## Friday, 08 November 2019

### 04:00 PM

Nature, Published online: 07 November 2019; doi:10.1038/d41586-019-03332-7

A neural network that teaches itself the laws of physics could help to solve quantum-mechanics mysteries.

Six days after the UK electorate refused to grant Theresa May the parliamentary majority she needed to deliver Brexit, 72 people were killed in an avoidable disaster in Grenfell Tower, west London. It was the worst UK residential fire since World War 2. Stung by the public response, May announced an inquiry, a £5 million fund for survivors, and a promise that all those affected would be given new housing.

In 1978, Edgar Rey was the director of a large and overcrowded neonatal unit in Bogota, Colombia. Shortage of incubators and prolonged hospital stay were harming and resulting in the deaths of already stable premature infants. To overcome those circumstances, Rey started an early discharge programme in which infants were placed in skin-to-skin contact on top of their mothers' chests to ensure thermal stability (kangaroo position), and if the babies tolerated the position well and regulated their temperature, the mother and baby were sent home in the kangaroo position while receiving breastmilk-based nutrition and close ambulatory follow-up—ie, after discharge from hospital, kangaroo mother care was provided in specifically designated ambulatory outpatient clinics, where the family came for follow-up visits from discharge until up to 1 year after birth.

Academia can be a rewarding place to work, but not always and not for everybody. Precarity, inequality, and discrimination are stubbornly persistent, and bullying and harassment can make for a toxic environment. In the UK, a range of scientific organisations and funders are addressing these problems by emphasising the need for a positive research culture to promote quality scholarship. In 2018, a conference convened by the Royal Society of London, UK, explored the cultures necessary to support excellent research and researchers,1 following on from work in 2014 by the Nuffield Council on Bioethics on the culture of scientific research in the UK.

The CRASH-3 trial collaborators. Effects of tranexamic acid on death, disability, vascular occlusive events and other morbidities in patients with acute traumatic brain injury (CRASH-3): a randomised, placebo-controlled trial. Lancet 2019; 394: 1713–23—In this Article, the Japanese translation of the abstract (appendix 5) has been resupplied. This correction has been made as of Nov 7, 2019.

Community-initiated kangaroo mother care substantially improves newborn baby and infant survival. In low-income and middle-income countries, incorporation of kangaroo mother care for all infants with low birthweight, irrespective of place of birth, could substantially reduce neonatal and infant mortality.

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.

Vision-based sign language translation (SLT) is a challenging task due to the complicated variations of facial expressions, gestures, and articulated poses involved in sign linguistics. As a weakly supervised sequence-to-sequence learning problem, in SLT there are usually no exact temporal boundaries of actions. To adequately explore temporal hints in videos, we propose a novel framework named Hierarchical deep Recurrent Fusion (HRF). Aiming at modeling discriminative action patterns, in HRF we design an adaptive temporal encoder to capture crucial RGB visemes and skeleton signees. Specifically, RGB visemes and skeleton signees are learned by the same scheme named Adaptive Clip Summarization (ACS), respectively. ACS consists of three key modules, i.e., variable-length clip mining, adaptive temporal pooling, and attention-aware weighting. Besides, based on unaligned action patterns (RGB visemes and skeleton signees), a query-adaptive decoding fusion is proposed to translate the target sentence. Extensive experiments demonstrate the effectiveness of the proposed HRF framework.

This article presents two novel algorithms for the estimation and diagnosis of the current unbalance factor (CUF) for three-phase power systems from single period of three-phase acquired data samples. The CUF is evaluated by an algorithm named circular phase shift (CPS), and the three-phase parameters are estimated by a circular cross-correlation (CCC) algorithm for unbalance diagnosis. The estimated CUF along with multisensory data is transmitted and monitored through a remote web server for diagnosis and detection of incipient three-phase power systems faults including three-phase machines. The CPS algorithm estimation has an accuracy that exceeds 95% for CUF values exceeding 5%. The CCC time-complexity and the Cramer–Rao Lower Bound analysis are presented for performance evaluation. The CCC algorithm outperforms the IEEE-Standard-1057 estimation method in both phase accuracy and processing memory requirements compatible with low-cost microcontrollers. Experimental results on condition monitoring of industrial induction machines (1.5 to 7.5 KW) are also presented with custom designed 2.4-GHz wireless sensor network and an IEEE 802.11 Internet of Things gateway with multisensory data which carries out the effectiveness of the system.

