Query: Active Sentiment Domain Adaptation

Relevant passages:
Support Vector Machine Active Learning with Applications to Text Classification <=SEP=> Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.

Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis <=SEP=> This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automatically identify thecontextual polarityfor a large subset of sentiment expressions, achieving results that are significantly better than baseline.

Active Learning Literature Survey <=SEP=> The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances if it is allowed to choose the data from which is learns. An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for active learning, a summary of several problem setting variants, and a discussion of related topics in machine learning research are also presented.

Predicting the Semantic Orientation of Adjectives <=SEP=> We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. Evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90% for adjectives that occur in a modest number of conjunctions in the corpus. 1 I n t r o d u c t i o n The semantic orientation or polarity of a word indicates the direction the word deviates from the norm for its semantic group or lezical field (Lehrer, 1974). It also constrains the word's usage in the language (Lyons, 1977), due to its evaluative characteristics (Battistella, 1990). For example, some nearly synonymous words differ in orientation because one implies desirability and the other does not (e.g., simple versus simplisfic). In linguistic constructs such as conjunctions, which impose constraints on the semantic orientation of their arguments (Anscombre and Ducrot, 1983; Elhadad and McKeown, 1990), the choices of arguments and connective are mutually constrained, as illustrated by: The tax proposal was simple and well-received } simplistic but well-received *simplistic and well-received by the public. In addition, almost all antonyms have different semantic orientations3 If we know that two words relate to the same property (for example, members of the same scalar group such as hot and cold) but have different orientations, we can usually infer that they are antonyms. Given that semantically similar words can be identified automatically on the basis of distributional properties and linguistic cues (Brown et al., 1992; Pereira et al., 1993; Hatzivassiloglou and McKeown, 1993), identifying the semantic orientation of words would allow a system to further refine the retrieved semantic similarity relationships, extracting antonyms. Unfortunately, dictionaries and similar sources (theusari, WordNet (Miller et al., 1990)) do not include semantic orientation information. 2 Explicit links between antonyms and synonyms may also be lacking, particularly when they depend on the domain of discourse; for example, the opposition bearbull appears only in stock market reports, where the two words take specialized meanings. In this paper, we present and evaluate a method that automatically retrieves semantic orientation information using indirect information collected from a large corpus. Because the method relies on the corpus, it extracts domain-dependent information and automatically adapts to a new domain when the corpus is changed. Our method achieves high precision (more than 90%), and, while our focus to date has been on adjectives, it can be directly applied to other word classes. Ultimately, our goal is to use this method in a larger system to automatically identify antonyms and distinguish near synonyms. 2 O v e r v i e w o f O u r A p p r o a c h Our approach relies on an analysis of textual corpora that correlates linguistic features, or indicators, with 1 Exceptions include a small number of terms that are both negative from a pragmatic viewpoint and yet stand in all antonymic relationship; such terms frequently lexicalize two unwanted extremes, e.g., verbose-terse. 2 Except implicitly, in the form of definitions and usage examples.

Transductive Inference for Text Classification using Support Vector Machines <=SEP=> This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassi cations of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classi cation. These theoretical ndings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs e ciently, handling 10,000 examples and more.

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing <=SEP=> With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs.
 Stochastic Computing (SC), which uses a bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has high potential for implementing DCNNs with high scalability and ultra-low hardware footprint. Since multiplications and additions can be calculated using AND gates and multiplexers in SC, significant reductions in power (energy) and hardware footprint can be achieved compared to the conventional binary arithmetic implementations. The tremendous savings in power (energy) and hardware resources allow immense design space for enhancing scalability and robustness for hardware DCNNs.
 This paper presents SC-DCNN, the first comprehensive design and optimization framework of SC-based DCNNs, using a bottom-up approach. We first present the designs of function blocks that perform the basic operations in DCNN, including inner product, pooling, and activation function. Then we propose four designs of feature extraction blocks, which are in charge of extracting features from input feature maps, by connecting different basic function blocks with joint optimization. Moreover, the efficient weight storage methods are proposed to reduce the area and power (energy) consumption. Putting all together, with feature extraction blocks carefully selected, SC-DCNN is holistically optimized to minimize area and power (energy) consumption while maintaining high network accuracy. Experimental results demonstrate that the LeNet5 implemented in SC-DCNN consumes only 17 mm2 area and 1.53 W power, achieves throughput of 781250 images/s, area efficiency of 45946 images/s/mm2, and energy efficiency of 510734 images/J.

