## Thursday, 03 October 2019

### 04:00 PM

Ride-hailing and ride-sharing applications have recently gained in popularity as a convenient alternative to traditional modes of travel. Current research into autonomous vehicles is accelerating rapidly and will soon become a critical component of a ride-hailing platform's architecture. Implementing an autonomous vehicle ride-hailing platform proves a difficult challenge due to the centralized nature of traditional ride-hailing architectures. In a traditional ride-hailing environment the drivers operate their own personal vehicles so it follows that a fleet of autonomous vehicles would be required for a centralized ride-hailing platform to succeed. Decentralization of the ride-hailing platform would remove a road block along the way to an autonomous vehicle ride-hailing platform by allowing owners of autonomous vehicles to add their vehicle to a community driven fleet when not in use. Blockchain technology is an attractive choice for this decentralized architecture due to its immutability and fault tolerance. This paper proposes a framework for developing a decentralized ride-hailing architecture implemented on the Hyperledger Fabric blockchain platform. The implementation is evaluated using a static analysis tool and performing a performance analysis under heavy network load.

The fast developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Most existing IIoT infrastructures are based on a centralized architecture, which is easier for management but cannot effectively support immutable and verifiable services among multiple parties. Blockchain technology provides many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. This paper presents a blockchain-based IIoT architecture to support immutable and verifiable services. However, when applying blockchain technology to the IIoT infrastructure, the required storage space posts a grant challenge to resource-constrained IIoT infrastructures. To address the storage issue, this paper proposes a hierarchical blockchain storage structure, \textit{ChainSplitter}. Specially, the proposed architecture features a hierarchical storage structure where the majority of the blockchain is stored in the clouds, while the most recent blocks are stored in the overlay network of the individual IIoT networks. The proposed architecture seamlessly binds local IIoT networks, the blockchain overlay network, and the cloud infrastructure together through two connectors, the \textit{blockchain connector} and the \textit{cloud connector}, to construct the hierarchical blockchain storage. The blockchain connector in the overlay network builds blocks in blockchain from data generated in IIoT networks, and the cloud connector resolves the blockchain synchronization issues between the overlay network and the clouds. We also provide a case study to show the efficiency of the proposed hierarchical blockchain storage in a practical Industrial IoT case.

We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefitting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the 'enterprise attacker' and shed light on the current state of enterprise phishing attacks.

Federated Learning (FL) systems are gaining popularity as a solution to training Machine Learning (ML) models from large-scale user data collected on personal devices (e.g., smartphones) without their raw data leaving the device. At the core of FL is a network of anonymous user devices sharing minimal training information (model parameter deltas) computed locally on personal data. However, the degree to which user-specific information is encoded in the model deltas is poorly understood. In this paper, we identify model deltas encode subtle variations in which users capture and generate data. The variations provide a powerful statistical signal, allowing an adversary to effectively deanonymize participating devices using a limited set of auxiliary data. We analyze resulting deanonymization attacks on diverse tasks on real-world (anonymized) user-generated data across a range of closed- and open-world scenarios. We study various strategies to mitigate the risks of deanonymization. As random perturbation methods do not offer convincing operating points, we propose data-augmentation strategies which introduces adversarial biases in device data and thereby, offer substantial protection against deanonymization threats with little effect on utility.

AI-synthesized face swapping videos, commonly known as the DeepFakes, have become an emerging problem recently. Correspondingly, there is an increasing interest in developing algorithms that can detect them. However, existing dataset of DeepFake videos suffer from low visual quality and abundant artifacts that do not reflect the reality of DeepFake videos circulated on the Internet. In this work, we present a new DeepFake dataset, Celeb-DF, for the development and evaluation of DeepFake detection algorithms. The Celeb-DF dataset is generated using a refined synthesis algorithm that reduces the visual artifacts observed in existing datasets. Based on the Celeb-DF dataset, we also benchmark existing DeepFake detection algorithms.

Plants are vulnerable to disease through pathogen manipulation of phytohormone levels, which otherwise regulate development, abiotic, and biotic responses. Here, we show that the wheat pathogen Xanthomonas translucens pv. undulosa elevates expression of the host gene encoding 9-cis-epoxycarotenoid dioxygenase (TaNCED-5BS), which catalyzes the rate-limiting step in the biosynthesis of the...