Due to the existence of various anomalies such as non-Gaussian process and measurement noises, gross measurement errors, and sudden changes of system status, the robust forecasting-aided state estimation is pivotal for power system stability. This paper develops a novel unscented Kalman filter (UKF) with the generalized correntropy loss (GCL) (termed as GCL-UKF) to estimate power system state with forecasting aid. The GCL is used to replace the mean square error loss in the original UKF framework. The advantage of such an approach is that it combines the strength of the GCL developed in robust information theoretic learning for addressing the non-Gaussian interference and the strength of the UKF in handling strong model nonlinearities. In addition, we take into account the nontrivial influences of the bad data for the innovation vector. An enhanced GCL-UKF method is established by introducing an exponential function of the innovation vector to adjust a covariance matrix so as to improve the GCL-UKF-based state estimation accuracy under the change of gain matrix caused by bad factors. Numerical simulation results carried out on IEEE 14-bus, 30-bus, and 57-bus test systems validate the efficacy of the proposed methods for state estimation under various types of measurement.

Digital holography (DH) can record optical field of a three-dimensional (3-D) object, and the 3-D information can be retrieved and processed digitally. Thus, DH has application potential in 3-D sensing and metrology. However, it is hard for conventional DH to record the off-axis object light because of the limitation of the arrayed sensor. In this paper, optical scanning holography (OSH) is proposed to record the off-axis object light. OSH is a scanning-type and single-pixel holographic technique for 3-D imaging. Therefore, OSH can directly record the off-axis object light by using a small sampling pitch. This is, however, a waste of both bandwidth and recording time. For this reason, a new scheme called optical scanning tilt holography (OSTH) is proposed in this paper. In OSTH, the scanning beam is tilted related to the object axis, and thus the off-axis object light can be recorded by using a large sampling pitch. Although the horizontal resolution of OSTH is worse than that of OSH in normal scanning, the axial resolution of OSTH is better than that of OSH. In addition, the reconstruction plane of OSTH is always parallel to that of OSH in normal scanning. Therefore, there is no need of coordinate transformation post to the holographic recording. These features enable OSTH to be applied in depth sensing and stereoscopic imaging.

Digital holographic imaging is a powerful technique that can provide wavefront information of a three-dimensional object for biological and industrial applications. However, due to the constraint and cost of imaging sensors, the acquired digital hologram is limited in terms of pixel count, thus affecting the resolution in holographic reconstruction. To overcome this constraint, in this paper we propose a deep learning-based method to super-resolve holograms and to improve the quality of low-resolution holograms by training a convolutional neural network with large-scale data for resolution enhancement. Moreover, this algorithm can be broadly adapted to enhance the space-bandwidth product of a holographic imaging system without the need of any advanced hardware. We experimentally validate its capability using a lens-free off-axis holographic system, and compare the performance of various loss functions and interpolation methods in training such a network.

A new study led by researchers at the University of California, Davis, finds that child mortality significantly drops when children receive nutritional supplements rich in vitamins, minerals and essential fatty acids. It found that supplements may decrease mortality among children 6-24 months old by as much as 27% in low- and middle-income countries.

A new technique to change the structure of liquid crystals could lead to the development of fast-responding liquid crystals suitable for next generation displays -- 3D, augmented and virtual reality -- and advanced photonic applications such as mirrorless lasers, bio-sensors and fast/slow light generation, according to an international team of researchers from Penn State, the Air Force Research Laboratory and the National Sun Yat-sen University, Taiwan.

French scientists led by a CNRS researcher have just revealed that eight succinate dehydrogenase inhibitor pesticide molecules do not just inhibit the SDH activity of fungi, but can also block that of earthworms, bees, and human cells in varying proportions. They demonstrated that the conditions of current regulatory tests for toxicity mask a very important effect that SDH inhibitors have on human cells: the pesticides induce oxidative stress in cells, leading to their death.

Forensic anthropologists have now discovered that several skull features in Asian and Asian-derived groups differ significantly with regard to shape, such that they can be distinguished using statistical analyses. These findings highlight the future potential for developing more nuanced statistical methods that can potentially differentiate between groups that comprise the broad 'Asian' ancestral category in forensic casework.