Look who's talking: speech style and social context in language input to infants are linked to concurrent and future speech development. <=SEP=> Language input is necessary for language learning, yet little is known about whether, in natural environments, the speech style and social context of language input to children impacts language development. In the present study we investigated the relationship between language input and language development, examining both the style of parental speech, comparing 'parentese' speech to standard speech, and the social context in which speech is directed to children, comparing one-on-one (1:1) to group social interactions. Importantly, the language input variables were assessed at home using digital first-person perspective recordings of the infants' auditory environment as they went about their daily lives (N =26, 11- and 14-months-old). We measured language development using (a) concurrent speech utterances, and (b) word production at 24 months. Parentese speech in 1:1 contexts is positively correlated with both concurrent speech and later word production. Mediation analyses further show that the effect of parentese speech-1:1 on infants' later language is mediated by concurrent speech. Our results suggest that both the social context and the style of speech in language addressed to children are strongly linked to a child's future language development.

The Internet of Things: a security point of view <=SEP=> ed as devices to provide services at low-levels as network discovery services, metadata exchange services, and asynchronous publish and subscribe event in (Kranenburg et al. 2011; Sundmaeker et al. 2010); In (Peris-Lopez et al. 2006), a representational state transfer (REST) is defined to increase interoperability between Page 14 of 32 http://mc.manuscriptcentral.com/intr Internet Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 or Peer Rview loosely coupled services and distributed applications. In (Hernandez-Castro et al. 2013), the services layer introduced a service provisioning process (SPP) that can provide the interaction between applications and services. It is important to design an effective security strategy to protect services against attacks in the service layer. The security requirements in the service layer include: • Authorization, service authentication, group authentication, privacy protection, integrity, integrity, security of keys, non-repudiation, anti-replay, availability,

Chest pathology detection using deep learning with non-medical training <=SEP=> In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.

Crowdsourcing of Pollution Data using Smartphones <=SEP=> In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.

Architecture, the Backbone of Robotic Systems <=SEP=> Architectures form the backbone of complete robotic systems. The right choice of architecture can go a long way in facilitating the specification, implementation and validation of robotic systems. Conversely, of course, the wrong choice can make one's life miserable. We present some of the needs of robotic systems, describe some general classes of robot architectures, and discuss how different architectural styles can help in addressing those needs. The paper, like the field itself, is somewhat preliminary, yet it is hoped that it will provide guidance for those who use, or develop, robot architectures.

Key parameters influencing the behavior of Steel Plate Shear Walls (SPSW) <=SEP=> The complex behavior of Steel Plate Shear Walls (SPSW) is investigated herein through nonlinear FE simulations. A 3D detailed FE model is developed and validated utilizing experimental results available in the literature. The influence of key parameters on the structural behavior is investigated. The considered parameters are: the infill plate thickness, the beam size, the column size, the infill plate material grade and the frame material grade. Several structural responses are used as criteria to quantify their influence on the SPSW behavior. The evaluated structural responses are: yield strength, yield displacement, ultimate strength, initial stiffness and secondary stiffness. The results show that, overall the most influential parameter is the infill plate thickness followed by the beam size. Also, it was found that the least influential parameter is the frame material grade.

