Query: Comparison Analysis of CPU Scheduling : FCFS, SJF and Round Robin

Relevant passages:
A Priority based Round Robin CPU Scheduling Algorithm for Real Time Systems <=SEP=> The main objective of this paper is to develop a new approach for round robin C P U scheduling a l g o r i t h m which improves the performance of CPU in real time operating system. The proposed Priority based Round-Robin CPU Scheduling algorithm is based on the integration of round-robin and priority scheduling algorithm. It retains the advantage of round robin in reducing starvation and also integrates the advantage of priority scheduling. The proposed algorithm also implements the concept of aging by assigning new priorities to the processes. Existing round robin CPU scheduling algorithm cannot be implemented in real time operating system due to their high context switch rates, large waiting time, large response time, large turnaround time and less throughput. The proposed algorithm improves all the drawbacks of round robin C P U scheduling algorithm. The paper also presents the comparative analysis of proposed algorithm with existing round robin scheduling algorithm on the basis of varying time quantum, average waiting time, average turnaround time and number of context switches.

A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average <=SEP=> Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.

FINDING TIME QUANTUM OF ROUND ROBIN CPU SCHEDULING ALGORITHM IN GENERAL COMPUTING SYSTEMS USING INTEGER PROGRAMMING <=SEP=> In Round Robin Scheduling, the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper, a method using integer programming has been proposed to solve equations that decide a value that is neither too large nor too small such that every process has reasonable response time and the throughput of the system is not decreased due to unnecessarily context switches.

Schedulability analysis of periodic fixed priority systems <=SEP=> Feasibility analysis of fixed priority systems has been widely studied in the real-time literature and several acceptance tests have been proposed to guarantee a set of periodic tasks. They can be divided in two main classes: polynomial time tests and exact tests. Polynomial time tests can efficiently be used for online guarantee of real-time applications, where tasks are activated at runtime. These tests introduce a negligible overhead, when executed upon a new task arrival, however provide only a sufficient schedulability condition, which may cause a poor processor utilization. On the other hand, exact tests, which are based on response time analysis, provide a necessary and sufficient schedulability condition, but are too complex to be executed on line for large task sets. As a consequence, for large task sets, they are often executed off line. This paper proposes a novel approach for analyzing the schedulability of periodic task sets on a single processor under an arbitrary fixed priority assignment: Using this approach, we derive a new schedulability test which can be tuned through a parameter to balance complexity versus acceptance ratio, so that it can be used on line to better exploit the processor, based on the available computational power. Extensive simulations show that our test, when used in its exact form, is significantly faster than the current response time analysis methods. Moreover the proposed approach, for its elegance and compactness, offers an explanation of some known phenomena of fixed priority scheduling and could be helpful for further work on schedulability analysis.

An Optimized Round Robin Scheduling Algorithm for CPU Scheduling <=SEP=> The main objective of this paper is to develop a new approach for round robin scheduling which help to improve the CPU efficiency in real time and time sharing operating system. There are many algorithms available for CPU scheduling. But we cannot implemented in real time operating system because of high context switch rates, large waiting time, large response time, large trn around time and less throughput. The proposed algorithm improves all the drawback of simple round robin architecture. The author have also given comparative analysis of proposed with simple round robin scheduling algorithm. Therefore, the author strongly feel that the proposed architecture solves all the problem encountered in simple round robin architecture by decreasing the performance parameters to desirable extent and thereby increasing the system throughput.

BranchGAN: Branched Generative Adversarial Networks for Scale-Disentangled Learning and Synthesis of Images. <=SEP=> We introduce BranchGAN , a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. The key novel feature of BranchGAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network, is explicitly split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively “defreeze” the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image manifolds and synthesis, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate three applications enabled or improved by BranchGAN.