Unmanned aerial vehicles (UAVs) are getting smarter with the help of an international team of researchers. They developed a way for multiple UAVS to fall into formation while still automatically controlling their own flight needs, just like the drones used by the villain portrayed by Jake Gyllenhaal in the 2019 Spiderman movie. They published their results in IEEE/CAA Journal of Automatica Sinica.

## Thursday, 07 November 2019

### 04:00 PM

Blockchains have received much attention recently since they provide decentralized approaches to the creation and management of value. Many banks, Internet companies, car manufacturers, and even governments worldwide have incorporated or started considering blockchains to improve the security, scalability, and efficiency of their services. In this paper, we survey blockchain applications in different areas. These areas include cryptocurrency, healthcare, advertising, insurance, copyright protection, energy, and societal applications. Our work provides a timely summary for individuals and organizations interested in blockchains. We envision our study to motivate more blockchain applications.

Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform privacy-preserving data analytics. In particular, each user locally perturbs his/her data to preserve privacy before sending it to the data collector, who aggregates the perturbed data to obtain statistics of interest. Over the past several years, researchers from multiple communities--such as security, database, and theoretical computer science-- have proposed many LDP protocols. These studies mainly focused on improving the utility of the LDP protocols. However, the security of LDP protocols is largely unexplored. In this work, we aim to bridge this gap. We focus on LDP protocols for frequency estimation and heavy hitter identification, which are two basic data analytics tasks. Specifically, we show that an attacker can inject fake users into an LDP protocol and the fake users send carefully crafted data to the data collector such that the LDP protocol estimates high frequencies for certain items or identifies them as heavy hitters. We call our attacks data poisoning attacks. We theoretically and/or empirically show the effectiveness of our attacks. We also explore two countermeasures against our attacks. Our experimental results show that they can effectively defend against our attacks in some scenarios but have limited effectiveness in others, highlighting the needs for new defenses against our attacks.

In this paper, we design DeLottery, a decentralized lottery system based on block chain technology and smart contracts. Lottery is a classical form of entertainment and charity for centuries. Facing the bottleneck of the combination between lottery and information technology, we use smart contracts and blockchain in decentralized, intelligent, and secure systems for lottery industries. Moreover, we are inspired by the algorithm of RANDAO, an outstanding way of random number generation in blockchain scenario. The components and the functions of the novel system are described in details. We implement DeLottery in a blockchain network and show functioning procedure and security of the proposed lottery system.

Zero-knowledge proofs are an essential building block in many privacy-preserving systems. However, implementing these proofs is tedious and error-prone. In this paper, we present zksk, a well-documented Python library for defining and computing sigma protocols: the most popular class of zero-knowledge proofs. In zksk proofs compose: programmers can convert smaller proofs into building blocks that then can be combined into bigger proofs. zksk features a modern Python-based domain-specific language. This makes possible to define proofs without learning a new custom language, and to benefit from the rich Python syntax and ecosystem.

Nature, Published online: 06 November 2019; doi:10.1038/s41586-019-1735-9

The bacterium Pseudomonas aeruginosa attacks competing bacteria using the toxin Tas1, which pyrophosphorylates adenosine nucleotides to generate (p)ppApp, thereby depleting ATP and disrupting multiple cellular functions.

Annual Review of Nutrition, Volume 39, Issue 1, Page 21-44, 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.

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.

