Tuesday, 02 March 2021

10:14 AM

Constrained Differentially Private Federated Learning for Low-bandwidth Devices. (arXiv:2103.00342v1 [cs.CR]) [cs.CR updates on arXiv.org]

Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learning has two main drawbacks. First, it is quite bandwidth inefficient as it involves a lot of message exchanges between the aggregating server and the participating entities. This bandwidth and corresponding processing costs could be prohibitive if the participating entities are, for example, mobile devices. Furthermore, although federated learning improves privacy by not sharing data, recent attacks have shown that it still leaks information about the training data. This paper presents a novel privacy-preserving federated learning scheme. The proposed scheme provides theoretical privacy guarantees, as it is based on Differential Privacy. Furthermore, it optimizes the model accuracy by constraining the model learning phase on few selected weights. Finally, as shown experimentally, it reduces the upstream and downstream bandwidth by up to 99.9% compared to standard federated learning, making it practical for mobile systems.

Countering Adaptive Network Covert Communication with Dynamic Wardens. (arXiv:2103.00433v1 [cs.CR]) [cs.CR updates on arXiv.org]

Network covert channels are hidden communication channels in computer networks. They influence several factors of the cybersecurity economy. For instance, by improving the stealthiness of botnet communications, they aid and preserve the value of darknet botnet sales. Covert channels can also be used to secretly exfiltrate confidential data out of organizations, potentially resulting in loss of market/research advantage. Considering the above, efforts are needed to develop effective countermeasures against such threats. Thus in this paper, based on the introduced novel warden taxonomy, we present and evaluate a new concept of a dynamic warden. Its main novelty lies in the modification of the warden's behavior over time, making it difficult for the adaptive covert communication parties to infer its strategy and perform a successful hidden data exchange. Obtained experimental results indicate the effectiveness of the proposed approach.

Secure Evaluation of Quantized Neural Networks. (arXiv:1910.12435v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

We investigate two questions in this paper: First, we ask to what extent "MPC friendly" models are already supported by major Machine Learning frameworks such as TensorFlow or PyTorch. Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e.g., TensorFlow to design, train and test models that later have to be evaluated securely. Second, we ask to what extent the functionality for evaluating Neural Networks already exists in general-purpose MPC frameworks. These frameworks have received more scrutiny, are better documented and supported on more platforms. Furthermore, they are typically flexible in terms of the threat model they support. In contrast, most secure evaluation protocols in the literature are targeted to a specific threat model and their implementations are only a "proof-of-concept", making it very hard for their adoption in practice. We answer both of the above questions in a positive way: We observe that the quantization techniques supported by both TensorFlow, PyTorch and MXNet can provide models in a representation that can be evaluated securely; and moreover, that this evaluation can be performed by a general purpose MPC framework. We perform extensive benchmarks to understand the exact trade-offs between different corruption models, network sizes and efficiency. These experiments provide an interesting insight into cost between active and passive security, as well as honest and dishonest majority. Our work shows then that the separating line between existing ML frameworks and existing MPC protocols may be narrower than implicitly suggested by previous works.

BE-RAN: Blockchain-enabled Open RAN with Decentralized Identity Management and Privacy-Preserving Communication. (arXiv:2101.10856v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Radio Access Networks (RAN) tends to be more distributed in the 5G new radio in order to provide low latency and flexible on-demanding services. In this paper, Blockchain-enabled Radio Access Networks (BE-RAN) is proposed as a novel decentralized RAN architecture to facilitate enhanced security and privacy on identification and authentication. It can offer user-centric identity management for User Equipment (UE) and Radio Access Networks (RAN) elements and mutual authentication to all entities while enabling on-demand point-to-point communication with accountable billing service add-on to the general public. Also, a potential operating model with thorough decentralization of RAN is envisioned. The distributed privacy-preserving P2P communication, as one of the core use cases for future mobile networks, is presented as an essential complement to the existing core network-based security and privacy management. The results show that BE-RAN significantly improves communication and computation overheads compared to the existing communication authentication protocols.

Revenue Attribution on iOS 14 using Conversion Values in F2P Games. (arXiv:2102.08458v3 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Mobile app developers use paid advertising campaigns to acquire new users, and they need to know the campaigns' performance to guide their spending. Determining the campaign that led to an install requires that the app and advertising network share an identifier that allows matching ad clicks to installs. Ad networks use the identifier to build user profiles that help with targeting and personalization. Modern mobile operating systems have features to protect the privacy of the user. The privacy features of Apple's iOS 14 enforces all apps to get system permission for tracking explicitly instead of asking the user to opt-out of tracking as before. If the user does not allow tracking, the identifier for advertisers (IDFA) required for attributing the installation to the campaign is not shared. The lack of an identifier for the attribution changes profoundly how user acquisition campaigns' performance is measured. For users who do not allow tracking, there is a new feature that still allows following campaign performance. The app can set an integer, so called conversion value for each user, and the developer can get the number of installs per conversion value for each campaign. This paper investigates the task of distributing revenue to advertising campaigns using the conversion values. Our contributions are to formalize the problem, find the theoretically optimal revenue attribution function for any conversion value schema, and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.

