title: Family Systems Theory, Attachment Theory, and Culture*

abstract: Family systems theory and attachment theory have important similarities and complementarities. Here we consider two areas in which the theories converge: (a) in family system theorists' description of an overly close, or “enmeshed,” mother-child dyad, which attachment theorists conceptualize as the interaction of children's ambivalent attachment and mothers' preoccupied attachment; (b) in family system theorists' description of the “pursuer-distance cycle” of marital conflict, which attachment theorists conceptualize as the interaction of preoccupied and dismissive partners. We briefly review family systems theory evidence, and more extensively review attachment theory evidence, pertaining to these points of convergence. We also review cross-cultural research, which leads us to conclude that the dynamics described in both theories reflect, in part, Western ways of thinking and Western patterns of relatedness. Evidence from Japan suggests that extremely close ties between mother and child are perceived as adaptive, and are more common, and that children experience less adverse effects from such relationships than do children in the West. Moreover, in Japan there is less emphasis on the importance of the exclusive spousal relationship, and less need for the mother and father to find time alone to rekindle romantic, intimate feelings and to resolve conflicts by openly communicating their differences. Thus, the “maladaptive” pattern frequently cited by Western theorists of an extremely close mother-child relationship, an unromantic, conflictual marriage characterized by little verbal communication and a peripheral, distant father, may function very differently in other cultures. While we believe that both theories will be greatly enriched by their integration, we caution against the application of either theory outside the cultures in which they were developed.


Irisin treatment improves healing of dystrophic skeletal muscle


title: A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

abstract: Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.


Private Information Retrieval by Keywords


title: Review of Efficient Resource Scheduling Algorithms in Cloud Computing

abstract: In this paper the computability of the various resource scheduling algorithms in different aspects are described. Which algorithm is best topology wise, aspect and many others ways and why. This describes the optimal algorithm for various activities that include resource scheduling as an efficient ways to allocate resources in cloud computing which are proposed till now. The energy efficient, dynamic priority based, virtual resources model and so on. These include the latest algorithms that are devised to work over cloud and help in optimal resource scheduling.


title: Neural circuits for social cognition: Implications for autism

abstract: Abstract Social neuroscience, the study of the neurobiological basis of social behavior, has become a major area of current research in behavioral neuroscience and psychiatry, since many psychiatric disorders are characterized by social deficits. Social behavior refers to the behavioral response with regard to socially relevant information, and requires the perception and integration of social cues through a complex cognition process (i.e. social cognition) that involves attention, memory, motivation and emotion. Neurobiological and molecular mechanisms underlying social behavior are highly conserved across species, and inter- and intra-specific variability observed in social behavior can be explained to large extent by differential activity of this conserved neural network. Human functional magnetic resonance imaging (fMRI) studies have greatly informed about the brain structures and their connectivity networks that are important for social cognition. Animal research has been crucial for identifying specific circuits and molecular mechanisms that modulate this structural network. From a molecular neurobiology perspective, activity in these brain structures is coordinated by neuronal circuits modulated by several neurotransmitters and neuromodulators. Thus, quantitative variation in the levels, release and/or receptor density of these molecules could affect the observed behavioral response. The present review presents an overall framework of the components of the social brain circuitry and its modulation. By integrating multiple research approaches, from human fMRI studies to animal models we can start shedding light into how dysfunction in these circuits could lead to disorders of social-functioning such as Autism.


title: Use case pitfalls: top 10 problems from real projects using use cases

abstract: One of the beauties of use cases is their accessible, informal format. Use cases are easy to write, and the graphical notation is trivial. Because of their simplicity, use cases are not intimidating, even for teams that have little experience with formal requirements specification and management. However, the simplicity can be deceptive; writing good use cases takes some skill and practice. Many groups writing use cases for the first time run into similar kinds of problems. The paper presents the author's "Top Ten" list of use case pitfalls and problems, based on observations from a number of real projects. The paper outlines the symptoms of the problems, and recommends pragmatic cures for each. Examples are provided to illustrate the problems and their solutions.


title: A Survey on Multipath Routing Protocols for QoS Assurances in Real-Time Wireless Multimedia Sensor Networks

abstract: The vision of wireless multimedia sensor networks (WMSNs) is to provide real-time multimedia applications using wireless sensors deployed for long-term usage. Quality of service assurances for both best effort data and real-time multimedia applications introduced new challenges in prioritizing multipath routing protocols in WMSNs. Multipath routing approaches with multiple constraints have received considerable research interest. In this paper, a comprehensive survey of both best effort data and real-time multipath routing protocols for WMSNs is presented. Results of a preliminary investigation into design issues affecting the development of strategic multipath routing protocols that support multimedia data in WMSNs are also presented and discussed from the network application perspective.


title: Multicultural assessment of child and adolescent psychopathology with ASEBA and SDQ instruments: research findings, applications, and future directions.

abstract: Around the world, cultural blending and conflict pose challenges for assessment and understanding of psychopathology. Economical, evidence-based, culturally robust assessment is needed for research, for answering public health questions, and for evaluating immigrant, refugee, and minority children. This article applies multicultural perspectives to behavioral, emotional, and social problems assessed on dimensions describing children's functioning, as rated by parents, teachers, children, and others. The development of Achenbach System of Empirically Based Assessment (ASEBA) and Strengths and Difficulties Questionnaire (SDQ) forms and their applications to multicultural research are presented. A primary aim of both questionnaires is to identify children at high risk of psychiatric disorders and who therefore warrant further assessment. The forms are self-administered or administered by lay interviewers. ASEBA problem items are scored on 6 DSM-oriented scales and 3 broader band scales, plus 8 syndromes derived statistically as taxonomic constructs and supported by uniform confirmatory factor analyses of samples from many populations. Comparisons of ASEBA scale scores, psychometrics, and correlates are available for diverse populations. SDQ forms are scored on one broad-band scale and 5 a priori behavioral dimensions supported by data from various populations. For both instruments, factor analyses, psychometrics, and correlates are available for diverse populations. The willingness and ability of hundreds of thousands of respondents from diverse groups to complete ASEBA and SDQ forms support this approach to multicultural assessment. Although particular items and scales may have differential relevance among groups and additional assessment procedures are needed, comparable results are found in many populations. Scale scores vary more within than between populations, and distributions of scores overlap greatly among different populations. Ratings of children's problems thus indicate more heterogeneity within populations than distinctiveness between populations. Norms from multiple populations can be used to compare children's scores with relevant peer groups. Multicultural dimensional research can advance knowledge by diversifying normative data; by comparing immigrant children with nonimmigrant compatriots and with host country children; by identifying outlier findings for elucidation by emic research; and by fostering efforts to dimensionalize DSM-V diagnostic criteria.


title: New support vector algorithms with parametric insensitive/margin model

abstract: In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter [email protected][email protected]?1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.


title: Grid-enabling data mining applications with DataMiningGrid : An architectural perspective

abstract: The DataMiningGrid system has been designed to meet the requirements of modern and distributed data mining scenarios. Based on the Globus Toolkit and other open technology and standards, the DataMiningGrid system provides tools and services facilitating the grid-enabling of data mining applications without any intervention on the application side. Critical features of the system include flexibility, extensibility, scalability, efficiency, conceptual simplicity and ease of use. The system has been developed and evaluated on the basis of a diverse set of use cases from different sectors in science and technology. The DataMiningGrid software is freely available under Apache License 2.0.


title: Applications of the self-organising map to reinforcement learning

abstract: This article is concerned with the representation and generalisation of continuous action spaces in reinforcement learning (RL) problems. A model is proposed based on the self-organising map (SOM) of Kohonen [Self Organisation and Associative Memory, 1987] which allows either the one-to-one, many-to-one or one-to-many structure of the desired state-action mapping to be captured. Although presented here for tasks involving immediate reward, the approach is easily extended to delayed reward. We conclude that the SOM is a useful tool for providing real-time, on-line generalisation in RL problems in which the latent dimensionalities of the state and action spaces are small. Scalability issues are also discussed.


title: Compaction Management in Distributed Key-Value Datastores

abstract: Compactions are a vital maintenance mechanism used by datastores based on the log-structured merge-tree to counter the continuous buildup of data files under update-intensive workloads. While compactions help keep read latencies in check over the long run, this comes at the cost of significantly degraded read performance over the course of the compaction itself. In this paper, we offer an in-depth analysis of compaction-related performance overheads and propose techniques for their mitigation. We offload large, expensive compactions to a dedicated compaction server to allow the datastore server to better utilize its resources towards serving the actual workload. Moreover, since the newly compacted data is already cached in the compaction server's main memory, we fetch this data over the network directly into the datastore server's local cache, thereby avoiding the performance penalty of reading it back from the filesystem. In fact, pre-fetching the compacted data from the remote cache prior to switching the workload over to it can eliminate local cache misses altogether. Therefore, we implement a smarter warmup algorithm that ensures that all incoming read requests are served from the datastore server's local cache even as it is warming up. We have integrated our solution into HBase, and using the YCSB and TPC-C benchmarks, we show that our approach significantly mitigates compaction-related performance problems. We also demonstrate the scalability of our solution by distributing compactions across multiple compaction servers.