Hydrogen peroxide (H2O2) is an important messenger molecule for diverse cellular processes. H2O2 oxidizes proteinaceous cysteinyl thiols to sulfenic acid, also known as S-sulfenylation, thereby affecting the protein conformation and functionality. Although many proteins have been identified as S-sulfenylation targets in plants, site-specific mapping and quantification remain largely unexplored. By...

Nature, Published online: 02 October 2019; doi:10.1038/d41586-019-02948-z

Can you pass the test?

Nature, Published online: 02 October 2019; doi:10.1038/d41586-019-02939-0

Stuart Russell’s latest book examines how artificial intelligence could spin out of control. David Leslie critiques it.

Nature, Published online: 01 October 2019; doi:10.1038/d41586-019-02946-1

An easily transmitted piece of DNA cranks up the virulence of Klebsiella bacteria, a major cause of infections in hospitals.

This narrative review discusses the diagnosis, evaluation, staging, and management of chronic kidney disease (CKD), including discussion of new calculators for determining risk of CKD progression, treatment of complications, and considerations for referral to a nephrologist and dialysis initiation.

In Reply In their letter regarding our JAMA Insights Clinical Update article, Dr Caturano and colleagues point out several additional considerations in prescribing metformin. These include comparable or superior glucose-lowering efficacy compared with most alternatives, as well as evidence for safety and efficacy in pediatric and older patients with diabetes.

This Viewpoint reviews evidence for associations between Medicaid expansion under the Affordable Care Act and self-reported health outcomes, condition-specific outcome measures from administrative data, and state and national health indicators for low-income populations.

Reducing the risk of macrovascular events, including myocardial infarction and stroke, is a major goal when treating diabetes. Despite this focus, of the major randomized trials used to support current diabetes guidelines (ACCORD, ADVANCE, UKPDS 33, UKPDS 34, VADT), none showed reductions in macrovascular events when the trial results were first reported. For example, UKPDS is still frequently cited in support of aggressive glucose lowering, yet when the primary outcome data were presented in 1998, the results showed no difference between groups in terms of reducing the risk of all-cause mortality (relative risk [RR], 0.94 [95% CI, 0.80-1.10]), myocardial infarction (RR, 0.84 [95% CI, 0.71-1.00]), heart failure (RR, 0.91 [95% CI, 0.54-1.52]), stroke (RR, 1.07 [95% CI, 0.68-1.69]), or kidney failure (RR, 0.73 [95% CI, 0.25-2.20]). Only 1 of 21 clinical end points showed a statistically significant difference attributable to intensive glucose control after 10 years of treatment. Of the 21 end points, only the need for retinal photocoagulation was significantly reduced with intensive glucose lowering, although the effect size was small (RR, 0.71 [95% CI, 0.53-0.98]). An effect of aggressive glucose lowering was only seen 10 years after the trial was completed, when patients were no longer receiving the assigned interventions and had similar glycated hemoglobin levels. This observational analysis of this randomized clinical trial eventually showed reduction in macrovascular disease, including for myocardial infarction (RR, 0.85 [95% CI, 0.74-0.97]) and all-cause mortality (RR, 0.80 [95% CI, 0.79-0.96]) but not for stroke (RR, 0.91 [95% CI, 0.73-1.13]).

## Tuesday, 01 October 2019

### 04:00 PM

Induction of eomesodermin-positive CD4+ T cells (Eomes+ T helper [Th] cells) has recently been correlated with the transition from an acute stage to a later stage of experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis. Moreover, these cells’ pathogenic role has been experimentally proven in EAE. While exploring...

## Monday, 30 September 2019

### 12:00 AM

Nature, Published online: 27 September 2019; doi:10.1038/d41586-019-02890-0

Crop irrigation saturated soil, helping to set off Indonesian slides that killed thousands of people.

Nature, Published online: 27 September 2019; doi:10.1038/d41586-019-02881-1

In the Nature PastCast series, we delve into the archives to tell the stories behind some of Nature’s biggest papers.

