA databases demonstrate the superiority of CaHDC compared with existing BIQA methods. Meanwhile, the CaHDC (with about 0.73M parameters) is lightweight comparing to other CNN-based BIQA models, which can be easily realized in the microprocessing system. The dataset and source code of the proposed method are available at https://web.xidian.edu.cn/wjj/paper.html.

Tuesday, 04 August 2020

02:02 AM

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

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

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

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

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

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

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

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

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

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

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

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

Friday, 31 July 2020

02:02 AM

Advances in the Science of Asking Questions [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 37-60, July 2020.

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

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

Multiracial Categorization, Identity, and Policy in (Mixed) Racial Formations [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 335-353, July 2020.

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

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

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

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

Thursday, 30 July 2020

06:02 PM

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

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

Tuesday, 28 July 2020

06:02 PM

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

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

Tuesday, 09 June 2020

02:01 AM

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

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

Tuesday, 12 May 2020

02:02 AM

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.

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.

Cardiomyocyte Polyploidy and Implications for Heart Regeneration [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 45-61, 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

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.

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