title: A technique to detect books from library bookshelf image

abstract: In this paper, we present a technique to recognize books from library bookshelf images. The proposed technique includes several modules such as - noise removal, bookshelf row detection and book detection and extraction from bookshelf. Firstly, the image is filtered for noise removal and edges are detected. The proposed technique counts the number of pixels of every candidate for horizontal lines on the edge detected image and compares the value with a threshold. Elected lines are extended which creates some horizontal lines segmenting the image into different portions. These individual portions are considered as bookshelf row regions of the bookshelf and each of them is passed into the book detection module for further processing. Applying vertical line detection in the manner of horizontal line detection method on the segmented regions is not adequate to detect a book from shelf row because of the angular orientation of book arrangements. The proposed technique detects books arranged in different angular orientation on a shelf and differentiates between the book and non-book components based on some properties of the book shape. Experimental results show that the proposed technique is capable of extracting bookshelf rows and detecting individual books from library bookshelf image successfully.


title: 3D Printed Waveguide Slot Array Antennas

abstract: Waveguide slot array antennas can have high gain, since the power distribution network (waveguide) has low loss, enabling a large aperture size. Rapid response to requirements for frequency, gain, and skew angle inspired a search for an alternative to conventional, commercial slot array acquisition. The solution to this requirement is high-resolution 3-D printing of modified slot arrays combined with metal plating. Modification of the structure included removal of wall material in regions where these openings would not cause radiation, opening the structure to enable metal plating while not affecting gain. A 3-D-printed slot array requires no further assembly, unlike conventional waveguide-based arrays that require soldering or brazing, block machining, or plate assembled, brazed structures. Stereolithography 3-D printing of plastic slot array antennas, modified to enable metal plating, has been used to produce a 30 dBi realized gain slot array at 21 GHz for under $1000 USD in a few weeks. Metal plated, 3-D printed plastic slot arrays are also very lightweight in comparison with conventional metal structures. Performance of 3-D printed, metal plated slot arrays has been shown to be identical to conventional metal structures at frequencies up to 22 GHz.


title: ERP Implementation: Chief Information Officers' Perceptions of Critical Success Factors

abstract: This article reports the results of a survey of Chief Information Officers (CIOs) from Fortune 1000 companies on their perceptions of the critical success factors in Enterprise Resource Planning (ERP) implementation. Through a review of the literature, 11 critical success factors were identified , with underlying subfactors, for successful ERP implementation. The degree of criticality of each of these factors were assessed in a survey administered to the CIOs. The 5 most critical factors identified by the CIOs were top management support, project champion, ERP teamwork and composition, project management, and change management program and culture. The importance of each of these factors is discussed.


title: Enhancing the Effectiveness of Work Groups and Teams

abstract: Teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend their communities. Human history is largely a story of people working together in groups to explore, achieve, and conquer. Yet, the modern concept of work in large organizations that developed in the late 19th and early 20th centuries is largely a tale of work as a collection of individual jobs. A variety of global forces unfolding over the last two decades, however, has pushed organizations worldwide to restructure work around teams, to enable more rapid, flexible, and adaptive responses to the unexpected. This shift in the structure of work has made team effectiveness a salient organizational concern.Teams touch our lives everyday and their effectiveness is important to well-being across a wide range of societal functions. There is over 50 years of psychological research—literally thousands of studies—fo...


title: An Approach to Capture Role-Based Access Control Models from Spring Web Applications

abstract: To mitigate potential misinterpretation and security violations, software developers should use tools that reflect the state of web applications and visualise them as graphical models. Modelling helps to ensure that functionality and access control mechanisms are consistently interconnected. In this paper, we propose an approach to support Web application development using the Spring platform. Our proposal is supported by the Eclipse IDE plugin tool, which recognises Spring Security configuration captures, its notations, and visualises them in the role-based access control (RBAC) models. The RBAC models are represented using SecureUML modelling language. The plugin is validated through survey taken by software developers.


Teachers' beliefs about purposes as reflected in teaching practices : a study in elementary school physical education


title: A Hybrid Approach to Rumour Detection in Microblogging Platforms

abstract: Microblogging platforms facilitate fast and frequent communication among very large numbers of users. Rumours, especially in times of crisis, tend to spread quickly, causing confusion, and impair people's ability to make decisions. Hence, it is of utmost importance to automatically detect a rumour as soon as possible. Using the PHEME dataset that contains rumours and non-rumours pertaining to five major events, we have developed a rumour detection system that classifies posts from Twitter, a popular microblogging website. We have first analyzed and ranked a number of content-based and user-based features. Some content-based features are derived using natural language processing techniques. We then trained multiple machine learning models (Naive Bayes, Random Forests and Support Vector Machines) using different combinations of the features. Finally, we compared the performance of these models. The performance of the models on one such event resulted in 78% accuracy.


title: Fast Mode Decision In Hevc Intra Prediction, Using Region Wise CNN Feature Classification

abstract: The computational time of HEVC encoder is increased mainly because of hierarchical quadtree based structure, recursive search for finding best coding units (CU), and the exhaustive prediction mode search upto 35. These advances improve coding efficiency but result in a high computational complexity. Therefore we propose \quad a convolutional neural network (CNN) based algorithm which learns the regionwise image features and performs a classification job. These classification results are later used in the encoder downstream systems for finding the optimal CUs in each of the tree blocks, and subsequently reduces number of prediction modes. For ourmodel training, we gathered a new dataset which includes diverse images for better generalization of our results. The experimental results show that our proposed algorithm reduces encoder time saving up to 66.89 % with a minimal BD-BR loss of 1.31% over the state-of-the-art machine learning approaches and reduces mode selection by 45.83 % with respect to HEVC baseline.


title: Cardiorespiratory fitness estimation in free-living using wearable sensors

abstract: HighlightsUsed machine learning methods to determine multiple level of context in free living and contextualize heart rate data.Estimated cardiorespiratory fitness (CRF) using contextualized heart rate in free living, without laboratory protocols.Reduced CRF estimation error by up to 22.6% compared to other methods.The proposed CRF estimation method does not require specific exercise and was validated against VO2max. ObjectiveIn this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. MethodsOur methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. ResultsWe show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. ConclusionsOur investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.


title: HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards.

abstract: Reinforcement Learning (RL) struggles in problems with delayed rewards, and one approach is to segment the task into sub-tasks with incremental rewards. We propose a framework called Hierarchical Inverse Reinforcement Learning (HIRL), which is a model for learning sub-task structure from demonstrations. HIRL decomposes the task into sub-tasks based on transitions that are consistent across demonstrations. These transitions are defined as changes in local linearity w.r.t to a kernel function. Then, HIRL uses the inferred structure to learn reward functions local to the sub-tasks but also handle any global dependencies such as sequentiality. ::: We have evaluated HIRL on several standard RL benchmarks: Parallel Parking with noisy dynamics, Two-Link Pendulum, 2D Noisy Motion Planning, and a Pinball environment. In the parallel parking task, we find that rewards constructed with HIRL converge to a policy with an 80% success rate in 32% fewer time-steps than those constructed with Maximum Entropy Inverse RL (MaxEnt IRL), and with partial state observation, the policies learned with IRL fail to achieve this accuracy while HIRL still converges. We further find that that the rewards learned with HIRL are robust to environment noise where they can tolerate 1 stdev. of random perturbation in the poses in the environment obstacles while maintaining roughly the same convergence rate. We find that HIRL rewards can converge up-to 6x faster than rewards constructed with IRL.


title: Direct Antisite Formation in Electron Irradiation of GaAs

abstract: We investigate the antisite formation in GaAs by molecular dynamics simulation with a realistic many-atom potential. The recoil energies are chosen to correspond to the values encountered in electron irradiation experiments. The probability of forming antisites directly during the cascade is substantial. The antisite defects are stable and are likely to survive during long-term annealing. We estimate the angle-dependent threshold for antisite formation and discuss the creation mechanism. The cross sections for antisite and vacancy formation are compared.


