In this paper, a high-throughput Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) using the bitslicing technique is proposed. In such technique, instead of the conventional row-major data representation, column-major data representation is employed which allows the bitsliced implementation to take full advantage of all the available datapath of the hardware platform. We use LFSR-based (Linear Feedback Shift Register) PRNG for our implementation since its register oriented architecture perfectly suits the GPU's many-core structure and allows for usage of bitslicing technique which can further improve its performance. In our GPU implementation, each GPU thread is capable of generating a remarkable number of 32 pseudo-random bits in each LFSR clock cycle. In order to obtain cryptographically suitable properties, we propose an SIMD vectorized fully parallel bitsliced implementation of the LFSR-based, cryptographically secure MICKEY 2.0 stream cipher algorithm for CSPRNG. To the best of our knowledge, our method not only achieves better performance, but also significantly outperforms optical solutions in terms of performance per cost while maintaining an acceptable measure of randomness. It should be mentioned that our implementation successfully passes the NIST test for statistical randomness and bitwise correlation criteria. Our proposed methodology significantly outperforms the current best implementations in the literature for computer-based PRNG. Moreover, our evaluations show 6.6x improvement over the Nvidia's proprietary high-performance PRNG, cuRAND library, achieving 5.2 Tb/s of throughput on the affordable Nvidia GTX 980 Ti.
Today's state-of-the-art image classifiers fail to correctly classify carefully manipulated adversarial images. In this work, we develop a new, localized adversarial attack that generates adversarial examples by imperceptibly altering the backgrounds of normal images. We first use this attack to highlight the unnecessary sensitivity of neural networks to changes in the background of an image, then use it as part of a new training technique: localized adversarial training. By including locally adversarial images in the training set, we are able to create a classifier that suffers less loss than a non-adversarially trained counterpart model on both natural and adversarial inputs. The evaluation of our localized adversarial training algorithm on MNIST and CIFAR-10 datasets shows decreased accuracy loss on natural images, and increased robustness against adversarial inputs.
Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbations of sparse attacks are typically large and thus can be potentially detected. We propose a new black-box technique to craft adversarial examples aiming at minimizing $l_0$-distance to the original image. Extensive experiments show that our attack is better or competitive to the state of the art. Moreover, we can integrate additional bounds on the componentwise perturbation. Allowing pixels to change only in region of high variation and avoiding changes along axis-aligned edges makes our adversarial examples almost non-perceivable. Moreover, we adapt the Projected Gradient Descent attack to the $l_0$-norm integrating componentwise constraints. This allows us to do adversarial training to enhance the robustness of classifiers against sparse and imperceivable adversarial manipulations.
Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently, DNNs are attacked through Trojan which can effectively infect the model during the training phase and get activated only through specific input patterns (i.e, trigger) during inference. However, in this work, for the first time, we propose a novel Targeted Bit Trojan(TBT), which eliminates the need for model re-training to insert the targeted Trojan. Our algorithm efficiently generates a trigger specifically designed to locate certain vulnerable bits of DNN weights stored in main memory (i.e., DRAM). The objective is that once the attacker flips these vulnerable bits, the network still operates with normal inference accuracy. However, when the attacker activates the trigger embedded with input images, the network classifies all the inputs to a certain target class. We demonstrate that flipping only several vulnerable bits founded by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan infected model. We perform extensive experiments of CIFAR-10, SVHN and ImageNet datasets on both VGG-16 and Resnet-18 architectures. Our proposed TBT could classify 93% of the test images to a target class with as little as 82 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset.
Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods.
Doxorubicin is a widely used chemotherapeutic agent that causes dose-dependent cardiotoxicity in a subset of treated patients, but the genetic determinants of this susceptibility are poorly understood. Here, we report that a noncanonical tumor suppressor activity of p53 prevents cardiac dysfunction in a mouse model induced by doxorubicin administered in...
Prior knowledge about the probabilistic structure of visual environments is necessary to resolve ambiguous information about objects in the world. Expectations based on stimulus regularities exert a powerful influence on human perception and decision making by improving the efficiency of information processing. Another type of prior knowledge, termed top-down attention,...
The emissions, deposition, and chemistry of volatile organic compounds (VOCs) are thought to be influenced by underlying landscape heterogeneity at intermediate horizontal scales of several hundred meters across different forest subtypes within a tropical forest. Quantitative observations and scientific understanding at these scales, however, remain lacking, in large part due...