## Wednesday, 06 November 2019

### 04:00 PM

We describe and evaluate an attack that reconstructs the histogram of any target attribute of a sensitive dataset which can only be queried through a specific class of real-world privacy-preserving algorithms which we call bounded perturbation algorithms. A defining property of such an algorithm is that it perturbs answers to the queries by adding zero-mean noise distributed within a bounded (possibly undisclosed) range. Other key properties of the algorithm include only allowing restricted queries (enforced via an online interface), suppressing answers to queries which are only satisfied by a small group of individuals (e.g., by returning a zero as an answer), and adding the same perturbation to two queries which are satisfied by the same set of individuals (to thwart differencing or averaging attacks). A real-world example of such an algorithm is the one deployed by the Australian Bureau of Statistics' (ABS) online tool called TableBuilder, which allows users to create tables, graphs and maps of Australian census data [30]. We assume an attacker (say, a curious analyst) who is given oracle access to the algorithm via an interface. We describe two attacks on the algorithm. Both attacks are based on carefully constructing (different) queries that evaluate to the same answer. The first attack finds the hidden perturbation parameter $r$ (if it is assumed not to be public knowledge). The second attack removes the noise to obtain the original answer of some (counting) query of choice. We also show how to use this attack to find the number of individuals in the dataset with a target attribute value $a$ of any attribute $A$, and then for all attribute values $a_i \in A$. Our attacks are a practical illustration of the (informal) fundamental law of information recovery which states that overly accurate estimates of too many statistics completely destroys privacy'' [9, 15].

Ketone bodies, including β-hydroxybutyrate and acetoacetate, are important alternative energy sources during energy shortage. β-Hydroxybutyrate also acts as a signaling molecule via specific G protein-coupled receptors (GPCRs); however, the specific associated GPCRs and physiological functions of acetoacetate remain unknown. Here we identified acetoacetate as an endogenous agonist for short-chain fatty...

From incoming sensory information, our brains make selections according to current behavioral goals. This process, selective attention, is controlled by parietal and frontal areas. Here, we show that another brain area, posterior inferotemporal cortex (PITd), also exhibits the defining properties of attentional control. We discovered this area with functional magnetic...

Nature, Published online: 06 November 2019; doi:10.1038/d41586-019-03367-w

As a scientific glassblower, Terri Adams commands a workshop that is filled with tools for crafting bespoke scientific glass apparatus.

Nature, Published online: 05 November 2019; doi:10.1038/d41586-019-03391-w

Scientists evaluate the seismic risk facing a portion of North America by comparing a centuries-old mega-quake with two recent events.

Nature, Published online: 04 November 2019; doi:10.1038/d41586-019-03396-5

UN fund attracts record pledges for climate adaptation, Indigenous communities will share the benefits of rooibos tea and 10 of the most influential Nature papers of all time.

This paper proposes a new robust field-weakening approach for reluctance synchronous motors regulated by direct torque and flux control. Compared to the existing direct torque and flux control based field-weakening methods that cannot achieve maximized DC-link voltage utilization and are parameter dependent, the proposed approach contributes to improve the field-weakening performance of reluctance synchronous motor in two aspects. First, it extends the constant power speed range through autonomous stator flux reference adjustment, which maximizes the dc-link voltage utilization. Smooth transition between the maximum torque per ampere trajectory and field-weakening trajectory is also realized. Second, it enhances the parameter robustness of drives in very high-speed region by employing a torque reference adjustment scheme. This effectively avoids the instability of drives caused by machine parameter variations. The proposed approach is verified experimentally on a laboratory setup.

This paper presents a new four-quadrant (4Q) boost-type converter with the use of active virtual ground (AVG) technology. The presented topology can step up the ac grid voltage to a regulated dc voltage under a stable bidirectional current flow and support the power transmission in either real power or reactive power delivery. With the use of the proposed modulation method, only two high-frequency switches are required through the 4Q operation. Under a full-bridge converter structure, an efficient system can be guaranteed. Also, benefiting from the AVG technology, an LCL filter is formed at the system input over the 4Q operation. Both magnitude of leakage current and grid current ripple are also minimized to a small value. Thus, a high-efficiency and low-noise 4Q converter is guaranteed. The presented topology is successfully implemented on a 750 VA prototype and the performance is experimentally verified on it, which shows good agreement with the theoretical knowledge.

Torque motor is one key component that directly influences the dynamic performance of jet pipe servo valve in aircraft. In this paper, a novel torque motor with hybrid-magnetization pole arrays is proposed. By changing the magnetization patterns of permanent magnets, the torque motor can significantly improve the output torque by range of 47–52% compared with traditional designs, while maintaining the system size and mass. The design concept and operating principle of the torque motor is presented. The magnetic field distribution is formulated analytically with equivalent magnetic circuit. Different from conventional study, the flux leakage of the permanent magnets and coils is included to improve the model precision. Subsequently, the output torque is derived mathematically from the airgap flux. Following that, the numerical calculation is conducted to validate the mathematical models of magnetic field and output torque. The design optimization is then carried out. One research prototype that can be mounted with either conventional magnet or the proposed hybrid array has been developed. The test rigs are constructed and experiments are conducted on the prototype. Both numerical computation and experimental results verify the significant improvement of torque generation of the proposed hybrid magnetization torque motor.