title: Transport on Riemannian Manifold for Connectivity-Based Brain Decoding

abstract: There is a recent interest in using functional magnetic resonance imaging (fMRI) for decoding more naturalistic, cognitive states, in which subjects perform various tasks in a continuous, self-directed manner. In this setting, the set of brain volumes over the entire task duration is usually taken as a single sample with connectivity estimates, such as Pearson's correlation, employed as features. Since covariance matrices live on the positive semidefinite cone, their elements are inherently inter-related. The assumption of uncorrelated features implicit in most classifier learning algorithms is thus violated. Coupled with the usual small sample sizes, the generalizability of the learned classifiers is limited, and the identification of significant brain connections from the classifier weights is nontrivial. In this paper, we present a Riemannian approach for connectivity-based brain decoding. The core idea is to project the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. For this, we propose a matrix whitening transport, and compare it against parallel transport implemented via the Schild's ladder algorithm. To validate our classification approach, we apply it to fMRI data acquired from twenty four subjects during four continuous, self-driven tasks. We show that our approach provides significantly higher classification accuracy than directly using Pearson's correlation and its regularized variants as features. To facilitate result interpretation, we further propose a non-parametric scheme that combines bootstrapping and permutation testing for identifying significantly discriminative brain connections from the classifier weights. Using this scheme, a number of neuro-anatomically meaningful connections are detected, whereas no significant connections are found with pure permutation testing.


title: Condom Versus Indwelling Urinary Catheters: A Randomized Trial

abstract: OBJECTIVES: To compare condom and indwelling urinary catheters in terms of infection risk and patient satisfaction. ::: ::: DESIGN: A prospective, randomized, unblinded, controlled trial. ::: ::: SETTING: An academically affiliated Veterans Affairs Medical Center. ::: ::: PARTICIPANTS: Hospitalized men aged 40 and older who required a urinary collection device. ::: ::: MEASUREMENTS: The incidence of adverse outcomes (bacteriuria, symptomatic urinary tract infection (UTI), or death) and patient device-related satisfaction as determined according to a questionnaire. Dementia status was recorded to assess effect modification by the presence of dementia. ::: ::: RESULTS: Seventy-five subjects were randomized: 41 receiving an indwelling catheter and 34 a condom catheter. The incidence of an adverse outcome was 131/1,000 patient-days with an indwelling catheter and 70/1,000 patient-days with a condom catheter (P=.07). The median time to an adverse event was 7 days in the indwelling group and 11 days in the condom group. After adjusting for other risk factors, it was found that condom catheter use reduced adverse outcomes (P=.04). Patients without dementia who had an indwelling catheter were approximately five times as likely to develop bacteriuria or symptomatic UTI or to die (hazard ratio=4.84, 95% confidence interval=1.46–16.02) as those with a condom catheter (P=.01). Patients reported that condom catheters were more comfortable (P=.02) and less painful (P=.02) than indwelling catheters. ::: ::: CONCLUSION: The use of condom catheters is less likely to lead to bacteriuria, symptomatic UTI, or death than the use of indwelling catheters. This protection is especially apparent in men without dementia.


title: Heap Taichi: exploiting memory allocation granularity in heap-spraying attacks

abstract: Heap spraying is an attack technique commonly used in hijacking browsers to download and execute malicious code. In this attack, attackers first fill a large portion of the victim process's heap with malicious code. Then they exploit a vulnerability to redirect the victim process's control to attackers' code on the heap. Because the location of the injected code is not exactly predictable, traditional heap-spraying attacks need to inject a huge amount of executable code to increase the chance of success. Injected executable code usually includes lots of NOP-like instructions leading to attackers' shellcode. Targeting this attack characteristic, previous solutions detect heap-spraying attacks by searching for the existence of such large amount of NOP sled and other shellcode. In this paper, we analyze the implication of modern operating systems' memory allocation granularity and present Heap Taichi, a new heap spraying technique exploiting the weakness in memory alignment. We describe four new heap object structures that can evade existing detection tools, as well as proof-of-concept heap-spraying code implementing our technique. Our research reveals that a large amount of NOP sleds is not necessary for a reliable heap-spraying attack. In our experiments, we showed that our heap-spraying attacks are a realistic threat by evading existing detection mechanisms. To detect and prevent the new heap-spraying attacks, we propose enhancement to existing approaches and propose to use finer memory allocation granularity at memory managers of all levels. We also studied the impact of our solution on system performance.


title: High-Resistivity SOI CMOS Cellular Antenna Switches

abstract: Results for cellular antenna switches using high- resistivity silicon-on-insulator (SOI) CMOS technology are presented. The performance of SOI RF switch FETs is presented and compared to a production GaAs pHEMT technology. Data from prototype high-resistivity SOI RF switch designs is presented and compared to pHEMT based designs.


On Real-Time LIDAR Data Segmentation and Classification


title: Accurate Junction Detection and Characterization in Natural Images

abstract: Accurate junction detection and characterization are of primary importance for several aspects of scene analysis, including depth recovery and motion analysis. In this work, we introduce a generic junction analysis scheme. The first asset of the proposed procedure is an automatic criterion for the detection of junctions, permitting to deal with textured parts in which no detection is expected. Second, the method yields a characterization of L-, Y- and X- junctions, including a precise computation of their type, localization and scale. Contrary to classical approaches, scale characterization does not rely on the linear scale-space. First, an a contrario approach is used to compute the meaningfulness of a junction. This approach relies on a statistical modeling of suitably normalized gray level gradients. Then, exclusion principles between junctions permit their precise characterization. We give implementation details for this procedure and evaluate its efficiency through various experiments.


title: Programs for machine learning Part I

abstract: This paper reports on a proposed schema and gives some detailed specifications for constructing a learning system by means of programming a computer. We have tried to separate learning processes and problem-solving techniques from specific problem content in order to achieve generality, i.e., in order to achieve a system capable of performing in a wide variety of learning and problem-solving situations. Behavior of the system is determined by both a direct and an indirect means. The former involves detailed, explicit specification of responses or response patterns in the form of built-in programs. The indirect means is by programs representing three mechanisms: a “community unit” (a program-providing mechanism), a planning mechanism, and an induction mechanism. These mechanisms have in common the following features: (1) a directly given repertory of response patterns; (2) general and less explicitly specified decision making rules and hierarchically distributed authority for decision making; (3) an ability to delegate some control over the system's behavior to the environment; and (4) a self-modifying ability which allows the decision-making rules and the repertory of response patterns to adapt and grow. In Part I of this paper, the community unit is described and an illustration of its operation is given. It is presented in a schematized framework as a team of routines connected by first and second-order feedback loops. The function of the community unit is to provide higher-level programs (its environment or customers) with programs capable of performing requested tasks, or to perform a customer-stipulated task by executing a program. If the community unit does not have a ready-made program in stock to fill a particular request, internal programming will be performed, i.e., the community unit will have to construct a program, and debug it, before outputting or executing it. The primary purpose of internal programming is to assist higher-level programs in performing tasks for which detailed preplanning by an external programmer is either impossible or impractical. Some heuristics are suggested for enabling the community unit to search for a usable sequence of operations more efficiently than if it were to search simply by exhaustive or random trial and error. These heuristics are of a step-by-step nature. For complex problems, however, such step-by-step heuristics alone will fail unless there is also a mechanism for analyzing problem structure and placing guideposts on the road to the goal. A planning mechanism capable of doing this is proposed in Part II. Under the control of a higher-level program which specifies the level of detail required in a plan being developed, this planning mechanism is to break up problems into a hierarchy of subproblems each by itself presumably easier to solve than the original problem. To manage classes of problems and to make efficient use of past experience, an induction mechanism is proposed in Part II. An illustration is given of the induction mechanism solving a specific sequence of tasks. The system is currently being programmed and tested in IPL-V on the Philco 2000 computer. The current stage of the programming effort is reported in an epilogue to Part II.


title: Low-rank matrix completion via preconditioned optimization on the Grassmann manifold

abstract: We address the numerical problem of recovering large matrices of low rank when most of the entries are unknown. We exploit the geometry of the low-rank constraint to recast the problem as an unconstrained optimization problem on a single Grassmann manifold. We then apply second-order Riemannian trust-region methods (RTRMC 2) and Riemannian conjugate gradient methods (RCGMC) to solve it. A preconditioner for the Hessian is introduced that helps control the conditioning of the problem and we detail preconditioned versions of Riemannian optimization algorithms. The cost of each iteration is linear in the number of known entries. The proposed methods are competitive with state-of-the-art algorithms on a wide range of problem instances. In particular, they perform well on rectangular matrices. We further note that second-order and preconditioned methods are well suited to solve badly conditioned matrix completion tasks.


title: Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data

abstract: We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting “machines reading regression data” in reach.


Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers


title: A Review on the Role of Vibrational Spectroscopy as An Analytical Method to Measure Starch Biochemical and Biophysical Properties in Cereals and Starchy Foods

abstract: Starch is the major component of cereal grains and starchy foods, and changes in its biophysical and biochemical properties (e.g., amylose, amylopectin, pasting, gelatinization, viscosity) will have a direct effect on its end use properties (e.g., bread, malt, polymers). The use of rapid and non-destructive methods to study and monitor starch properties, such as gelatinization, retrogradation, water absorption in cereals and starchy foods, is of great interest in order to improve and assess their quality. In recent years, near infrared reflectance (NIR) and mid infrared (MIR) spectroscopy have been explored to predict several quality parameters, such as those generated by instrumental methods commonly used in routine analysis like the rapid visco analyser (RVA) or viscometers. In this review, applications of both NIR and MIR spectroscopy to measure and monitor starch biochemical (amylose, amylopectin, starch) and biophysical properties (e.g., pasting properties) will be presented and discussed.


title: On the risk prediction and analysis of soft information in finance reports

abstract: We attempt in this paper to utilize soft information in financial reports to analyze financial risk among companies. Specifically, on the basis of the text information in financial reports, which is the so-called soft information, we apply analytical techniques to study relations between texts and financial risk. Furthermore, we conduct a study on financial sentiment analysis by using a finance-specific sentiment lexicon to examine the relations between financial sentiment words and financial risk. A large collection of financial reports published annually by publicly-traded companies is employed to conduct our experiments; moreover, two analytical techniques – regression and ranking methods – are applied to conduct these analyses. The experimental results show that, based on a bag-of-words model, using only financial sentiment words results in performance comparable to using the whole texts; this confirms the importance of financial sentiment words with respect to risk prediction. In addition to this performance comparison, via the learned models, we draw attention to some strong and interesting correlations between texts and financial risk. These valuable findings yield greater insight and understanding into the usefulness of soft information in financial reports and can be applied to a broad range of financial and accounting applications.


title: Supporting soft real-time tasks in the xen hypervisor

abstract: Virtualization technology enables server consolidation and has given an impetus to low-cost green data centers. However, current hypervisors do not provide adequate support for real-time applications, and this has limited the adoption of virtualization in some domains. Soft real-time applications, such as media-based ones, are impeded by components of virtualization including low-performance virtualization I/O, increased scheduling latency, and shared-cache contention. The virtual machine scheduler is central to all these issues. The goal in this paper is to adapt the virtual machine scheduler to be more soft-real-time friendly. We improve two aspects of the VMM scheduler -- managing scheduling latency as a first-class resource and managing shared caches. We use enterprise IP telephony as an illustrative soft real-time workload and design a scheduler S that incorporates the knowledge of soft real-time applications in all aspects of the scheduler to support responsiveness. For this we first define a laxity value that can be interpreted as the target scheduling latency that the workload desires. The load balancer is also designed to minimize the latency for real-time tasks. For cache management, we take cache-affinity into account for real time tasks and load-balance accordingly to prevent cache thrashing. We measured cache misses and demonstrated that cache management is essential for soft real time tasks. Although our scheduler S employs a different design philosophy, interestingly enough it can be implemented with simple modifications to the Xen hypervisor's credit scheduler. Our experiments demonstrate that the Xen scheduler with our modifications can support soft real-time guests well, without penalizing non-real-time domains.


title: Research Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using

abstract: We describe here a multiplexed protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures. At the core of this methodology is a multiplexed set of isobaric reagents that yield amine-derivatized peptides. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantitation. In this study, we have examined the global protein expression of a wild-type yeast strain and the isogenic upf1Delta and xrn1Delta mutant strains that are defective in the nonsense-mediated mRNA decay and the general 5' to 3' decay pathways, respectively. We also demonstrate the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards. We find that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression.


title: Hybrid CMOS/memristor circuits

abstract: This is a brief review of recent work on the prospective hybrid CMOS/memristor circuits. Such hybrids combine the flexibility, reliability and high functionality of the CMOS subsystem with very high density of nanoscale thin film resistance switching devices operating on different physical principles. Simulation and initial experimental results demonstrate that performance of CMOS/memristor circuits for several important applications is well beyond scaling limits of conventional VLSI paradigm.


title: A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network

abstract: Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.


title: System design and performance evaluation of high altitude platform: Link budget and power budget

abstract: High altitude platform (HAP) is an emerging technology to cope with the increasing demand of multimedia applications, capacity and coverage of wireless service communication. The purpose of HAP is to provide broadband communication over wide area from a single node. Link budgeting, power system designing and planning for a 50 kg HAP has been presented. Moreover protocols for communicating with HAP are also investigated. The proposed link budget is based on simplex model over 31/28 GHz frequency band as this has been allocated by FCC as well as Frequency Allocation Board Pakistan for communication with HAPs. Power budget is presented for solar powered autonomous or unmanned airborne aircraft for line of sight (LOS) condition in the stratosphere at an altitude of 17 Km above ground level where air turbulence is nearly static and minimum cloud are present to maximize solar energy. Link budget with transmission power of 30 dBm using QPSK for varying code rates is proposed to minimize power consumption. The power system is designed for 10 hours of daylight and 14 hours of the night operation, direct energy transfer (DET) architecture is used with battery banks working at 80% depth of discharge (DOD). For communication, Digital Video Broadcasting protocol (DVB-S2) and Worldwide Interoperability for Microwave Access (WiMAX) 802.16e using QPSK modulation scheme are used. Number of simulations with varying code rates to minimize bit error rate, minimizing power consumption are carried out and results are presented. Results shows that DVB-S2 gives better performance with minimum signal energy which minimize power consumption.


An Overview of Localization Methods for Multi-Agent Systems


title: Seq-NMS for Video Object Detection

abstract: Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip. Recently, there have been major advances for doing object detection in a single image. These methods typically contain three phases: (i) object proposal generation (ii) object classification and (iii) post-processing. We propose a modification of the post-processing phase that uses high-scoring object detections from nearby frames to boost scores of weaker detections within the same clip. We show that our method obtains superior results to state-of-the-art single image object detection techniques. Our method placed 3rd in the video object detection (VID) task of the ImageNet Large Scale Visual Recognition Challenge 2015 (ILSVRC2015).


title: Accelerating Relational Databases by Leveraging Remote Memory and RDMA

abstract: Memory is a crucial resource in relational databases (RDBMSs). When there is insufficient memory, RDBMSs are forced to use slower media such as SSDs or HDDs, which can significantly degrade workload performance. Cloud database services are deployed in data centers where network adapters supporting remote direct memory access (RDMA) at low latency and high bandwidth are becoming prevalent. We study the novel problem of how a Symmetric Multi-Processing (SMP) RDBMS, whose memory demands exceed locally-available memory, can leverage available remote memory in the cluster accessed via RDMA to improve query performance. We expose available memory on remote servers using a lightweight file API that allows an SMP RDBMS to leverage the benefits of remote memory with modest changes. We identify and implement several novel scenarios to demonstrate these benefits, and address design challenges that are crucial for efficient implementation. We implemented the scenarios in Microsoft SQL Server engine and present the first end-to-end study to demonstrate benefits of remote memory for a variety of micro-benchmarks and industry-standard benchmarks. Compared to using disks when memory is insufficient, we improve the throughput and latency of queries with short reads and writes by 3X to 10X, while improving the latency of multiple TPC-H and TPC-DS queries by 2X to 100X.


title: Traumatic oral mucosal lesions: a mini review and clinical update.

abstract: Oral mucosal lesions are a common occurrence and very often dentists are called upon to see such patients. Among many causes trauma is one of the leading for oral mucosal diseases. Oral traumatic lesions are diverse in which some present as acute lesions while the majority are chronic lesions. Clinical presentation of traumatic lesions vary significantly and most of the occasions the cause and the effect can be established with thorough history and clinical examination. Although, biopsy of such lesions are not required in most of the occasions some may warrant histological investigations to exclude conditions which clinically mimic traumatic lesions. This paper provides an overview of common and some rare traumatic conditions of the oral mucosa.