Hypoxia is a ubiquitous feature of cancers, encouraging glycolytic metabolism, proliferation, and resistance to therapy. Nonetheless, hypoxia is a poorly defined term with confounding features described in the literature. Redox biology provides an important link between the external cellular microenvironment and the cell’s response to changing oxygen pressures. In this...
Nature, Published online: 11 September 2019; doi:10.1038/d41586-019-02721-2The exoplanet is just twice the diameter of Earth, and could potentially host life.
Nature, Published online: 11 September 2019; doi:10.1038/s41586-019-1556-xGalaxy GSN 069 has unprecedented eruptions of X-ray light every nine hours, which indicate fast transitions between cold and warm states and may shed light on black hole accretion.
Nature, Published online: 11 September 2019; doi:10.1038/d41586-019-02675-5The race to cash in is draining universities of talent, fracturing the field and closing off avenues of enquiry, warn Jacob D. Biamonte, Pavel Dorozhkin and Igor Zacharov.
Nature, Published online: 11 September 2019; doi:10.1038/s41586-019-1555-yPalaeoproteomic analysis of dental enamel from an Early Pleistocene Stephanorhinus resolves the phylogeny of Eurasian Rhinocerotidae, by enabling the reconstruction of molecular evolution beyond the limits of ancient DNA preservation.
Nature, Published online: 11 September 2019; doi:10.1038/d41586-019-02728-9Bulgaria’s Mariya Gabriel picked to lead newly named policy department that combines research with education and youth affairs.
On Aug 26, a judge ruled that Johnson & Johnson—parent company of Janssen Pharmaceuticals—must pay the state of Oklahoma, USA, $572 million for knowingly overstating the benefits and downplaying the dangers of opioids and contributing to the crisis that has claimed the lives of 400 000 Americans over the past two decades. That amount represents only a small fraction of what the state was seeking, estimating that it would need $17 billion over 20 years to adequately address the crisis that has severely affected Oklahoma and other states, especially in the midwest and northeast.
In the next 5 years, Gavi aims to vaccinate 300 million children and will have a greater focus on building primary health-care systems. Ann Danaiya Usher reports.
As a paediatric cardiologist in Depression-era America, Helen Brooke Taussig (1898–1986) saw many “blue” babies, their blood starved of oxygen as it failed to circulate properly through the lungs. The infants gasped for breath after the least exertion and usually died at an early age. After studying the condition, which was caused by a cluster of congenital heart defects, Taussig proposed to her surgical colleague at Johns Hopkins University, Alfred Blalock (1899–1964), that it might be alleviated by a procedure to divert another artery to the lungs.
We welcome Richard Horton's Comment,1 in which he highlights the lack of prioritisation of the health needs of a region that has faced tremendous human tragedy over the past several decades. Our ongoing research in Jordan has elucidated a hidden and at-risk population: displaced, married adolescent girls. Our concern for this population has become more urgent since the Jordanian Parliament rejected a measure to eliminate the exceptions under which girls younger than 18 years can be legally married, citing a lack of evidence of the health consequences of child marriage.
Christopher Parker and colleagues1 build on the body of work that emerges from the STAMPEDE initiative. Once again we are shown the timely contributions that STAMPEDE provides to set the course in prostate cancer research. The study1 screened for the effects of prostate radiation on outcomes of men with metastases. The authors conclude that the findings establish a new standard of care that includes prostate radiation in men with so-called oligometastatic cancer. We agree with this conclusion; however, are the findings adequate evidence to make this the standard to offer to all patients?
Humans are capable of learning a new fine-grained concept with very little supervision, e.g., few exemplary images for a species of bird, yet our best deep learning systems need hundreds or thousands of labeled examples. In this paper, we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting, termed few-shot fine-grained recognition (FSFG). The task of FSFG requires the learning systems to build classifiers for the novel fine-grained categories from few examples (only one or less than five). To solve this problem, we propose an end-to-end trainable deep network, which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task. Specifically, our network consists of a bilinear feature learning module and a classifier mapping module: while the former encodes the discriminative information of an exemplar image into a feature vector, the latter maps the intermediate feature into the decision boundary of the novel category. The key novelty of our model is a “piecewise mappings” function in the classifier mapping module, which generates the decision boundary via learning a set of more attainable sub-classifiers in a more parameter-economic way. We learn the exemplar-to-classifier mapping based on an auxiliary dataset in a meta-learning fashion, which is expected to be able to generalize to novel categories. By conducting comprehensive experiments on three fine-grained datasets, we demonstrate that the proposed method achieves superior performance over the competing baselines.