In this paper, the noncooperative optimal control problem is investigated for a class of discrete time-varying networked control systems subject to exogenous nonlinear disturbances. To relive the transmission burden in the sensor-to-controller channel, the Round-Robin protocol is adopted to schedule the sensor transmissions. On the other hand, the controller operates in an event-triggered manner so as to reduce the transmission frequency and thereby preserving the energy in the controller-to-actuator channel. In the presence of the underlying scheduling and triggering mechanism, it is literally impossible to acquire the accurate value of the individual cost function for each controller and, as an effective alternative, a certain upper bound is derived on the individual cost function. Then, in virtue of the completing-the-square technique and the Moore–Penrose pseudo inverse, such an upper bound is minimized at each time instant. Furthermore, a sufficient condition is established to guarantee the boundedness of the derived upper bound over the infinite horizon. Finally, a numerical example on the power grid is provided to verify the validity of the proposed methodology.

Due to the limits on market requirements, material conditions, and production situations in manufacturing process, conventional optimization approaches are difficult to obtain optimal economical and technical indices with physical constraints. To optimize several conflicting objects such as production rate, economic benefits, and gas emission, a hybrid-model-based intelligent optimization method that consists of an improved genetic algorithm and derived deep learning is put forward in this paper. Integration of the hybrid model has made modeling and optimizing an indivisible whole, in which the fitness of the genetic algorithm comes from deep neural networks by weighted sum of the output variables that correspond to the input solutions. The recurrent neural network (RNN) with disposition-gated recurrent unit (dGRU) is applied to capture the dynamics of blast furnace by training the model over datasets recorded in the production scene. Meanwhile, the self-adaptive population genetic algorithm (SAPGA) with a varied population size depending on the fitness distribution is used to locate the optimal solutions under current working conditions. The hybrid intelligent optimization model, validated by both numerical tests and practical data, has been running in an ironmaking plant for one year. It has proved to be successful in meeting industry demands by optimizing multiproduction indices simultaneously.

This study monitored online posts from readers seeking specific health information on a social media platform to evaluate the volume of information requests, whether readers sought an initial or second opinion for diagnosis, and the amount of response time before a reply answer was sent.

This JAMA Guide to Statistics and Methods reviews the use of whole genome association studies to quantify the association between single-nucleotide polymorphisms (SNPs) and human disease, and the importance of using the information to identify the actual effector transcripts responsible for the underlying pathophysiology.

In the Original Investigation entitled “Switching to Another SSRI or to Venlafaxine With or Without Cognitive Behavioral Therapy for Adolescents With SSRI-Resistant Depression: the TORDIA Randomized Controlled Trial,” published in the February 27, 2008, issue of JAMA, the coding used for the Suicidal Ideation Questionnaire-Jr was incorrect. The scale ranges from 0 to 6; we had coded the scores on a scale from 1 to 7, which inflated the total scores. The resulting means, CIs, and cut point for clinical significance were incorrect. The data in Table 1, the Results section, and Table 4 were affected, but the errors did not affect the conclusions or interpretations of the study. A letter of explanation has been published and this article has been corrected online.

This Viewpoint discusses the importance of structuring value-based purchasing models around principles of physician professionalism to ensure that measures that lead to more payment are clinically meaningful, do not increase administrative burden, do not displace clinicians’ intrinsic motivation to help patients with financial motivations, and do not incentivize physicians to avoid sicker, more complicated patients.

This Viewpoint summarizes recommendations from an International Society for Stem Cell Research task force charged with developing professional standards of patient consent for stem cell–based interventions offered outside a clinical trial given the spread of unregulated stem cell clinics offering unproven therapies.

## Monday, 04 November 2019

### 04:50 PM

Animal mating displays provide some of nature’s most dramatic and curious spectacles. Ring doves (Streptopelia risoria) are a case in point (Fig. 1). According to Cheng (ref. 1, p. 2), “When a male ring dove courts a female, he starts with majestic bowing and cooing (bow coo) interspersed with strutting...