Monday, 01 March 2021

06:16 PM

COVID-19 RCTs registered in 1st 100 days of pandemic [EurekAlert! - Breaking News]

Researchers assessed the recruitment and results reporting of randomized clinical trials (RCTs) to treat or prevent COVID-19 registered within 100 days of the first case reported to the World Health Organization.

How 'great' was the great oxygenation event? [EurekAlert! - Breaking News]

Around 2.5 billion years ago, our planet experienced what was possibly the greatest change in its history: According to the geological record, molecular oxygen suddenly went from nonexistent to becoming freely available everywhere. Evidence for the "great oxygenation event" (GOE) is clearly visible, for example, in banded iron formations containing oxidized iron. The GOE, of course, is what allowed oxygen-using organisms - respirators - and ultimately ourselves, to evolve.

Saturday, 27 February 2021

02:22 AM

Oahu marine protected areas offer limited protection of coral reef herbivorous fishes [EurekAlert! - Breaking News]

Marine protected areas around O?ahu do not adequately protect populations of herbivorous reef fishes that eat algae on coral reefs. That is the primary conclusion of a study published in Coral Reefs by researchers from the University of Hawai?i at Mānoa.

Friday, 26 February 2021

06:16 PM

Sub-diffraction optical writing information bits: towards a high-capacity optical disk for Big Data [EurekAlert! - Breaking News]

Researchers at USST, RMIT and NUS have overcome Abbe's diffraction barrier by using earth-rich lanthanide-doped upconversion nanoparticles and graphene oxide flakes. Sub-diffraction information bits have been written in the nanocomposite using upconversion nanoparticles to reduce graphene oxide flakes through upconversion resonance energy transfer upon engineered illumination. A much-improved data density has been achieved for an estimated storage capacity of 700 TB on a 12-cm optical disk, comparable to a storage capacity of 28,000 Blu-ray disks.

02:23 AM

[Editorial] 50 years of the inverse care law [The Lancet]

“The availability of good medical care tends to vary with the need for it in the population served. This inverse care law operates more completely where medical care is most exposed to market forces, and less so where such exposure is reduced.”

[Comment] PSMA-targeted radiopharmaceutical therapy in patients with metastatic castration-resistant prostate cancer [The Lancet]

The treatment of patients with metastatic castration-resistant prostate cancer has mostly involved androgen receptor-targeted therapies (ARTTs) and cytotoxic chemotherapy for over a decade. Prostate-specific membrane antigen (PSMA)-targeted radiopharmaceutical therapy with lutetium-177 [177Lu]Lu-PSMA-617 delivers β radiation to cells expressing PSMA. The retrospective data from Germany investigating PSMA-targeted radiopharmaceutical therapy in men with metastatic castration-resistant prostate cancer were promising,1 but the first prospective results from Australia, the LuPSMA study,2 provided credible safety and efficacy data.

[Comment] Averting malaria transmission with lethal house lure intervention [The Lancet]

In 2019, malaria accounted for 229 million cases and 409 000 deaths globally, with 94% of the burden occurring in sub-Saharan Africa.1 Despite determined efforts to combat malaria through definitive diagnoses, case management, and preventive interventions, the huge disease burden persists. Although effective vector control unequivocally curtails malaria transmission, integrated vector management has faced numerous challenges,2 including diminished effectiveness and uncertain sustainability of interventions, partly caused by insecticide resistance and residual transmission.

[Perspectives] Julian Tudor Hart: medical pioneer and social advocate [The Lancet]

Julian Tudor Hart is seen variously as a researcher, an expert on high blood pressure, an epidemiologist, scientist, writer, political commentator, and social advocate. But at heart he was always a practising family doctor. Few physicians manage to be expert in so many fields and none while also looking after the primary care needs of some 2100 people, which Tudor Hart did at Glyncorrwg, a former colliery village in south Wales, UK. His dedication to general practice meant his work was relevant and valued by fellow general practitioners (GPs).