Fault diagnosis of a thermal system under varying operating conditions is of great importance for the safe and reliable operation of a power plant involved in peak shaving. However, it is a difficult task due to the lack of sufficient labeled data under some operating conditions. In practical applications, the model built on the labeled data under one operating condition will be extended to such operating conditions. Data distribution discrepancy can be triggered by variation of operating conditions and may degenerate the performance of the model. Considering the fact that data distributions are different but related under different operating conditions, this paper proposes a hierarchical deep domain adaptation (HDDA) approach to transfer a classifier trained on labeled data under one loading condition to identify faults with unlabeled data under another loading condition. In HDDA, a hierarchical structure is developed to reveal the effective information for final diagnosis by layerwisely capturing representative features. HDDA learns domain-invariant and discriminative features with the hierarchical structure by reducing distribution discrepancy and preserving discriminative information hidden in raw process data. For practical applications, the Taguchi method is used to obtain the optimized model parameters. Experimental results and comprehensive comparison analysis demonstrate its superiority.
This paper proposes a distributed fixed-time multiagent control strategy for the frequency restoration, voltage regulation, state of charge balancing, and proportional reactive power sharing between photovoltaic battery systems distributed in a microgrid with communication time delays. First, the feedback linearization method is applied to find the direct relationships between explicit states and control inputs. Then, based on the model, the distributed fixed-time cooperative control system restores the frequency, regulates the average voltage to the nominal value, and achieves accurate power sharing. For the state of charge balancing, a fixed-time observer is proposed to estimate the average state of charge of a battery using only information from neighbors. Based on the estimated value, a local fixed-time sliding mode control is applied to achieve the balanced state of charge. Due to robustness of the fixed-time control strategy, the balanced state of charge can be maintained despite intermittent photovoltaic generation and variable loads. The Artstein's transformation is applied to ensure the stability of the time delayed system. The dynamic performance is verified with an RTDS Technologies real-time digital simulator, using switching converter models, nonlinear lead-acid battery models, photovoltaic generation, and communication delays in a European benchmark microgrid.
DC short-circuit fault ride through is one of the most important characteristics for the modular-multilevel converters (MMC) employed in high-voltage direct-current (HVdc) transmission systems. During the faults, for providing a fault-tolerant control with reactive power compensation to the grid, the MMC normally changes its structure, which requires a remarkable modification and burden computation for implementing the modulation technique in the MMC. This paper proposes an alternative submodule configuration of the MMC based on a carrier-phase-shift PWM scheme, which is easily implemented for switching the operation modes of the MMC from normal condition to fault-tolerant control. The arms of the proposed MMC are configured by typical half-bridge submodules (HBSM) and suggested series-connected triple SMs (SCTSM) in interleaving series, where the SCTSM is composed of three HBSMs connected in series through an additional IGBT and a clamp diode. With the additional IGBTs and diodes, the SCTSMs can produce bipolar output voltages and the MMC can be restructured to operate as three-phase cascaded multilevel converter during pole-to-pole short circuits to control the converter currents. In addition, the cost and power loss of the proposed MMC are lower than those of the existing MMCs based on full-bridge SMs (FBSM), a hybrid of HBSMs and FBSMs, and clamp-double SMs. PSIM simulation results for the 300 MW–300 kV HVdc system with the proposed MMCs are shown to verify the effectiveness of the scheme.
With the increased penetration of renewable energy sources (RESs) and plug-and-play loads, Microgrids (MGs) bring direct challenges in energy management due to the uncertainties in both supply and demand sides. In this paper, we present a coordinated energy dispatch based on Distributed Model Predictive Control (DMPC), where the upper level provides an optimal scheduling for energy exchange between Distribution Network Operator (DNO) and MGs, whereas the lower level guarantees a satisfactory tracking between supply and demand. With the proposed scheme, not only we maintain a supply–demand balance in an economic way, but also improve the renewable energy utilization of distributed MG systems. To describe the dynamic process of energy trading, a novel conditional probability distribution model is introduced, which can characterize randomness of charging/discharging and uncertainties of energy dispatch. Moreover, we formulate a two-layer optimization problem and the corresponding algorithm is given. Finally, simulation results show the effectiveness of the proposed method.