Underground Mine Communications: A Survey <=SEP=> After a recent series of unfortunate underground mining disasters, the vital importance of communications for underground mining is underlined one more time. Establishing reliable communication is a very difficult task for underground mining due to the extreme environmental conditions. Until now, no single communication system exists which can solve all of the problems and difficulties encountered in underground mine communications. However, combining research with previous experiences might help existing systems improve, if not completely solve all of the problems. In this survey, underground mine communication is investigated. Major issues which underground mine communication systems must take into account are discussed. Communication types, methods, and their significance are presented.

Heteroscedastic Gaussian process regression <=SEP=> This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori estimation in exponential families. Unlike previous work, we obtain a convex optimization problem which can be solved via Newton's method.

Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series <=SEP=> Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.

Group Buying on the Web: A Comparison of Price-Discovery Mechanisms <=SEP=> Web-based Group-Buying mechanisms, a refinement of quantity discounting, are being used for both Business-to-Business (B2B) and Business-toConsumer (B2C) transactions. In this paper, we survey currently operational online Group-Buying markets, and then study this phenomenon using analytical models. We surveyed over fifty active Group-Buying sites, and provide a comprehensive review of Group-Buying practices in the B2B, B2C and non-profit sectors, across three continents. On the modeling side, we build on the coordination literature in Information Economics and the quantity-discounts literature in Operations to develop an analytical model of a monopolist who uses web-based Group-Buying mechanisms under different kinds of demand uncertainty. We derive the monopolist's optimal Group-Buying schedule, and compare his profits with those that obtain under the more conventional posted-price mechanism. We also study the effect of heterogeneity in the demand regimes, in combination with uncertainty, on the relative performance of the two mechanisms. We further study the impact of the timing of the pricing decision (vis-à-vis the production decision) by modeling it as a two-stage game between the monopolist and buyers. Finally, we investigate how Group-Buying schemes compare with posted price markets when buyers can revise their prior valuation of products based on information received from third parties (infomediaries). In all cases, we characterize the conditions under which one mechanism outperforms the other, and those under which the posted price and Group-Buy mechanisms lead to identical seller revenues. Our results have implications for firms' choice of price discovery mechanisms in electronic markets and scheduling of production and pricing decisions in the presence (and absence) of scale economies of production. 1 Email: anandk@wharton.upenn.edu; Tel: (215) 898 1175. 2 Email: raviaron@wharton.upenn.edu; Tel:(215) 573 5677.

Tamperproof transmission of fingerprints using visual cryptography schemes <=SEP=> Visual Cryptography and biometrics have been identified as the two most important aspects of digital security. In this paper, we propose a method for the preparation and secure transmission of fingerprint images using visual cryptography scheme. Visual cryptography (VC) is a kind of secret image sharing scheme that uses the human visual system to perform the decryption computations. A visual cryptography scheme (VCS) allows confidential messages to be encrypted into k-out-of-n secret sharing schemes. Whenever the number of participants from the group (n) is larger than or equal to the predetermined threshold value (k), the confidential message can be obtained by these participants. VCS is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. In this paper, the fingerprint image is broken up into n pieces, which individually yield no information about the image. These pieces of the images, called shares or shadows, may then be distributed among a group of n participants/dealers. By combining any k (k ≤ n) of these shares, the original fingerprint image can be recovered, but combining less than k of them will not reveal any information about the image. The scheme is perfectly secure and very easy to implement. To decode the encrypted information, i.e., to get the original information back, the shares are stacked and the secret image pops out. The only drawback of the VCS is the loss in contrast of reconstructed image. The proposed method for the reconstruction of fingerprint image is based on XNOR operation. This will enable one to obtain back perfect fingerprint image.

Consumer buying behavior towards online shopping stores in Malaysia <=SEP=> The global nature of communication and shopping has as well redefined, seeing that it is the perfect vehicle for online shopping stores. Online convenient shop is mostly reflected in shorter time and less energy spent, including shipping cost reduction, less crowd and queues than real markets, unlimited time and space, which all increase convenience of shopping. Internet shopping for businesses and consumers are being accepted as an alternative shop mode rather than visiting the stores. However, convincing the consumers to shop online is still a challenging task for web retailers in Malaysia. The growth of Internet technology in Malaysia has enormous potential as it reduces the costs of product and service delivery and extends geographical boundaries in bringing buyers and sellers together. This study is conducted to identify factors influencing consumers towards online shopping in Malaysia. The study focused on nine independent variables namely appearance, quick loading, security, sitemap, validity, promotion, attractiveness, believability, and originality. We applied Five-point Likert Scale to measure the influential factors on intention for online shopping.

Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain <=SEP=> Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.

A 35.7–64.2 GHz low power Miller Divider with Weak Inversion Mixer in 65 nm CMOS <=SEP=> A 60 GHz wide locking range Miller divider is presented in this letter. To enhance the locking range of divider and save power consumption, we proposed a Miller divider based on weak inversion mixer. The proposed Miller divider is implemented in 65 nm CMOS and exhibits 57% locking range from 35.7 to 64.2 GHz at an input power of 0 dBm while consuming 1.6-mW dc power at 0.4 V supply. Compared to the previously reported CMOS millimeter wave frequency dividers, the proposed divider achieves the widest fractional bandwidth without any frequency tuning mechanism.

Identification of Refused Bequest Code Smells <=SEP=> Accumulated technical debt can be alleviated by means of refactoring application aiming at architectural improvement. A prerequisite for wide scale refactoring application is the automated identification of the corresponding refactoring opportunities, or code smells. One of the major architectural problems that has received limited attention is the so called 'Refused Bequest' which refers to inappropriate use of inheritance in object-oriented systems. This code smell occurs when subclasses do not take advantage of the inherited behavior, implying that replacement by delegation should be used instead. In this paper we propose a technique for the identification of Refused Bequest code smells whose major novelty lies in the intentional introduction of errors in the inherited methods. The essence of inheritance is evaluated by exercising the system's functionality through the corresponding unit tests in order to reveal whether inherited methods are actually employed by clients. Based on the results of this approach and other structural information, an indication of the smell strength on a 'thermometer' is obtained. The proposed approach has been implemented as an Eclipse plug in.

Effectivecollaborative movie recommender system using asymmetric user similarity and matrix factorization <=SEP=> Recommender systems are becoming ubiquitous these days to advise important products to users. Conventional collaborative filtering methods suffer from sparsity, scalability, and cold start problem. In this work, we have implemented a novel and improved method of recommending movies by combining the asymmetric method of calculating similarity with matrix factorization and Tyco (typicality-based collaborative filtering). The asymmetric method describes that similarity of user A with B is not the similar as the similarity of B with A. Matrix factorization shows items (movies) as well as users by vectors of factors derived from rating pattern of items (movies). In Tyco clusters of movies of the same genre are created, and typicality degree (a measure of how much a movie belongs to that genre) of each movie in that cluster was considered and subsequently of each user in a genre was calculated. The similarity between users was calculated by using their typicality in genres rather than co-rated items. We had combined these methods and employed Pearson correlation coefficient method to calculate similarity to optimize results when compared to cosine similarity, Linear Regression to make predictions that gave better results. In this research work stochastic gradient descent is also used for optimization and regularization to avoid the problem of over fitting. All these approaches together provide better prediction and handle problems of sparsity, cold start, and scalability well as compared to conventional methods. Experimental results confirm that our HYBRTyco gives improved results than Tyco regarding mean absolute error (MAE)and mean absolute percentage error (MAPE), especially on the sparse dataset.

An Analysis of Black-Box Web Application Security Scanners against Stored SQL Injection <=SEP=> Web application security scanners are a compilation of various automated tools put together and used to detect security vulnerabilities in web applications. Recent research has shown that detecting stored SQL injection, one of the most critical web application vulnerabilities, is a major challenge for black-box scanners. In this paper, we evaluate three state of art black-box scanners that support detecting stored SQL injection vulnerabilities. We developed our custom test bed that challenges the scanners capability regarding stored SQL injections. The results show that existing vulnerabilities are not detected even when these automated scanners are taught to exploit the vulnerability. The weaknesses of black-box scanners identified reside in many areas: crawling, input values and attack code selection, user login, analysis of server replies, miss-categorization of findings, and the automated process functionality. Because of the poor detection rate, we discuss the different phases of black-box scanners' scanning cycle and propose a set of recommendations that could enhance the detection rate of stored SQL injection vulnerabilities.