CLASSIFICATION OF DEFECTS IN SOFTWARE USING DECISION TREE ALGORITHM <=SEP=> Software defects due to coding errors continue to plague the industry with disastrous impact, especially in the enterprise application software category. Identifying how much of these defects are specifically due to coding errors is a challenging problem. Defect prevention is the most vivid but usually neglected aspect of software quality assurance in any project. If functional at all stages of software development, it can condense the time, overheads and wherewithal entailed to engineer a high quality product. In order to reduce the time and cost, we will focus on finding the total number of defects if the test case shows that the software process not executing properly. That has occurred in the software development process. The proposed system classifying various defects using decision tree based defect classification technique, which is used to group the defects after identification. The classification can be done by employing algorithms such as ID3 or C4.5 etc. After the classification the defect patterns will be measured by employing pattern mining technique. Finally the quality will be assured by using various quality metrics such as defect density, etc. The proposed system will be implemented in JAVA.

SympGraph: a framework for mining clinical notes through symptom relation graphs <=SEP=> As an integral part of Electronic Health Records (EHRs), clinical notes pose special challenges for analyzing EHRs due to their unstructured nature. In this paper, we present a general mining framework SympGraph for modeling and analyzing symptom relationships in clinical notes.
 A SympGraph has symptoms as nodes and co-occurrence relations between symptoms as edges, and can be constructed automatically through extracting symptoms over sequences of clinical notes for a large number of patients. We present an important clinical application of SympGraph: symptom expansion, which can expand a given set of symptoms to other related symptoms by analyzing the underlying SympGraph structure. We further propose a matrix update algorithm which provides a significant computational saving for dynamic updates to the graph. Comprehensive evaluation on 1 million longitudinal clinical notes over 13K patients shows that static symptom expansion can successfully expand a set of known symptoms to a disease with high agreement rate with physician input (average precision 0.46), a 31% improvement over baseline co-occurrence based methods. The experimental results also show that the expanded symptoms can serve as useful features for improving AUC measure for disease diagnosis prediction, thus confirming the potential clinical value of our work.

Using Design Science Research to Incorporate Gamification into Learning Activities <=SEP=> Gamifying learning activities can be beneficial as it can better engage students and result in improved learning. However, incorporating game elements into learning activities can be difficult because it requires an appropriate mix of science, art, and experience. Design science research can help to address this issue. In particular, its search process for determining an appropriate solution for a given problem is useful as it allows for a number of iterative cycles over which the solution is incrementally refined, and ultimately resulting in a successful implementation. Our objective in this study is to develop a non-discipline-specific instantiation that can be embedded into a learning activity to motivate students and improve the quality of their learning. We provide a detailed explanation of how we designed and developed a gamified multiple choice quiz software tool over multiple iterations. The tool was trialled in three undergraduate IT-related courses and evaluated using a questionnaire survey. Results showed that the tool was well received as 76 per cent of students believed it was effective for learning and would for it to be used in their other courses.

Network failure detection and graph connectivity <=SEP=> We consider a model for monitoring the connectivity of a network subject to node or edge failures. In particular, we are concerned with detecting (ε, <i>k</i>)-<i>failures</i>: events in which an adversary deletes up to network elements (nodes or edges), after which there are two sets of nodes <i>A</i> and <i>B</i>, each at least an ε fraction of the network, that are disconnected from one another. We say that a set <i>D</i> of nodes is an (ε <i>k</i>)-detection set if, for any (ε <i>k</i>)-failure of the network, some two nodes in <i>D</i> are no longer able to communicate; in this way, <i>D</i> "witnesses" any such failure. Recent results show that for any graph <i>G</i>, there is an is (ε <i>k</i>)-detection set of size bounded by a polynomial in <i>k</i> and ε, independent of the size of <i>G</i>.In this paper, we expose some relationships between bounds on detection sets and the edge-connectivity λ and node-connectivity κ of the underlying graph. Specifically, we show that detection set bounds can be made considerably stronger when parameterized by these connectivity values. We show that for an adversary that can delete κλ edges, there is always a detection set of size <i>O</i>((κ/ε) log (1/ε)) which can be found by random sampling. Moreover, an (ε, &lambda)-detection set of minimum size (which is at most 1/εε) can be computed in polynomial time. A crucial point is that these bounds are independent not just of the size of <i>G</i> but also of the value of λ.Extending these bounds to node failures is much more challenging. The most technically difficult result of this paper is that a random sample of <i>O</i>((κ/ε) log (1/ε)) nodes is a detection set for adversaries that can delete a number of nodes up to κ, the node-connectivity.For the case of edge-failures we use VC-dimension techniques and the cactus representation of all minimum edge-cuts of a graph; for node failures, we develop a novel approach for working with the much more complex set of all minimum node-cuts of a graph.

Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping <=SEP=> Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In this contribution we propose a batch approach and a novel multi-view frame fusion technique to exploit multiple views for improving the semantic labelling results. The batch approach works offline and is the direct application of an existing single-view method to scene reconstructions with multiple views. The multi-view frame fusion works in an incremental fashion accumulating the single-view results, hence allowing the online multi-view semantic segmentation of single frames and the offline reconstruction of semantic maps. Our experiments show the superiority of the approaches based on our fusion scheme, which leads to a more accurate semantic labelling.

Coupled hidden Markov models for complex action recognition <=SEP=> We present algorithms for coupling and training hidden Markov models ( HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signal—that it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.

UMAN WAYFINDING IN UNFAMILIAR BUILDINGS : A SIMULATION WITH A COGNIZING AGENT <=SEP=> Existing cognitively based computational models for wayfinding focus primarily on the exploration of mental representations rather than the information needs for wayfinding. It is important to consider information needs because people trying to find their ways in unfamiliar environments do not have a previously acquired mental representation but depend on external information. The fundamental tenet of this work is that all information must be presented to the wayfinder at each decision point as “knowledge in the world.” Simulating people’s wayfinding behavior in a cognitively plausible way requires the integration of structures for information perception and cognition in the underlying model. In this work we use a cognizing agent to simulate people’s wayfinding processes in unfamiliar buildings. The agent-based model consists of two tiers: simulated states of the environment and simulated beliefs of the agent. The agent is modeled with state, an observation schema, a specific wayfinding strategy, and commonsense knowledge. The environment is modeled as a graph, where nodes represent decision points and edges represent lines of movement. The perceptual wayfinding model integrates the agent and its environment within a “Sense-Plan-Act” framework. It focuses on knowledge in the world to explain actions of the agent. The concepts of affordance and information are used to describe the kinds of knowledge the agent derives from the world by means of visual perception. Affordances are possibilities for action with reference to the agent. Information is necessary for the agent to decide which affordances to utilize. During the navigation process the agent accumulates beliefs about the environment by observing task-relevant affordances and information at decision points. The utilization of a “go-to” affordance leads the agent from one node to another where it is again provided with percepts. A successful navigation corresponds to the agent’s traversal from a start to a goal node. The proposed formal specifications of the agent-based model can be used to simulate people’s wayfinding behavior in spatial information and design systems in a cognitively plausible way. Such simulation helps to determine where and why people face wayfinding difficulties and what needs to be done to avoid them. The case of wayfinding in an airport, in which the signage in the airport is tested, is employed to demonstrate the perceptual wayfinding model.

A multi-view intelligent editor for digital video libraries <=SEP=> Silver is an authoring tool that aims to allow novice users to edit di gital video. The goal is to make editing of digital video as easy as text editing. Silver provides multiple coordinated views, including project, source, outline, subject, storyboard, textual transcript and timeline views. Selections and edits in any view are synchronized with all other views. A variety of recognition algorithms are applied to the video and audio content and then are used to aid in the editing tasks. The Informedia Digital Library supplies the recognition algorithms and metadata used to support intelligent editing, and Informedia also provides search and a repository. The metadata includes shot boundaries and a time-synchronized transcript, which are used to support intelligent selection and intelligent cut/copy/paste.