## Friday, 27 September 2019

### 04:00 PM

In The Lancet, Maigeng Zhou and colleagues1 present a systematic analysis for the Global Burden of Disease Study (GBD) 2017 on mortality, morbidity, and risk factors in China and its provinces, from 1990 to 2017. Their report is, to our knowledge, the first comprehensive subnational assessment of health in the Chinese population after previous GBD China 2010 on mortality, morbidity, combined health loss, and risks at the national level2 and GBD China 2013 on mortality at the national and province level.

Pre-eclampsia kills approximately 76 000 women and 500 000 babies every year.1 The mortality rate is high in low-income countries without sufficient antenatal and obstetric care. The only current available pre-eclampsia cure is delivery of the dysfunctional placenta, which sheds excessive proinflammatory substances to the maternal cardiovasculature.2,3 High-risk women, identified in the first trimester by screening of clinical risk factors4 and dysregulated placental function biomarkers,1,5 are recommended low-dose aspirin as prophylaxis against preterm pre-eclampsia.

2019 is the tenth anniversary of China's latest health system reform.1 Over the past decade, China has invested much time and effort in improving health-care access and equity. Most noticeably, China has expanded health insurance coverage to more than 95% of its population.2 However, gaps in health-care quality remain, and resolving these gaps will be key to realising Healthy China 2030, an important long-term national strategic plan.3

We agree with the Editors that health-care professionals have a duty to care for vulnerable populations.1 We report on the situation for refugees in France. Since the shutdown of the so-called Calais Jungle refugee camp in Calais, France, in autumn 2016, more refugees have moved to the streets of Paris.2

In 2009, China launched a major health-care reform and pledged to provide all citizens with equal access to basic health care with reasonable quality and financial risk protection. The government has since quadrupled its funding for health. The reform's first phase (2009–11) emphasised expanding social health insurance coverage for all and strengthening infrastructure. The second phase (2012 onwards) prioritised reforming its health-care delivery system through: (1) systemic reform of public hospitals by removing mark-up for drug sales, adjusting fee schedules, and reforming provider payment and governance structures; and (2) overhaul of its hospital-centric and treatment-based delivery system.

## Tuesday, 24 September 2019

### 04:00 PM

A new evaluation of previously published data suggested to us that the accumulation of mutations might slow, rather than increase, as individuals age. To explain this unexpected finding, we hypothesized that normal stem cell division rates might decrease as we age. To test this hypothesis, we evaluated cell division rates...

Metal-reducing bacteria direct electrons to their outer surfaces, where insoluble metal oxides or electrodes act as terminal electron acceptors, generating electrical current from anaerobic respiration. Geobacter sulfurreducens is a commonly enriched electricity-producing organism, forming thick conductive biofilms that magnify total activity by supporting respiration of cells not in direct contact...

## Friday, 13 September 2019

### 12:00 AM

Electricity theft is the third largest form of theft in the United States. It not only leads to significant revenue losses, but also creates the risk of fires and fatal electrical shocks. In the past, utilities have fought electricity theft by sending field operation groups to conduct physical inspections of electrical equipment based on suspicious activity reported by the public. However, the recent rapid penetration of advanced metering infrastructure makes it possible to detect electricity theft by analyzing the information gathered from smart meters. In this paper, we develop a physically inspired data driven model to detect electricity theft with smart meter data. The main advantage of the proposed model is that it only leverages the electricity usage and voltage data from smart meters instead of unreliable parameter and topology information of the secondary network. Hence, a speedy and widespread adoption of the proposed model is feasible. We show that a modified linear regression model accurately captures the physical relationship between electricity usage and voltage magnitude on the Kron-reduced distribution secondaries. Our results show that electricity theft on a distribution secondary will lead to negative and positive residuals from the regression for dishonest and honest customers, respectively. The proposed model is validated with real-world smart-meter data. The results show that the model is effective in identifying electricity theft cases.

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.