Practical Option Pricing with Support Vector Regression and MART


title: Repurposing the CRISPR-Cas9 system for targeted DNA methylation

abstract: Epigenetic studies relied so far on correlations between epigenetic marks and gene expression pattern. Technologies developed for epigenome editing now enable direct study of functional relevance of precise epigenetic modifications and gene regulation. The reversible nature of epigenetic modifications, including DNA methylation, has been already exploited in cancer therapy for remodeling the aberrant epigenetic landscape. However, this was achieved non-selectively using epigenetic inhibitors. Epigenetic editing at specific loci represents a novel approach that might selectively and heritably alter gene expression. Here, we developed a CRISPR-Cas9-based tool for specific DNA methylation consisting of deactivated Cas9 (dCas9) nuclease and catalytic domain of the DNA methyltransferase DNMT3A targeted by co-expression of a guide RNA to any 20 bp DNA sequence followed by the NGG trinucleotide. We demonstrated targeted CpG methylation in a ∼35 bp wide region by the fusion protein. We also showed that multiple guide RNAs could target the dCas9-DNMT3A construct to multiple adjacent sites, which enabled methylation of a larger part of the promoter. DNA methylation activity was specific for the targeted region and heritable across mitotic divisions. Finally, we demonstrated that directed DNA methylation of a wider promoter region of the target loci IL6ST and BACH2 decreased their expression.


title: Weakly Supervised Deep Matrix Factorization for Social Image Understanding

abstract: The number of images associated with weakly supervised user-provided tags has increased dramatically in recent years. User-provided tags are incomplete, subjective and noisy. In this paper, we focus on the problem of social image understanding, i.e., tag refinement, tag assignment, and image retrieval. Different from previous work, we propose a novel weakly supervised deep matrix factorization algorithm, which uncovers the latent image representations and tag representations embedded in the latent subspace by collaboratively exploring the weakly supervised tagging information, the visual structure, and the semantic structure. Due to the well-known semantic gap, the hidden representations of images are learned by a hierarchical model, which are progressively transformed from the visual feature space. It can naturally embed new images into the subspace using the learned deep architecture. The semantic and visual structures are jointly incorporated to learn a semantic subspace without overfitting the noisy, incomplete, or subjective tags. Besides, to remove the noisy or redundant visual features, a sparse model is imposed on the transformation matrix of the first layer in the deep architecture. Finally, a unified optimization problem with a well-defined objective function is developed to formulate the proposed problem and solved by a gradient descent procedure with curvilinear search. Extensive experiments on real-world social image databases are conducted on the tasks of image understanding: image tag refinement, assignment, and retrieval. Encouraging results are achieved with comparison with the state-of-the-art algorithms, which demonstrates the effectiveness of the proposed method.


title: iCONCUR: informed consent for clinical data and bio-sample use for research

abstract: Background: Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings. Methods: We developed a web-based tiered informed consent tool called informed consent for clinical data and bio-sample use for research (iCONCUR) that honors granular patient preferences for use of electronic health record data in research. We piloted this tool in 4 outpatient clinics of an academic medical center. Results: Of patients offered access to iCONCUR, 394 agreed to participate in this study, among whom 126 patients accessed the website to modify their records according to data category and data recipient. The majority consented to share most of their data and specimens with researchers. Willingness to share was greater among participants from an Human Immunodeficiency Virus (HIV) clinic than those from internal medicine clinics. The number of items declined was higher for for-profit institution recipients. Overall, participants were most willing to share demographics and body measurements and least willing to share family history and financial data. Participants indicated that having granular choices for data sharing was appropriate, and that they liked being informed about who was using their data for what purposes, as well as about outcomes of the research. Conclusion: This study suggests that a tiered electronic informed consent system is a workable solution that respects patient preferences, increases satisfaction, and does not significantly affect participation in research.


title: The Reactive Species Interactome: Evolutionary Emergence, Biological Significance, and Opportunities for Redox Metabolomics and Personalized Medicine

abstract: Abstract Significance: Oxidative stress is thought to account for aberrant redox homeostasis and contribute to aging and disease. However, more often than not, administration of antioxidants is ineffective, suggesting that our current understanding of the underlying regulatory processes is incomplete. Recent Advances: Similar to reactive oxygen species and reactive nitrogen species, reactive sulfur species are now emerging as important signaling molecules, targeting regulatory cysteine redox switches in proteins, affecting gene regulation, ion transport, intermediary metabolism, and mitochondrial function. To rationalize the complexity of chemical interactions of reactive species with themselves and their targets and help define their role in systemic metabolic control, we here introduce a novel integrative concept defined as the reactive species interactome (RSI). The RSI is a primeval multilevel redox regulatory system whose architecture, together with the physicochemical characteristics of its constitu...


Anaerobic digestion of agricultural wastes.


title: Farmbeats: an IoT platform for data-driven agriculture

abstract: Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In this paper, we present FarmBeats, an end-to-end IoT platform for agriculture that enables seamless data collection from various sensors, cameras and drones. FarmBeats's system design that explicitly accounts for weather-related power and Internet outages has enabled six month long deployments in two US farms.


title: HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving

abstract: Large computer-understandable proofs consist of millions of intermediate logical steps. The vast majority of such steps originate from manually selected and manually guided heuristics applied to intermediate goals. So far, machine learning has generally not been used to filter or generate these steps. In this paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. We make this dataset publicly available under the BSD license. We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving. We also benchmark a set of simple baseline machine learning models suited for the tasks (including logistic regression, convolutional neural networks and recurrent neural networks). The results of our baseline models show the promise of applying machine learning to HOL theorem proving.


title: What went wrong? A survey of problems in game development

abstract: Despite its growth and profitability, many reports about game projects show that their production is not a simple task, but one beset by common problems and still distant from having a healthy and synergetic work process. The goal of this article is to survey the problems in the development process of electronic games, which are mainly collected from game postmortems, by exploring their similarities and differences to well-known problems in traditional information systems.


title: Language Geometry Using Random Indexing

abstract: Random Indexing is a simple implementation of Random Projections with a wide range of applications. It can solve a variety of problems with good accuracy without introducing much complexity. Here we demonstrate its use for identifying the language of text samples, based on a novel method of encoding letter N-grams into high-dimensional Language Vectors. Further, we show that the method is easily implemented and requires little computational power and space. As proof of the method’s statistical validity, we show its success in a language-recognition task. On a difficult data set of 21,000 short sentences from 21 different languages, we achieve 97.4% accuracy, comparable to state-of-the-art methods.


title: Upregulation of miR-196a and HOTAIR Drive Malignant Character in Gastrointestinal Stromal Tumors

abstract: Large intergenic noncoding RNAs (lincRNA) have been less studied than miRNAs in cancer, although both offer considerable theranostic potential. In this study, we identified frequent upregulation of miR-196a and lincRNA HOTAIR in high-risk gastrointestinal stromal tumors (GIST). Overexpression of miR-196a was associated with high-risk grade, metastasis and poor survival among GIST specimens. miR-196a genes are located within the HOX gene clusters and microarray expression analysis revealed that the HOXC and HOTAIR gene were also coordinately upregulated in GISTs which overexpress miR-196a. In like manner, overexpression of HOTAIR was also strongly associated with high-risk grade and metastasis among GIST specimens. RNA interference–mediated knockdown of HOTAIR altered the expression of reported HOTAIR target genes and suppressed GIST cell invasiveness. These findings reveal concurrent overexpression of HOX genes with noncoding RNAs in human cancer in this setting, revealing miR-196a and HOTAIR as potentially useful biomarkers and therapeutic targets in malignant GISTs. Cancer Res; 72(5); 1126–36. ©2012 AACR .


title: Privacy Preserving Data Analytics for Smart Homes

abstract: A framework for maintaining security & preserving privacy for analysis of sensor data from smart homes, without compromising on data utility is presented. Storing the personally identifiable data as hashed values withholds identifiable information from any computing nodes. However the very nature of smart home data analytics is establishing preventive care. Data processing results should be identifiable to certain users responsible for direct care. Through a separate encrypted identifier dictionary with hashed and actual values of all unique sets of identifiers, we suggest re-identification of any data processing results. However the level of re-identification needs to be controlled, depending on the type of user accessing the results. Generalization and suppression on identifiers from the identifier dictionary before re-introduction could achieve different levels of privacy preservation. In this paper we propose an approach to achieve data security & privacy through out the complete data lifecycle: data generation/collection, transfer, storage, processing and sharing.


title: PriVaricator: Deceiving Fingerprinters with Little White Lies

abstract: Researchers have shown that, in recent years, unwanted web tracking is on the rise, with browser-based fingerprinting being adopted by more and more websites as a viable alternative to third-party cookies. In this paper we propose PriVaricator, a solution to the problem of browser-based fingerprinting. A key insight is that when it comes to web tracking, the real problem with fingerprinting is not uniqueness of a fingerprint, it is linkability, i.e., the ability to connect the same fingerprint across multiple visits. Thus, making fingerprints non-deterministic also makes them hard to link across browsing sessions. In PriVaricator we use the power of randomization to "break" linkability by exploring a space of parameterized randomization policies. We evaluate our techniques in terms of being able to prevent fingerprinting and not breaking existing (benign) sites. The best of our randomization policies renders all the fingerprinters we tested ineffective, while causing minimal damage on a set of 1000 Alexa sites on which we tested, with no noticeable performance overhead.