Closed-loop control is commonly used in industrial processes to track setpoints or regulate process disturbances. Process dynamics resulting from closed-loop control are reflected in data mainly in two aspects, namely serial correlation and variation of response speed. Concurrent analysis of both aspects from data has not been fully investigated in the literature. In this work, a combined strategy of canonical variate analysis and slow feature analysis is proposed to monitor process dynamics resulting from closed-loop control by exploring both serial correlations and variation speed of process data. First, the canonical subspaces reflecting serial correlation are modeled by maximizing correlation between the past and future values of the process data. Then, both the serially correlated canonical subspace and its residual subspace are further explored to extract the slow features, which are representations of process variation speed. The proposed method provides a meaningful physical interpretation and in-depth process analysis with considerations of process dynamics under closed-loop control. Besides, it provides a concurrent monitoring of both process faults and operating condition deviations, resulting in fine-scale identification of different operation statuses. To demonstrate the feasibility and effectiveness, the proposed strategy is tested in a simulated typical chemical process under closed-loop control, namely the three-phase flow process.
In this narrative medicine essay, a dermatologist recalls his moments with his grandmother a fierce educator and administrator in the New York City Schools who taught him about life and death with advice and by example and whom he helped guide through her transition at 96 years—proud to be her medical advocate, sad to let her go.
This JAMA Patient Page describes the symptoms, diagnosis, treatment, and prevention of mumps.
To the Editor:—I am interested to note your editorial of August 12 on the matter of claims for disability said to have resulted from cancer caused by a single injury. In my opinion this editorial is timely but scarcely goes far enough. Traumatic cancer attracted my interest for a rather brief period following the death of the late Dr. James Ewing. Dr. Ewing had been interested in this matter for years and at his death a number of unfinished cases were turned over to me for completion.
The Trump administration continues its focus on making information about medical costs more available as a strategy to help patients become more price- and cost-conscious in their choice of hospitals, physicians, and prescription drugs. The administration’s current effort to increase price transparency for hospitals stems from the 2019 inpatient and long-term care hospital proposed payment final rule.
This JAMA Insights Clinical Update reviews what is known and not known about the efficacy and safety of cannabis and cannabinoids for managing medical conditions, distinguishing between FDA-approved indications, nonapproved indications supported by high-quality emerging evidence, and current claims and uses for which there is inadequate evidence.
This paper studies a multilevel multiphase dc–ac conversion system configured by a neutral-point-clamped converter fed by multiple battery packs connected in series. A virtual-vector modulation is selected and a state-of-charge (SoC) balancing control is designed to be able to employ the full battery bank capacity, even under different battery initial SoC values or different battery nominal capacities. The SoC balancing among battery packs is accomplished through the multilevel converter operation in a lossless manner, by simply distributing the dc-to-ac power flow among the batteries according to their SoC. A simple average system model is also presented, which allows performing very fast system simulations over long periods of time and serves as a convenient tool to tune the compensator parameters. The satisfactory performance of the proposed system configuration and control, which can be applied with any number of levels and phases, has been verified through simulations and experiments in a four-level three-phase dc–ac converter fed by three lithium-ion battery packs. The results prove the feasibility and advantages of the proposed system configuration, which can be used to implement conversion systems with different specifications combining several instances of a standard battery pack and a standard power semiconductor device.
Analysis, Design, and Experimental Verification of a Mixed High-Order Compensations-Based WPT System with Constant Current Outputs for Driving Multistring LEDs [IEEE Transactions on Industrial Electronics - new TOC]
Current imbalance in multistring light-emitting diodes (LEDs) is a critical issue. It may cause overcurrent in one or more LED strings, leading to rapid degradation. In this paper, a mixed high-order compensation networks-based wireless power transfer system is proposed to generate multiple constant current outputs. It is composed of an LCC resonant network in the transmitting side, a series resonant network, and multiple CLC resonant rectifiers in the receiving side. The CLC resonant rectifiers are connected in parallel to form multiple independent output channels, and each channel is then connected to an LED string. Based on the analysis of the T resonant circuit and the modeling of coupling coils, multiple constant output currents can be derived. As a result, current balance can be achieved, which is very suitable for driving multistring LEDs. The proposed system also offers a modular, scalable, and maintenance-free design, which can significantly reduce the construction cost and the control complexity. In addition, the inverter in the transmitting side can achieve zero phase angle. A laboratory prototype with dual independent output currents is built to verify the proposed method. The experimental results agree well with the theoretical analysis.
