Based on the fundamental rules of quantum mechanics, two communicating parties can generate and share a secret random key that can be used to encrypt and decrypt messages sent over an insecure channel. This process is known as quantum key distribution (QKD). Contrary to classical encryption schemes, the security of a QKD system does not depend on the computational complexity of specific mathematical problems. However, QKD systems can be subject to different kinds of attacks, exploiting engineering and technical imperfections of the components forming the systems. Here, we review the security vulnerabilities of QKD. We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks. We equally highlight the methods for quantifying backflash emission and the different ways to mitigate this effect.
In recent years, as electronic files include personal records and business activities, these files can be used as important evidences in a digital forensic investigation process. In general, the data that can be verified using its own application programs is largely used in the investigation of document files. However, in the case of the PDF file that has been largely used at the present time, certain data, which include the data before some modifications, exist in electronic document files unintentionally. Because such residual information may present the writing process of a file, it can be usefully used in a forensic viewpoint. This paper introduces why the residual information is stored inside the PDF file and explains a way to extract the information. In addition, we demonstrate the attributes of PDF files can be used to hide data.
Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior work on membership inference attacks may severely underestimate the privacy risks by relying solely on training custom neural network classifiers to perform attacks and focusing only on the aggregate results over data samples, such as the attack accuracy. To overcome these limitations, we first propose to benchmark membership inference privacy risks by improving existing non-neural network based inference attacks and proposing a new inference attack method based on a modification of prediction entropy. We also propose benchmarks for defense mechanisms by accounting for adaptive adversaries with knowledge of the defense and also accounting for the trade-off between model accuracy and privacy risks. Using our benchmark attacks, we demonstrate that existing defense approaches are not as effective as previously reported.
Next, we introduce a new approach for fine-grained privacy analysis by formulating and deriving a new metric called the privacy risk score. Our privacy risk score metric measures an individual sample's likelihood of being a training member, which allows an adversary to perform membership inference attacks with high confidence. We experimentally validate the effectiveness of the privacy risk score metric and demonstrate that the distribution of the privacy risk score across individual samples is heterogeneous. Finally, we perform an in-depth investigation for understanding why certain samples have high privacy risk scores, including correlations with model sensitivity, generalization error, and feature embeddings. Our work emphasizes the importance of a systematic and rigorous evaluation of privacy risks of machine learning models.
As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has been widely used in federated learning for various machine learning models. To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently. However, the existing solutions have a dimension dependency problem: the injected noise is substantially proportional to the dimension $d$. In this work, we propose a two-stage framework FedSel for federated SGD under LDP to relieve this problem. Our key idea is that not all dimensions are equally important so that we privately select Top-k dimensions according to their contributions in each iteration of federated SGD. Specifically, we propose three private dimension selection mechanisms and adapt the gradient accumulation technique to stabilize the learning process with noisy updates. We also theoretically analyze privacy, accuracy and time complexity of FedSel, which outperforms the state-of-the-art solutions. Experiments on real-world and synthetic datasets verify the effectiveness and efficiency of our framework.
Layout camouflaging can protect the intellectual property of modern circuits. Most prior art, however, incurs excessive layout overheads and necessitates customization of active-device manufacturing processes, i.e., the front-end-of-line (FEOL). As a result, camouflaging has typically been applied selectively, which can ultimately undermine its resilience. Here, we propose a low-cost and generic scheme---full-chip camouflaging can be finally realized without reservations. Our scheme is based on obfuscating the interconnects, i.e., the back-end-of-line (BEOL), through design-time handling for real and dummy wires and vias. To that end, we implement custom, BEOL-centric obfuscation cells, and develop a CAD flow using industrial tools. Our scheme can be applied to any design and technology node without FEOL-level modifications. Considering its BEOL-centric nature, we advocate applying our scheme in conjunction with split manufacturing, to furthermore protect against untrusted fabs. We evaluate our scheme for various designs at the physical, DRC-clean layout level. Our scheme incurs a significantly lower cost than most of the prior art. Notably, for fully camouflaged layouts, we observe average power, performance, and area overheads of 24.96%, 19.06%, and 32.55%, respectively. We conduct a thorough security study addressing the threats (attacks) related to untrustworthy FEOL fabs (proximity attacks) and malicious end-users (SAT-based attacks). An empirical key finding is that only large-scale camouflaging schemes like ours are practically secure against powerful SAT-based attacks. Another key finding is that our scheme hinders both placement- and routing-centric proximity attacks; correct connections are reduced by 7.47X, and complexity is increased by 24.15X, respectively, for such attacks.
Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences (ΔTs) exhibits distinctive hysteretic cycles whose shape...
Ehrlichia chaffeensis, a cholesterol-rich and cholesterol-dependent obligate intracellular bacterium, partially lacks genes for glycerophospholipid biosynthesis. We found here that E. chaffeensis is dependent on host glycerolipid biosynthesis, as an inhibitor of host long-chain acyl CoA synthetases, key enzymes for glycerolipid biosynthesis, significantly reduced bacterial proliferation. E. chaffeensis cannot synthesize phosphatidylcholine...
Range expansions lead to distinctive patterns of genetic variation in populations, even in the absence of selection. These patterns and their genetic consequences have been well studied for populations advancing through successive short-ranged migration events. However, most populations harbor some degree of long-range dispersal, experiencing rare yet consequential migration events...
Nature, Published online: 25 March 2020; doi:10.1038/d41586-020-00154-wUpdates on the respiratory illness that has infected hundreds of thousands of people and killed several thousand.
Nature, Published online: 25 March 2020; doi:10.1038/d41586-020-00840-9Detecting tumours earlier and more precisely could lead to more effective treatment.
Nature, Published online: 25 March 2020; doi:10.1038/d41586-020-00849-0Cancer traps, artificial intelligence and other highlights from clinical trials and laboratory studies.
Nature, Published online: 24 March 2020; doi:10.1038/d41586-020-00891-yA fossil the size of a grain of rice appears to be the earliest known bilaterian, the group of animals (including humans) with two-sided symmetry, two openings and a through-gut. Plus: the diagnostic tests for coronavirus available now and in the works, and two approaches to solving the global challenge of climate change.
Nature, Published online: 18 March 2020; doi:10.1038/s41586-020-2121-3Apoptotic cells communicate with neighbouring cells by the regulated release of specific metabolites, and a cocktail of select apoptotic metabolites reduces disease severity in mouse models of inflammatory arthritis and lung transplant rejection.
Addressing the Increased Expectations of Nutrition [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page v-vi, August 2019.
FADS1 and FADS2 Polymorphisms Modulate Fatty Acid Metabolism and Dietary Impact on Health [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 21-44, August 2019.
Ocular Carotenoid Status in Health and Disease [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 95-120, August 2019.
The Role of Brain Barriers in Maintaining Brain Vitamin Levels [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 147-173, August 2019.
Evidence Collection and Evaluation for the Development of Dietary Guidelines and Public Policy on Nutrition [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 227-247, August 2019.
After years of war and political instability, Afghanistan is at a potentially historic crossroads. On Feb 29, 2020, a peace deal was agreed between the Taliban and the USA. The anticipated resumption of talks between the Afghan Government and the Taliban could signal a new phase, but what this means for the Afghan people is unclear. Weak health systems and disrupted access to health care have left many in poor health for decades, but since the fall of the Taliban some extraordinary gains in health have occurred.
Leading African cardiologist. He was born in Hanover, South Africa, on May 6, 1933, and died in Cape Town, South Africa, on Feb 20, 2020, aged 86 years.
We have read with great interest the Correspondence by Shibo Jiang and colleagues,1 in which they propose a name change for the newly emerged coronavirus,2 which was recently designated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the Coronavirus Study Group of the International Committee on Taxonomy of Viruses.3 The authors argued that the use of SARS in the virus name could confuse the public about the disease that it causes; in addition, they noted that the name SARS-CoV-2 is not consistent with the disease name chosen by WHO, coronavirus disease 2019.
