Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework and Method for Online Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of {\em online} IRL---where the observations are incrementally accrued, yet the demands of the application often prohibit a full rerun of an IRL method---has received relatively less attention. We introduce the first formal framework for online IRL, called incremental IRL (I2RL), and a new method that advances maximum entropy IRL with hidden variables, to this setting. Our formal analysis shows that the new method has a monotonically improving performance with more demonstration data, as well as probabilistically bounded error, both under full and partial observability. Experiments in a simulated robotic application of penetrating a continuous patrol under occlusion shows the relatively improved performance and speed up of the new method and validates the utility of online IRL.
CNN Region featuresInput image Guided loss SGA SGA Class semantic features Semantic embedding label Attention features Attention features Embedded loss Softm

Zero-Shot Learning (ZSL) is achieved via aligning the semantic relationships between the global image feature vector and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semanticsguided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-toend. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.
Understanding uplift of the Ethiopian Plateau from longitudinal profile analysis of the Blue Nile drainage system

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Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning

Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work \cite{efroni2018beyond}, multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analyzed. In this work, we study multiple-step greedy algorithms in more practical setups. We begin by highlighting a counter-intuitive difficulty, arising with soft-policy updates: even in the absence of approximations, and contrary to the 1-step-greedy case, monotonic policy improvement is not guaranteed unless the update stepsize is sufficiently large. Taking particular care about this difficulty, we formulate and analyze online and approximate algorithms that use such a multi-step greedy operator.
An Enhanced Technique for Secure Image Transmission Via Visual Cryptography and Secret Fragment Visible Mosaic Images

: In this paper a new type of computer art image called secret fragment visible mosaic image is proposed which transforms automatically a given large-volume secret image into a so called secret fragment visible mosaic image. The mosaic image, which looks similar to an arbitrarily selected target image and may be used as a camouﬂage of the secret image, is yielded by dividing the secret image into fragments. The information required for recovering the secret image is embedded into the created mosaic image by a lossless data hiding scheme using a key. Shamir secret sharing algorithm plays an important role in this project. Shamir’s secret sharing is an algorithm that divides a secret into shares. For encryption of secret image Shamir Encryption method is used and for decryption process Shamir Decryption algorithm is used. Secret can be recovered by combining certain numbers of shares. An additional measure to enhance the embedded data security is also proposed.
Neighbourhood socioeconomic position and risks of major chronic diseases and all-cause mortality: a quasi-experimental study

Objectives This study estimated the health impacts of neighbourhood socioeconomic position (SEP) among public housing residents. Because applicants to public housing were assigned to housing projects primarily based on factors other than personal choice, we capitalised on a quasirandom source of variation in neighbourhood of residence to obtain more valid estimates of the health impacts of neighbourhood SEP. Design Quasiexperimental study. Setting Greater Metropolitan Toronto area, Canada. Participants Residents (24 019–28 858 adults age ≥30 years in 1994 for all outcomes except for asthma, for which the sample was expanded to 66 627 individuals age ≥4 years) of public housing on 1 January 1994. Outcome measures Incident hypertension, diabetes, asthma, and acute myocardial infarction (MI) and all-cause mortality between 1 January 1994 and 31 December 2006. We used multivariate Cox proportional hazards models to estimate hazard ratios (HRs) for the associations between the quartile of census tract-level SEP and the risk of diagnosis of each health outcome as well as death from any cause. Results Living in a public housing project in the second highest neighbourhood SEP quartile (Q3) was associated with lower hazards of acute MI (HR=0.76, 95% CI 0.54 to 1.07; P=0.11), incident asthma (HR=0.80, 95% CI 0.67 to 0.96; P=0.02) and all-cause mortality (HR=0.86, 95% CI 0.73 to 1.01; P=0.06) compared to living in the lowest neighbourhood SEP quartile (Q1), although only the trend for incident asthma reached statistical significance (P for trend=0.04). By contrast, the associations corresponding to living in the highest versus lowest quartile of median household income (Q4 vs Q1) were neither consistent in direction nor significant. The inconsistent associations may partly be attributed to selection and status incongruity. Conclusion This study provides new evidence compatible with protective influences of higher neighbourhood SEP on health outcomes, particularly asthma.
Design and development of vitamin C-encapsulated proliposome with improved in-vitro and ex-vivo antioxidant efficacy

