Wednesday, 04 December 2019

04:00 PM

Estimating Numerical Distributions under Local Differential Privacy. (arXiv:1912.01051v1 [cs.CR]) [cs.CR updates on arXiv.org]

When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator. We study the problem of recovering the distribution over a numerical domain while satisfying LDP. While one can discretize a numerical domain and then apply the protocols developed for categorical domains, we show that taking advantage of the numerical nature of the domain results in better trade-off of privacy and utility. We introduce a new reporting mechanism, called the square wave SW mechanism, which exploits the numerical nature in reporting. We also develop an Expectation Maximization with Smoothing (EMS) algorithm, which is applied to aggregated histograms from the SW mechanism to estimate the original distributions. Extensive experiments demonstrate that our proposed approach, SW with EMS, consistently outperforms other methods in a variety of utility metrics.

Drndalo: Lightweight Control Flow Obfuscation Through Minimal Processor/Compiler Co-Design. (arXiv:1912.01560v1 [cs.DC]) [cs.CR updates on arXiv.org]

Binary analysis is traditionally used in the realm of malware detection. However, the same technique may be employed by an attacker to analyze the original binaries in order to reverse engineer them and extract exploitable weaknesses. When a binary is distributed to end users, it becomes a common remotely exploitable attack point. Code obfuscation is used to hinder reverse engineering of executable programs. In this paper, we focus on securing binary distribution, where attackers gain access to binaries distributed to end devices, in order to reverse engineer them and find potential vulnerabilities. Attackers do not however have means to monitor the execution of said devices. In particular, we focus on the control flow obfuscation --- a technique that prevents an attacker from restoring the correct reachability conditions for the basic blocks of a program. By doing so, we thwart attackers in their effort to infer the inputs that cause the program to enter a vulnerable state (e.g., buffer overrun). We propose a compiler extension for obfuscation and a minimal hardware modification for dynamic deobfuscation that takes advantage of a secret key stored in hardware. We evaluate our experiments on the LLVM compiler toolchain and the BRISC-V open source processor. On PARSEC benchmarks, our deobfuscation technique incurs only a 5\% runtime overhead. We evaluate the security of Drndalo by training classifiers on pairs of obfuscated and unobfuscated binaries. Our results shine light on the difficulty of producing obfuscated binaries of arbitrary programs in such a way that they are statistically indistinguishable from plain binaries.

On Training Robust PDF Malware Classifiers. (arXiv:1904.03542v2 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task.

In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the evasion cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks.

Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 92.27% average verified robust accuracy over three properties, while maintaining 99.74% accuracy and 0.56% false positive rate. With simple robustness properties, our robust model maintains 7% higher robust accuracy than all the baseline models against unrestricted whitebox attacks. Moreover, the state-of-the-art and new adaptive evolutionary attackers need up to 10 times larger $L_0$ feature distance and 21 times more PDF basic mutations (e.g., inserting and deleting objects) to evade our robust model than the baselines.

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models. (arXiv:1908.01297v4 [cs.SI] UPDATED) [cs.CR updates on arXiv.org]

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain. Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss. However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system. This paper promotes current frameworks in a more general and flexible sense -- we demand to attack various kinds of graph embedding model with black-box driven. To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter. As such, a generalized adversarial attacker: GF-Attack is constructed by the graph filter and feature matrix. Instead of accessing any knowledge of the target classifiers used in graph embedding, GF-Attack performs the attack only on the graph filter in a black-box attack fashion. To validate the generalization of GF-Attack, we construct the attacker on four popular graph embedding models. Extensive experimental results validate the effectiveness of our attacker on several benchmark datasets. Particularly by using our attack, even small graph perturbations like one-edge flip is able to consistently make a strong attack in performance to different graph embedding models.

MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection. (arXiv:1910.13063v3 [cs.CR] UPDATED) [cs.CR updates on arXiv.org]

Differential Power Analysis (DPA) has been an active area of research for the past two decades to study the attacks for extracting secret information from cryptographic implementations through power measurements and their defenses. Unfortunately, the research on power side-channels have so far predominantly focused on analyzing implementations of ciphers such as AES, DES, RSA, and recently post-quantum cryptography primitives (e.g., lattices). Meanwhile, machine-learning, and in particular deep-learning applications are becoming ubiquitous with several scenarios where the Machine Learning Models are Intellectual Properties requiring confidentiality. Expanding side-channel analysis to Machine Learning Model extraction, however, is largely unexplored.

This paper expands the DPA framework to neural-network classifiers. First, it shows DPA attacks during inference to extract the secret model parameters such as weights and biases of a neural network. Second, it proposes the $\textit{first countermeasures}$ against these attacks by augmenting $\textit{masking}$. The resulting design uses novel masked components such as masked adder trees for fully-connected layers and masked Rectifier Linear Units for activation functions. On a SAKURA-X FPGA board, experiments show that the first-order DPA attacks on the unprotected implementation can succeed with only 200 traces and our protection respectively increases the latency and area-cost by 2.8x and 2.3x.

Daily briefing: Black hole is ‘impossibly’ massive [Nature - Issue - nature.com science feeds]

Nature, Published online: 29 November 2019; doi:10.1038/d41586-019-03718-7

Current theory says heavyweight black hole shouldn’t exist, a chink in the armour of drug-resistant MRSA and the highs and lows of doing a PhD.

Addressing the Increased Expectations of Nutrition [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page v-vi, August 2019.

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 Role of Brain Barriers in Maintaining Brain Vitamin Levels [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 147-173, August 2019.

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.

Pancreas–Microbiota Cross Talk in Health and Disease [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 249-266, August 2019.

[Editorial] Research integrity: time for global action [The Lancet]

China has become a formidable global leader in scientific—including medical—research, with the world's largest publication output, a rapid surge in the number of highly cited researchers, and an increasingly unparalleled quality of scientific publications. However, there is often a shadow hanging over any country's progress, especially a nation that has advanced with spectacular velocity. China is no exception. And the current concern, escalated to the highest levels of the Chinese Government, is research integrity.

[Editorial] The unfolding migrant crisis in Latin America [The Lancet]

With the political crisis intensifying across Latin America, the difficulties in a region already struggling with massive migration and economic instability are becoming more complex. The number of people migrating across borders within this region has increased by 36% in the past 15 years, reaching 63·7 million in 2015; and of these migrants, 808 000 were defined as refugees, who are the most vulnerable type of migrants and often have insufficient access to appropriate health services. People smuggled by human trafficking and victims of violence are among these migrants.

[Comment] The political determinants of health and wellbeing in the Lebanese uprising [The Lancet]

The uprising in Lebanon, known as the October 17 Revolution, began in protest against a tax on WhatsApp voice calls. At a time of severe economic crisis, the uprising grew and hundreds of thousands of people took to the streets on Oct 19, 2019. Health and wellbeing, and their political determinants have been among the protesters' demands. Protests are ongoing in squares all over Lebanon, as well as in front of government institutions such as the parliament, the central bank, Electricity of Lebanon, telecommunication companies, and private residences of select politicians.

[Correspondence] The harsh effects of sanctions on Iranian health [The Lancet]

We commend The Lancet for bringing to the attention of readers the devastating health effects of the sanction regime imposed on Iran by the USA.1 Sanctions, a form of collective punishment,2 kill. Although sanctions are not physical weapons of war, they are just as lethal, if not more. For example, as of 1994, more than half a million Iraqi children had died under UN Security Council sanctions3 and more recently, in 2017 and 2018, 40 000 innocent civilians died in Venezuela under the US Government sanctions.

[Seminar] Muscular dystrophies [The Lancet]

Muscular dystrophies are primary diseases of muscle due to mutations in more than 40 genes, which result in dystrophic changes on muscle biopsy. Now that most of the genes responsible for these conditions have been identified, it is possible to accurately diagnose them and implement subtype-specific anticipatory care, as complications such as cardiac and respiratory muscle involvement vary greatly. This development and advances in the field of supportive medicine have changed the standard of care, with an overall improvement in the clinical course, survival, and quality of life of affected individuals.