“We now have a name for the disease caused by coronavirus and it's COVID-19”, said Dr Tedros Adhanom Ghebreyesus, Director-General of WHO on Feb 11, 2020.1 WHO recently updated the name novel coronavirus pneumonia, previously named by Chinese scientists,2 to coronavirus disease 2019 (COVID-19).
A 55-year-old man was referred to our dermatology clinic because of progressive, changing, painful skin lesions, a burning sensation in his mouth, and irritation of his tongue. He had also lost 12 kg and reported increased thirst. The patient also developed delusions of grandeur and persecution at the same time as the skin problem, and weight loss started 4 months earlier. Further, 7 days before these difficulties, he had been depressed and had suicidal ideas. He had initially been admitted to a psychiatric ward for 3 weeks, where he had been given a diagnosis of bipolar affective disorder and started on lithium.
Fecal microbiota transplant (FMT) using a single “superdonor” produced high rates of clinical response in patients with irritable bowel syndrome (IBS) in a trial published in Gut. Two previous randomized clinical trials of FMT for IBS had conflicting results.
Patients whose depression lingered after treatment had less severe residual symptoms, higher rates of remission, and lower rates of relapse following online mindfulness-based cognitive therapy, a trial in JAMA Psychiatry reported.
The proportions of certain white blood cell types in a woman’s blood might predict her risk of being diagnosed with breast cancer in the short-term and in the long-term, according to a recent study in JAMA Network Open by researchers from the National Institute of Environmental Health Sciences (NIEHS).
In this issue of JAMA, we are pleased to publish the names of the 2816 reviewers who completed reviews of manuscripts for JAMA in 2019. The thoughtful comments and recommendations of each reviewer for each manuscript are carefully considered in the editorial evaluation and are exceedingly helpful in assessing the novelty and importance of submitted manuscripts and in improving the presentation and quality of published articles. JAMA could not be successful without the efforts of the reviewers. We extend our appreciation to all reviewers for their service to the journal and hope that publishing your names in this issue provides recognition of the critical importance of the often underrecognized and underappreciated academic activity of scholarly peer review.
Prof. Hyung Joon Cha and his research team developed a stem cell therapy on myocardial infarction, using proteins that can be found in mussels, mussel adhesive proteins.
Older people are generally more emotionally stable and better able to resist temptations in their daily lives, a new study says. Researchers from Duke and Vanderbilt pinged 123 study participants aged 20 to 80 on their cell phones three times a day for ten days. They indicated how they felt on a five-point scale for each of eight emotional states, including contentment, enthusiasm, relaxation and sluggishness, and whether they were craving chocolate, cigarettes or sex.
Men are more prone to competitive risk taking and violent behavior, so what happens when the number of men is greater than the number of women in a population? According to research by Florida State University Professor of Psychology Jon Maner, the answers might not be what you expect.
Telemental health services are a practical and feasible way to support patients, family members, and healthcare providers who may experience psychological side-effects of the COVID-19 pandemic, including anxiety, fear, depression, and the impact of long-term isolation.
The field of 'brain-mimicking' neuromorphic electronics shows great potential for basic research and commercial applications, and researchers in Germany and Switzerland recently explored the possibility of reproducing the physics of real neural circuits by using the physics of silicon. In Applied Physics Letters, they present their work to understand neural processing systems, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials.
Irinotecan treats a range of solid tumors, but its effectiveness is severely limited by gastrointestinal (GI) tract toxicity caused by gut bacterial β-glucuronidase (GUS) enzymes. Targeted bacterial GUS inhibitors have been shown to partially alleviate irinotecan-induced GI tract damage and resultant diarrhea in mice. Here, we unravel the mechanistic basis...
The endoplasmic reticulum (ER) is the site of synthesis of secretory and membrane proteins and contacts every organelle of the cell, exchanging lipids and metabolites in a highly regulated manner. How the ER spatially segregates its numerous and diverse functions, including positioning nanoscopic contact sites with other organelles, is unclear....