[Correspondence] Continue rare cancers collaboration with European Reference Networks after Brexit [The Lancet]

We read the Correspondence by Marc Tischkowitz and colleagues,1 signed by UK participants in the European Reference Networks (ERNs) programme and related to the risk of Brexit on patients with rare diseases.

Table of Contents [IEEE Transactions on Industrial Informatics - new TOC]

Presents the table of contents for this issue of the publication.

A Bearing Fault Diagnosis Method Based on Enhanced Singular Value Decomposition [IEEE Transactions on Industrial Informatics - new TOC]

For the two shortcomings of singular value decomposition (SVD), the determination of the reconstruction order and the poor noise reduction ability, an enhanced SVD is introduced in this article. The core ideas include: first, an efficient method to determine the reconstructed order of SVD and the relative-change rate of the singular envelope kurtosis is presented, composed of improved SVD (ISVD). Then, the method to select the optimal node of wavelet packet transform (WPT) by the criterion of envelope kurtosis maximum is presented, composed of improved WPT (IWPT). The flexible filter design and superior noise reduction abilities of the IWPT and the passband denoise ability of the ISVD are organicly combined to form enhanced singular value decomposition (E-SVD) method. In addition, an indicator is introduced to evaluate the performance of the results. First, the reconstructed signal is obtained by performing ISVD on the original signal. Second, IWPT is executed on the reconstructed signal to achieve the optimal node. Finally, the filtered signal is combined with the envelope power spectrum to extract the bearing fault characteristic frequency. The method's validity and superiority are verified by the analysis of simulated data and actual cases of rolling bearing.

Intelligent Spectrum Controlled Supplemental Lighting for Daylight Harvesting [IEEE Transactions on Industrial Informatics - new TOC]

This article presents a neural network-based control method for daylight harvesting in a proof-of-concept greenhouse consisting of emulated sunlight and dimmable light emitting diode light fixtures. The objective of this multi-input–multi-output lighting system is to deliver desired levels of light, within a specific spectrum range, to locations of interest in a grow tent. To this end, a learning neural network controller with online adaptive weights is presented which can achieve stability with small errors in the presence of disturbances and modeling uncertainties. A stability analysis of the closed-loop system is presented along with a selection method for obtaining the control parameters. The neural controller is enhanced with an antiwindup mechanism to account for the nonlinear effect of actuator saturation. Experimental results are presented to verify the proposed daylighting control strategy which confirm analytic and simulation studies.

A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life [IEEE Transactions on Industrial Informatics - new TOC]

Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery (LIB). However, accurately predicting the remaining useful life (RUL) of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods is greatly limited. Besides, mathematical models established by model-driven methods to represent degradation process are unstable because of external factors like temperature. To solve this problem, a new LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (LSTM), namely Auto-CNN-LSTM, is proposed in this article. This method is developed based on deep CNN and LSTM to mine deeper information in finite data. In this method, an autoencoder is utilized to augment the dimensions of data for more effective training of CNN and LSTM. In order to obtain continuous and stable output, a filter to smooth the predicted value is used. Comparing with other commonly used methods, experiments on a real-world dataset demonstrate the effectiveness of the proposed method.

A Novel Heavy-Tailed Mixture Distribution Based Robust Kalman Filter for Cooperative Localization [IEEE Transactions on Industrial Informatics - new TOC]

In cooperative localization for autonomous underwater vehicles (AUVs), the practical stochastic noise may be heavy-tailed, and nonstationary distributed because of acoustic speed variation, multipath effect of acoustic channel, and changeable underwater environment. To address such noise, a novel heavy-tailed mixture (HTM) distribution is first proposed in this article, and then expressed as a hierarchical Gaussian form by employing a categorical distributed auxiliary vector. Based on that, a novel HTM distribution based robust Kalman filter is proposed, where the one-step prediction, and measurement likelihood probability density functions are, respectively, modeled as an HTM distribution, and a Normal-Gamma-inverse Wishart distribution. The proposed filter is verified by a lake experiment about cooperative localization for AUVs. Compared with the cutting-edge filter, the proposed filter has been improved by 50.27% in localization error but no more than twice computational time is required.

Urban Americans more likely to follow covid-19 prevention behaviors than rural Americans [EurekAlert! - Breaking News]

Timothy Callaghan, PhD, and Alva Ferdinand, DrPH, JD, from the Southwest Rural Health Research Center at Texas A&M University School of Public Health, joined colleagues in the first national study of how often people in urban and rural areas in the United States follow COVID-19 guidelines. These include public health best practices like wearing masks in public, sanitizing homes and work areas, maintaining physical distancing, working from home and avoiding dining in restaurants or bars.