Recurrent Residual Learning for Sequence Classification <=SEP=> In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) for modeling sequential data. We show that for sequence classification tasks, incorporating residual connections into recurrent structures yields similar accuracy to Long Short Term Memory (LSTM) RNN with much fewer model parameters. In addition, we propose two novel models which combine the best of both residual learning and LSTM. Experiments show that the new models significantly outperform LSTM.

Design and Evaluation of Visual Interpenetration Cues in Virtual Grasping <=SEP=> We present design and impact studies of visual feedback for virtual grasping. The studies suggest new or updated guidelines for feedback. Recent grasping techniques incorporate visual cues to help resolve undesirable visual or performance artifacts encountered after real fingers enter a virtual object. Prior guidelines about such visuals are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We address this with a two-stage study. In the first stage, users adjusted parameters of various feedback types, including some novel aspects, to identify promising settings and to give insight into preferences regarding the parameters. In the next stage, the tuned feedback techniques were evaluated in terms of objective performance (finger penetration, release time, and precision) and subjective rankings (visual quality, perceived behavior impact, and overall preference). Additionally, subjects commented on the techniques while reviewing them in a final session. Performance wise, the most promising techniques directly reveal penetrating hand configuration in some way. Subjectively, subjects appreciated visual cues about interpenetration or grasp force, and color changes are most promising. The results enable selection of the best cues based on understanding the relevant tradeoffs and reasonable parameter values. The results also provide a needed basis for more focused studies of specific visual cues and for choosing conditions in comparisons to other feedback modes, such as haptic, audio, or multimodal. Considering results, we propose that 3D interaction guidelines must be updated to capture the importance of interpenetration cues, possible performance benefits of direct representations, and tradeoffs involved in cue selection.

An Attribute-aware Neural Attentive Model for Next Basket Recommendation <=SEP=> Next basket recommendation is a new type of recommendation, which recommends a set of items, or a basket, to the user. Purchase in basket is a common behavior of consumers. Recently, deep neural networks have been applied to model sequential transactions of baskets in next basket recommendation. However, current methods do not track the user's evolving appetite for items explicitly, and they ignore important item attributes such as product category. In this paper, we propose a novel Attribute-aware Neural Attentive Model (ANAM) to address these problems. ANAM adopts an attention mechanism to explicitly model user's evolving appetite for items, and utilizes a hierarchical architecture to incorporate the attribute information. In specific, ANAM utilizes a recurrent neural network to model the user's sequential behavior over time, and relays the user's appetite toward items and their attributes to next basket through attention weights shared across baskets on the two different hierarchies. Experiment results on two public datasets (ıe Ta-Feng and JingDong) demonstrate the effectiveness of our ANAM model for next basket recommendation.

A Context-Based Detection Framework for Advanced Persistent Threats <=SEP=> Besides a large set of malware categories such as worms and Trojan horses, Advanced Persistent Threat (APT) is another more sophisticated attack entity emerging in the cyber threats environment. In this paper we propose a model of the APT detection problem as well as a methodology to implement it on a generic organization network. From our knowledge, the proposed method is the first to address the problem of modeling an APT and to provide a possible detection framework.

Data Semantics <=SEP=> 

On the write energy of non-volatile resistive crossbar arrays with selectors <=SEP=> Crossbar arrays based on non-volatile resistive devices such as resistive RAM and phase change memory have become an important technology due to the applications to memory systems. The energy consumption of integrated circuits has become a primary issue due to thermal constraints in high performance systems and limited battery time in mobile and IoT applications. In this paper, the energy efficiency of a crossbar array of a one-selector-one-resistor (1S1R) configuration during a write operation is explored for the V/2 and V/3 bias schemes. The characteristics that affect the most energy efficient bias scheme are demystified. The write energy of a crossbar array is modeled in terms of the array size, number of selected cells, and the nonlinearity factor. For a specific array size and selector technology, the number of selected cells during a write operation can affect the choice of bias scheme. Moreover, the effect of leakage current due to partially biased unselected cells is explored.

