Query: On Parallelizing the Multiprocessor Scheduling Problem

Relevant passages:
Hypertool: A Programming Aid for Message-Passing Systems <=SEP=> |As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more di cult and errorprone. This paper discusses programming assistance and automation concepts and their application to a program development tool for message-passing systems called Hypertool. It performs scheduling and handles the communication primitive insertion automatically. Two algorithms, based on the critical-path method, are presented for scheduling processes statically. Hypertool also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs.

Efficient Scheduling of Arbitrary TAsk Graphs to Multiprocessors Using a Parallel Genetic Algorithm <=SEP=> Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptions and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinatorial optimization problems. While a few GAs for scheduling have already been suggested, in this paper we propose a novel GA-based algorithm with an objective to simultaneously meet the goals of high performance, scalability, and fast running time. The proposed parallel genetic scheduling (PGS) algorithm itself is a parallel algorithm which generates high quality solutions in a short time. By encoding the scheduling list as a chromosome, the PGS algorithm can potentially generate an optimal scheduling list which in turn leads to an optimal schedule. The major strength of the PGS algorithm lies in its two efficient genetic operators: the order crossover and mutation. These operators effectively combine the building-blocks of good scheduling lists to construct better lists. The proposed algorithm is evaluated through a robust comparison with two heuristics best known in terms of performance and time complexity. It outperforms both heuristics while taking considerably less running time. When evaluated with random task graphs for which optimal solutions are known, the PGS algorithm generates optimal solutions for more than half of the test cases and close-to-optimal for the other half. © 1997 Academic Press

A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems <=SEP=> One measure of usefulness of a general-purpose distributed computing system is the system’s ability to provide a level of performance commensurate to the degree of multiplicity of resources present in the system. Many different approaches and metrics of performance have been proposed in an attempt to achieve this goal in existing systems. In addition, analogous problem formulations exist in other fields such as control theory, operations research, and production management. However, due to the wide variety of approaches to this problem, it is difficult to meaningfully compare different systems since there is no uniform means for qualitatively or quantitatively evaluating them. It is difficult to successfully build upon existing work or identify areas worthy of additional effort without some understanding of the relationships between past efforts. In this paper, a taxonomy of approaches to the resource management problem is presented in an attempt to provide a common terminology and classification mechanism necessary in addressing this problem. The taxonomy, while presented and discussed in terms of distributed scheduling, is also applicable to most types of resource management. As an illustration of the usefulness of the taxonomy an annotated bibliography is given which classifies a large number of distributed scheduling approaches according to the taxonomy.

A Microprocessor-based Hypercube Supercomputer <=SEP=> Each node in the NCUBE/ten parallel processor is organized around a custom, VAX-like, 32-bit CPU chip. With 1024 nodes, the NCUBE/ten provides a throughput of 500 MELOPS.

Scheduling Precedence Graphs in Systems with Interprocessor Communication Times <=SEP=> The problem of nonpreemptively scheduling a set of rn partially ordered tasks on n identical processors subject to interprocessor communication delays is studied in an effort to minimize the makespan. A new heuristic, called Earliest Task First (ETF), is designed and analyzed. It is shown that the makespan wEx-generated by ETF always satisfies to Eq.. (2 1/n wopti) + C, where Wopti) is the optimal makespan without considering communication delays and C is the communication requirements over some immediate predecessor-immediate successor pairs along one chain. An algorithm is also provided to calculate C. The time complexity of Algorithm ETF is O(nm2).

GIPSY: Automated Geographic Indexing of Text Documents <=SEP=> In this paper we present an algorithm which automatically extracts geopositional coordinate index terms from text to support georeferenced document indexing and retrieval. Under this algorithm, words and phrases containing geographic place names or characteristics are extracted from a text document and used as input to database functions which use spatial reasoning to approximate statistically the geoposition being referenced in the text. We conclude with a discussion of preliminary results and future work.

Hybrid supervised clustering based ensemble scheme for text classification <=SEP=> PurposeThe immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. Providing diversity among the classification algorithms is a key issue in the ensemble design. Design/methodology/approachAn ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, Bagging and Random Subspace) using eleven text benchmarks. FindingThe experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification. Originality/valueThe presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

Web evaluation: heuristic evaluation vs. user testing. <=SEP=> It is very important that designers recognize the benefits and limitations of different usability inspection methods. This is because the quality of the usability evaluation is dependent on the method used. Two of the most popular usability evaluation techniques are user testing and heuristic analysis. The main objective of this study was to compare the efficiency and effectiveness between user testing and heuristic analysis in evaluating four different commercial web sites. The results showed that both user testing and heuristic analysis addressed different usability problems. Analysis by severity of problems found and diminishing return analysis model on the relationship between the number of new problems discovered with users and evaluators used showed that both methods are equally efficient and effective in addressing different categories of usability problems. These significant differences found between these two methods suggested that the two methods are complimentary and should not be competing. In order for better evaluation results, both user testing and heuristic analysis are still needed. Relevance to industry: The research findings from this study will be of particular value to the web development industry and communities. Knowledge regarding the differences between user testing and heuristic evaluation will enable appropriate business decisions to be made on when and how to apply these methods to improve the overall efficiency of the design process. Published by Elsevier B.V.