Abstract Vitamin C, as an antioxidant additive in pharmaceutical and food products, is susceptible to environmental conditions, and new design strategies are needed to enhance its stability. The aim of this study is to prepare vitamin C proliposome using film deposition on the carrier by applying different factors, and optimise the characteristics of the obtained powder using the design expert® software. The optimised formulation demonstrated acceptable flowability with 20% vitamin C loading. This formulation released about 90% vitamin C within 2 h and showed higher (1.7-fold) in-vitro antioxidant activity. Ex-vivo antioxidant activity was 1.9 and 1.6 times higher in brain and liver cells, respectively. A 27% reduction in malondialdehyde (MDA) level of liver cell was obtained comparing free vitamin C. Therefore, this study results suggest that the vitamin C-encapsulated proliposome powder might be an appropriate carrier for oral drug delivery of vitamin C with better antioxidant efficacy.
A Measure of Information Available for Inference

The mutual information between the state of a neural network and the state of the external world represents the amount of information stored in the neural network that is associated with the external world. In contrast, the surprise of the sensory input indicates the unpredictability of the current input. In other words, this is a measure of inference ability, and an upper bound of the surprise is known as the variational free energy. According to the free-energy principle (FEP), a neural network continuously minimizes the free energy to perceive the external world. For the survival of animals, inference ability is considered to be more important than simply memorized information. In this study, the free energy is shown to represent the gap between the amount of information stored in the neural network and that available for inference. This concept involves both the FEP and the infomax principle, and will be a useful measure for quantifying the amount of information available for inference.
Discovering a regulatory network topology by Markov Chain Monte-Carlo on GPGPUs with special reference to the biological clock of Neurospora crassa

Discovering Regulatory Network Topology by Markov Chain Monte-Carlo on Gpgpus with Special Reference to the Biological Clock of Neurospora Crassa، للحصول على النص الكامل يرجى زيارة مكتبة الحسين بن طلال في جامعة اليرموك او زيارة موقعها الالكتروني
Long‐Term Triple Therapy De‐escalation to Indacaterol/Glycopyrronium in Patients with Chronic Obstructive Pulmonary Disease (SUNSET): A Randomized, Double‐Blind, Triple‐Dummy Clinical Trial

&NA; Rationale: There are no studies on withdrawal of inhaled corticosteroids in patients on long‐term triple therapy in the absence of frequent exacerbations. Objectives: To evaluate the efficacy and safety of direct de‐escalation from long‐term triple therapy to indacaterol/glycopyrronium in nonfrequently exacerbating patients with chronic obstructive pulmonary disease (COPD). Methods: This 26‐week, randomized, double‐blind, triple‐dummy study assessed the direct change from long‐term triple therapy to indacaterol/glycopyrronium (110/50 &mgr;g once daily) or continuation of triple therapy (tiotropium [18 &mgr;g] once daily plus combination of salmeterol/fluticasone propionate [50/500 &mgr;g] twice daily) in nonfrequently exacerbating patients with moderate‐to‐severe COPD. Primary endpoint was noninferiority on change from baseline in trough FEV1. Moderate or severe exacerbations were predefined secondary endpoints. Measurements and Main Results: A total of 527 patients were randomized to indacaterol/glycopyrronium and 526 to triple therapy. Inhaled corticosteroids withdrawal led to a reduction in trough FEV1 of −26 ml (95% confidence interval, −53 to 1 ml) with confidence limits exceeding the noninferiority margin of −50 ml. The annualized rate of moderate or severe COPD exacerbations did not differ between treatments (rate ratio, 1.08; 95% confidence interval, 0.83 to 1.40). Patients with ≥300 blood eosinophils/&mgr;l at baseline presented greater lung function loss and higher exacerbation risk. Adverse events were similar in the two groups. Conclusions: In patients with COPD without frequent exacerbations on long‐term triple therapy, the direct de‐escalation to indacaterol/glycopyrronium led to a small decrease in lung function, with no difference in exacerbations. The higher exacerbation risk in patients with ≥300 blood eosinophils/&mgr;l suggests that these patients are likely to benefit from triple therapy. Clinical trial registered with www.clinicaltrials. gov (NCT 02603393).
Big Data for LITTLE Cores Combining Learning and Control for Mobile Energy Efficiency

