Miracle-dz/UniFAR-FTUs huggingface dataset:
Prunable Authenticated Log and Authenticable Snapshot in Distributed Collaborative Systems

In distributed collaborative systems, participants maintain a replicated copy of shared documents. They edit their own copy and then share their modifications without any coordination. Copies follow successions of divergence and convergence. Convergence is a liveness property of collaborative systems. Some malicious participants may find an advantage to make the collaboration fail. To that end, they can preclude convergence of the copies. To protect convergence of copies, participants can exploit an authenticated log of modifications. New participants have to retrieve the entire log in order to contribute. Unfortunately, the cost of joining a collaboration increases with the size of this log. Causal Stability allows to prune authenticated logs in a static collaborative group without any malicious participants. In this paper, we first tailor Causal Stability to dynamic groups in presence of malicious participants. We then propose a mechanism to verify the consistency of a pruned log and a mechanism to authenticate a snapshot from a pruned log.
Data fusion through simulated annealing of registered range and reflectance images

In this paper we present results of a study of registered range and reflectance images acquired using a prototype amplitude-modulated cw laser radar. Ranging devices such as laser radars represent new technologies which are being applied in aerospace, nuclear and other hazardous environments where remote inspections, 3D identifications, and measurements are required. However, data acquired using devices of this type may contain non-stationary, signal- dependent noise, range-reflectance crosstalk, and low-reflectance range artifacts. Low level fusion algorithms play an essential role in achieving reliable performance by handling the complex noise, systematic errors, and artifacts. The objective of our study is the development of a stochastic fusion algorithm which takes as its input the registered image pair and produces as its output a reliable description of the underlying physical scene in terms of locally smooth surfaces separated by well-defined depth discontinuities. To construct the algorithm we model each image as a set of coupled Markov random fields representing pixel and several orders of line processes. Within this framework we (i) impose local smoothness constraints, introducing a simple linearity property in place of the usual sums over clique potentials; (ii) fuse the range and reflectance images through line process couplings; and (iii) use nonstationary, signal- dependent variances, adaptive thresholding, and a form of Markov natural selection. We show that the resulting algorithm yields reliable results even in worst-case scenarios.
How far from automatically interpreting deep learning

In recent years, deep learning researchers have focused on how to find the interpretability behind deep learning models. However, today cognitive competence of human has not completely covered the deep learning model. In other words, there is a gap between the deep learning model and the cognitive mode. How to evaluate and shrink the cognitive gap is a very important issue. In this paper, the interpretability evaluation, the relationship between the generalization performance and the interpretability of the model and the method for improving the interpretability are concerned. A universal learning framework is put forward to solve the equilibrium problem between the two performances. The uniqueness of solution of the problem is proved and condition of unique solution is obtained. Probability upper bound of the sum of the two performances is analyzed.
Design of a Low Power 24 GHz VCO in 45 nm RF-SOI CMOS

This work presents the design of a low power 24 GHz LC cross-coupled voltage-controlled oscillator (VCO) for a frequency modulated continuous wave (FMCW) radar base station. The VCO is fabricated using 45 nm RF -SOI technology since its passive components provide better quality factor than those of bulk CMOS technologies. When measured on a printed circuit board (PCB), the fabricated VCO consumes only 12.3 mA from a 1 V supply, achieving a power efficiency of 2.15 GHz/mW which outperforms other state-of-the-art LC cross-coupled VCOs. The design employs a two bit capacitor bank to achieve a tuning range of 9%. The measured phase noise is −96.4 and −119 dBc/Hz at 1 and 10 MHz offset frequency respectively. It achieves a figure of merit (FOM) of −179.5 dBc/Hz at 10 MHz offset frequency.
Tuning of Finest Controller for AVR Unit – An Examination with Firefly-Algorithm

Firefly Algorithm (FA) tuned PID controller is employed to regulate the benchmark Automatic-Voltage-Regulator (AVR) unit. The finest controller values (Kp, Ki, Kd) for the PID is acquired by reducing the multi-objective function. The FA explores the three-dimensional investigating space, till it discover the finest PID values. A relative assessment on various searching procedure on FA, such as Brownian, Levy and chaotic exploration is also presented in this work. The experiment is implemented with traditional and modified form of PID controllers with AVR unit. The performance of PID is computed based on time-domain and its error values. The performances of selected FAs are also appraised based on its run time. This work confirms that, all the exploring techniques tender related time domain and error values. The iteration time taken for the Chaotic-FA is smaller than other FAs. Proposed approach is also authenticated against the other related existing works and which confirms; FA based PID controller offers improved outcome.
Arena Model: Inference About Competitions