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
Digital power management systems allow power converters to operate with multiple changes in voltage reference. This asks for a new paradigm that shifts from achieving faster response to achieving best efficiency during multiple transients, with transient being eventually time constrained. State-space-based design of the control system fits perfectly this goal, since it is a time-domain design tool. It can further be enhanced with a linear quadratic regulator (LQR) optimization, configured with converter loss equation as cost function. The LQR mathematics guarantees a solution to the algebraic Riccati equation that produces the best system efficiency for the converter system during multiple transients. This article documents the design of the LQR control system having converter loss as a cost function and demonstrates the advantages of this method. The closed-loop control software yields very simple, running with a 250 kHz sampling frequency capability on a general use Microchip microcontroller platform. While a loss reduction of 2.5% is demonstrated by experiment in our setup, the advantage depends on the actual system work profile.
This article focuses on the design of a magnetic sensor (MS) in position detection of a permanent magnet linear motor (PMLM). The MS is composed of a hall-effect-integrated-circuit and magnetic materials, and it is well known as a contactless sensor with a simple structure, and low cost. As a result, it is preferred for precise linear positioning applications. The work presented in this article consists of the following three steps to achieve high accuracy and economy in the mover position detection for a PMLM. First, the initial design of the MS is described which has been optimized using response surface methodology to obtain very low total harmonic distortion value of the sensing signal. Second, the leakage flux of PMLM is analytically investigated, from which it is determined that shielding the leakage flux is most appropriate to ensure the accuracy of the MS. Finally, in an attempt to reduce manufacturing cost, three MS prototypes using different materials (silicon steel laminated core, steel solid core, and ferrite solid core) are fabricated and tested. To verify the analytical results, several experiments are conducted. The experimental data prove that the accuracy of the MS is sufficient to use for mover position detection for the PMLM.
Time delay of the finite control set model predictive current control caused by a large amount of calculations will degrade the control performance of the permanent magnet synchronous machine system. This letter first demonstrates the delay issue at length. Then, a new direct compensation method, by predicting the current variation within the delay time, is proposed. In comparison with the traditional two-step prediction strategy, the novel method is also easy to implement and can single out the best switching state in each period for optimal control. Simulation and experimental results are presented to verify the effectiveness of the proposed method.
A high-frequency (HF) voltage injection method can accurately estimate the rotor position of an interior permanent magnet synchronous motor at low speed. However, the fixed injection frequency for the traditional HF voltage injection method will produce a loud audible noise that limits the actual application of HF voltage injection method. In order to reduce the audible noise, a novel HF square-wave voltage injection method with the random switching frequency is proposed in this article. The frequency of the injected HF voltage varies with the random switching frequency, so it can effectively spread power spectrum caused by HF current and pulsewidth modulation (PWM), which will effectively reduce the audible noise caused by the HF current and PWM. Additionally, the power spectral density of the HF current based on the proposed method is analyzed to verify the effectiveness in reducing audible noise. Then, a corresponding demodulation method for induced HF current is presented. Finally, the experimental results show that the proposed method has continuous spectra and the audible noises can be suppressed significantly.
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512 × 384 ). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.
In modern plants, industrial processes typically operate under different states to meet the different requirements of high-quality products. Many monitoring models for industrial processes were constructed based on the prior knowledge (the mechanism's model or the process data characteristics) to monitor such processes (called multimode processes). However, obtaining this prior knowledge is difficult in practice. Efficiently monitoring nonlinear multimode processes without any prior knowledge is an open problem that demands further exploration. Since data from different modes follow different distributions while data from the same mode are considered to be sampled from the same distribution, the modes of multimode processes can be uncovered based on the characteristics of the process data. This article proposes using a Dirichlet process Gaussian mixed model to classify the modes of multimode processes based on historical data, and then, determine the mode types of the monitored data. A nonlinear monitoring strategy based on the t-distributed stochastic neighbor embedding is then proposed to achieve nonlinear dimensionality reduction and visualize the data. Finally, a monitoring index that is integrated with support vector data description is constructed for comprehensive monitoring. The proposed nonlinear multimode framework completely realizes data-driven mode identification and unsupervised fault detection without knowing any prior knowledge. The effectiveness and feasibility of the proposed model are demonstrated using data from a simulated wastewater treatment plant.