Wednesday, 24 February 2021

06:16 PM

Sulfur sequestration promotes multicellularity during nutrient limitation [Nature - Issue - nature.com science feeds]

Nature, Published online: 24 February 2021; doi:10.1038/s41586-021-03270-3

Depriving unicellular Dictyostelium discoideum of nutrients generates reactive oxygen species that sequester cysteine within glutathione, which maintains this amoeba in a nonproliferating state that promotes aggregation into a multicellular organism.

Tuesday, 23 February 2021

06:16 PM

Mars video reveals Perseverance rover’s daring touchdown [Nature - Issue - nature.com science feeds]

Nature, Published online: 22 February 2021; doi:10.1038/d41586-021-00475-4

The NASA spacecraft has also snapped more shots of its surroundings and listened to a Martian wind gust.

Drug-Resistant Yeast Infections Spread in COVID-19 Unit [JAMA Current Issue]

Pandemic-related lapses in infection control practices may have caused an outbreak of multidrug-resistant Candida auris yeast infections in a Florida hospital’s coronavirus disease 2019 (COVID-19) unit, investigators from the CDC and the Florida Department of Health reported.

Diagnosis and Treatment of Multiple Sclerosis [JAMA Current Issue]

This narrative review discusses the epidemiology and pathophysiology of multiple sclerosis and summarizes current evidence on its diagnosis and treatment using disease-modifying therapies and nonpharmacological interventions.

Covid-19 Communication—The Need for Humanity, Empathy, and Grace [JAMA Current Issue]

In this narrative medicine essay, a medical school professor expresses gratitude for the caring and empathy expressed by the team caring for her mother hospitalized with COVID-19 and emphasizes the importance of humanity and compassion over facts and statistics for families physically separated from their critically ill loved ones.

Effect of Baclofen on Agitation Events in Patients With Unhealthy Alcohol Use Receiving Mechanical Ventilation [JAMA Current Issue]

This randomized clinical trial compares the effects of high-dose baclofen vs placebo on agitation-related events among adults in the ICU with unhealthy alcohol use receiving mechanical ventilation.

Hydrocortisone, Vitamin C, and Thiamine for Treatment of Sepsis—Making Evidence Matter [JAMA Current Issue]

The randomized trial, alone or as part of a meta-analysis, is the pinnacle of the evidence pyramid. Rigorously performed randomized trials offer key insights into the benefit of a treatment vs a control or of a new treatment compared with a standard treatment, although clinicians do not always adopt trial findings into practice. The resulting practice variability is inherent to the art of medicine but not always to the benefit of patients.

02:21 AM

Best Papers and Distinguished Reviewers [IEEE Transactions on Industrial Electronics - new TOC]

Presents a listing of the best papers and distinguished reviewers for this publication in 2020.

Sensorless Fault-Tolerant Control With Phase Delay Compensation for Aerospace FTPMSM Drives With Phase Open-Circuit and Short-Circuit Faults [IEEE Transactions on Industrial Electronics - new TOC]

To enhance the reliability of fault-tolerant permanent-magnet synchronous motor (FTPMSM) drives, a new sensorless control based on the robust observer, nonorthogonal phase-locked loop (PLL), and variable phase delay compensation is proposed, which can guarantee the medium- and high-speed sensorless control performance for the FTPMSM even in the phase open-circuit and short-circuit fault conditions. A robust observer is proposed to achieve any two healthy phase back electromotive force (EMF) estimation, regardless of the parameter variation, external disturbance, and the phase fault. The nonorthogonal PLL is presented to extract the information of the rotor position from the observed two nonorthogonal phase back-EMFs. To enhance the estimation accuracy of the rotor position as the speed changes, the variable cut-off frequency low-pass filter (VLPF) is proposed to eliminate the high-frequency noises of the observed phase back-EMFs, while a phase delay compensation is presented to compensate for the estimation deviation caused by the VLPF. The resulting sensorless FTPMSM system has excellent speed control performance both in normal and fault conditions, which is also demonstrated by a six-phase FTPMSM system experimental platform.