Forecasting spot prices of electricity is challenging because it not only contains seasonal variations, but also random, abrupt spikes, which depend on market conditions and network contingencies. In this paper, a hybrid model has been developed to forecast the spot prices of electricity in two main stages. In the first stage, the prices are forecasted using autoregressive time varying (ARXTV) model with exogenous variables. To improve the forecasting ability of the ARXTV model, the price variations in the training process have been smoothened using the wavelet technique. In the second stage, a kernel regression is used to estimate the price spikes, which are detected using support vector machine based model. In addition, mutual information technique is employed to select appropriate input variables for the model. A case study is carried out with the aid of price data obtained from the Australian energy market operator. It is demonstrated that the proposed hybrid method can accurately forecast electricity prices containing spikes.

## Tuesday, 03 September 2019

### 04:00 PM

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.

Failure of the commutation process is a serious malfunction in line-commutated high-voltage direct current (HVdc) converters, which mainly occurs due to inverter ac faults and may lead to outage of the HVdc system. In this paper, an improved strategy is developed that functions based on the SIEMENS HVdc control system under normal conditions and switches to a designed commutation failure inhibition module (CFIM) during an inverter ac fault. From the response speed point of view, since the designed CFIM does not require any proportional-integral controller, the inverter control system has a quick performance in prevention of the commutation failure. This is achieved by direct measurement of the overlap area using the waveforms of the valves anode–cathode and commuting voltages. In addition, from the accuracy aspect, the proposed method has a superior performance in comparison with the existing strategies. It is because of the fact that by direct measurement of the overlap area, variations of both direct current and the commutation inductance are considered, and hence, the unnecessary increase of the inverter reactive power consumption during the fault and the repetitive commutation failures are prevented. The practical performance and feasibility of the proposed strategy is validated through the laboratory testing, using the real-time Opal-RT hardware prototyping platform. The experimental results demonstrate that the proposed strategy can effectively inhibit the commutation failure or repetitive commutation failures under different fault types by considering the lowest possible reactive power consumption.

Selective catalytic reduction (SCR) systems have been widely used to meet the emission regulations and two-cell SCR systems have shown the advantages of high NO$_{x}$ conversion efficiency and low NH$_{3}$ slip simultaneously. However, it is noteworthy that the catalyst performance of SCR device would degrade over the service time gradually. If the performance degrading is not well-compensated, the NO$_{x}$ conversion efficiency would reduce significantly. Different from detecting the aging issue in the laboratory, a practical method is to design an observer to estimate the aging factor online such that the urea injection can be modified accordingly. In this paper, we aim to construct an aging-factor observer for two-cell SCR systems. In order to reduce the algorithm computational load and guarantee the implementation performance, we propose a dual time-scale algorithm based on two-cell SCR model and unscented Kalman filter. There is a fast time scale and a slow time scale in the algorithm. Two simulation studies of constant aging factor and time-varying aging factor are investigated in the simulation environment of MATLAB/SIMULINK. The simulation results indicate that the proposed dual time-scale observer works well under different conditions and the calculational load is reduced significantly.

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.

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.

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.

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.

## Monday, 26 August 2019

### 04:00 PM

Annual Review of Economics, Volume 11, Issue 1, Page 329-354, August 2019.

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.

Annual Review of Economics, Volume 11, Issue 1, Page 859-893, August 2019.

Annual Review of Economics, Volume 11, Issue 1, Page 929-958, August 2019.

## Friday, 23 August 2019

### 04:00 PM

As data grow in quantity and complexity, data anonymization is becoming increasingly challenging. On one side, a great diversity of masking methods, synthetic data generation methods, and privacy models exists, and this diversity is often perceived as unsettling by practitioners. On the other side, most of the anonymization methodology was designed for static, structured, and small data, whereas the current landscape includes big data and, in particular, data streams. We explore here a unified and conceptually simple anonymization approach, by presenting a primitive called steered microaggregation that can be tailored to enforce various privacy models on static data sets and also on data streams. Steered microaggregation is based on adding artificial attributes that are properly initialized and weighted in order to guide the microaggregation process into meeting certain desired constraints. To demonstrate the potential of this type of microaggregation, we show how it can be used to achieve $k$ -anonymity, $t$ -closeness, $l$ -diversity, and $epsilon$ -differential privacy in the context of static data sets; furthermore, we discuss how it can be used to achieve $k$ -anonymity of data streams while controlling tuple reordering. Beyond its flexibility and theoretical appeal, steered microaggregation can drastically reduce information loss, as shown by our experimental evaluation.