With the deployment of phasor measurement units (PMUs), machine learning based data-driven methods have been applied to online power system stability assessment. This article proposes a novel temporal-adaptive intelligent system (IS) for post-fault short-term voltage stability (STVS) assessment. Unlike existing methods using a single learning algorithm, the proposed IS incorporates multiple randomized learning algorithms in an ensemble form, including random vector functional link networks and extreme learning machine, to obtain a more diversified machine learning outcome. Moreover, under a multi-objective optimization programming framework, the STVS is assessed in an optimized temporal-adaptive way to balance STVS accuracy and speed. The simulation results on New England 39-bus system and Nordic test system verify its superiority over a single learning algorithm and its excellent accuracy and speed without increased computational efficiency. In particular, its real-time assessment speed is 27.5–37.3% faster than the single algorithm based methods. Given such faster assessment speed, the proposed method can enable earlier and more timely stability control (load shedding) for less load shedding amount and stronger effectiveness.
Accurate clock synchronization in the industr-ial-Internet-of-Things systems forms the cornerstone of distributed interaction and coordination among various infrastructures and machines in an industrial environment. However, due to the widespread use of wireless networks in industrial applications, constraints inherent to wireless networks including uncertain propagation delays, random packets losses, and unguaranteed communication resources are unavoidable, leading to dramatically increased clock synchronization error and unreliable or even outdated information. Meanwhile, time information transmissions are vulnerable to suffer from malicious attacks, causing unreliable timestamps and insecure synchronization. In this article, we proposed a distributed clock synchronization protocol based on an intelligent clustering algorithm to achieve accurate, secure, and packet-efficient clock synchronization. The varying rate of skew of every clock is collected and utilized for cluster formation as well as malicious node detection. According to established clusters, various synchronization frequencies are assigned, which can avoid excessive network access contention, reduce overall communication resource consumption, and improve synchronization accuracy. Meanwhile, a two-tier fault detection algorithm consists of outlier detection and second-order regressive model prediction is applied to determine potential malicious nodes. The simulation results demonstrate that the proposed protocol overwhelms simultaneous synchronization protocols in terms of synchronization performance and faulty node detection.
The current optimization-based algorithms to operate grid-tied battery energy storage systems (BESS) typically do not look much under the hood of the BESS, i.e., the device-level characteristics of the batteries. This is often due to modeling as well as optimization complexities. However, simplified models may significantly degrade the performance of BESS operation in practice. Therefore, in this article, we propose a new BESS scheduling optimization framework that accounts for features such as cell-to-cell variations in maximum capacity, charge level balance, and internal resistance. The proposed framework is in the form of tractable mixed integer linear programs. Our approach is to estimate and update the device-level battery model parameters continuously, without the need to interrupt BESS normal operation. We validate the performance compared to an offline approach, which is based on dedicated model testing and calibration. To assure accurate performance evaluation, this article also includes developing a power hardware-in-the-loop testbed that allows for flexible operation and detailed monitoring of BESS under different design scenarios.
With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.
New Approaches to Target Inflammation in Heart Failure: Harnessing Insights from Studies of Immune Cell Diversity [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 82, Issue 1, Page 1-20, February 2020.
Neuronal Mechanisms that Drive Organismal Aging Through the Lens of Perception [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 82, Issue 1, Page 227-249, February 2020.
Diurnal Regulation of Renal Electrolyte Excretion: The Role of Paracrine Factors [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 82, Issue 1, Page 343-363, February 2020.
Marrow Adipocytes: Origin, Structure, and Function [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 82, Issue 1, Page 461-484, February 2020.