Hazy Image Decolorization With Color Contrast Restoration [IEEE Transactions on Image Processing - new TOC]

It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortion-recovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.

Parallax Tolerant Light Field Stitching for Hand-Held Plenoptic Cameras [IEEE Transactions on Image Processing - new TOC]

Light field (LF) stitching is a potential solution to improve the field of view (FOV) for hand-held plenoptic cameras. Existing LF stitching methods cannot provide accurate registration for scenes with large depth variation. In this paper, a novel LF stitching method is proposed to handle parallax in the LFs more flexibly and accurately. First, a depth layer map (DLM) is proposed to guarantee adequate feature points on each depth layer. For the regions of nondeterministic depth, superpixel layer map (SLM) is proposed based on LF spatial correlation analysis to refine the depth layer assignments. Then, DLM-SLM-based LF registration is proposed to derive the location dependent homography transforms accurately and to warp LFs to its corresponding position without parallax interference. 4D graph-cut is further applied to fuse the registration results for higher LF spatial continuity and angular continuity. Horizontal, vertical and multi-LF stitching are tested for different scenes, which demonstrates the superior performance provided by the proposed method in terms of subjective quality of the stitched LFs, epipolar plane image consistency in the stitched LF, and perspective-averaged correlation between the stitched LF and the input LFs.

Front-End Parameter Monitoring Method Based on Two-Layer Adaptive Differential Evolution for SS-Compensated Wireless Power Transfer Systems [IEEE Transactions on Industrial Informatics - new TOC]

In this paper, a two-layer adaptive differential evolution (ADE) algorithm is adopted to monitor the parameters of the receiving resonators and the mutual inductances of series-series (SS)-compensated wireless power transfer (WPT) systems. By only measuring the primary coils' voltages and currents, the proposed monitoring method can be applied for multiple-coil SS-compensated WPT systems without any feedback signals from the receivers. Compared to the conventional monitoring method based on the genetic algorithm (GA), which may find local optimal solutions by the manually tuned parameters of the mutation rate, the crossover rate, and the generations, the proposed method based on the two-layer ADE can always find global optimal solutions by the automatically tuned parameters of the differential weight, the crossover rate, and the generations. Experimental results validate that the ADE and the proposed two-layer ADE can monitor the parameters of both two- and three-coil SS-compensated WPT systems more steadily and accurately than the conventional GA. Additionally, the proposed two-layer ADE is verified to monitor the parameters of three-coil SS-compensated WPT systems with three different arrangements more accurately than the ADE.

Offline Inductance Identification of IPMSM With Sequence-Pulse Injection [IEEE Transactions on Industrial Informatics - new TOC]

Identification of the stator d-axis and q-axis inductances is essential for the interior permanent magnet synchronous motor (IPMSM) sensorless control system. The signal injection method is an effective way to identify the inductance parameters; however, it requires the rotor position to ensure the accuracy of the injection reference position. To overcome the dependence on the rotor position, a sequence-pulse voltage signal injection method is presented. The matrix least-squares fitting algorithm is utilized to fit the current responses for the inductance identification. Based on the fitting method, the discrete standard orthogonal polynomial with look-up table is introduced to reduce the computation complexity. To reduce the disturbance of the injected signal to the rotor position during the identification process, a double-direction injection position trajectory planning is presented. In addition, the saturation effect on the identification method is also analyzed. The proposed method is verified via a 3.0 kW IPMSM motor drive platform. The identification results are compared with those measured by a standard method and experiment results show that the proposed method can identify the inductance parameters with acceptable accuracy without obtaining the rotor position information.

Mobile stroke units could expedite treatment and improve patient outcomes in urban areas [EurekAlert! - Breaking News]

Stroke patients received time-sensitive, lifesaving treatment approximately 30 minutes faster via an ambulance specially designed to treat stroke called a Mobile Stroke Unit (MSU).Patients diagnosed and treated in an MSU received stroke care faster, even in a densely populated city such as New York City, where this study was conducted.