In the United States, the iconic groundfish fishery for Gulf of Maine cod has endured several dramatic reductions in annual catch limits and been federally declared an economic disaster. Using a repeated cross-sectional survey of fishing captains to assess potential social impacts of the fishery failure, we found that psychological...

Wilson (1) proposes a multiple testing procedure based on the harmonic mean p-value (HMP). While this is a potentially useful method, he makes several claims that are not supported by the theory. Herein we identify 4 errors, for clarity described in terms of the version with equal weights 1/L, so...

Massive MIMO attains high spectral and power efficiency transmission by leveraging a large number of transmit antennas. However, to capture the benefits of massive MIMO, each antenna should be accompanied with a dedicated RF chain, and consequently, the hardware costs would scale up tremendously with the increase of the antennas. Cheap implementations of massive MIMO have recently gained considerable attention, and constant modulus (CM) signaling is seen as a promising solution, owing to its low peak-to-average power ratio (PAPR). This paper investigates the physical-layer (PHY) security in massive MIMO with an emphasis on the CM signaling. In particular, we consider a transmitter with massive antennas broadcast common confidential information to a group of legitimate receivers, and a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our goal is to design the CM beamforming at the transmitter so that the multicast secrecy rate is maximized. This secrecy rate maximization (SRM) problem is generally NP-hard. To tackle it, two tractable approaches are developed. The first one employs the semidefinite relaxation (SDR) technique and the Charnes-Copper transformation to obtain a convex relaxation of the SRM problem. However, due to the dimension lifting of SDR, this approach is feasible only for small to medium antenna sizes. The second approach leverages the Dinkelbach method to work directly over the beamformer domain; a custom-build nonconvex alternating direction method of multipliers (ADMM) algorithm is proposed to efficiently perform each Dinkelbach update. Simulation results demonstrate that the second approach is computationally more efficient and can achieve nearly optimal performance when the number of antennas is large.

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.

This paper studies the optimal tradeoff between secrecy and non-secrecy rates of the MISO wiretap channels for different power constraint settings: sum power constraint only, per-antenna power constraints only, and joint sum and per-antenna power constraints. The problem is motivated by the fact that channel capacity and secrecy capacity are generally achieved by different transmit strategies. First, a necessary and sufficient condition to ensure a positive secrecy capacity is shown. The optimal tradeoff between secrecy rate and transmission rate is characterized by a weighted rate sum maximization problem. Since this problem is not necessarily convex, equivalent problem formulations are introduced to derive the optimal transmit strategies. Under sum power constraint only, a closed-form solution is provided. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are derived. Sufficient conditions are provided for the special case of two transmit antennas. For the special case of aligned channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. Last, the theoretical results are illustrated by numerical simulations.

Anomaly detection is an important technique used to identify patterns of unusual network behavior and keep the network under control. Today, network attacks are increasing in terms of both their number and sophistication. To avoid causing significant traffic patterns and being detected by existing techniques, many new attacks tend to involve gradual adjustment of behaviors, which always generate incomplete sessions due to their running mechanisms. Accordingly, in this work, we employ the behavior symmetry degree to profile the anomalies and further identify unusual behaviors. We first proposed a symmetry degree to identify the incomplete sessions generated by unusual behaviors; we then employ a sketch to calculate the symmetry degree of internal hosts to improve the identification efficiency for online applications. To reduce the memory cost and probability of collision, we divide the IP addresses into four segments that can be used as keys of the hash functions in the sketch. Moreover, to further improve detection accuracy, a threshold selection method is proposed for dynamic traffic pattern analysis. The hash functions in the sketch are then designed using Chinese remainder theory, which can analytically trace the IP addresses associated with the anomalies. We tested the proposed techniques based on traffic data collected from the northwest center of CERNET (China Education and Research Network); the results show that the proposed methods can effectively detect anomalies in large-scale networks.