title: A link-correlation-aware cross-layer protocol for IoT devices

abstract: The Internet of Things (IoT) applications is envisioned to require higher throughput protocols because of the increasing data amount. To significantly enhance the network throughput between IoT devices, this paper proposes a new link-layer data forwarding technique that is aware of link correlation (LC) and supports receiver initiated acknowledgement (RI-ACK). We also propose a multicast communication protocol based on LC-aware forwarding and RI-ACKs to further enhance the throughput. In a simulation study, our protocol improves the throughput by 35%–55% comparing to a state-of-the-art baseline.


title: Automatic acknowledgement indexing: expanding the semantics of contribution in the CiteSeer digital library

abstract: Acknowledgements in research publications, like citations, indicate influential contributions to scientific work; however, large-scale acknowledgement analyses have traditionally been impractical due to the high cost of manual information extraction. In this paper we describe a mixture method for automatically mining acknowledgements from research documents using a combination of a Support Vector Machine and regular expressions. The algorithm has been implemented as a plug-in to the CiteSeer Digital Library and the extraction results have been integrated with the traditional metadata and citation index of the CiteSeer system. As a demonstration, we use CiteSeer's autonomous citation indexing (ACI) feature to measure the relative impact of acknowledged entities, and present the top twenty acknowledged entities within the archive.


Emotional Intelligence: The Context for Successful Nursing Leadership: A Literature Review


title: Reconfigurable Solar Converter for PV battery application

abstract: A photovoltaic system is an arrangement of components designed to supply usable electric power using the sun as the power source. PV systems have many advantages like it provide clean energy, it can be made available almost anywhere there is sunlight, the operating and maintenance costs for PV panels are considered to be low. The conventionally used converters in solar systems have many disadvantages such as multiple conversion stages, complex control, less efficient, high cost, bulky, etc. A new converter, for photovoltaic battery applications, called Reconfigurable Solar Converter (RSC), uses only single-stage three phase grid-tie solar PV converter to perform dc/ac and dc/dc operations, overcomes the drawback of conventional converters. The RSC concept is based on energy storage integration for utility-scale solar PV systems and it provides a minimal overlap between the PV energy storage and energy release time. In this paper a RSC which uses minimum number of conversion stages which in turn improves the efficiency and reduces the cost, weight and volume has been realized.


title: YSmart: Yet Another SQL-to-MapReduce Translator

abstract: MapReduce has become an effective approach to big data analytics in large cluster systems, where SQL-like queries play important roles to interface between users and systems. However, based on our Face book daily operation results, certain types of queries are executed at an unacceptable low speed by Hive (a production SQL-to-MapReduce translator). In this paper, we demonstrate that existing SQL-to-MapReduce translators that operate in a one-operation-to-one-job mode and do not consider query correlations cannot generate high-performance MapReduce programs for certain queries, due to the mismatch between complex SQL structures and simple MapReduce framework. We propose and develop a system called Y Smart, a correlation aware SQL-to-MapReduce translator. Y Smart applies a set of rules to use the minimal number of MapReduce jobs to execute multiple correlated operations in a complex query. Y Smart can significantly reduce redundant computations, I/O operations and network transfers compared to existing translators. We have implemented Y Smart with intensive evaluation for complex queries on two Amazon EC2 clusters and one Face book production cluster. The results show that Y Smart can outperform Hive and Pig, two widely used SQL-to-MapReduce translators, by more than four times for query execution.


title: A Novel Control Scheme of Synchronous Buck Converter for ZVS in Light-Load Condition

abstract: This paper proposes a new control technology that enables a synchronous rectifier (SR) buck converter to realize zero voltage switching (ZVS) in light load condition. The SR control technique is applicable to dc low voltage output because the replacement of output rectifier diode by MOSFET can minimize conduction losses and increase the efficiency of the whole circuit. However, the technique cannot achieve good efficiency in light load condition. The control technique introduced in this paper enables an SR buck converter to carry out ZVS in light load condition to increase efficiency. No extra auxiliary switches or RLC passive components are required; thus, it is low cost and easy to control. The output of buck converter with 5 V/25 W featured by SR is developed in this paper; the availability of the technique introduced, herein, is also proven. The experimental results are satisfactory.


Ethical theory, codes of ethics and IS practice


title: BoostFS: A Boosting-Based Irrelevant Feature Selection Algorithm

abstract: In a learning process, features play a fundamental role. In this paper, we propose a Boosting-based feature selection algorithm called BoostFS. It extends AdaBoost which is designed for classification problems to feature selection. BoostFS maintains a distribution over training samples which is initialized from the uniform distribution. In each iteration, a decision stump is trained under the sample distribution and then the sample distribution is adjusted so that it is orthogonal to the classification results of all the generated stumps. Because a decision stump can also be regarded as one selected feature, BoostFS is capable to select a subset of features that are irrelevant to each other as much as possible. Experimental results on synthetic datasets, five UCI datasets and a real malware detection dataset all show that the features selected by BoostFS help to improve learning algorithms in classification problems, especially when the original feature set contains redundant features.


title: Distance Metric Based Oversampling Method for Bioinformatics and Performance Evaluation

abstract: An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority class but significantly less accurate for the minority class. To overcome this problem, this study took several imbalanced datasets from the famed UCI datasets and designed and implemented an efficient algorithm which couples Top-N Reverse k-Nearest Neighbor (TRkNN) with the Synthetic Minority Oversampling TEchnique (SMOTE). The proposed algorithm was investigated by applying it to classification methods such as logistic regression (LR), C4.5, Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN). This research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrate that the Euclidean and Manhattan distances are not only more accurate, but also show greater computational efficiency when compared to the Chebyshev and Cosine distances. Therefore, the proposed algorithm based on TRkNN and SMOTE can be widely used to handle imbalanced datasets. Our recommendations on choosing suitable distance metrics can also serve as a reference for future studies.


title: Effects of a best-possible-self mental imagery exercise on mood and dysfunctional attitudes

abstract: Abstract Dispositional optimism has been related to positive physical and mental health outcomes, increased positive mood ratings and cognitions about the future. In order to determine the causal relation between optimism and mood and cognitions optimism should be manipulated experimentally. The current study tested the effects of a best-possible-self mental imagery exercise on affect and mood ratings and dysfunctional cognitions following a sad mood induction in undergraduate students (N=40). Participants in the experimental condition wrote about their best possible self in the future for 15 min and engaged in a mental imagery task about their best possible self in the future for 5 min in order to experimentally induce optimism. Participants in the control condition wrote about a typical day for 15 min and engaged in mental imagery about a typical day for 5 min. We assessed affect, mood and dysfunctional cognitions before and after the experimental manipulation. Participants in the experimental condition had higher positive mood ratings and higher positive affect compared to participants in the control condition. Participants in the control condition reported decreased dysfunctional cognitions whereas dysfunctional cognitions in the experimental group remained unchanged. Future studies should replicate these findings in clinical groups with more profound levels of negative affect and dysfunctional cognitions.


title: Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring

abstract: Computerized liver volumetry has been studied, because the current "gold-standard" manual volumetry is subjective and very time-consuming. Liver volumetry is done in either CT or MRI. A number of researchers have developed computerized liver segmentation in CT, but there are fewer studies on ones for MRI. Our purpose in this study was to develop a general framework for liver segmentation in both CT and MRI. Our scheme consisted of 1) an anisotropic diffusion filter to reduce noise while preserving liver structures, 2) a scale-specific gradient magnitude filter to enhance liver boundaries, 3) a fast-marching algorithm to roughly determine liver boundaries, and 4) a geodesic-active-contour model coupled with a level-set algorithm to refine the initial boundaries. Our CT database contained hepatic CT scans of 18 liver donors obtained under a liver transplant protocol. Our MRI database contains 23 patients with 1.5T MRI scanners. To establish "gold-standard" liver volumes, radiologists manually traced the contour of the liver on each CT or MR slice. We compared our computer volumetry with "gold-standard" manual volumetry. Computer volumetry in CT and MRI reached excellent agreement with manual volumetry (intra-class correlation coefficient = 0.94 and 0.98, respectively). Average user time for computer volumetry in CT and MRI was 0.57 ± 0.06 and 1.0 ± 0.13 min. per case, respectively, whereas those for manual volumetry were 39.4 ± 5.5 and 24.0 ± 4.4 min. per case, respectively, with statistically significant difference (p < .05). Our computerized liver segmentation framework provides an efficient and accurate way of measuring liver volumes in both CT and MRI.


