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.
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.
Context: Post-release user feedback plays an integral role in improving software quality and informing new features. Given its growing importance, feedback concerning security enhancements is particularly noteworthy. In considering the rapid uptake of Android we have examined the scale and severity of Android security threats as reported by its stakeholders. Objective: We systematically mine Android issue logs to derive insights into stakeholder perceptions and experiences in relation to certain Android security issues. Method: We employed contextual analysis techniques to study issues raised regarding confidentiality and privacy in the last three major Android releases, considering covariance of stakeholder comments, and the level of consistency in user preferences and priorities. Results: Confidentiality and privacy concerns varied in severity, and were most prevalent over Jelly Bean releases. Issues raised in regard to confidentiality related mostly to access, user credentials and permission management, while privacy concerns were mainly expressed about phone locking. Community users also expressed divergent preferences for new security features, ranging from more relaxed to very strict. Conclusion: Strategies that support continuous corrective measures for both old and new Android releases would likely maintain stakeholder confidence. An approach that provides users with basic default security settings, but with the power to configure additional security features if desired, would provide the best balance for Android's wide cohort of stakeholders.
Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a user's location, to their text messages. This information can be harvested by apps and sent to third parties, which can use it for a variety of purposes. In this paper we perform the largest study of private information collection (PIC) on Android to date. Leveraging an anonymized dataset collected from the customers of a popular mobile security product, we analyze the flows of sensitive information generated by 2.1M unique apps installed by 17.3M users over a period of 21 months between 2018 and 2019. We find that 87.2% of all devices send private information to at least five different domains, and that actors active in different regions (e.g., Asia compared to Europe) are interested in collecting different types of information. The United States (62% of the total) and China (7% of total flows) are the countries that collect most private information. Our findings raise issues regarding data regulation, and would encourage policymakers to further regulate how private information is used by and shared among the companies and how accountability can be truly guaranteed.
Code initialization -- the step of loading code, executing static code, filling caches, and forming re-used connections -- tends to dominate cold-start time in serverless compute systems such as AWS Lambda. Post-initialization memory snapshots, cloned and restored on start, have emerged as a viable solution to this problem, with incremental snapshot and fast restore support in VMMs like Firecracker.
Saving memory introduces the challenge of managing high-value memory contents, such as cryptographic secrets. Cloning introduces the challenge of restoring the uniqueness of the VMs, to allow them to do unique things like generate UUIDs, secrets, and nonces. This paper examines solutions to these problems in the every microsecond counts context of serverless cold-start, and discusses the state-of-the-art of available solutions. We present two new interfaces aimed at solving this problem -- MADV\_WIPEONSUSPEND and SysGenId -- and compare them to alternative solutions.
Smart speakers and voice-based virtual assistants are core components for the success of the IoT paradigm. Unfortunately, they are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic. To cope with that, deep adversarial learning approaches can be used to build black-box countermeasures altering the network traffic (e.g., via packet padding) and its statistical information. This letter showcases the inadequacy of such countermeasures against machine learning attacks with a dedicated experimental campaign on a real network dataset. Results indicate the need for a major re-engineering to guarantee the suitable protection of commercially available smart speakers.
Password security hinges on an in-depth understanding of the techniques adopted by attackers. Unfortunately, real-world adversaries resort to pragmatic guessing strategies such as dictionary attacks that are inherently difficult to model in password security studies. In order to be representative of the actual threat, dictionary attacks must be thoughtfully configured and tuned. However, this process requires a domain-knowledge and expertise that cannot be easily replicated. The consequence of inaccurately calibrating dictionary attacks is the unreliability of password security analyses, impaired by a severe measurement bias. In the present work, we introduce a new generation of dictionary attacks that is consistently more resilient to inadequate configurations. Requiring no supervision or domain-knowledge, this technique automatically approximates the advanced guessing strategies adopted by real-world attackers. To achieve this: (1) We use deep neural networks to model the proficiency of adversaries in building attack configurations. (2) Then, we introduce dynamic guessing strategies within dictionary attacks. These mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets. Our techniques enable more robust and sound password strength estimates within dictionary attacks, eventually reducing overestimation in modeling real-world threats in password security. Code available: https://github.com/TheAdamProject/adams
“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.”
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.
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.
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).
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.
Presents the table of contents for this issue of the publication.
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.
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.
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.
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.
New research which looked at data from over 28,000 users of the website 'Germ Defence' since May 2020 highlights the continued, critical importance of breaking chains of virus transmission within our homes.