Interactions between location and task affect the spatial and directional firing of hippocampal neurons. <=SEP=> When rats forage for randomly dispersed food in a high walled cylinder the firing of their hippocampal "place" cells exhibits little dependence on the direction faced by the rat. On radial arm mazes and similar tasks, place cells are strongly directionally selective within their fields. These tasks differ in several respects, including the visual environment, configuration of the traversable space, motor behavior (e.g., linear and angular velocities), and behavioral context (e.g., presence of specific, consistent goal locations within the environment). The contributions of these factors to spatial and directional tuning of hippocampal neurons was systematically examined in rats performing several tasks in either an enriched or a sparse visual environment, and on different apparati. Place fields were more spatially and directionally selective on a radial maze than on an open, circular platform, regardless of the visual environment. On the platform, fields were more directional when the rat searched for food at fixed locations, in a stereotypic and directed manner, than when the food was scattered randomly. Thus, it seems that place fields are more directional when the animal is planning or following a route between points of special significance. This might be related to the spatial focus of the rat's attention (e.g., a particular reference point). Changing the behavioral task was also accompanied by a change in firing location in about one-third of the cells. Thus, hippocampal neuronal activity appears to encode a complex interaction between locations, their significance and the behaviors the rat is called upon to execute.

Design of a Novel Broadband Skeletal Discone Antenna With a Compact Configuration <=SEP=> A novel broadband skeletal discone antenna with a compact configuration is proposed in this letter. The antenna consists of three components including a circular metal disc, a small upside-down cone, and a skeletal cone. The operating principle of this antenna is investigated by simulation, and the corresponding simulated results are also demonstrated in this letter. According to the simulation, the antenna was fabricated and followed by the measurement. The comparison between the simulated and measured results is made for validating the design strategy. Although some discrepancies can be obviously found at some discrete frequencies, both the simulated and measured results indicate that this antenna is capable of operating in the frequency range of 400 MHz to almost 16.4 GHz with VSWR below 2.5, while maintaining stable omnidirectional radiation. Meanwhile, this antenna also has a distinct advantage over the traditional discone antenna for its compact configuration, which makes it a more competitive candidate for applications as wireless communication.

Distributed Data Clustering in Peer -to-Peer Networks: A Technical Review <=SEP=> Clustering as one of the main branches of data mining, has gained an important place in the different applied fields. On the other hand, Peer-to-Peer (P2P) networks with features such as simplicity, low cost communication, and high availability resources, have gained a worldwide popularity in the present days. In P2P network, high volumes of data are distributed between dispersed data sources. In such distributed systems, sending data to a central site is not possible, due to processing, storage and handling costs. This issue makes the need for specific methods for distributed data clustering in P2P networks. This paper makes a technical review of distributed data clustering approaches in P2P network, to understand the research trends in this area. Keywords—Peer to Peer Networks, Distributed Data Mining, Distributed Data Clustering

Omni-Directional Mobility Using Active Split Offset Castors <=SEP=> An omni-directional mobility platform design concept using two active split offset cas (ASOC) and one or more conventional castors is presented. An ASOC module cons two coaxial conventional wheels driven independently and connected to the platfor an offset link. The kinematics and implementation of the omni-directional platform described and analyzed. Particular attention is paid to the system performance on u floors. The fundamental mechanics of the ASOC wheel scrubbing, which is critic system wear and energy use, is analyzed and compared to conventional active designs. The effectiveness of the design is shown experimentally using an inte mobility aid for the elderly.@DOI: 10.1115/1.1767181 #