While convolutional neural network (CNN)-based pedestrian detection methods have proven to be successful in various applications, detecting small-scale pedestrians from surveillance images is still challenging. The major reason is that the small-scale pedestrians lack much detailed information compared to the large-scale pedestrians. To solve this problem, we propose to utilize the relationship between the large-scale pedestrians and the corresponding small-scale pedestrians to help recover the detailed information of the small-scale pedestrians, thus improving the performance of detecting small-scale pedestrians. Specifically, a unified network (called JCS-Net) is proposed for small-scale pedestrian detection, which integrates the classification task and the super-resolution task in a unified framework. As a result, the super-resolution and classification are fully engaged, and the super-resolution sub-network can recover some useful detailed information for the subsequent classification. Based on HOG+LUV and JCS-Net, multi-layer channel features (MCF) are constructed to train the detector. The experimental results on the Caltech pedestrian dataset and the KITTI benchmark demonstrate the effectiveness of the proposed method. To further enhance the detection, multi-scale MCF based on JCS-Net for pedestrian detection is also proposed, which achieves the state-of-the-art performance.

## Thursday, 22 August 2019

### 04:00 PM

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.

Annual Review of Nutrition, Volume 39, Issue 1, Page 201-226, 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.

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.

## Tuesday, 20 August 2019

### 04:00 PM

The electroencephalography (EEG) method has recently attracted increasing attention in the study of brain activity-based biometric systems because of its simplicity, portability, noninvasiveness, and relatively low cost. However, due to the low signal-to-noise ratio of EEG, most of the existing EEG-based biometric systems require a long duration of signals to achieve high accuracy in individual identification. Besides, the feasibility and stability of these systems have not yet been conclusively reported, since most studies did not perform longitudinal evaluation. In this paper, we proposed a novel EEG-based individual identification method using code-modulated visualevoked potentials (c-VEPs). Specifically, this paper quantitatively compared eight code-modulated stimulation patterns, including six 63-bit (1.05 s at 60-Hz refresh rate) m-sequences (M1-M6) and two spatially combined sequence groups (M×4: M1-M4 and M× 6: M1-M6) in recording the c-VEPs from a group of 25 subjects for individual identification. To further evaluate the influence of inter-session variability, we recorded two data sessions for each individual on different days to measure intra-session and cross-session identification performance. State-of-the-art VEP detection algorithms in brain-computer interfaces (BCIs) were employed to construct a template-matching-based identification framework. For intra-session identification, we achieved a 100% correct recognition rate (CRR) using 5.25-s EEG data (average of five trials for M5). For cross-session identification, 99.43% CRR was attained using 10.5-s EEG signals (average of ten trials for M5). These results suggest that the proposed c-VEP based individual identification method is promising for real-world applications.

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.

## Thursday, 08 August 2019

### 04:00 PM

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.

Annual Review of Sociology, Volume 45, Issue 1, Page 515-534, 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.

## Wednesday, 17 July 2019

### 04:00 PM

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.

## Tuesday, 16 July 2019

### 04:00 PM

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.

They outlived mammoths and saber-toothed tigers. But without dramatic action to reduce climate change, new research shows Joshua trees won't survive much past this century.

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.

## Sunday, 09 June 2019

### 07:32 PM

Annual Review of Political Science, Volume 22, Issue 1, Page 165-185, May 2019.

Beyond the “Sinew of War”: The Political Economy of Security as a Subfield [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 223-239, May 2019.

Annual Review of Political Science, Volume 22, Issue 1, Page 241-259, May 2019.

Annual Review of Political Science, Volume 22, Issue 1, Page 343-360, May 2019.

Annual Review of Political Science, Volume 22, Issue 1, Page 399-417, May 2019.

## Tuesday, 12 February 2019

### 04:00 PM

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.

Unexpected Roles for the Second Brain: Enteric Nervous System as Master Regulator of Bowel Function [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 235-259, February 2019.

Annual Review of Physiology, Volume 81, Issue 1, Page 309-333, February 2019.

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.

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