Eddy Current Probe With Three-Phase Excitation and Integrated Array Tunnel Magnetoresistance Sensors [IEEE Transactions on Industrial Electronics - new TOC]

Detecting defects in metal structures quickly and robustly is critical to maintaining safe operation in industry. This work discloses an eddy current (EC) testing probe with coils carrying three-phase currents as excitation and integrated array tunnel magnetoresistance (TMR) sensors measuring the magnetic field as receiver. The three-phase currents in the planar layout coils induce EC that migrates electrically in the material under test. This excitation method does not require multiplexing of the coils and is sensitive to defects of any orientations. The TMR array contains 64 sensors, which are microfabricated and packaged in a line. Within single probe pass, the sensors generate magnetic field image with high sensitivity ($text{1.99};text{nT}/sqrt {text{Hz}} @text{30};text{kHz}$) and fine spatial resolution (0.5 mm pitch). By analyzing the output image, defects are identified and localized. The operating principle of the probe was investigated based on a finite-element method model. A prototype probe was developed and tested, with which machined defects in aluminum samples were inspected. Experimental results show that the probe has comparable sensitivity for horizontal and vertical defects, and it can recognize a small defect with length × width × depth = 1 × 0.2 × 1 mm3. The defects are located within 1 mm with an artificial neural network.

Fast Multi-View Clustering via Nonnegative and Orthogonal Factorization [IEEE Transactions on Image Processing - new TOC]

The rapid growth of the number of data brings great challenges to clustering, especially the introduction of multi-view data, which collected from multiple sources or represented by multiple features, makes these challenges more arduous. How to clustering large-scale data efficiently has become the hottest topic of current large-scale clustering tasks. Although several accelerated multi-view methods have been proposed to improve the efficiency of clustering large-scale data, they still cannot be applied to some scenarios that require high efficiency because of the high computational complexity. To cope with the issue of high computational complexity of existing multi-view methods when dealing with large-scale data, a fast multi-view clustering model via nonnegative and orthogonal factorization (FMCNOF) is proposed in this paper. Instead of constraining the factor matrices to be nonnegative as traditional nonnegative and orthogonal factorization (NOF), we constrain a factor matrix of this model to be cluster indicator matrix which can assign cluster labels to data directly without extra post-processing step to extract cluster structures from the factor matrix. Meanwhile, the F-norm instead of the L2-norm is utilized on the FMCNOF model, which makes the model very easy to optimize. Furthermore, an efficient optimization algorithm is proposed to solve the FMCNOF model. Different from the traditional NOF optimization algorithm requiring dense matrix multiplications, our algorithm can divide the optimization problem into three decoupled small size subproblems that can be solved by much less matrix multiplications. Combined with the FMCNOF model and the corresponding fast optimization method, the efficiency of the clustering process can be significantly improved, and the computational complexity is nearly O(n). Extensive experiments on various benchmark data sets validate our approach can greatly improve the efficiency when achieve acceptable performance.

Volume 16 (2021): Issue 3 (Mar 2021) [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance

The Association Between Sleep and In-Game Performance in Basketball Players [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 16
Issue: 3
Pages: 333-341

Morning Priming Exercise Strategy to Enhance Afternoon Performance in Young Elite Soccer Players [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 16
Issue: 3
Pages: 407-414

Single and Combined Effect of Acute Sleep Restriction and Mental Fatigue on Basketball Free-Throw Performance [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 16
Issue: 3
Pages: 415-420

Monday, 22 February 2021

06:16 PM

Fuel for world’s largest fusion reactor ITER is set for test run [Nature - Issue - nature.com science feeds]

Nature, Published online: 22 February 2021; doi:10.1038/d41586-021-00408-1

Nuclear fusion experiments with deuterium and tritium at the Joint European Torus are a crucial dress rehearsal for the mega-experiment.

Friday, 19 February 2021

06:16 PM

Eight priorities for calculating the social cost of carbon [Nature - Issue - nature.com science feeds]

Nature, Published online: 19 February 2021; doi:10.1038/d41586-021-00441-0

Advice to the Biden administration as it seeks to account for mounting losses from storms, wildfires and other climate impacts.

Electrons are caught in the act of relaxing — over quadrillionths of a second [Nature - Issue - nature.com science feeds]

Nature, Published online: 19 February 2021; doi:10.1038/d41586-021-00434-z

Physicists fire lasers at electrons to understand how the particles gain and shed energy.

02:20 AM

Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks [IEEE Transactions on Information Forensics and Security - new TOC]

Decentralized Applications (DApps) are increasingly developed and deployed on blockchain platforms such as Ethereum. DApp fingerprinting can identify users’ visits to specific DApps by analyzing the resulting network traffic, revealing much sensitive information about the users, such as their real identities, financial conditions and religious or political preferences. DApps deployed on the same platform usually adopt the same communication interface and similar traffic encryption settings, making the resulting traffic less discriminative. Existing encrypted traffic classification methods either require hand-crafted and fine-tuning features or suffer from low accuracy. It remains a challenging task to conduct DApp fingerprinting in an accurate and efficient way. In this paper, we present GraphDApp, a novel DApp fingerprinting method using Graph Neural Networks (GNNs). We propose a graph structure named Traffic Interaction Graph (TIG) as an information-rich representation of encrypted DApp flows, which implicitly reserves multiple dimensional features in bidirectional client-server interactions. Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier. We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows. The experimental results show that GraphDApp is superior to the other state-of-the-art methods in terms of classification accuracy in both closed- and open-world scenarios. In addition, GraphDApp maintains its high accuracy when being applied to the traditional mobile application classification.