title: Precise X-ray and video overlay for augmented reality fluoroscopy

abstract: Purpose ::: The camera-augmented mobile C-arm (CamC) augments any mobile C-arm by a video camera and mirror construction and provides a co-registration of X-ray with video images. The accurate overlay between these images is crucial to high-quality surgical outcomes. In this work, we propose a practical solution that improves the overlay accuracy for any C-arm orientation by: (i) improving the existing CamC calibration, (ii) removing distortion effects, and (iii) accounting for the mechanical sagging of the C-arm gantry due to gravity.


title: Design of a cloud robotics middleware based on web service technology

abstract: In the process of robotics applications development, Robot Operating System (ROS) has been a defactostandard tool. However, ROS nodes always run at the on-board computer. Limited resources of CPU and storage restrict the ability of robotics when performing a task. A good solution is to transfer the computing-intensive tasks to the Cloud. In view of this, we deploy ROS packages in Cloud virtual machines and encapsulate them as web services. Meanwhile, we design a middleware based on web service technology. As the core of the whole Cloud Robotics system, the middleware is responsible for parsing the Cloud Robotics task request and schedule ROS nodes in a distributed network. Experiments have been performed to validate the reliability of the middleware.


title: Analysis and Design of Mixed-Mode Operation for Noninverting Buck–Boost DC–DC Converters

abstract: A noninverting buck–boost dc–dc converter can work in the buck, boost, or buck–boost mode, but it has been analyzed that operating in the buck–boost mode has the lowest efficiency. However, if the buck–boost mode is excluded, the converter may jump between the buck and boost modes when the input voltage approaches the output voltage. This transition region is called the mixed mode, and larger output voltage ripples are expectable. In this brief, the conditions for the converter to operate in the mixed mode are analyzed, including the impact of mismatches between ramp signals, and a ramp generator is designed accordingly. Moreover, a full-cycle current-sensing circuit is proposed, and it can effectively inhibit the switching noise on the sensed current signal. The proposed chip was fabricated by the 0.35- $\mu\mbox{m}$ 2P4M 3.3-V/5-V mixed-signal polycide process. The maximal measured efficiency is 93.5%.


Visual Relationship Detection With Deep Structural Ranking


title: Augmenting photographs with textures using the Laplacian pyramid

abstract: We introduce a method to stylize photographs with auxiliary textures, by means of the Laplacian pyramid. Laplacian pyramid coefficients from a synthetic texture are combined with the coefficients from the original image by means of a smooth maximum function. The final result is a stylized image which maintains the structural characteristics from the input, including edges, color, and existing texture, while enhancing the image with additional fine-scale details. Further, we extend patch-based texture synthesis to include a guidance channel so that texture structures are aligned with an orientation field, obtained through the image structure tensor.


title: Converting a Trained Neural network to a . . .

abstract: Neural Networks are very successful in acquiring hidden knowledge in datasets. Their most important weakness is that the knowledge they acquired is represented in a form not very understandable to humans. In real life applications understandability of the knowledge acquired by a machine learning system is important. ::: In this thesis, we address the understandability problem of neural networks by converting the knowledge acquired by the neural network into a more understandable form. We do this by extracting a classical decision tree from neural network. We tested the new method on neural networks trained by using real life datasets. We showed that the decision trees extracted have high fidelity to the neural networks they are extracted from. Fidelity measures the closeness of decision tree's outputs to neural network's outputs. ::: We introduced four new splitting techniques to make DecText increase the fidelity of the extracted trees. We showed that the new methods are effective in extracting high fidelity trees. We also introduced a new discretization technique to make DecText be able to handle continuous features. New discretization technique can be used in other induction algorithms if a neural network is trained by using the dataset. We also introduced a new technique to prune the trees extracted. New pruning technique tries to find the simplest tree with the highest fidelity. ::: Another contribution of this thesis is a new feature subset selection technique. Real world classification applications usually have many features. This increases the complexity of the classification task. Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge. We tested the new technique on real world and artificial datasets and compared its results with existing methods. We showed that the new method chooses good subsets by searching fewer states than the existing methods. In the new method, we first sort the features according to their relevance and test the subsets formed by the most relevant features to find a starting subset for searching the subset space. We show that this technique speeds up the search considerably for most of the problem domains.


Islamic Beliefs in Compulsory Islamic Studies (Islamiyat) and Cognitive Development: an investigation


title: Managing Bad News in Social Media: A Case Study on Domino’s Pizza Crisis

abstract: Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its spread. We investigated the Domino’s Pizza crisis in 2009, where bad news spread rapidly through social media followed by an official apology from the company. Our work shows that bad news spreads faster than other types of information, such as an apology, and sparks a great degree of negative sentiments in the network. However, when users converse about bad news repeatedly, their negative sentiments are softened. We discuss various reactions of users towards the bad news in social media such as negative purchase intent.


title: An efficient authentication and key agreement protocol for 4G (LTE) networks

abstract: Long Term Evolution (LTE) networks designed by 3rd Generation Partnership Project (3GPP) represent a widespread technology. LTE is mainly influenced by high data rates, minimum delay and the capacity due to scalable bandwidth and its flexibility. With the rapid and widespread use LTE networks, and increase the use in data/video transmission and Internet applications in general, accordingly, the challenges of securing and speeding up data communication in such networks is also increased. Authentication in LTE networks is very important process because most of the coming attacks occur during this stage. Attackers try to be authenticated and then launch the network resources and prevent the legitimate users from the network services. The basics of Extensible Authentication Protocol-Authentication and Key Agreement (EAP-AKA) are used in LTE AKA protocol which is called Evolved Packet System AKA (EPS-AKA) protocol to secure LTE network, However it still suffers from various vulnerabilities such as disclosure of the user identity, computational overhead, Man In The Middle (MITM) attack and authentication delay. In this paper, an Efficient EPS-AKA protocol (EEPS-AKA) is proposed to overcome those problems. The proposed protocol is based on the Simple Password Exponential Key Exchange (SPEKE) protocol. Compared to previous proposed methods, our method is faster, since it uses a secret key method which is faster than certificate-based methods, In addition, the size of messages exchanged between User Equipment (UE) and Home Subscriber Server (HSS) is reduced, this reduces authentication delay and storage overhead effectively. The automated validation of internet security protocols and applications (AVISPA) tool is used to provide a formal verification. Results show that the proposed EEPS-AKA is efficient and secure against active and passive attacks.


Personalised Patient Embeddings - Towards a Whole Data Health Profile


title: Learn from experience: Probabilistic prediction of perception performance to avoid failure

abstract: Despite significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging time-varying environments. When...


title: Users’ experiences of wearable activity trackers: a cross-sectional study

abstract: Wearable activity trackers offer considerable promise for helping users to adopt healthier lifestyles. This study aimed to explore users’ experience of activity trackers, including usage patterns, sharing of data to social media, perceived behaviour change (physical activity, diet and sleep), and technical issues/barriers to use. A cross-sectional online survey was developed and administered to Australian adults who were current or former activity tracker users. Results were analysed descriptively, with differences between current and former users and wearable brands explored using independent samples t-tests, Mann-Whitney, and chi square tests. Participants included 200 current and 37 former activity tracker users (total N = 237) with a mean age of 33.1 years (SD 12.4, range 18–74 years). Fitbit (67.5%) and Garmin devices (16.5%) were most commonly reported. Participants typically used their trackers for sustained periods (5–7 months) and most intended to continue usage. Participants reported they had improved their physical activity (51–81%) more commonly than they had their diet (14–40%) or sleep (11–24%), and slightly more participants reported to value the real time feedback (89%) compared to the long-term monitoring (78%). Most users (70%) reported they had experienced functionality issues with their devices, most commonly related to battery life and technical difficulties. Results suggest users find activity trackers appealing and useful tools for increasing perceived physical activity levels over a sustained period.


title: Opinion mining of customer feedback data on the web

abstract: As people leave on the Web their opinions on products and services they have used, it has become important to develop methods of (semi-)automatically classifying and gauging them. The task of analyzing such data, collectively called customer feedback data, is known as opinion mining. Opinion mining consists of several steps, and multiple techniques have been proposed for each step. In this paper, we survey and analyze various techniques that have been developed for the key tasks of opinion mining. On the basis of our survey and analysis of the techniques, we provide an overall picture of what is involved in developing a software system for opinion mining.