Mobile systems must deliver performance to interactive applications while simultaneously conserving resources to extend battery life. There are two central challenges to meeting these conflicting goals: (1) the complicated optimization spaces arising from hardware heterogeneity and (2) dynamic changes in application behavior and resource availability. Machine learning techniques handle complicated optimization spaces, but do not incorporate models of system dynamics; control theory provides formal guarantees of dynamic behavior, but struggles with non-linear system models. In this paper, we propose CALOREE, a combination of learning and control techniques to meet performance requirements on heterogeneous devices in unpredictable environments. CALOREE combines a hierarchical Bayesian model (HBM) with a lightweight control system (LCS). The HBM runs remotely, learning customized performance/power models. The LCS runs on the mobile system and tunes resource usage to meet performance goals. The Performance Hash Table (PHT) is the interface between the two and allows the LCS to apply the learned models in constant time. We test CALOREE’s ability to manage ARM big.LITTLE systems. Compared to existing learning and control methods, CALOREE delivers more reliable performance – only 2% error compared to 4.5-5.4% for learning and 4.7% for control – and lower energy – within 7% of optimal on average as compared to 25-52% for learning and 26% for control. Furthermore, we demonstrate CALOREE’s ability to meet performance and energy goals in dynamic systems with phase changes and multiple applications running on the same system.
Confidence-Rated Discriminative Partial Label Learning

Partial label learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one label is valid. The common discriminative solution to learn from partial label examples assumes one parametric model for each class label, whose predictions are aggregated to optimize specific objectives such as likelihood or margin over the training examples. Nonetheless, existing discriminative approaches treat the predictions from all parametric models in an equal manner, where the confidence of each candidate label being the ground-truth label is not differentiated. In this paper, a boosting-style partial label learning approach is proposed to enabling confidence-rated discriminative modeling. Specifically, the ground-truth confidence of each candidate label is maintained in each boosting round and utilized to train the base classifier. Extensive experiments on artificial as well as real-world partial label data sets validate the effectiveness of the confidence-rated discriminative modeling.
Indian Manufacturing Sector: A Review on the Problems & Declining Scenario of Indian Industries

Industries are the backbone of any growing economy and things go worst when the same is on decline. As our topic lies, Indian industries are not registering substantial growth or are on a far decline in this behalf. Considering to the north-west region of India, industries are not a much even started or have been in their shut down stage at large, with some opposite positive instances in the state of Punjab and Rajasthan. Still in these clusters too, in the state of Punjab particularly, industries are feeling heats out of nonresponsive state policies, well visible in days-to-day regional news. Except a few Industrial Clusters in India, Else regions are well away from the age of industrialisation and are facing challenges in their growth there by. In this research we have afforded out to address out the same with the help of existing available literature and research work as well as other secondary data available on industries. We have defined out some areas and goals where industry as well as concerned governments need to focus upon. It is also tried define out the research grey area, in order to let the researchers work ahead for the discovery of the same.
D C ] 8 J ul 2 01 4 Clique counting in MapReduce : theory and experiments

We tackle the problem of counting the number of k-cliques in large-scale graphs, for any constant k ≥ 3. Clique counting is essential in a variety of applications, among which social network analysis. Due to its computationally intensive nature, we settle for parallel solutions in the MapReduce framework, which has become in the last few years a de facto standard for batch processing of massive data sets. We give both theoretical and experimental contributions. On the theory side, we design the first exact scalable algorithm for counting (and listing) kcliques. Our algorithm uses O(m) total space and O(m) work, where m is the number of graph edges. This matches the best-known bounds for triangle listing when k = 3 and is workoptimal in the worst case for any k, while keeping the communication cost independent of k. We also design a sampling-based estimator that can dramatically reduce the running time and space requirements of the exact approach, while providing very accurate solutions with high probability. We then assess the effectiveness of different clique counting approaches through an extensive experimental analysis over the Amazon EC2 platform, considering both our algorithms and their state-of-the-art competitors. The experimental results clearly highlight the algorithm of choice in different scenarios and prove our exact approach to be the most effective when the number of kcliques is large, gracefully scaling to non-trivial values of k even on clusters of small/medium size. Our approximation algorithm achieves extremely accurate estimates and large speedups, especially on the toughest instances for the exact algorithms. As a side effect, our study also sheds light on the number of k-cliques of several real-world graphs, mainly social networks, and on its growth rate as a function of k.
The marine toad, Bufo marinus : a natural history resumé of native populations