The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full advantage of the structure of competitions. Third, the model provides an easy method to quantify the uncertainty in competitions. Fourth, some of our methods can be directly generalized for comparisons among three or more individuals. Furthermore, the authors identify an invariant Bayes estimator with regard to the prior distribution and prove the consistency of the estimations of uncertainty. Currently, the arena model is not effective in tracking the change of strengths of individuals, but its basic framework provides a solid foundation for future study of such cases.
Multi-Antenna Directional Backscatter Tags

A directional backscatter tag is proposed to enhance the range of backscattering communications. The tag uses multiple antennas acting as a reflectarray by configuring constant phase gradients depending on the direction of arrival (DoA) of the signal. The DoA is determined using RSSI measurements across different tag reflection configuration. As a proof of concept, a directional backscatter tag using a $\mathbf{3x3}$ antenna array is implemented. It provides a 19dB sensitivity enhancement resulting in an up to $\mathbf{3x}$ increase in range compared to backscatter communications with a single antenna tag.
Polar Decoding on Sparse Graphs with Deep Learning

In this paper, we present a sparse neural network decoder (SNND) of polar codes based on belief propagation (BP) and deep learning. At first, the conventional factor graph of polar BP decoding is converted to the bipartite Tanner graph similar to low-density parity-check (LDPC) codes. Then the Tanner graph is unfolded and translated into the graphical representation of deep neural network (DNN). The complex sum-product algorithm (SPA) is modified to min-sum (MS) approximation with low complexity. We dramatically reduce the number of weight by using single weight to parameterize the networks. Optimized by the training techniques of deep learning, proposed SNND achieves comparative decoding performance of SPA and obtains about 0.5 dB gain over MS decoding on (128,64) and (256,128) codes. Moreover, 60% complexity reduction is achieved and the decoding latency is significantly lower than the conventional polar BP.
GDF15 and Growth Control

Growth/differentiation factor-15 (GDF-15) is a distant member of the transforming growth factor β (TGF-β) superfamily and is widely expressed in multiple mammalian tissues. Its expression is highly regulated and is often induced in response to conditions associated with cellular stress. GDF15 serum levels have a strong association with many diseases, including inflammation, cancer, cardiovascular diseases, and obesity, and potentially serve as reliable predictor of disease progression. A functional role for GDF15 has been suggested in cancer, cardiovascular disease, kidney disease and metabolic disease. However, the knowledge of its pathophysiological function at the molecular level is still limited and requires more investigation. Recent identification of the endogenous receptor for GDF15 may provide additional insight in to its’ molecular mechanisms and relationship to disease states.
Design and Demonstration of Linearly-Polarized Transmit-Arrays in X-Band

This paper describes the design and demonstration of planar transmit-arrays operating in X-band with 1-bit phase quantization and based on wideband high efficiency unit cells. The unit cell consists of two identical square patch antennas loaded by C-loop slots and interconnected in their centers by a metalized via hole. The proposed transmit-array achieves a maximum directivity of 25.1 dBi and a gain of 22.8 dBi. Radiation efficiency of 56.3% is also obtained with a 1-dB gain bandwidth of up to 9.2% around 9.85 GHz and very low cross-polarization levels. The beam-steering up to 30° is achieved by tilting the focal source.
Predictors of response to synchronized transcranial magnetic stimulation for major depressive disorder

Synchronized transcranial magnetic stimulation (sTMS) is a new modality to reduce symptoms of major depressive disorder (MDD). sTMS uses rotating neodymium magnets to deliver low‐field stimulation matched to the individual alpha frequency (IAF). A previous multisite study showed that sTMS significantly reduced MDD symptoms in the per‐protocol sample. To this end, we evaluated clinical features associated with optimal sTMS outcomes.
The metabolic syndrome in developing countries

The occurrence of the metabolic syndrome in various ethnic groups – including Caucasians, Africans, Latin Americans, Asian Indians, Chinese, Aboriginal Australians, Polynesians and Micronesians – has been confirmed in several epidemiological studies. In developing countries, the lifestyle changes resulting from industrialization and rural-urban migration involve decreased levels of physical activity and the increased intake of energy. As reported by Viswanathan Mohan and Mohan Deepa in this article, the consequent rise in rates of obesity has led to a huge increase in the numbers of people with the metabolic syndrome in developing regions.
Cross Hardware-Software Boundary Exploration for Scalable and Optimized Deep Learning Platform Design

Deep learning system composed with multiple levels of layers is increasingly presented in diverse areas nowadays. To achieve good performance, multicore CPUs and accelerators are widely used in real system. Previous study shows that GPU can significantly speed up computation in deep neural networks, while the performance does not scale very well on multicore CPUs. In this letter, we run Caffe on various hardware platforms using different computation setups to train LeNet-5 on MNIST dataset and measure individual time durations of forward and backward passes for each layer. We find that the speedups perform diversely and the scalability of multicore CPU varies when processing different stages of the network. Based on the observation, we show it is worth applying different policies for each layer separately to achieve the overall optimized performance. In addition, our benchmarking results can be used for references to develop dedicated acceleration methods for individual layer of the network.
Men’s health – the impact of stroke