Integrated Locomotion and Deformation of a Magnetic Soft Robot: Modeling, Control, and Experiments [IEEE Transactions on Industrial Electronics - new TOC]

Magnetic robots have shown great potential in small-scale applications due to their wireless control mode. However, the existing efforts only deal with solid magnetic materials that could not deform. In this article, we focus on integrated locomotion and deformation of a class of magnetic soft robot made of ferrofluid. To this end, the magnetic model and dynamics model that takes the nonlinearity into account are first established. Then, the corresponding motion controllers are proposed, based on the results of feedback linearization and frequency-domain test results. Furthermore, an extended state observer is designed to reduce the perturbation due to model uncertainties. By integrating the control strategies of locomotion and active deformation, we demonstrate that the soft robot possesses the capability of conducting complex tasks such as passing through narrow environment and transporting multiple objects. Various experiments are also performed to demonstrate the effectiveness of the proposed control methods.

Control of Pendulum-Sloshing Dynamics in Suspended Liquid Containers [IEEE Transactions on Industrial Electronics - new TOC]

Motion-induced unwanted cable swing and liquid sloshing degrade the effectiveness and safety of suspended liquid container. The manipulations of cable-suspended liquid container become more challenging due to the coupled pendulum-sloshing dynamics. This article develops the dynamic modeling of cable-suspended liquid container with coupled pendulum-sloshing dynamics. Afterward, a novel control method is proposed for controlling the coupled pendulum-sloshing dynamics. Numerous simulated and experimental results obtained from a small-scale industrial crane carrying a liquid container validated the dynamic behavior of the nonlinear model and demonstrated the effectiveness of the control method.

Graph-Embedded Lane Detection [IEEE Transactions on Image Processing - new TOC]

Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm. The former reduces the over-reliance on customized annotated/labeled lane data. We leveraged several open-source semantic segmentation datasets (e.g., Cityscape, Vistas, and Apollo) and designed a dedicated network that can be trained across these heterogeneous datasets to extract lane attributes. The latter algorithm constructs a graph to represent the lane geometry and topology. It does not rely on strong geometric assumptions such as lane lines are a set of parallel polynomials. Instead, it constructs a graph based on detected lane nodes. The lane parameters in the world coordinate are inferred by efficient graph-based searching and calculation. The performance of the proposed method is verified on both open source and our own collected data. On-vehicle experiments were also conducted and the comparison with Mobileye EyeQ2 shows favorable results.

Friday, 12 February 2021

02:19 AM

Online Rain/Snow Removal From Surveillance Videos [IEEE Transactions on Image Processing - new TOC]

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can- be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC_matlab_2020.

Thursday, 11 February 2021

Thursday, 21 January 2021

10:36 AM

Gendered accountability: when and why do women’s policy priorities get implemented? [European Political Science Review]

Recent decades have seen a surge in women occupying positions of political power. This has been welcomed in part as a means of achieving better policy outcomes for women. We interrogate this proposition, developing a “gendered accountability” framework to explain when and how female representation promotes the implementation of policies that women prioritize. Our empirical analysis applies this framework to sub-Saharan Africa, home to the largest recent expansion in women’s political representation. We find that increased female representation in the legislature is robustly associated with reduced infant and child mortality as well as greater spending on health. Effects are magnified when women are more active in civil society and appear primarily in countries that have gender quotas and proportional electoral systems. Thus, while female representation can lead to improved policy outcomes for women, the process is not automatic and is unlikely to occur absent key institutional and societal conditions.

Elite cueing and attitudes towards trade agreements: the case of TTIP [European Political Science Review]

How does elite communication affect citizens’ attitudes towards trade agreements? Building on a growing literature on context factors influencing public opinion about trade and trade agreements; we argue that citizens rely on cues provided by political elites, especially political parties, when forming their views towards these agreements. Such cueing effects are most likely for citizens with little information about a trade agreement and for citizens receiving cues from trusted elites. In addition, citizens exposed to cues from non-trusted elites should exhibit a source-opposing effect. Our key contribution is to test these expectations relying on a survey experiment on the Transatlantic Trade and Investment Partnership (TTIP) carried out in Germany and Spain. The findings from our experiment support the existence of elite cueing effects, although to a limited degree. Overall, the paper contributes to a better understanding of public opinion towards TTIP, trade policy attitudes, and public opinion more generally.