In this paper, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient's finger is demonstrated in human experiments. Specifically, the net force required to move a subject's finger joints can be accounted for by the Lagrange model.
Oriented-plane curvature reproduction, the control of fingertip deformation by orientating a flat plate on contact points to generate a sensation of curvature, is one of the curvature reproduction methods with favorable effects. However, among the three geometric cues of curvature, only the zeroth- and first-order information is recovered with this method, whereas the second-order information (i.e., local curvature) is neglected due to the oriented plate's inherent characteristic of having a planar surface. The second-order information will change the contact length between the finger and the surface and thus affects the feeling. As a solution for this problem, this paper proposes to control the indentation depth of the finger pulp on the oriented plate such that the correct contact length is reproduced during the rendering. A finger wearable device is developed to control that indentation depth. Three psychophysical experiments are carried out and the results indicate that the second-order information plays a positive role in curvature rendering.
In a typical communication pipeline, images undergo a series of processing steps that can cause visual distortions before being viewed. Given a high quality reference image, a reference (R) image quality assessment (IQA) algorithm can be applied after compression or transmission. However, the assumption of a high quality reference image is often not fulfilled in practice, thus contributing to less accurate quality predictions when using stand-alone R IQA models. This is particularly common on social media, where hundreds of billions of user-generated photos and videos containing diverse, mixed distortions are uploaded, compressed, and shared annually on sites like Facebook, YouTube, and Snapchat. The qualities of the pictures that are uploaded to these sites vary over a very wide range. While this is an extremely common situation, the problem of assessing the qualities of compressed images against their pre-compressed, but often severely distorted (reference) pictures has been little studied. Towards ameliorating this problem, we propose a novel two-step image quality prediction concept that combines NR with R quality measurements. Applying a first stage of NR IQA to determine the possibly degraded quality of the source image yields information that can be used to quality-modulate the R prediction to improve its accuracy. We devise a simple and efficient weighted product model of R and NR stages, which combines a pre-compression NR measurement with a post-compression R measurement. This first-of-a-kind two-step approach produces more reliable objective prediction scores. We also constructed a new, first-of-a-kind dedicated database specialized for the design and testing of two-step IQA models. Using this new resource, we show that two-step approaches yield outstanding performance when applied to compressed images whose original, pre-compression quality covers a wide range of realistic distortion types and severities. The two-step concept is versatile as it can use - ny desired R and NR components. We are making the source code of a particularly efficient model that we call 2stepQA publicly available at https://github.com/xiangxuyu/2stepQA. We are also providing the dedicated new two-step database free of charge at http://live.ece.utexas.edu/research/twostep/index.html.
Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and handcrafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a handcrafted feature model. To fuse handcrafted feature information into CNNs and realize feature embeddings, a general fusion unit is proposed (called Fusion-Net). We also define a network loss function with image label information to realize supervised deep metric learning. Our extensive experimental results on the Stanford online products’ data set and the in-shop clothes retrieval data set demonstrate that our proposed methods outperform the existing state-of-the-art methods of image retrieval by a large margin. Moreover, we also explore the applications of the proposed methods in person re-identification and vehicle re-identification; the experimental results demonstrate both the effectiveness and efficiency of the proposed methods.
In this paper, we focus on restoring high-resolution facial images under noisy low-resolution scenarios. This problem is a challenging problem as the most important structures and details of captured facial images are missing. To address this problem, we propose a novel local patch-based face super-resolution (FSR) method via the joint learning of the contextual model. The contextual model is based on the topology consisting of contextual sub-patches, which provide more useful structural information than the commonly used local contextual structures due to the finer patch size. In this way, the contextual models are able to recover the missing local structures in target patches. In order to further strengthen the structural compensation function of contextual topology, we introduce the recognition feature as additional regularity. Based on the contextual model, we formulate the super-resolved procedure as a contextual joint representation with respect to the target patch and its adjacent patches. The high-resolution image is obtained by weighting contextual estimations. Both quantitative and qualitative validations show that the proposed method performs favorably against state-of-the-art algorithms.