Automated Evaluation of Out-of-Context Errors <=SEP=> We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human performance. The outcome of the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and humans, showing that the errors cannot be identified when limited to a single sentence.

Part-Activated Deep Reinforcement Learning for Action Prediction <=SEP=> In this paper, we propose a part-activated deep reinforcement learning (PA-DRL) method for action prediction. Most existing methods for action prediction utilize the evolution of whole frames to model actions, which cannot avoid the noise of the current action, especially in the early prediction. Moreover, the loss of structural information of human body diminishes the capacity of features to describe actions. To address this, we design the PA-DRL to exploit the structure of the human body by extracting skeleton proposals under a deep reinforcement learning framework. Specifically, we extract features from different parts of the human body individually and activate the action-related parts in features to enhance the representation. Our method not only exploits the structure information of the human body, but also considers the saliency part for expressing actions. We evaluate our method on three popular action prediction datasets: UT-Interaction, BIT-Interaction and UCF101. Our experimental results demonstrate that our method achieves the performance with state-of-the-arts.

Design patterns: elements of reuseable object-oriented software <=SEP=> Factory (87) Provide an interface for creating families of related or dependent objects without specifying their concrete classes. Adapter (139) Convert the interface of a class into another interface clients expect. Adapter lets classes work together that couldn't otherwise because of incompatible interfaces. Bridge (151) Decouple an abstraction from its implementation so that the two can vary independently. Builder (97) Separate the construction of a complex object from its representation so that the same construction process can create different representations. Chain of Responsibility (223) Avoid coupling the sender of a request to its receiver by giving more than one object a chance to handle the request. Chain the receiving objects and pass the request along the chain until an object handles it. Command (233) Encapsulate a request as an object, thereby letting you parameterize clients with different requests, queue or log requests, and support undoable operations. Composite (163) Compose objects into tree structures to represent part-whole hierarchies. Composite lets clients treat individual objects and compositions of objects uniformly. Decorator (175) Attach additional responsibilities to an object dynamically. Decorators provide a flexible alternative to subclassing for extending functionality. Facade (185) Provide a unified interface to a set of interfaces in a subsystem. Facade defines a higher-level interface that makes the subsystem easier to use. Factory Method (107) Define an interface for creating an object, but let subclasses decide which class to instantiate. Factory Method lets a class defer instantiation to subclasses. Flyweight (195) Use sharing to support large numbers of fine-grained objects efficiently. Interpreter (243) Given a language, define a represention for its grammar along with an interpreter that uses the representation to interpret sentences in the language. Iterator (257) Provide a way to access the elements of an aggregate object sequentially without exposing its underlying representation. Mediator (273) Define an object that encapsulates how a set of objects interact. Mediator promotes loose coupling by keeping objects from referring to each other explicitly, and it lets you vary their interaction independently. Memento (283) Without violating encapsulation, capture and externalize an object's internal state so that the object can be restored to this state later. Observer (293) Define a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically. Prototype (117) Specify the kinds of objects to create using a prototypical instance, and create new objects by copying this prototype. Proxy (207) Provide a surrogate or placeholder for another object to control access to it. Singleton (127) Ensure a class only has one instance, and provide a global point of access to it. State (305) Allow an object to alter its behavior when its internal state changes. The object will appear to change its class. Strategy (315) Define a family of algorithms, encapsulate each one, and make them interchangeable. Strategy lets the algorithm vary independently from clients that use it. Template Method (325) Define the skeleton of an algorithm in an operation, deferring some steps to subclasses. Template Method lets subclasses redefine certain steps of an algorithm without changing the algorithm's structure. Visitor (331) Represent an operation to be performed on the elements of an object structure. Visitor lets you define a new operation without changing the classes of the elements on which it operates. Organizing the Catalog Design patterns vary in their granularity and level of abstraction. Because there are many design patterns, we need a way to organize them. This section classifies design patterns so that we can refer to families of related patterns. The classification helps you learn the patterns in the catalog faster, and it can direct efforts to find new patterns as well. We classify design patterns by two criteria (Table 1.1). The first criterion, called purpose, reflects what a pattern does. Patterns can have either creational, structural, or behavioral purpose. Creational patterns concern the process of object creation. Structural patterns deal with the composition of classes or objects. Behavioral patterns characterize the ways in which classes or objects interact and distribute responsibility.