title: A convolutional neural network for visual object recognition in marine sector

abstract: Object detection and recognition are crucial elements of any high level image analysis system. Convolutional Neural Networks (CNNs) or ConvNets have been applied for recognizing the category of the principal entity in an image for several years. One major benefit of convolutional networks is the use of shared weights in the intermediate convolutional layers, which reduces the required memory size and improves performance. In this work, we created a small database of vessels to analyse different aspects of CNN for recognizing the types of marine vessels in sail. A huge network like CNN may possibly be over fitted due to lack of data. We tried to overcome this serious issue by augmenting the data as well as varying the network parameters and achieved an accuracy of 81.6%. This will be further more investigated to improve the total efficacy of the system.


title: An exploratory study of data sketching for visual representation

abstract: Hand-drawn sketching on napkins or whiteboards is a common, accessible method for generating visual representations. This practice is shared by experts and non-experts and is probably one of the faster and more expressive ways to draft a visual representation of data. In order to better understand the types of and variations in what people produce when sketching data, we conducted a qualitative study. We asked people with varying degrees of visualization expertise, from novices to experts, to manually sketch representations of a small, easily understandable dataset using pencils and paper and to report on what they learned or found interesting about the data. From this study, we extract a data sketching representation continuum from numeracy to abstraction; a data report spectrum from individual data items to speculative data hypothesis; and show the correspondence between the representation types and the data reports from our results set. From these observations we discuss the participants' representations in relation to their data reports, indicating implications for design and potentially fruitful directions for research.


title: Relational concept analysis: mining concept lattices from multi-relational data

abstract: The processing of complex data is admittedly among the major concerns of knowledge discovery from data (kdd). Indeed, a major part of the data worth analyzing is stored in relational databases and, since recently, on the Web of Data. This clearly underscores the need for Entity-Relationship and rdf compliant data mining (dm) tools. We are studying an approach to the underlying multi-relational data mining (mrdm) problem, which relies on formal concept analysis (fca) as a framework for clustering and classification. Our relational concept analysis (rca) extends fca to the processing of multi-relational datasets, i.e., with multiple sorts of individuals, each provided with its own set of attributes, and relationships among those. Given such a dataset, rca constructs a set of concept lattices, one per object sort, through an iterative analysis process that is bound towards a fixed-point. In doing that, it abstracts the links between objects into attributes akin to role restrictions from description logics (dls). We address here key aspects of the iterative calculation such as evolution in data description along the iterations and process termination. We describe implementations of rca and list applications to problems from software and knowledge engineering.


title: A simple and effective unsupervised word segmentation approach

abstract: In this paper, we propose a new unsupervised approach for word segmentation. The core idea of our approach is a novel word induction criterion called WordRank, which estimates the goodness of word hypotheses (character or phoneme sequences). We devise a method to derive exterior word boundary information from the link structures of adjacent word hypotheses and incorporate interior word boundary information to complete the model. In light of WordRank, word segmentation can be modeled as an optimization problem. A Viterbistyled algorithm is developed for the search of the optimal segmentation. Extensive experiments conducted on phonetic transcripts as well as standard Chinese and Japanese data sets demonstrate the effectiveness of our approach. On the standard Brent version of Bernstein-Ratner corpora, our approach outperforms the state-of-the-art Bayesian models by more than 3%. Plus, our approach is simpler and more efficient than the Bayesian methods. Consequently, our approach is more suitable for real-world applications.


title: SHERIFF: precise detection and automatic mitigation of false sharing

abstract: False sharing is an insidious problem for multithreaded programs running on multicore processors, where it can silently degrade performance and scalability. Previous tools for detecting false sharing are severely limited: they cannot distinguish false sharing from true sharing, have high false positive rates, and provide limited assistance to help programmers locate and resolve false sharing. This paper presents two tools that attack the problem of false sharing: Sheriff-Detect and Sheriff-Protect. Both tools leverage a framework we introduce here called Sheriff. Sheriff breaks out threads into separate processes, and exposes an API that allows programs to perform per-thread memory isolation and tracking on a per-page basis. We believe Sheriff is of independent interest. Sheriff-Detect finds instances of false sharing by comparing updates within the same cache lines by different threads, and uses sampling to rank them by performance impact. Sheriff-Detect is precise (no false positives), runs with low overhead (on average, 20%), and is accurate, pinpointing the exact objects involved in false sharing. We present a case study demonstrating Sheriff-Detect's effectiveness at locating false sharing in a variety of benchmarks. Rewriting a program to fix false sharing can be infeasible when source is unavailable, or undesirable when padding objects would unacceptably increase memory consumption or further worsen runtime performance. Sheriff-Protect mitigates false sharing by adaptively isolating shared updates from different threads into separate physical addresses, effectively eliminating most of the performance impact of false sharing. We show that Sheriff-Protect can improve performance for programs with catastrophic false sharing by up to 9×, without programmer intervention.


title: Physical activity recognition from accelerometer data using a multi-scale ensemble method

abstract: Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.


title: Clickonomics: Determining the Effect of Anti-Piracy Measures for One-Click Hosting.

abstract: Piracy is a mass phenomenon on the Internet today. Various file sharing platforms offer free access to unauthorised copies of copyrighted works such as media content and software. Copyright holders are using a range of legal and technical methods to protect their rights, and they are lobbying for legislation that would give them additional ways of enforcing their copyright online. However, little is known about how effective current forms of copyright enforcement are and how enduring the effect of proposed new measures can be. In this paper, we report on the results of a largescale measurement study of the piracy ecosystem that has emerged around One-Click Hosters or “cyberlockers” such as Rapidshare, Megaupload, Mediafire, and Hotfile. Our data shows that current anti-piracy efforts are visible, but their overall impact appears to be rather limited. Furthermore, our analysis of the file sharing ecosystem suggests that future antipiracy measures that are currently under discussion may not be as successful as their proponents might expect. Ongoing legal proceedings and efforts by payment processors, however, may force hosters to increase their own anti-piracy efforts.


title: Exact robot navigation using artificial potential functions

abstract: A methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design is presented. Complete information about a freespace and goal is encoded in the form of a special artificial potential function, called a navigation function, that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. The navigation function automatically gives rise to a bounded-torque feedback controller for the robot's actuators that guarantees collision-free motion and convergence to the destination from almost all initial free configurations. A formula for navigation functions that guide a point-mass robot in a generalized sphere world is developed. The simplest member of this family is a space obtained by puncturing a disk by an arbitrary number of smaller disjoint disks representing obstacles. The other spaces are obtained from this model by a suitable coordinate transformation. Simulation results for planar scenarios are provided. >


title: Using Cameras to Improve Wi-Fi Based Indoor Positioning

abstract: Indoor positioning systems are increasingly being deployed to enable indoor navigation and other indoor location-based services. Systems based on Wi-Fi and video cameras rely on different technologies and techniques and have so far been developed independently by different research communities; we show that integrating information provided by a video system into a Wi-Fi based system increases its maintainability and avoid drops in accuracy over time. Specifically, we consider a Wi-Fi system that uses fingerprints measurements collected in the space for positioning. We improve the system’s room-level accuracy by means of automatic, video-driven collection of fingerprints. Our method is able to relate a Wi-Fi user to unidentified movements detected by cameras by exploiting the existing Wi-Fi system, thus generating fingerprints automatically. This use of video for fingerprint collection reduces the need for manual collection and allows online updating of fingerprints. Hence, increasing system accuracy. We report on an empirical study that shows that automatic fingerprinting induces only few false positives and yields a substantial accuracy improvement.


title: An Over-110-GHz-Bandwidth 2:1 Analog Multiplexer in 0.25-μm InP DHBT Technology

abstract: This paper presents an over-110-GHz-bandwidth 2:1 analog multiplexer (AMUX) for ultra-broadband digital-to-analog (D/A) conversion subsystems. The AMUX was designed and fabricated by using newly developed $\pmb{0.25-\mu \mathrm{m}}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $\pmb{f_{\mathrm{T}}}$ and $\pmb{ f\displaystyle \max}$ of 460 and 480 GHz, respectively. The AMUX IC consists of lumped building blocks, including data-input linear buffers, a clock-input limiting buffer, an AMUX core, and an output linear buffer. The measured 3-dB bandwidth for data and clock paths are both over 110 GHz. In addition, it measures and obtains time-domain large-signal sampling operations of up to 180 GS/s. A 224-Gb/s (112-GBaud) four-level pulse-amplitude modulation (PAM4) signal was successfully generated by using this AMUX. To the best of our knowledge, this AMUX IC has the broadest bandwidth and the fastest sampling rate compared with any other previously reported AMUXes.