A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network (IPS-cGAN) is proposed for intelligent vehicular applications. The convolution backbone architecture of the generator is based on the improved U-Net with residual blocks for well utilizing regional semantic information. Moreover, cross entropy loss for segmentation is introduced as the condition for the generator. SandwichNet, a novel convolutional network with symmetrical input, is proposed as the discriminator for real–fake segmented images. Based on the c-GAN framework, good segmentation performance could be achieved for thermal infrared pedestrians. Compared to some supervised and unsupervised segmentation algorithms, the proposed algorithm achieves higher accuracy with better robustness, especially for complex scenes.
Biobased C4-dicarboxylic acids are attractive sustainable precursors for polymers and other materials. Commercial scale production of these acids at high titers requires efficient secretion by cell factories. In this study, we characterized 7 dicarboxylic acid transporters in Xenopus oocytes and in Saccharomyces cerevisiae engineered for dicarboxylic acid production. Among the...
The Economics of Kenneth J. Arrow: A Selective Review [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 1-26, August 2019.
Evolutionary Models of Preference Formation [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 329-354, August 2019.
Social Networks in Policy Making [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 473-494, August 2019.
Taking State-Capacity Research to the Field: Insights from Collaborations with Tax Authorities [Annual Reviews: Annual Review of Economics: Table of Contents]
Annual Review of Economics, Volume 11, Issue 1, Page 755-781, August 2019.
To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.
Dietary Fuels in Athletic Performance [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 45-73, August 2019.
The Benefits and Risks of Iron Supplementation in Pregnancy and Childhood [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 121-146, August 2019.
Mitochondrial DNA Mutation, Diseases, and Nutrient-Regulated Mitophagy [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 201-226, August 2019.
The Microbiota and Malnutrition: Impact of Nutritional Status During Early Life [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 267-290, August 2019.
Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases [Annual Reviews: Annual Review of Nutrition: Table of Contents]
Annual Review of Nutrition, Volume 39, Issue 1, Page 291-315, August 2019.
In many communication channels, secrecy constraints usually incur a penalty in capacity, as well as generalized degrees-of-freedom (GDoF). In this paper, we show an interesting observation that adding a helper can totally remove the penalty in sum GDoF for a two-user symmetric Gaussian interference channel. For the interference channel where each transmitter sends a message to an intended receiver without secrecy constraints, the sum GDoF is a well-known “W” curve, characterized by Etkin-Tse-Wang in 2008. If the secrecy constraints are imposed on this interference channel, where the message of each transmitter must be secure from the unintended receiver (eavesdropper), then a GDoF penalty is incurred and the secure sum GDoF is reduced to a modified “W” curve, derived by Chen recently. In this paper, we show that, by adding a helper into this interference channel with secrecy constraints, the secure sum GDoF turns out to be a “W” curve, which is the same as the sum GDoF of the setting without secrecy constraints. The proposed scheme is based on the cooperative jamming and a careful signal design such that the jamming signal of the helper is aligned at a specific direction and power level with the information signals of the transmitters, which allows us to totally remove the penalty in GDoF due to the secrecy constraints. Furthermore, the estimation approaches of noise removal and signal separation due to the rational independence are used in the secure rate analysis.
In cyberspace, evolutionary strategies are commonly used by both attackers and defenders. For example, an attacker's strategy often changes over the course of time, as new vulnerabilities are discovered and/or mitigated. Similarly, a defender's strategy changes over time. These changes may or may not be in direct response to a change in the opponent's strategy. In any case, it is important to have a set of quantitative metrics to characterize and understand the effectiveness of attackers' and defenders' evolutionary strategies, which reflect their cyber agility. Despite its clear importance, few systematic metrics have been developed to quantify the cyber agility of attackers and defenders. In this paper, we propose the first metric framework for measuring cyber agility in terms of the effectiveness of the dynamic evolution of cyber attacks and defenses. The proposed framework is generic and applicable to transform any relevant, quantitative, and/or conventional static security metrics (e.g., false positives and false negatives) into dynamic metrics to capture dynamics of system behaviors. In order to validate the usefulness of the proposed framework, we conduct case studies on measuring the evolution of cyber attacks and defenses using two real-world datasets. We discuss the limitations of the current work and identify future research directions.