Stroke is a leading cause of adult death and the most common cause of complex disability in the UK. This article discusses the incidence and impact of stroke, focusing on a range of issues from a male perspective, including stroke prevention, psychological needs, sexuality and return to work. There are some gender differences in modifiable risk factors for stroke, and women have better knowledge of stroke symptomatology. For men, the development of post-stroke depression is associated with greater physical disability. (c) Sherborne Gibbs Limited
Evaluating the End-User Experience of Private Browsing Mode

In this paper, we investigate why users of private browsing mode misunderstand the benefits and limitations of private browsing. We design and conduct a three-part study: (1) an analytic evaluation of the user interface of private mode in different browsers; (2) a qualitative user study to explore user mental models of private browsing; (3) a participatory design study to investigate why existing browser disclosures, the in-browser explanations of private mode, do not communicate the actual protection of private mode. We find the user interface of private mode in different browsers violated well-established design guidelines and heuristics. Further, most participants had incorrect mental models of private browsing, influencing their understanding and usage of private mode. We also find existing browser disclosures did not explain the primary security goal of private mode. Drawing from the results of our study, we extract a set of recommendations to improve the design of disclosures.
Scene Classification Based on Multiscale Convolutional Neural Network

With the large amount of high-spatial resolution images now available, scene classification aimed at obtaining high-level semantic concepts has drawn great attention. The convolutional neural networks (CNNs), which are typical deep learning methods, have widely been studied to automatically learn features for the images for scene classification. However, scene classification based on CNNs is still difficult due to the scale variation of the objects in remote sensing imagery. In this paper, a multiscale CNN (MCNN) framework is proposed to solve the problem. In MCNN, a network structure containing dual branches of a fixed-scale net (F-net) and a varied-scale net (V-net) is constructed and the parameters are shared by the F-net and V-net. The images and their rescaled images are fed into the F-net and V-net, respectively, allowing us to simultaneously train the shared network weights on multiscale images. Furthermore, to ensure that the features extracted from MCNN are scale invariant, a similarity measure layer is added to MCNN, which forces the two feature vectors extracted from the image and its corresponding rescaled image to be as close as possible in the training phase. To demonstrate the effectiveness of the proposed method, we compared the results obtained using three widely used remote sensing data sets: the UC Merced data set, the aerial image data set, and the google data set of SIRI-WHU. The results confirm that the proposed method performs significantly better than the other state-of-the-art scene classification methods.
Are We Making Progress on Communication with People Who Are Near the End of Life in the Australian Health System? A Thematic Analysis

ABSTRACT Initiating end-of-life (EoL) discussions with patients is often delayed or avoided altogether by healthcare practitioners even in light of imminent death. This continues despite the availability of guidelines and conceptual frameworks on how to communicate prognoses at EoL. We surveyed healthcare practitioners to elicit their exposure to and confidence in EoL discussions and to better understand factors that enable or challenge the initiation of discussions in Australian healthcare settings. Thematic analysis identified that EoL discussions could be emotionally burdensome for healthcare practitioners but were regarded as valuable. Effective communications were challenged by conflict with families and between healthcare practitioners as to appropriate care goal transition, and by prognostic uncertainty. Communication skills appeared to be developed more from experience, and beneficial strategies such as role play and mentoring particularly for younger nurses and doctors were identified. Specific training in EoL communications should target undergraduates and new healthcare practitioners
RIPA: Real-Time Image Privacy Alert System

The problem of privacy and security threats arising from images uploaded onto popular social media and content sharing websites is prevalent now more than ever. As our digital footprints grow exponentially, the need to find a solution to these problems has become that much more significant. In order to address these problems, a lot of research work has been carried out for image privacy protection through privacy policy recommendations and configurations. Due to the recent advancement in the field of computer vision and deep learning we can now gain more detailed insights about the context of an image and about the relationships between objects within it, this makes it possible to better address these problems. The privacy and security threats arising from an image uploaded on-line are not only limited to the data owners. Unlike previous works that are mostly focused on individual privacy policies, we take into account privacy concerns of multiple objects depicted on the same photo (even people, animals or other objects in the background of a scenery photo) whereby these privacy concerns may not be those from the user who uploads the photo. Specifically, we first build a general knowledge base by leveraging convolution neural networks to classify sensitive and non-sensitive image content and then use our proposed metadata analysis module to analyze metadata embedded within the image. Next, we extract objects present in the photo and validate if there is any privacy violation of the objects' privacy concerns. If any sensitive object is found, we toggle the object and issue a privacy violation alert to the user who is uploading the image as well as the service provider.
A low-voltage high-speed sampling technique