Double-sided cooling and thermo-electrical management of power transients for silicon chips on DCB-substrates for converter applications: Design, technology and test <=SEP=> This paper deals with the system design, technology and test of a novel concept of integrating Si and SiC power dies along with thermo-electric coolers in order to thermally manage transients occurring during operation. The concept features double-sided cooling as well as new materials and joining technologies to integrate the dies such as transient liquid phase bonding/soldering and sintering. Coupled-field simulations are used to predict thermal performance and are verified by especially designed test stands to very good agreement. This paper is the second in a series of publications on the ongoing work.

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations <=SEP=> In recent years, the interest in unsupervised learning of disentangled representations has significantly increased. The key assumption is that real-world data is generated by a few explanatory factors of variation and that these factors can be recovered by unsupervised learning algorithms. A large number of unsupervised learning approaches based on auto-encoding and quantitative evaluation metrics of disentanglement have been proposed; yet, the efficacy of the proposed approaches and utility of proposed notions of disentanglement has not been challenged in prior work. In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12 000 models covering the six most prominent methods, and evaluate them across six disentanglement metrics in a reproducible large-scale experimental study on seven different data sets. On the positive side, we observe that different methods successfully enforce properties “encouraged” by the corresponding losses. On the negative side, we observe that in our study (1) “good” hyperparameters seemingly cannot be identified without access to ground-truth labels, (2) good hyperparameters neither transfer across data sets nor across disentanglement metrics, and (3) that increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. These results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey <=SEP=> Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are used to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

Production-run software failure diagnosis via hardware performance counters <=SEP=> Sequential and concurrency bugs are widespread in deployed software. They cause severe failures and huge financial loss during production runs. Tools that diagnose production-run failures with low overhead are needed. The state-of-the-art diagnosis techniques use software instrumentation to sample program properties at run time and use off-line statistical analysis to identify properties most correlated with failures. Although promising, these techniques suffer from high run-time overhead, which is sometimes over 100%, for concurrency-bug failure diagnosis and hence are not suitable for production-run usage.
 We present PBI, a system that uses existing hardware performance counters to diagnose production-run failures caused by sequential and concurrency bugs with low overhead. PBI is designed based on several key observations. First, a few widely supported performance counter events can reflect a wide variety of common software bugs and can be monitored by hardware with almost no overhead. Second, the counter overflow interrupt supported by existing hardware and operating systems provides a natural and effective mechanism to conduct event sampling at user level. Third, the noise and non-determinism in interrupt delivery complements well with statistical processing.
 We evaluate PBI using 13 real-world concurrency and sequential bugs from representative open-source server, client, and utility programs, and 10 bugs from a widely used software-testing benchmark. Quantitatively, PBI can effectively diagnose failures caused by these bugs with a small overhead that is never higher than 10%. Qualitatively, PBI does not require any change to software and presents a novel use of existing hardware performance counters.

Predicting Online Extremism, Content Adopters, and Interaction Reciprocity <=SEP=> We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

The Moderating Role of Organizational Culture in the Relationship between Power, Trust, and eSCMS Adoption Intention <=SEP=> Building on multiple theoretical perspectives, we examined how organizational culture moderates the relationship between power, trust, and a firm’s eSCMS adoption intention. We tested the hypotheses using survey data collected from senior executives in China. Our findings reveal that a target firm’s perceived mediated power would negatively impact its trust toward a dominant firm, while its perceived non-mediated power would positively affect its trust. Meanwhile, trust can positively influence the target firm’s eSCMS adoption intention. Further, an internally focused culture weakens the negative effects of mediated power on trust. Meanwhile, an externally focused culture weakens the positive relationships between non-mediated power and trust, and between trust and eSCMS adoption intention. The externally focused culture could weaken the negative relationship between mediated power and trust either. The theoretical contributions and managerial implications of the study are discussed.

An Injectable 64 nW ECG Mixed-Signal SoC in 65 nm for Arrhythmia Monitoring <=SEP=> A syringe-implantable electrocardiography (ECG) monitoring system is proposed. The noise optimization and circuit techniques in the analog front-end (AFE) enable 31 nA current consumption while a minimum energy computation approach in the digital back-end reduces digital energy consumption by 40%. The proposed SoC is fabricated in 65 nm CMOS and consumes 64 nW while successfully detecting atrial fibrillation arrhythmia and storing the irregular waveform in memory in experiments using an ECG simulator, a live sheep, and an isolated sheep heart.