Birds are shrinking as the climate warms [EurekAlert! - Breaking News]

After 40 years of collecting birds that ran into Chicago buildings, scientists have been able to show that the birds have been shrinking as the climate's warmed up.

Transition to exhaustion: clues for cancer immunotherapy [EurekAlert! - Breaking News]

Emory research on immune cells 'exhausted' by chronic viral infection provides clues on how to refine cancer immunotherapy. The Immunity paper defines a transitional stage in between stem-like and truly exhausted cells.

NASA's exoplanet-hunting mission catches a natural comet outburst in unprecedented detail [EurekAlert! - Breaking News]

Using data from NASA's Transiting Exoplanet Survey Satellite (TESS), astronomers at the University of Maryland (UMD), in College Park, Maryland, have captured a clear start-to-finish image sequence of an explosive emission of dust, ice and gases during the close approach of comet 46P/Wirtanen in late 2018.

Virtual reality could help flu vaccination rates [EurekAlert! - Breaking News]

Using a virtual reality simulation to show how flu spreads and its impact on others could be a way to encourage more people to get a flu vaccination, according to a study by researchers at the University of Georgia and the Oak Ridge Associated Universities in Oak Ridge, Tennessee.

Thursday, 28 November 2019

04:00 PM

Otogelin, otogelin-like, and stereocilin form links connecting outer hair cell stereocilia to each other and the tectorial membrane [Neuroscience] [Early Edition]

The function of outer hair cells (OHCs), the mechanical actuators of the cochlea, involves the anchoring of their tallest stereocilia in the tectorial membrane (TM), an acellular structure overlying the sensory epithelium. Otogelin and otogelin-like are TM proteins related to secreted epithelial mucins. Defects in either cause the DFNB18B and...

E. coli bacteria engineered to eat carbon dioxide [Nature - Issue - nature.com science feeds]

Nature, Published online: 27 November 2019; doi:10.1038/d41586-019-03679-x

Feat could turn bacteria into biological factories for energy and even food.

The tough dogs that helped people to settle a frozen land [Nature - Issue - nature.com science feeds]

Nature, Published online: 27 November 2019; doi:10.1038/d41586-019-03659-1

Heritage of Arctic dogs traced in part to canines that immigrated from Siberia more than a millennium ago.

An acute immune response underlies the benefit of cardiac stem-cell therapy [Nature - Issue - nature.com science feeds]

Nature, Published online: 27 November 2019; doi:10.1038/s41586-019-1802-2

An acute immune response underlies the benefit of cardiac stem-cell therapy

Academics should not need to live in fear for the pursuit of their science [Nature - Issue - nature.com science feeds]

Nature, Published online: 27 November 2019; doi:10.1038/d41586-019-03626-w

Attacks on scholars are on the rise at the same time as universities find themselves at the centre of student protests.

H pylori Treatment May Reduce Gastric Cancer Risk [JAMA Current Issue]

Short-term treatment for Helicobacter pylori infection and vitamin or garlic supplementation may afford long-term protection against gastric cancer among people at high risk, according to a BMJ follow-up study.

Use of an ICU Diary and Patient Posttraumatic Stress Disorder Symptoms—Reply [JAMA Current Issue]

In Reply The widespread use of ICU diaries has occurred without a high standard of proof. Our study showed that 30% of patients developed PTSD symptoms 3 months after ICU discharge. Given this prevalence, it is important to identify efficient interventions. Our study was the first multicenter and assessor-blinded study in this field, to our knowledge.

The Search for a Universal Flu Vaccine Heats Up [JAMA Current Issue]

This Medical News article discusses the urgent need for a more broadly protective, durable influenza vaccine—and advancements toward it.