Intrusion Detection System using Support Vector Machine and Decision Tree <=SEP=> Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying an algorithm, Tree structured multiclass SVM, which has been used for classifying data. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.

DESIGNING A LAYOUT USING THE MODIFIED TRIANGLE METHOD , AND GENETIC ALGORITHMS <=SEP=> This paper describes the use of genetic algorithms (GA) for solving the facility layout problem (FLP) within manufacturing systems’ design. The paper considers a specific heuristic layout planning method, known as the modified triangle method. This method seeks for an optimal layout solution based on the degrees of flows between workstations. The search for an optimal solution is extremely time-consuming and not suitable for larger systems; therefore we have developed a system based on evolutionary computation. Our paper presents a system, based on GA, and the results obtained regarding several numerical cases from the literature. We propose the usage of this system by presenting numerous advantages over other methods of solving FLP for problem representation and evolutionary computation for solution search. (Received in September 2012, accepted in June 2013. This paper was with the authors 2 months for 2 revisions.)

Mapping Unseen Words to Task-Trained Embedding Spaces <=SEP=> Education University of California, Berkeley (2008-2013) Ph.D. in Computer Science Thesis: Surface Web Semantics for Structured Natural Language Processing Advisor: Dan Klein. Committee members: Dan Klein, Marti Hearst, Line Mikkelsen, Nelson Morgan University of California, Berkeley (2012) Master of Science (M.S.) in Computer Science Thesis: An All-Fragments Grammar for Simple and Accurate Parsing Advisor: Dan Klein Indian Institute of Technology, Kanpur (2004-2008) Bachelor of Technology (B.Tech.) in Computer Science and Engineering GPA: 3.96/4.00 (Institute and Department Rank 2) Cornell University (Summer 2007) CS490 (Independent Research and Reading) GPA: 4.00/4.00 Advisors: Lillian Lee, Claire Cardie

Interpreting and Unifying Outlier Scores <=SEP=> Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary “outlier factors” to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.

Feature Analysis for Fake Review Detection through Supervised Classification <=SEP=> Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

DRAFT: COMMENTS WELCOME BUT PLEASE DO NOT QUOTE <=SEP=> The Experimental Study of Adaptive User Interfaces Pat Langley (langley@isle.org) Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, CA 94306 Michael Fehling (fehling@odc.stanford.edu) Organizational Dynamics Center, EES/OR Department Stanford University, Stanford, CA 94305 Abstract In this paper we examine some issues that arise in the experimental evaluation of adaptive user interfaces, which are computational decision aids that use machine learning to improve their interaction with users. We begin by reviewing the notion of an adaptive interface and presenting examples for a number of application areas. After this, we discuss plausible dependent measures of their behavior, including solution speed, solution quality, user satisfaction, and predictive accuracy. Next we turn to independent variables that are likely to in uence these measures, including the number of user interactions and characteristics of the system, the user, and the task at hand. In closing, we comment on the role that experimentation plays in the larger scienti c process.

Learning to reinforcement learn <=SEP=> In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.

BLINKS: ranked keyword searches on graphs <=SEP=> Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.

Modeling and control of a three-port DC-DC converter for PV-battery systems <=SEP=> This paper presents modeling and decoupled control design for an isolated three-port DC-DC converter (TPC), which interfaces a photovoltaic (PV) panel, a rechargeable battery, and an isolated load. A small signal model is derived from the state-space equations of the TPC. Based on the model, two decoupled controllers are designed to control the power flows of the two sources and the load voltage independently. Experimental studies are performed to verify that the two controllers are capable of regulating the power flows of the PV panel and the battery while maintaining a constant output voltage for the TPC.