Machine Learning for Sociology [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 27-45, July 2019.
The Role of Space in the Formation of Social Ties [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 111-132, July 2019.
The Social Structure of Time: Emerging Trends and New Directions [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 301-320, July 2019.
Retail Sector Concentration, Local Economic Structure, and Community Well-Being [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 321-343, July 2019.
Analyzing Age-Period-Cohort Data: A Review and Critique [Annual Reviews: Annual Review of Sociology: Table of Contents]
Annual Review of Sociology, Volume 45, Issue 1, Page 467-492, July 2019.
At present, the fusion of different unimodal biometrics has attracted increasing attention from researchers, who are dedicated to the practical application of biometrics. In this paper, we explored a multi-biometric algorithm that integrates palmprints and dorsal hand veins (DHV). Palmprint recognition has a rather high accuracy and reliability, and the most significant advantage of DHV recognition is the biopsy (Liveness detection). In order to combine the advantages of both and implement the fusion method, deep learning and graph matching were, respectively, introduced to identify palmprint and DHV. Upon using the deep hashing network (DHN), biometric images can be encoded as 128-bit codes. Then, the Hamming distances were used to represent the similarity of two codes. Biometric graph matching (BGM) can obtain three discriminative features for classification. In order to improve the accuracy of open-set recognition, in multi-modal fusion, the score-level fusion of DHN and BGM was performed and authentication was provided by support vector machine (SVM). Furthermore, based on DHN, all four levels of fusion strategies were used for multi-modal recognition of palmprint and DHV. Evaluation experiments and comprehensive comparisons were conducted on various commonly used datasets, and the promising results were obtained in this case where the equal error rates (EERs) of both palmprint recognition and multi-biometrics equal 0, demonstrating the great superiority of DHN in biometric verification.
Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and convolutional neural networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data. To achieve a high-recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprint-specific filters through an unsupervised procedure based on Gabor responses and principal component analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in this paper was obtained.
Researchers at Boston Children's Hospital report creating the first human tissue model of an inherited heart arrhythmia, replicating two patients' abnormal heart rhythms in a dish, and then suppressing the arrhythmia with gene therapy in a mouse model.
Women tend to have a greater immune response to a flu vaccination compared to men, but their advantage largely disappears as they age and their estrogen levels decline, suggests a study from researchers at the Johns Hopkins Bloomberg School of Public Health.
Cryptococcus neoformans is a fungal pathogen that infects people with weakened immune systems, particularly those with advanced HIV/AIDS. New University of Minnesota Medical Research could mean a better understanding of this infection and potentially better treatments for patients.
In a massive new analysis of findings from 277 clinical trials using 24 different interventions, Johns Hopkins Medicine researchers say they have found that almost all vitamin, mineral and other nutrient supplements or diets cannot be linked to longer life or protection from heart disease.
A new study led by Dr. Antonella Fioravanti in the lab of Prof. Han Remaut (VIB-VUB Center for Structural Biology) has shown that removing the armor of the bacterium that causes anthrax slows its growth and negatively affects its ability to cause disease. This work will be published in the prestigious journal Nature Microbiology can lead the way to new, effective ways of fighting anthrax and various other diseases.
The Economics and Politics of Preferential Trade Agreements [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 75-92, May 2019.
The Politics of Housing [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 165-185, May 2019.
Bias and Judging [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 241-259, May 2019.
Climate Change and Conflict [Annual Reviews: Annual Review of Political Science: Table of Contents]
Annual Review of Political Science, Volume 22, Issue 1, Page 343-360, May 2019.
Cysteine-Based Redox Sensing and Its Role in Signaling by Cyclic Nucleotide–Dependent Kinases in the Cardiovascular System [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 63-87, February 2019.
Biomarkers of Acute and Chronic Kidney Disease [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 309-333, February 2019.
Cellular Metabolism in Lung Health and Disease [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 403-428, February 2019.
Regulation of Blood and Lymphatic Vessels by Immune Cells in Tumors and Metastasis [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 535-560, February 2019.
Steps in Mechanotransduction Pathways that Control Cell Morphology [Annual Reviews: Annual Review of Physiology: Table of Contents]
Annual Review of Physiology, Volume 81, Issue 1, Page 585-605, February 2019.
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