Vaping—Seeking Clarity in a Time of Uncertainty [JAMA Current Issue]

This Viewpoint attempts to provide public health context to the 2019 emergence of e-cigarette, or vaping, product use–associated lung injury (EVALI), emphasizing that EVALI is likely caused by vaping exposure to THC-containing products and is a public health risk far less than that of smoking combustible tobacco, and suggesting ways physicians might use awareness of the phenomenon to counsel patients about the risks of smoking electronic and traditional cigarettes.

Baclofen Use in CKD and Risk of Encephalopathy [JAMA Current Issue]

This pharmacoepidemiology study uses Canadian health care database data to estimate the risk of encephalopathy among patients with chronic kidney disease (CKD) in Ontario prescribed higher (≥20 mg) vs lower (<20 mg) doses of baclofen.

Tuesday, 26 November 2019

11:42 AM

Evolutionary regain of lost gene circuit function [Applied Mathematics] [Early Edition]

Evolutionary reversibility—the ability to regain a lost function—is an important problem both in evolutionary and synthetic biology, where repairing natural or synthetic systems broken by evolutionary processes may be valuable. Here, we use a synthetic positive-feedback (PF) gene circuit integrated into haploid Saccharomyces cerevisiae cells to test if the population...

Quelling germ cell pluripotency on the genital ridge [Commentaries] [Early Edition]

The most critical step of mammalian embryogenesis in securing the future of the species comes just days after fertilization, and immediately after implantation into the uterine wall. At this time, a small subset of epiblast cells receives an inductive signal from neighboring extraembryonic tissue, the germ lineage is specified, and...

DJ-1 regulates the integrity and function of ER-mitochondria association through interaction with IP3R3-Grp75-VDAC1 [Neuroscience] [Early Edition]

Loss-of-function mutations in DJ-1 are associated with autosomal recessive early onset Parkinson’s disease (PD), yet the underlying pathogenic mechanism remains elusive. Here we demonstrate that DJ-1 localized to the mitochondria-associated membrane (MAM) both in vitro and in vivo. In fact, DJ-1 physically interacts with and is an essential component of...

A portrait of wellbore leakage in northeastern British Columbia, Canada [Environmental Sciences] [Early Edition]

Oil and gas well leakage is of public concern primarily due to the perceived risks of aquifer contamination and greenhouse gas (GHG) emissions. This study examined well leakage data from the British Columbia Oil and Gas Commission (BC OGC) to identify leakage pathways and initially quantify incident rates of leakage...

Electrothermal Transient Behavioral Modeling of Thyristor-Based Ultrafast Mechatronic Circuit Breaker for Real-Time DC Grid Emulation [IEEE Transactions on Industrial Electronics - new TOC]

The accuracy of power electronics simulation relies on the semiconductor switch model employed. Thus, in this paper where an ultrafast mechatronic circuit breaker (UFMCB) is implemented in real-time on the field programmable gate array, a detailed nonlinear thyristor model is proposed for extra device-level information regarding design evaluation. The cascaded thyristors impose a heavy computational burden on the UFMCB simulation, and node elimination is achieved following the proposal of a scalable thyristor model. For the convenience of the circuit breaker's integration into dc grid, a pair of coupled voltage-current sources is inserted as its interface, which achieves a reduction in the dimension of system admittance matrix, and the subsequent proposal of a relaxed scalar Newton-Raphson method further expedites the simulation by decomposing the nodal matrix equation. Meanwhile, the modular multilevel converter as a dc grid terminal adopts half-bridge and clamped double submodule topologies to test system performance in conjunction with the UFMCB. Real-time execution is achieved and the results are validated by ANSYS/Simplorer and PSCAD/EMTDC in device- and system-level, respectively.

A High Efficiency Nonisolated Buck–Boost Converter Based on ZETA Converter [IEEE Transactions on Industrial Electronics - new TOC]

In this paper, a new transformerless buck-boost converter based on a ZETA converter is introduced. The proposed converter has the ZETA converter advantages, such as buck-boost capability, input-to-output dc insulation, and continuous output current. The suggested converter voltage gain is higher than the classic ZETA converter. In the presented converter, only one main switch is utilized. The proposed converter offers low voltage stress of the switch; therefore, the low on-state resistance of the main switch can be selected to decrease the losses of the switch. The presented converter topology is simple; hence, the control of the converter is simple. The converter has the continuous output current. The mathematical analyses of the presented converter are given. The experimental results confirm the correctness of the analysis.