A new sampling technique is proposed, which integrates input current instead of tracking input voltage to realize high speed and low voltage sampling. Both the theoretical deduction and the simulation prove the advantages of improving the speed and accuracy of CMOS sampling circuits. A practical charge sampling circuit with 3 V supply, 500 MS/s and 9-bit accuracy is suggested.
HER2 protein overexpression in non‐amplified ductal carcinoma in situ: quality issue or transcription mechanisms gone awry?

we read with interest the recent report by Hui et al. entitled 'Discordant HER2 immunohistochemical expression and gene amplification in ductal carcinoma in situ - evaluating HER2 in synchronous in situ and invasive carcinoma'. 1 This study compares HER2 immunohistochemistry (IHC) and chromogenic in situ hybridization (CISH) between admixed synchronous in situ and invasive carcinomas exhibiting an equivocal 2+ HER2 IHC score in the invasive component. This article is protected by copyright. All rights reserved.
Lack of acute or chronic effects of epicatechin-rich and procyanidin-rich apple extracts on blood pressure and cardiometabolic biomarkers in adults with moderately elevated blood pressure: a randomized, placebo-controlled crossover trial.

Background The reported effects of flavanol-rich foods such as cocoa, dark chocolate, and apples on blood pressure and endothelial function may be due to the monomeric flavanols [mainly (-)-epicatechin (EC)], the oligomeric flavanols [procyanidins (PCs)], or other components. Reports of well-controlled intervention studies that test the effects of isolated oligomeric flavanols on biomarkers of cardiovascular health are lacking. Objective We studied the acute and chronic effects of an EC-rich apple flavanol extract and isolated apple PCs on systolic blood pressure (BP) and other cardiometabolic biomarkers. Design Forty-two healthy men and women with moderately elevated BP completed this randomized, double-blind, placebo-controlled, 4-arm crossover trial. Participants ingested a single dose of an apple flavanol extract (70 mg monomeric flavanols, 65 mg PCs), a double dose of this extract (140 mg monomeric flavanols, 130 mg PCs), an apple PC extract (130 mg PCs, 6.5 mg monomeric flavanols), or placebo capsules once daily for 4 wk, in random order. Biomarkers of cardiovascular disease risk and vascular function were measured before and 2 h after ingestion of the first dose and after the 4-wk intervention. Results Compared with placebo, none of the isolated flavanol treatments significantly (P < 0.05) changed systolic or diastolic BP (peripheral and aortic), plasma nitric oxide (NO) reaction products, or measures of arterial stiffness (carotid femoral pulse-wave velocity, brachial-ankle pulse-wave velocity, or Augmentation Index) after 2 h or 4 wk of the intervention. There were no changes in plasma endogenous metabolite profiles or circulating NO; endothelin 1; total, HDL, or LDL cholesterol; triglycerides; fasting glucose; fructosamine; or insulin after 4 wk of the intervention. Conclusions Our data suggest that, in isolation, neither monomeric flavanols nor PCs affect BP, blood lipid profiles, endothelial function, or glucose control in individuals with moderately elevated BP. The reported benefits of consuming flavanol-rich cocoa, chocolate, and apple products appear to be dependent on other components, which may work in combination with monomeric flavanols and PCs. This trial was registered at www.clinicaltrials. gov as NCT02013856.
Sex moderates the relationship between resting heart rate variability and self-reported difficulties in emotion regulation.

Lower resting vagally mediated heart rate variability (HRV) is thought to reflect poorer function of the neurophysiological pathways underlying emotion regulation (ER) and thus, poorer ER abilities. Sex differences in resting HRV exists such that women typically exhibit higher resting HRV than men. It is proposed that greater HRV in women reflects compensation for greater negative affect such as anxiety and depression. However, research has not yet investigated how the association between resting HRV and every day perceived difficulties in ER may be moderated by sex. The current study sought to test this in a sample of 362 young participants (207 females, mean age of 19). Resting HRV was assessed during a 5-min baseline period using an electrocardiogram. Participants then completed the 36-item Difficulties in Emotion Regulation Scale (DERS) designed to evaluate participant's daily difficulties in ER. Controlling for several covariates, sex significantly moderated the relationship between resting HRV and ER difficulties, such that women showed a much stronger relationship compared with men. Specifically, women with lower HRV reported greater difficulties in ER compared with men with lower HRV, whereas women with higher HRV reported slightly lesser difficulties in ER compared with all men. Overall, this study supports a deeper understanding of how neurophysiological differences in ER between men and women-as indexed by resting HRV-may contribute to how effectively individuals regulate their emotions on a day-to-day basis, with implications for well-being. (PsycINFO Database Record (c) 2018 APA, all rights reserved).