A study by the Mays Cancer Center, home to UT Health San Antonio MD Anderson, found bladder cancer is more advanced and more aggressive in South Texas compared to other areas of the U.S. Latinos and women have reduced five-year survival rates from bladder cancer, the analysis also found.
Glycomics researchers at the University of Alberta and CHU Sainte-Justine have reported a discovery that could lead to new treatments for cardiovascular disease.
Nature, Published online: 24 February 2021; doi:10.1038/s41586-021-03270-3Depriving 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.
Nature, Published online: 22 February 2021; doi:10.1038/d41586-021-00475-4The NASA spacecraft has also snapped more shots of its surroundings and listened to a Martian wind gust.
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.
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.
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.
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.
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.
Presents a listing of the best papers and distinguished reviewers for this publication in 2020.
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.
Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness.
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.
Journal Name: International Journal of Sports Physiology and Performance
Journal Name: International Journal of Sports Physiology and Performance
Journal Name: International Journal of Sports Physiology and Performance
Journal Name: International Journal of Sports Physiology and Performance
Journal Name: International Journal of Sports Physiology and Performance
Nature, Published online: 22 February 2021; doi:10.1038/d41586-021-00408-1Nuclear fusion experiments with deuterium and tritium at the Joint European Torus are a crucial dress rehearsal for the mega-experiment.
Nature, Published online: 19 February 2021; doi:10.1038/d41586-021-00434-zPhysicists fire lasers at electrons to understand how the particles gain and shed energy.
Nature, Published online: 19 February 2021; doi:10.1038/d41586-021-00441-0Advice to the Biden administration as it seeks to account for mounting losses from storms, wildfires and other climate impacts.
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.
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.
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.
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.
Annual Review of Physiology, Volume 83, Issue 1, Page 83-106, February 2021.
Annual Review of Physiology, Volume 83, Issue 1, Page 153-181, February 2021.
Annual Review of Physiology, Volume 83, Issue 1, Page 257-278, February 2021.
Annual Review of Physiology, Volume 83, Issue 1, Page 405-427, February 2021.
Annual Review of Physiology, Volume 83, Issue 1, Page 451-475, February 2021.
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.
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.
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.
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.
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.
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.
Unsupervised latent variable models-blind source separation (BSS) especially-enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz distribution for subspace modeling, in conjunction with a novel combinatorial optimization for evasion of local minima, enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for multimodal information fusion, including sample-poor regimes (N = 600) and low signal-to-noise ratio, promoting novel applications in both unimodal and multimodal brain imaging data.
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...
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...
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...
An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight...
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...
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.
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.
This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability.
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.
Annual Review of Nutrition, Volume 40, Issue 1, Page 1-23, September 2020.
Annual Review of Nutrition, Volume 40, Issue 1, Page 135-159, September 2020.
Annual Review of Nutrition, Volume 40, Issue 1, Page 221-245, September 2020.
Annual Review of Nutrition, Volume 40, Issue 1, Page 299-321, September 2020.
Annual Review of Nutrition, Volume 40, Issue 1, Page 345-373, September 2020.
Annual Review of Economics, Volume 12, Issue 1, Page 213-238, August 2020.
Annual Review of Economics, Volume 12, Issue 1, Page 273-297, August 2020.
Annual Review of Economics, Volume 12, Issue 1, Page 355-389, August 2020.
Annual Review of Economics, Volume 12, Issue 1, Page 603-629, August 2020.
Annual Review of Economics, Volume 12, Issue 1, Page 801-831, August 2020.
Annual Review of Sociology, Volume 46, Issue 1, Page 37-60, July 2020.
Annual Review of Sociology, Volume 46, Issue 1, Page 175-194, July 2020.
Annual Review of Sociology, Volume 46, Issue 1, Page 273-294, July 2020.
Annual Review of Sociology, Volume 46, Issue 1, Page 399-417, July 2020.
Annual Review of Sociology, Volume 46, Issue 1, Page 693-706, July 2020.
Annual Review of Political Science, Volume 23, Issue 1, Page 171-185, May 2020.
Annual Review of Political Science, Volume 23, Issue 1, Page 221-240, May 2020.
Annual Review of Political Science, Volume 23, Issue 1, Page 241-257, May 2020.
Annual Review of Political Science, Volume 23, Issue 1, Page 277-294, May 2020.
Annual Review of Political Science, Volume 23, Issue 1, Page 333-356, May 2020.
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