Query: DIRECT GEOMETRY PROCESSING FOR TELE-FABRICATION

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Digital Material Fabrication Using Mask-Image-Projection-based Stereolithography <=SEP=> Purpose – The purpose of this paper is to present a mask-image-projection-based Stereolithography process that can combine two base materials with various concentrations and structures to produce a solid object with desired material characteristics. Stereolithography is an additive manufacturing process in which liquid photopolymer resin is cross-linked and converted to solid. The fabrication of digital material requires frequent resin changes during the building process. The process presented in this paper attempts to address the related challenges in achieving such fabrication capability. Design/methodology/approach – A two-channel system design is presented for the multi-material mask-imageprojection-based Stereolithography process. In such a design, a coated thick film and linear motions in two axes are used to reduce the separation force of a cured layer. The material cleaning approach to thoroughly remove resin residue on built surfaces is presented for the developed process. Based on a developed testbed, experimental studies were conducted to verify the effectiveness of the presented process on digital material fabrication. Findings – The proposed two-channel system can reduce the separation force of a cured layer by an order of magnitude in the bottom-up projection system. The developed two-stage cleaning approach can effectively remove resin residue on built surfaces. Several multi-material designs have been fabricated to highlight the capability of the developed multi-material mask-image-projection-based Stereolithography process. Research limitations/implications – A proof-of-concept testbed has been developed. Its building speed and accuracy can be further improved. Our tests were limited to the same type of liquid resins. In addition, the removal of trapped air is a challenge in the presented process. Originality/value – This paper presents a novel and a pioneering approach towards digital material fabrication based on the Stereolithography process. This research contributes to the additive manufacturing development by significantly expanding the selection of base materials in fabricating solid objects with desired material characteristics.

Calibrating Large-area Mask Projection Stereolithography for Its Accuracy and Resolution Improvements <=SEP=> Solid freeform fabrication (SFF) processes based on mask image projection such as digital micro-mirror devices (DMD) have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on such digital devices. However, a digital light processing (DLP) projector based on DMD has limited resolution and certain image blurring. In order to use a DLP projector in the large-area mask projection stereolithography, it is critical to plan mask images in order to achieve high accuracy and resolution. Based on our previous work on optimized pixel blending, we present a calibration method for capturing the non-uniformity of a projection image by a low cost off-the-shelf DLP projector. Our method is based on two calibration systems, a geometric calibration system that can calibrate the position, shape, size, and orientation of a pixel and an energy calibration system that can calibrate the light intensity of a pixel. Based on both results, the light intensity at various grayscale levels can be approximated for each pixel. Developing a library of such approximation functions is critical for the optimized pixel blending to generate a better mask image plan. Experimental results verify our calibration results.

Direct least squares fitting of ellipses <=SEP=> This work presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4 2 1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: (i) It is ellipse-specific so that even bad data will always return an ellipse; (ii) It can be solved naturally by a generalized eigensystem and (iii) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other non-ellipse-specific approaches would fail or require computationally expensive iterative refinements. Source code for the algorithm is supplied and a demonstration is available on ! " ! $#% '& () "*) & +, ./10 0 32, . 4) "*) "*) % 5* 0

Projection micro-stereolithography using digital micro-mirror dynamic mask <=SEP=> We present in this paper the development of a high-resolution projection micro-stereolithography (P SL) process by using the Digital Micromirror Device (DMDTM, Texas Instruments) as a dynamic mask. This unique technology provides a parallel fabrication of complex three-dimensional (3D) microstructures used for micro electro-mechanical systems (MEMS). Based on the understanding of underlying mechanisms, a process model has been developed with all critical parameters obtained from the experimental measurement. By coupling the experimental measurement and the process model, the photon-induced curing behavior of the resin has been quantitatively studied. The role o erty of the r n d ©

A Layerless Additive Manufacturing Process based on CNC Accumulation <=SEP=> Most current additive manufacturing processes are layer-based, that is building a physical model layer-by-layer. By converting 3-dimensional geometry into 2-dimensional contours, the layer-based approach can dramatically simplify the process planning steps. However, there are also drawbacks associated with the layer-based approach such as inconsistent material properties between various directions. In a recent NSF workshop on additive manufacturing, it is suggested to investigate alternative non-layer based approaches. In this paper, we present an additive manufacturing process without planar layers. In the developed testbed, an additive tool based on a fiber optics cable and a UV-LED has been developed. By merging such tools inside a liquid resin tank, we demonstrate its capability of building various 2D and 3D structures. The technical challenges related to the development of such a process are discussed. Some potential applications including part repairing and building around inserts have also been demonstrated.

X band septum polarizer as feed for parabolic antenna <=SEP=> Contemporary RHCP and LHCP wave occurs in several applications of microwave communication and measurement system. From this point of view the septum polarizer can be useful. The septum polarizer is a four-port waveguide device. The square waveguide at one end constitutes two ports because it can support two orthogonal modes. A slopping (or stepped) septum divides the square waveguide into two standard rectangular waveguides sharing a common broad-wall. The size of the septum as well as two versions of the waveguides excitation were analyzed and are described in this paper.