Zug, George R. , and Patricia B. Zug. The Marine Toad, Bufo marinus: A Natural History Resume of Native Populations. Smithsonian Contributions to Zoology, number 284, 58 pages, 20 figures, 22 tables, 1979. —Bufo marinus is one of the most common and widespread anurans of Central and South America, yet little is known of its natural history in native habitats. Through a survey of the literature and brief ecological studies in Panama, we attempted to summarize the natural history of the toads. Emphasis is placed on distribution, sexual maturity, reproductive cycles, body size and growth, population structure and size, food and feeding behavior, activity and movements, thermal physiology, and water balance. In general, marine toads have one or two breeding seasons each year, apparently timed so metamorphosis occurs during periods of high humidity and prey abundance. Toadlets grow rapidly and reacn the size of sexual maturity (ap proximately 90 mm snout-vent length) at about one year from metamorphosis, although they may not breed their first year. Populations in semi-natural habitats are of moderately high density, 50 to 150 late term juvenile to adult toads per hectare. One-year-old toads comprise at least 50 percent of total population with a progressive and rapid decline of the older year classes. Apparently twothirds of the population turns over each year. The toads are opportunistic feeders with ants and beetles predominating in their diet. Most toads are quite mobile, changing feeding sites regularly within a forage area of roughly 160 sq m. A toad is generally active one evening out of every three or four; however, weather conditions do alter activity patterns. The toads have a broad temperature tolerance but have no or little control of water loss. The dry season and the resulting desiccation may be a major mortality factor. OFFICIAL PUBLICATION DATE is handstamped in a limited number of initial copies and is recorded in the Institution's annual report, SMITHSONIAN YEAR. SERIES COVER DESIGN: The coral Montastrea cavernosa (Linnaeus). Library of Congress Cataloging in Publication Data Zug, George R 1938— The marine toad, Bufo marinus. (Smithsonian contributions to zoology ; no. 284) Bibliography: p. 1. Bufo marinus. I. Zug, Patricia B. , joint author. II. Title. III. Series: Smithsonian Institution Smithsonian contributions to zoology ; no. 284 QL1.S54 no. 284 [QL668.E227] 59V.8 78-606171
Characterization of the outer membrane proteome of Francisella noatunensis subsp. orientalis

The aims of the current study were to characterize the outer membrane proteins (OMPs) of Francisella noatunensis subsp. orientalis (Fno) STIR‐GUS‐F2f7, and identify proteins recognized by sera from tilapia, Oreochromis niloticus, (L) that survived experimental challenge with Fno.
Review on Shear Thickening Fluids and Applications

Recently, research in the area of shear thickening fluids (STFs) has increased due to the potential for commercial application. They are considered possible candidate materials for liquid body armour and sporting protective clothing and other applications because of their unique properties. Here the state of the art of shear thickening mechanisms, ingredient materials, fabrication methods, rheological properties, and factors influencing shear thickening behaviour have been reviewed. Basic models used to describe shear thickening behavior are also discussed. Relevant patent applications, particularly in the area of body armour, as well as other industrial applications, such as smart structures, and devices with adaptive stiffness and damping, are also summarized. Recent advances, including the effects of particle surface properties, relationship to carrier fluids and electric or magnetic fields applied on the transition of STFs are included in the review.
Review on Relevant Top-k Neighbor Search with Keywords Sonali

Internet has became most integral part of today’s modern era. Need for having required information on fingertip creating new challenges in engineering and technology world. As data size is increasing providing required information which can satisfy user’s criteria is tedious job. Data mining help to process such huge data. Using these techniques people can access information like stores, hospitals, technical information, books etc. but today’s trend is changing an along with just information people need geographical information too so they can reach particular shop or hospital. Today’s applications also required to process such queries which need to satisfy both textual as well as spatial information need of user. For example instead of searching the hospital only, one can search the hospital having particular specialist doctors, no. of words, etc. To satisfy such queries the solution that is been used is the IR2-tree; but IR2-tree is having some pitfalls hence an alternative to this is to use WIBRTree which is capable to handle multidimensional data and process nearest neighbor queries with keywords.
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks

Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the inverse of the model. The former class of techniques require explicit knowledge of the measurement process which can be unrealistic, and rely on strong analytical regularizers to constrain the solution space, which often do not generalize well. The latter approaches have had remarkable success in part due to deep learning, but require a large collection of source-observation pairs, which can be prohibitively expensive. In this paper, we propose an unsupervised technique to solve inverse problems with generative adversarial networks (GANs). Using a pre-trained GAN in the space of source signals, we show that one can reliably recover solutions to under determined problems in a `blind' fashion, i.e., without knowledge of the measurement process. We solve this by making successive estimates on the model and the solution in an iterative fashion. We show promising results in three challenging applications -- blind source separation, image deblurring, and recovering an image from its edge map, and perform better than several baselines.