In this paper, we propose a novel deep sparse coding network (SCN) capable of efficiently adapting its own regularization parameters for a given application. The network is trained end-to-end with a supervised task-driven learning algorithm via error backpropagation. During training, the network learns both the dictionaries and the regularization parameters of each sparse coding layer so that the reconstructive dictionaries are smoothly transformed into increasingly discriminative representations. In addition, the adaptive regularization also offers the network more flexibility to adjust sparsity levels. Furthermore, we have devised a sparse coding layer utilizing a “skinny” dictionary. Integral to computational efficiency, these skinny dictionaries compress the high-dimensional sparse codes into lower dimensional structures. The adaptivity and discriminability of our 15-layer SCN are demonstrated on six benchmark datasets, namely Cifar-10, Cifar-100, STL-10, SVHN, MNIST, and ImageNet, most of which are considered difficult for sparse coding models. Experimental results show that our architecture overwhelmingly outperforms traditional one-layer sparse coding architectures while using much fewer parameters. Moreover, our multilayer architecture exploits the benefits of depth with sparse coding's characteristic ability to operate on smaller datasets. In such data-constrained scenarios, our technique demonstrates a highly competitive performance compared with the deep neural networks.

Scene recognition is challenging due to the intra-class diversity and inter-class similarity. Previous works recognize scenes either with global representations or with the intermediate representations of objects. In contrast, we investigate more discriminative image representations of object-to-object relations for scene recognition, which are based on the triplets of <;object, relation, object> obtained with detection techniques. Particularly, two types of representations, including co-occurring frequency of object-to-object relation (denoted as COOR) and sequential representation of object-to-object relation (denoted as SOOR), are proposed to describe objects and their relative relations in different forms. COOR is represented as the intermediate representation of co-occurring frequency of objects and their relations, with a three order tensor that can be fed to scene classifier without further embedding. SOOR is represented in a more explicit and freer form that sequentially describe image contents with local captions. And a sequence encoding model (e.g., recurrent neural network (RNN)) is implemented to encode SOOR to the features for feeding the classifiers. In order to better capture the spatial information, the proposed COOR and SOOR are adapted to RGB-D data, where a RGB-D proposal fusion method is proposed for RGB-D object detection. With the proposed approaches COOR and SOOR, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.

We propose an algorithm to efficiently compute approximate solutions of the piecewise affine Mumford-Shah model. The algorithm is based on a novel reformulation of the underlying optimization problem in terms of Taylor jets. A splitting approach leads to linewise segmented jet estimation problems for which we propose an exact and efficient solver. The proposed method has the combined advantages of prior algorithms: it directly yields a partition, it does not need an initialization procedure, and it is highly parallelizable. The experiments show that the algorithm has lower computation times and that the solutions often have lower functional values than the state-of-the-art.

## 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.

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

Annual Review of Economics, Volume 11, Issue 1, Page 895-928, August 2019.

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

## Thursday, 08 August 2019

### 04:00 PM

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.

Social Background and Children's Cognitive Skills: The Role of Early Childhood Education and Care in a Cross-National Perspective [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 45, Issue 1, Page 557-579, July 2019.

## Sunday, 09 June 2019

### 07:32 PM

Annual Review of Political Science, Volume 22, Issue 1, Page 75-92, May 2019.

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

Annual Review of Political Science, Volume 22, Issue 1, Page 277-295, 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.

Annual Review of Physiology, Volume 81, Issue 1, Page 261-284, February 2019.

Annual Review of Physiology, Volume 81, Issue 1, Page 285-308, February 2019.

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

## Feeds

Annual Reviews: Annual Review of Economics: Table of Contents 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Annual Reviews: Annual Review of Nutrition: Table of Contents 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Annual Reviews: Annual Review of Physiology: Table of Contents 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Annual Reviews: Annual Review of Political Science: Table of Contents 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Annual Reviews: Annual Review of Sociology: Table of Contents 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
cs.CR updates on arXiv.org 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Early Edition 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
EurekAlert! - Breaking News 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
IEEE Transactions on Image Processing - new TOC 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
IEEE Transactions on Industrial Electronics - new TOC 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
IEEE Transactions on Industrial Informatics - new TOC 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
IEEE Transactions on Information Forensics and Security - new TOC 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
JAMA Current Issue 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Latest BMJ Research 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
Nature - Issue - nature.com science feeds 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019
The Lancet 04:00 PM, Friday, 08 November 2019 07:00 PM, Friday, 08 November 2019