Claiming the right to rule: regime legitimation strategies from 1900 to 2019 [European Political Science Review]

Governments routinely justify why the regime over which they preside is entitled to rule. These claims to legitimacy are both an expression of and shape of how a rule is being exercised. In this paper, we introduce new expert-coded measures of regime legitimation strategies (RLS) for 183 countries in the world from 1900 to 2019. Country experts rated the extent to which governments justify their rule based on performance, the person of the leader, rational-legal procedures, and ideology. They were also asked to qualify the ideology of the regime. The main purposes of this paper are to present the conceptual basis for the measure, describe the data, and provide convergent, content, and construct validity tests for new measures. Our measure of regime legitimation performs well in all these three validation tests, most notably, the construct validity exercise which explores commonly held beliefs about leadership under populist rule.

A tough trade-off? The asymmetrical impact of populist radical right inclusion on satisfaction with democracy and government [European Political Science Review]

Populist radical right (PRR) parties are increasingly included in coalition governments across Western Europe. How does such inclusion affect satisfaction with democracy (SWD) in these societies? While some citizens will feel democracy has grown more responsive, others will abhor the inclusion of such controversial parties. Using data from the European Social Survey (2002–2018) and panel data from the Netherlands, we investigate how nativists’ and non-nativists’ SWD depends on mainstream parties’ strategies towards PRR parties. We show that the effect is asymmetrical: at moments of inclusion nativists become substantially more satisfied with democracy, while such satisfaction among non-nativists decreases less or not at all. This pattern, which we attribute to Easton’s ‘reservoir of goodwill’, that is, a buffer of political support generated by a track-record of good performance and responsiveness, can account for the seemingly contradictory increase in SWD in many Western European countries in times of populism.

Tuesday, 19 January 2021

03:02 AM

Convex and Compact Superpixels by Edge- Constrained Centroidal Power Diagram [IEEE Transactions on Image Processing - new TOC]

Superpixel segmentation, as a central image processing task, has many applications in computer vision and computer graphics. Boundary alignment and shape compactness are leading indicators to evaluate a superpixel segmentation algorithm. Furthermore, convexity can make superpixels reflect more geometric structures in images and provide a more concise over-segmentation result. In this paper, we consider generating convex and compact superpixels while satisfying the constraints of adhering to the boundary as far as possible. We formulate the new superpixel segmentation into an edge-constrained centroidal power diagram (ECCPD) optimization problem. In the implementation, we optimize the superpixel configurations by repeatedly performing two alternative operations, which include site location updating and weight updating through a weight function defined by image features. Compared with existing superpixel methods, our method can partition an image into fully convex and compact superpixels with better boundary adherence. Extensive experimental results show that our approach outperforms existing superpixel segmentation methods in boundary alignment and compactness for generating convex superpixels.

Tuesday, 12 January 2021

02:54 AM

Just Noticeable Distortion Profile Inference: A Patch-Level Structural Visibility Learning Approach [IEEE Transactions on Image Processing - new TOC]

In this paper, we propose an effective approach to infer the just noticeable distortion (JND) profile based on patch-level structural visibility learning. Instead of pixel-level JND profile estimation, the image patch, which is regarded as the basic processing unit to better correlate with the human perception, can be further decomposed into three conceptually independent components for visibility estimation. In particular, to incorporate the structural degradation into the patch-level JND model, a deep learning-based structural degradation estimation model is trained to approximate the masking of structural visibility. In order to facilitate the learning process, a JND dataset is further established, including 202 pristine images and 7878 distorted images generated by advanced compression algorithms based on the upcoming Versatile Video Coding (VVC) standard. Extensive experimental results further show the superiority of the proposed approach over the state-of-the-art. Our dataset is available at: https://github.com/ShenXuelin-CityU/PWJNDInfer.

Thursday, 17 December 2020

10:03 AM

Evolutionary determinism and convergence associated with water-column transitions in marine fishes [Evolution] [Early Edition]

Repeatable, convergent outcomes are prima facie evidence for determinism in evolutionary processes. Among fishes, well-known examples include microevolutionary habitat transitions into the water column, where freshwater populations (e.g., sticklebacks, cichlids, and whitefishes) recurrently diverge toward slender-bodied pelagic forms and deep-bodied benthic forms. However, the consequences of such processes at deeper...