People constantly share their photographs with others through various social media sites. With the aid of the privacy settings provided by social media sites, image owners can designate scope of sharing, e.g., close friends and acquaintances. However, even if the owner of a photograph carefully sets the privacy setting to exclude a given individual who is not supposed to see the photograph, the photograph may still eventually reach a wider audience, including those clearly undesired through unanticipated channels of disclosure, causing a privacy breach. Moreover, it is often the case that a given image involves multiple stakeholders who are also depicted in the photograph. Due to various personalities, it is even more challenging to reach agreement on the privacy settings for these multi-owner photographs. In this paper, we propose a privacy risk reminder system, called REMIND, which estimates the probability that a shared photograph may be seen by unwanted people-through the social graph-who are not included in the original sharing list. We tackle this problem from a novel angle by digging into the big data regarding image sharing history. Specifically, the social media providers possess a huge amount of image sharing information (e.g., what photographs are shared with whom) of their users. By analyzing and modeling such rich information, we build a sophisticated probability model that efficiently aggregates the image disclosure probabilities along different possible image propagation chains and loops. If the computed disclosure probability indicates high risks of privacy breach, a reminder is issued to the image owner to help revise the privacy settings (or, at least, inform the user about this accidental disclosure risk). The proposed REMIND system also has a nice feature of policy harmonization that helps resolve privacy differences in multi-owner photographs. We have carried out a user study to validate the rationale of our proposed solutions and also conducte- experimental studies to evaluate the efficiency of the proposed REMIND system.
The side-channel leakage is a consequence of program execution in a computer processor, and understanding relationship between code execution and information leakage is a necessary step in estimating information leakage and its capacity limits. This paper proposes a methodology to relate program execution to electromagnetic side-channel emanations and estimates side-channel information capacity created by execution of series of instructions (e.g., a function, a procedure, or a program) in a processor. To model dependence among program instructions in a code, we propose to use Markov source model, which includes the dependencies among sequence of instructions as well as dependencies among instructions as they pass through a pipeline of the processor. The emitted electromagnetic (EM) signals during instruction executions are natural choice for the inputs into the model. To obtain the channel inputs for the proposed model, we derive a mathematical relationship between the emanated instruction signal power (ESP) and total emanated signal power while running a program. Then, we derive the leakage capacity of EM side channels created by execution of series of instructions in a processor. Finally, we provide experimental results to demonstrate that leakages could be severe and that a dedicated attacker could obtain important information.
Ratings provided by advisors can help an advisee to make decisions, e.g., which seller to select in e-commerce. Unfair rating attacks-where dishonest ratings are provided to mislead the advisee-impact the accuracy of decision making. Current literature focuses on specific classes of unfair rating attacks, which does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behavior that is possible within a given system. We propose a probabilistic modeling of rating behavior, and apply information theory to quantitatively measure the impact of attacks. In particular, we can identify the attack with the worst impact. In the simple case, honest advisors report the truth straightforwardly, and attackers rate strategically. In real systems, the truth (or an advisor's view on it) may be subjective, making even honest ratings inaccurate. Although there exist methods to deal with subjective ratings, whether subjectivity influences the effect of unfair rating attacks was an open question. We discover that subjectivity decreases the robustness against attacks.
Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.
We investigate the effect of outdated channel state information (CSI) on the secrecy performance of wireless-powered untrusted relay networks, in which the relay is a potential eavesdropper. To keep the information secret from this untrusted relay, the destination sends a jamming signal to the relay when the source transmits an information signal. At the same time, the relay harvests energy from the radio-frequency power, and forwards the received signals to the destination using this harvested energy. To determine the proportions of energy harvesting and information processing, the relay makes use of relaying based on power splitting or time switching policy. Although the destination tries to remove the jamming signal from the relaying signal, it cannot cancel out the jamming signal perfectly due to imperfect channel reciprocity caused by the outdated CSI; this residual jamming signal therefore has a negative impact on secrecy performance. In this scenario, we derive the closed-form expressions for the outage probability and average secrecy rate, and find the jamming power ratio, power splitting ratio, and time switching ratio to optimize these secrecy performance metrics. The numerical results demonstrate the accuracy of our analysis, and show that the proposed secure relaying protocols achieve a near-optimal secrecy performance, as well as outperforming the conventional scheme without the jamming power control.
Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel top-down reverse attention block to guide the above side-output residual learning. Specifically, the current predicted salient regions are used to erase its side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in more complete detection and high accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art approaches, and shows advantages in simplicity, compactness and efficiency.
Seismic image interpolation is a currently popular research subject in modern reflection seismology. The interpolation problem is generally treated as a process of inversion. Under the compressed sensing framework, various sparse transformations and low-rank constraints based methods have great performances in recovering irregularly missing traces. However, in the case of regularly missing traces, their applications are limited because of the strong spatial aliasing energies. In addition, the erratic noise always poses a serious impact on the interpolation results obtained by the sparse transformations and low-rank constraints-based methods,. This is because the erratic noise is far from satisfying the statistical assumption behind these methods. In this study, we propose a mathematical morphology-based interpolation technique, which constrains the morphological scale of the model in the inversion process. The inversion problem is solved by the shaping regularization approach. The mathematical morphological constraint (MMC)-based interpolation technique has a satisfactory robustness to the spatial aliasing and erratic energies. We provide a detailed algorithmic framework and discuss the extension from 2D to higher dimensional version and the back operator in the shaping inversion. A group of numerical examples demonstrates the successful performance of the proposed technique.
The human visual system tends to consider saliency of an object as a whole. Some object-level saliency detection methods have been proposed by leveraging object proposals in bounding boxes, and regarding the entire bounding box as one candidate salient region. However, the bounding boxes can not provide exact object position and a lot of pixels in bounding boxes belong to the background. Consequently, background pixels in bounding box also show high saliency. Besides, acquiring object proposals needs high time cost. In order to compute object-level saliency, we consider region growing from some seed superpixels, to find one surrounding region which probably represents the whole object. The desired surrounding region has similar appearance inside and obvious difference with the outside, which is proposed as maximally stable region (MSR) in this paper. In addition, one effective seed superpixel selection strategy is presented to improve speed. MSR based saliency detection is more robust than pixel or superpixel level methods and object proposal based methods. The proposed method significantly outperforms the state-of-the-art unsupervised methods at 50 FPS. Compared with deep learning based methods, we show worse performance, but with about 1200-1600 times faster, which means better trade-off between performance and speed.
In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution. Compared with two representative network architectures with skip connections, ResNet and DenseNet, a linear compressing layer is designed in LCSCNet for skip connection, which connects former feature maps and distinguishes them from newly-explored feature maps. In this way, the proposed LCSCNet enjoys the merits of the distinguish feature treatment of DenseNet and the parameter-economic form of ResNet. Moreover, to better exploit hierarchical information from both low and high levels of various receptive fields in deep models, inspired by gate units in LSTM, we also propose an adaptive element-wise fusion strategy with multi-supervised training. Experimental results in comparison with state-of-the-art algorithms validate the effectiveness of LCSCNet.
Transitional Dynamics in Aggregate Models of Innovative Investment [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 273-301, August 2019.
Echo Chambers and Their Effects on Economic and Political Outcomes [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 303-328, August 2019.
Auction Market Design: Recent Innovations [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 383-405, August 2019.
History, Microdata, and Endogenous Growth [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 615-633, August 2019.
Aging Populations, Mortality, and Life Expectancy [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 69-89, July 2019.
Theories of the Causes of Poverty [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 155-175, July 2019.
Divergent Destinies: Children of Immigrants Growing Up in the United States [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 383-399, July 2019.
Examining Public Opinion About LGBTQ-Related Issues in the United States and Across Multiple Nations [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 401-423, July 2019.
Family Instability in the Lives of American Children [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 493-513, July 2019.
A Conversation with Theda Skocpol [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 1-16, May 2019.
The Return of the Single-Country Study [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 187-203, May 2019.
Political Responses to Economic Shocks [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 277-295, May 2019.
Integrating the Civil–Military Relations Subfield [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 379-398, May 2019.
Not So Civic: Is There a Difference Between Ethnic and Civic Nationalism? [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 419-434, May 2019.
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