Brain of the African elephant (Loxodonta africana): neuroanatomy from magnetic resonance images. <=SEP=> We acquired magnetic resonance images of the brain of an adult African elephant, Loxodonta africana, in the axial and parasagittal planes and produced anatomically labeled images. We quantified the volume of the whole brain (3,886.7 cm3) and of the neocortical and cerebellar gray and white matter. The white matter-to-gray matter ratio in the elephant neocortex and cerebellum is in keeping with that expected for a brain of this size. The ratio of neocortical gray matter volume to corpus callosum cross-sectional area is similar in the elephant and human brains (108 and 93.7, respectively), emphasizing the difference between terrestrial mammals and cetaceans, which have a very small corpus callosum relative to the volume of neocortical gray matter (ratio of 181-287 in our sample). Finally, the elephant has an unusually large and convoluted hippocampus compared to primates and especially to cetaceans. This may be related to the extremely long social and chemical memory of elephants.

Convolutional auto-encoder for image denoising of ultra-low-dose CT <=SEP=> OBJECTIVES
The purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom.


MATERIALS AND METHODS
Standard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively. Ultra-low-dose CT images were denoised with our proposed method using neural network, large-scale nonlocal mean, and block-matching and 3D filtering. Five radiologists and three technologists assessed the denoised ultra-low-dose CT images visually and recorded their subjective impressions of streak artifacts, noise other than streak artifacts, visualization of pulmonary vessels, and overall image quality.


RESULTS
For the streak artifacts, noise other than streak artifacts, and visualization of pulmonary vessels, the results of our proposed method were statistically better than those of block-matching and 3D filtering (p-values < 0.05). On the other hand, the difference in the overall image quality between our proposed method and block-matching and 3D filtering was not statistically significant (p-value = 0.07272). The p-values obtained between our proposed method and large-scale nonlocal mean were all less than 0.05.


CONCLUSION
Neural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.

Collaborative GAN Sampling <=SEP=> Generative adversarial networks (GANs) have shown great promise in generating complex data such as images. A standard practice in GANs is to discard the discriminator after training and use only the generator for sampling. However, this loses valuable information of real data distribution learned by the discriminator. In this work, we propose a collaborative sampling scheme between the generator and discriminator for improved data generation. Guided by the discriminator, our approach refines generated samples through gradient-based optimization, shifting the generator distribution closer to the real data distribution. Additionally, we present a practical discriminator shaping method that can further improve the sample refinement process. Orthogonal to existing GAN variants, our proposed method offers a new degree of freedom in GAN sampling. We demonstrate its efficacy through experiments on synthetic data and image generation tasks.

Neural correlates of mindfulness meditation-related anxiety relief. <=SEP=> Anxiety is the cognitive state related to the inability to control emotional responses to perceived threats. Anxiety is inversely related to brain activity associated with the cognitive regulation of emotions. Mindfulness meditation has been found to regulate anxiety. However, the brain mechanisms involved in meditation-related anxiety relief are largely unknown. We employed pulsed arterial spin labeling MRI to compare the effects of distraction in the form of attending to the breath (ATB; before meditation training) to mindfulness meditation (after meditation training) on state anxiety across the same subjects. Fifteen healthy subjects, with no prior meditation experience, participated in 4 d of mindfulness meditation training. ATB did not reduce state anxiety, but state anxiety was significantly reduced in every session that subjects meditated. Meditation-related anxiety relief was associated with activation of the anterior cingulate cortex, ventromedial prefrontal cortex and anterior insula. Meditation-related activation in these regions exhibited a strong relationship to anxiety relief when compared to ATB. During meditation, those who exhibited greater default-related activity (i.e. posterior cingulate cortex) reported greater anxiety, possibly reflecting an inability to control self-referential thoughts. These findings provide evidence that mindfulness meditation attenuates anxiety through mechanisms involved in the regulation of self-referential thought processes.