Convolutional deep maxout networks for phone recognition <=SEP=> Convolutional neural networks have recently been shown to outperform fully connected deep neural networks on several speech recognition tasks. Their superior performance is due to their convolutional structure that processes several, slightly shifted versions of the input window using the same weights, and then pools the resulting neural activations. This pooling operation makes the network less sensitive to translations. The convolutional network results published up till now used sigmoid or rectified linear neurons. However, quite recently a new type of activation function called the maxout activation has been proposed. Its operation is closely related to convolutional networks, as it applies a similar pooling step, but over different neurons evaluated on the same input. Here, we combine the two technologies, and experiment with deep convolutional neural networks built from maxout neurons. Phone recognition tests on the TIMIT database show that switching to maxout units from rectifier units decreases the phone error rate for each network configuration studied, and yields relative error rate reductions of between 2% and 6%.

Automatic quantification of crack patterns by image processing <=SEP=> Image processing technologies are proposed to quantify crack patterns. On the basis of the technologies, a software “Crack Image Analysis System” (CIAS) has been developed. An image of soil crack network is used as an example to illustrate the image processing technologies and the operations of the CIAS. The quantification of the crack image involves the following three steps: image segmentation, crack identification and measurement. First, the image is converted to a binary image using a cluster analysis method; noise in the binary image is removed; and crack spaces are fused. Then, the medial axis of the crack network is extracted from the binary image, with which nodes and crack segments can be identified. Finally, various geometric parameters of the crack network can be calculated automatically, such as node number, crack number, clod area, clod perimeter, crack area, width, length, and direction. The thresholds used in the operations are specified by cluster analysis and other innovative methods. As a result, the objects (nodes, cracks and clods) in the crack network can be quantified automatically. The software may be used to study the generation and development of soil crack patterns and rock fractures. & 2013 Elsevier Ltd. All rights reserved.

BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings <=SEP=> This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledgebased Word Sense Disambiguation (WSD) method based on sense embeddings. Punbased jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation.

Unifying Logical and Statistical AI <=SEP=> Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to a wide variety of problems in natural language understanding, vision, computational biology, social networks and others, and is the basis of the open-source Alchemy system.

A Multiband Antenna for Satellite Communications on the Move <=SEP=> The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included

An efficient probabilistic occupancy map-based people localization approach <=SEP=> The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.

Tracing female gamer identity. An empirical study into gender and stereotype threat perceptions <=SEP=> It seems that women stand outside game culture resulting in a low gamer identity profile. A nuanced and detailed examination of how gender identity and threatening experiences tap into their play practices has hitherto been lacking however. The present study fills this gap by examining how female players express a gamer identity and how this relates to perceptions of threat and stigmatization. Based on a large-scale survey directed at female players, a statistical model is specified taking into account how respondents attribute a gamer label to their self-concept. Results suggest that the cognitive, evaluative, and affective dimensions of female identity predict gamer identification in distinct ways. Whereas the mere cognitive process of categorizing as a woman is not related to gamer identity, women who feel closely connected to other women are less inclined to self-identify as gamer. However, group appraisal of womanhood is positively related to identifying as a gamer. When taking into account stigmatization, experienced discrimination by male players seems to discourage women to label themselves as gamer. © 2017 Elsevier Ltd. All rights reserved.

A New Compact Microstrip-Fed Dual-Band Coplanar Antenna for WLAN Applications <=SEP=> A novel compact microstrip fed dual-band coplanar antenna for wireless local area network is presented. The antenna comprises of a rectangular center strip and two lateral strips printed on a dielectric substrate and excited using a 50 Omega microstrip transmission line. The antenna generates two separate resonant modes to cover 2.4/5.2/5.8 GHz WLAN bands. Lower resonant mode of the antenna has an impedance bandwidth (2:1 VSWR) of 330 MHz (2190-2520 MHz), which easily covers the required bandwidth of the 2.4 GHz WLAN, and the upper resonant mode has a bandwidth of 1.23 GHz (4849-6070 MHz), covering 5.2/5.8 GHz WLAN bands. The proposed antenna occupy an area of 217 mm2 when printed on FR4 substrate (epsivr=4.7). A rigorous experimental study has been conducted to confirm the characteristics of the antenna. Design equations for the proposed antenna are also developed