High Torque Density Torque Motor With Hybrid Magnetization Pole Arrays for Jet Pipe Servo Valve [IEEE Transactions on Industrial Electronics - new TOC]

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.

Progressive Object Transfer Detection [IEEE Transactions on Image Processing - new TOC]

Recent development of object detection mainly depends on deep learning with large-scale benchmarks. However, collecting such fully-annotated data is often difficult or expensive for real-world applications, which restricts the power of deep neural networks in practice. Alternatively, humans can detect new objects with little annotation burden, since humans often use the prior knowledge to identify new objects with few elaborately-annotated examples, and subsequently generalize this capacity by exploiting objects from wild images. Inspired by this procedure of learning to detect, we propose a novel Progressive Object Transfer Detection (POTD) framework. Specifically, we make three main contributions in this paper. First, POTD can leverage various object supervision of different domains effectively into a progressive detection procedure. Via such human-like learning, one can boost a target detection task with few annotations. Second, POTD consists of two delicate transfer stages, i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer Detection (WSTD). In LSTD, we distill the implicit object knowledge of source detector to enhance target detector with few annotations. It can effectively warm up WSTD later on. In WSTD, we design a recurrent object labelling mechanism for learning to annotate weakly-labeled images. More importantly, we exploit the reliable object supervision from LSTD, which can further enhance the robustness of target detector in the WSTD stage. Finally, we perform extensive experiments on a number of challenging detection benchmarks with different settings. The results demonstrate that, our POTD outperforms the recent state-of-the-art approaches. The codes and models are available at https://github.com/Cassie94/LSTD/tree/lstd.

Friday, 08 November 2019

04:00 PM

Unscented Kalman Filter With Generalized Correntropy Loss for Robust Power System Forecasting-Aided State Estimation [IEEE Transactions on Industrial Informatics - new TOC]

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.

Review on Feature Extraction for 3-D Incoherent Image Processing Using Optical Scanning Holography [IEEE Transactions on Industrial Informatics - new TOC]

The advancement of digital holography in the past two decades has enabled precise capturing of three-dimensional (3-D) images of physical objects. This important technology has been widely applied in numerous industrial sectors such as, but not limited to remote sensing, metrology, biomedical imaging, advertising, and entertainment. Most of the hologram acquisition techniques developed to date is employing digital cameras for recording the hologram, hence imposing rigid restrictions on the size and resolution of the captured 3-D image. This limitation, however, is not found in optical scanning holography (OSH). Based on a scanning mechanism and a single pixel sensor, OSH is capable of capturing digital holograms of both macroscopic and microscopic, as well as fluorescent objects with high precision. Since its invention in the late 70s, numerous research works have been conducted to enhance this technology, optimizing important factors such as acquisition speed, precision, data size, and security. The objective of this article is to provide a walkthrough of the state-of-the-art of the OSH technology, from its original principle, to different variants that have been developed over the years with emphasis on their feature extraction capabilities under the incoherent mode of operation. Whenever possible, we shall provide the key formulations of each approach, and experimental outcomes for demonstrating the pros and cons of the method.

Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography [IEEE Transactions on Industrial Informatics - new TOC]

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.

Wednesday, 06 November 2019

04:00 PM

A Robust Field-Weakening Approach for Direct Torque and Flux Controlled Reluctance Synchronous Motors With Extended Constant Power Speed Region [IEEE Transactions on Industrial Electronics - new TOC]

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.