Tuesday, 15 December 2020

06:02 PM

Structure, self-assembly, and properties of a truncated reflectin variant [Biochemistry] [Early Edition]

Naturally occurring and recombinant protein-based materials are frequently employed for the study of fundamental biological processes and are often leveraged for applications in areas as diverse as electronics, optics, bioengineering, medicine, and even fashion. Within this context, unique structural proteins known as reflectins have recently attracted substantial attention due to...

Transcriptional readout of neuronal activity via an engineered Ca2+-activated protease [Biochemistry] [Early Edition]

Molecular integrators, in contrast to real-time indicators, convert transient cellular events into stable signals that can be exploited for imaging, selection, molecular characterization, or cellular manipulation. Many integrators, however, are designed as complex multicomponent circuits that have limited robustness, especially at high, low, or nonstoichiometric protein expression levels. Here, we...

Tuesday, 08 December 2020

10:03 AM

ARP2/3-independent WAVE/SCAR pathway and class XI myosin control sperm nuclear migration in flowering plants [Plant Biology] [Early Edition]

After eukaryotic fertilization, gamete nuclei migrate to fuse parental genomes in order to initiate development of the next generation. In most animals, microtubules control female and male pronuclear migration in the zygote. Flowering plants, on the other hand, have evolved actin filament (F-actin)-based sperm nuclear migration systems for karyogamy. Flowering...

Tuesday, 01 December 2020

10:03 AM

Intergenerational transfer of DNA methylation marks in the honey bee [Evolution] [Early Edition]

The evolutionary significance of epigenetic inheritance is controversial. While epigenetic marks such as DNA methylation can affect gene function and change in response to environmental conditions, their role as carriers of heritable information is often considered anecdotal. Indeed, near-complete DNA methylation reprogramming, as occurs during mammalian embryogenesis, is a major...

02:03 AM

Treating Interference as Noise Is Optimal for Covert Communication Over Interference Channels [IEEE Transactions on Information Forensics and Security - new TOC]

We study the covert communication over K-user-pair discrete memoryless interference channels (DM-ICs) with a warden. It is assumed that the warden's channel output distribution induced by K “off” input symbols, which are sent when no communication occurs, is not a convex combination of those induced by any other combination of input symbols (otherwise, the square-root law does not hold). We derive the exact covert capacity region and show that a simple point-to-point based scheme with treating interference as noise is optimal. In addition, we analyze the secret key length required for the reliable and covert communication with the desired rates, and present a channel condition where a secret key between each user pair is unnecessary. The results are extended to the Gaussian case and the case with multiple wardens.

Tuesday, 17 November 2020

02:02 AM

Identification of Malicious Injection Attacks in Dense Rating and Co-Visitation Behaviors [IEEE Transactions on Information Forensics and Security - new TOC]

Personalized recommender systems are pervasive in different domains, ranging from e-commerce services, financial transaction systems to social networks. The generated ratings and reviews by users toward products are not only favourable to make targeted improvements on the products for online businesses, but also beneficial for other users to get a more insightful review of the products. In reality, recommender systems can also be deliberately manipulated by malicious users due to their fundamental vulnerabilities and openness. However, improving the detection performance for defending malicious threats including profile injection attacks and co-visitation injection attacks is constrained by the challenging issues: (1) various types of malicious attacks in real-world data coexist; (2) it is difficult to balance the commonality and speciality of rating behaviors in terms of accurate detection; and (3) rating behaviors between attackers and anchor users caused by the consistency of attack intentions are extremely similar. In this article, we develop a unified detection approach named IMIA-HCRF, to progressively discriminate malicious injection behaviors for recommender systems. First, disturbed data are empirically eliminated by implementing both the construction of association graph and enhancement of dense behaviors, which can be adapted to different attacks. Then, the smooth boundary of dense rating (or co-visitation) behaviors is further segmented using higher order potentials, which is finally leveraged to determine the concerned injection behaviors. Extensive experiments on both synthetic data and real-world data demonstrate that the proposed IMIA-HCRF outperforms all baselines on various metrics. The detection performance of IMIA-HCRF can achieve an improvement of 7.8% for mixed profile injection attacks as well as 6% for mixed co-visitation injection attacks over the baselines in terms of FAR (false alarm rate) while keeping the highest- DR (detection rate). Additional experiments on real-world data show that IMIA-HCRF brings an improvement with the advantage of 11.5% FAR in average compared with the baselines.

Thursday, 12 November 2020

Wednesday, 04 November 2020

Thursday, 29 October 2020

Friday, 23 October 2020

Friday, 09 October 2020

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, 24 September 2020

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.

Tuesday, 04 August 2020

Friday, 31 July 2020

Thursday, 30 July 2020

Tuesday, 12 May 2020

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