Privacy-Preserving Multiple Linear Regression of Vertically Partitioned Real Medical Datasets <=SEP=> This paper studies the feasibility of privacy-preservingdata mining in epidemiological study. As for the data-miningalgorithm, we focus to a linear multiple regression thatcan be used to identify the most significant factorsamong many possible variables, such as the historyof many diseases. We try to identify the linear model to estimate a lengthof hospital stay from distributed dataset related tothe patient and the disease information. In this paper, we have done experiment usingthe real medical dataset related to stroke andattempt to apply multiple regression with sixpredictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include(1) to propose a practical privacy-preserving protocols for linear multiple regressionwith vertically partitioned datasets, and(2) to show the feasibility of the proposed system usingthe real medical dataset distributed into two parties, the hospital who knows the technical details of diseasesduring the patients are in the hospital, and the local government who knows the residence even afterthe patients left hospital. (3) to show the accuracy and the performance of thePPDM system which allows us to estimate the expectedprocessing time with arbitrary number of predictors.

Local potential connectivity in cat primary visual cortex. <=SEP=> Time invariant description of synaptic connectivity in cortical circuits may be precluded by the ongoing growth and retraction of dendritic spines accompanied by the formation and elimination of synapses. On the other hand, the spatial arrangement of axonal and dendritic branches appears stable. This suggests that an invariant description of connectivity can be cast in terms of potential synapses, which are locations in the neuropil where an axon branch of one neuron is proximal to a dendritic branch of another neuron. In this paper, we attempt to reconstruct the potential connectivity in local cortical circuits of the cat primary visual cortex (V1). Based on multiple single-neuron reconstructions of axonal and dendritic arbors in 3 dimensions, we evaluate the expected number of potential synapses and the probability of potential connectivity among excitatory (pyramidal and spiny stellate) neurons and inhibitory basket cells. The results provide a quantitative description of structural organization of local cortical circuits. For excitatory neurons from different cortical layers, we compute local domains, which contain their potentially pre- and postsynaptic excitatory partners. These domains have columnar shapes with laminar specific radii and are roughly of the size of the ocular dominance column. Therefore, connections between most excitatory neurons in the ocular dominance column can be implemented by local synaptogenesis. Structural connectivity involving inhibitory basket cells is generally weaker than excitatory connectivity. Here, only nearby neurons are capable of establishing more than one potential synapse, implying that within the ocular dominance column these connections have more limited potential for circuit remodeling.

Synthetic aperture ultrasound imaging. <=SEP=> The paper describes the use of synthetic aperture (SA) imaging in medical ultrasound. SA imaging is a radical break with today's commercial systems, where the image is acquired sequentially one image line at a time. This puts a strict limit on the frame rate and the possibility of acquiring a sufficient amount of data for high precision flow estimation. These constrictions can be lifted by employing SA imaging. Here data is acquired simultaneously from all directions over a number of emissions, and the full image can be reconstructed from this data. The paper demonstrates the many benefits of SA imaging. Due to the complete data set, it is possible to have both dynamic transmit and receive focusing to improve contrast and resolution. It is also possible to improve penetration depth by employing codes during ultrasound transmission. Data sets for vector flow imaging can be acquired using short imaging sequences, whereby both the correct velocity magnitude and angle can be estimated. A number of examples of both phantom and in vivo SA images will be presented measured by the experimental ultrasound scanner RASMUS to demonstrate the many benefits of SA imaging.

Facial soft-tissue spaces and retaining ligaments of the midcheek: defining the premaxillary space. <=SEP=> BACKGROUND
This anatomical study was undertaken to define the soft-tissue spaces, retaining ligaments, and their relations in the midcheek.


METHODS
Sixty fresh hemifaces were dissected. The retaining ligaments and facial spaces were defined and their dimensions recorded. The course of the key vessels and branches of the facial and infraorbital nerves were defined and their anatomical relations noted.


RESULTS
The preseptal and prezygomatic spaces underlie the lid-cheek and malar segments of the midcheek. A previously undocumented soft-tissue space, the premaxillary space, was found to underlie the nasolabial segment. The retaining ligaments of the midcheek are the tear trough-orbicularis retaining ligament complex in the upper midcheek and the zygomatic and maxillary ligaments in the lower midcheek. The tear trough-orbicularis retaining ligament complex separates the preseptal space above from the prezygomatic and premaxillary spaces below. Facial nerve branches in the midcheek are closely associated with the zygomatic ligaments located outside the lower boundary of the prezygomatic space and are protected so long as the dissection is kept within this space. The infraorbital nerve is protected by the floor of the premaxillary space, formed by the levator labii superioris and, at the inferior boundary of the space, by the close relation with the maxillary ligaments.


CONCLUSIONS
This study completely defined the spaces and retaining ligaments of the midcheek. Knowledge of this anatomy is key to safe and atraumatic suborbicular dissection for effective midcheek lifts.