Path-Following Control of A Quadrotor UAV With A Cable-Suspended Payload Under Wind Disturbances [IEEE Transactions on Industrial Electronics - new TOC]

A path-following controller based on an uncertainty and disturbance estimator (UDE) for a quadrotor with a cable-suspended payload is proposed in this paper. The quadrotor and the payload are subject to unknown wind disturbances. The controller resembles a cascade architecture. For the outer loop, a UDE-based translational control law is proposed. The controller asymptotically stabilizes the quadrotor along a given path and estimates the lumped disturbances with a low-pass filter. For the inner loop, an attitude tracking controller is used to control the direction of the lift vector so that the actual lift force can asymptotically follow the reference force generated by the translational controller. The stability of the system with the translational controller and the attitude tracking controller has been shown to be asymptotically stable using the reduction theorem. With the help of the reduction theorem, the design of the translational and the attitude control can be decoupled, providing the flexibility of implementing different attitude controllers without redoing the stability analysis. As shown in the simulation, the control law can stabilize the quadrotor on the desired path under different wind disturbances.

Monday, 04 November 2019

04:50 PM

Constant Modulus Secure Beamforming for Multicast Massive MIMO Wiretap Channels [IEEE Transactions on Information Forensics and Security - new TOC]

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.

An Intrusion Detection Method for Line Current Differential Relays [IEEE Transactions on Information Forensics and Security - new TOC]

The U.S. Department of Homeland Security (DHS) has recently identified digital relays as targets vulnerable to cyber-attacks. The DHS has also noted that attacks to multiple relays can bring about cascading outages of transmission lines, leading to blackouts. As a result, making protective relays cyber-resilient is a prominent security issue in power networks. Line current differential relays (LCDRs) are among the potentially vulnerable digital relays that are increasingly deployed for protecting critical transmission lines. LCDRs, however, lack the required resiliency against cyber attacks, due to their high dependence on communication systems. This paper unveils that such susceptibilities can result in unwarranted trip signals through false data injection attacks (FDIAs), and so cause instability if several attacks are coordinated. It also presents a solution for detecting FDIAs and distinguishing them from real internal faults. To detect attacks, the proposed method compares the estimated and locally measured voltages at an LCDR's terminal for both the positive sequence (PS) and negative sequence (NS). To estimate the local voltage for each sequence, the proposed technique uses an unknown input observer (UIO), the state-space model of the faulty line, and remote and local measurements, all associated with that sequence. The difference between the measured and estimated local voltages for each sequence remains close to zero during real internal faults because, in this condition, the state-space model based on which the UIO operates correctly represents the line. Nevertheless, the state-space model mismatch during FDIAs leads to a large difference between measured and estimated values in both sequences. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus network.

Information Theoretical Analysis of Unfair Rating Attacks Under Subjectivity [IEEE Transactions on Information Forensics and Security - new TOC]

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.

Optimal Transmit Strategies for Gaussian MISO Wiretap Channels [IEEE Transactions on Information Forensics and Security - new TOC]

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.

Symmetry Degree Measurement and its Applications to Anomaly Detection [IEEE Transactions on Information Forensics and Security - new TOC]

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.

Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration [IEEE Transactions on Image Processing - new TOC]

Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.

Supervised Deep Sparse Coding Networks for Image Classification [IEEE Transactions on Image Processing - new TOC]

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.

Monday, 26 August 2019

04:00 PM

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.

The International Monetary and Financial System [Annual Reviews: Annual Review of Economics: Table of Contents]

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

Universal Basic Income: Some Theoretical Aspects [Annual Reviews: Annual Review of Economics: Table of Contents]

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

Universal Basic Income in the United States and Advanced Countries [Annual Reviews: Annual Review of Economics: Table of Contents]

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

Thursday, 08 August 2019

04:00 PM

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.

Sunday, 09 June 2019

07:32 PM

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.

The Return of the Single-Country Study [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 187-203, May 2019.

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.

Political Responses to Economic Shocks [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 22, Issue 1, Page 277-295, May 2019.

Firms in Trade and Trade Politics [Annual Reviews: Annual Review of Political Science: Table of Contents]

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.

Plasticity of the Maternal Vasculature During Pregnancy [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 89-111, February 2019.

Epithelial-Stromal Interactions in Pancreatic Cancer [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 81, Issue 1, Page 211-233, 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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