Single‐cell sequencing maps gene expression to mutational phylogenies in PDGF‐ and EGF‐driven gliomas <=SEP=> Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor. Epidermal growth factor (EGF) and platelet-derived growth factor (PDGF) receptors are frequently amplified and/or possess gain-of-function mutations in GBM However, clinical trials of tyrosine-kinase inhibitors have shown disappointing efficacy, in part due to intra-tumor heterogeneity. To assess the effect of clonal heterogeneity on gene expression, we derived an approach to map single-cell expression profiles to sequentially acquired mutations identified from exome sequencing. Using 288 single cells, we constructed high-resolution phylogenies of EGF-driven and PDGF-driven GBMs, modeling transcriptional kinetics during tumor evolution. Descending the phylogenetic tree of a PDGF-driven tumor corresponded to a progressive induction of an oligodendrocyte progenitor-like cell type, expressing pro-angiogenic factors. In contrast, phylogenetic analysis of an EGFR-amplified tumor showed an up-regulation of pro-invasive genes. An in-frame deletion in a specific dimerization domain of PDGF receptor correlates with an up-regulation of growth pathways in a proneural GBM and enhances proliferation when ectopically expressed in glioma cell lines. In-frame deletions in this domain are frequent in public GBM data.

A very-high output impedance current mirror for very-low voltage biomedical analog circuits <=SEP=> In this paper, we present the design of a new very-high output impedance CMOS current mirror with enhanced output voltage compliance. The proposed current mirror uses MOS current dividers to sample the output current and a feedback action is used to force it to be equal to the input current; yielding very high impedance with a very large output voltage range. The proposed implementation yields an increase of the output impedance by a factor of about gmro compared with that of the super-Wilson current mirror, thus offering a potential solution to mitigate the effect of the low output impedance of ultra-deep submicron CMOS transistors used in sub 1-V current mirrors and current sources. A NMOS version of the proposed current mirror circuit was implemented using STMicroelectronics 1-V 90-nm CMOS process and simulated using Spectre to validate its performance. The output current is mirrored with a transfer error lower than 1% down to an output voltage as low as 80 mV for an input current of 5 muA, and 111 mV when the input current is increased to 50 muA.

Dune: Safe User-level Access to Privileged CPU Features <=SEP=> Dune is a system that provides applications with direct but safe access to hardware features such as ring protection, page tables, and tagged TLBs, while preserving the existing OS interfaces for processes. Dune uses the virtualization hardware in modern processors to provide a process, rather than a machine abstraction. It consists of a small kernel module that initializes virtualization hardware and mediates interactions with the kernel, and a user-level library that helps applications manage privileged hardware features. We present the implementation of Dune for 64bit x86 Linux. We use Dune to implement three userlevel applications that can benefit from access to privileged hardware: a sandbox for untrusted code, a privilege separation facility, and a garbage collector. The use of Dune greatly simplifies the implementation of these applications and provides significant performance advantages.

Applications of approximation algorithms to cooperative games <=SEP=> The Internet, which is intrinsically a common playground for a large number of players with varying degrees of collaborative and sel sh motives, naturally gives rise to numerous new game theoretic issues. Computational problems underlying solutions to these issues, achieving desirable economic criteria, often turn out to be NP-hard. It is therefore natural to apply notions from the area of approximation algorithms to these problems. The connection is made more meaningful by the fact that the two areas of game theory and approximation algorithms share common methodology { both heavily use machinery from the theory of linear programming. Various aspects of this connection have been explored recently by researchers [8, 10, 15, 20, 21, 26, 27, 29]. In this paper we will consider the problem of sharing the cost of a jointly utilized facility in a \fair" manner. Consider a service providing company whose set of possible customers, also called users, is U . For each set S U C(S) denotes the cost incurred by the company to serve the users in S. The function C is known as the cost function. For concreteness, assume that the company broadcasts news of common interest, such as nancial news, on the net. Each user, i, has a utility, u0i, for receiving the news. This utility u 0 i is known only to user i. User i enjoys a bene t of u0i xi if she gets the news at the price xi. If she does not get the news then her bene t is 0. Each user is assumed to be sel sh, and hence in order to maximize bene t, may misreport her utility as some other number, say ui. For the rest of the discussion, the utility of user i will mean the number ui. A cost sharing mechanism determines which users receive the broadcast and at what price. The mechanism is strategyproof if the dominant strategy of each user is to reveal the

Automatic generation of destination maps <=SEP=> Destination maps are navigational aids designed to show anyone within a region how to reach a location (the destination). Hand-designed destination maps include only the most important roads in the region and are non-uniformly scaled to ensure that all of the important roads from the highways to the residential streets are visible. We present the first automated system for creating such destination maps based on the design principles used by mapmakers. Our system includes novel algorithms for selecting the important roads based on mental representations of road networks, and for laying out the roads based on a non-linear optimization procedure. The final layouts are labeled and rendered in a variety of styles ranging from informal to more formal map styles. The system has been used to generate over 57,000 destination maps by thousands of users. We report feedback from both a formal and informal user study, as well as provide quantitative measures of success.

The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking <=SEP=> Algorithms for acoustic source localization and tracking are essential for a wide range of applications such as personal assistants, smart homes, tele-conferencing systems, hearing aids, or autonomous systems. Numerous algorithms have been proposed for this purpose which, however, are not evaluated and compared against each other by using a common database so far. The IEEE-AASP Challenge on sound source localization and tracking (LOCATA) provides a novel, comprehensive data corpus for the objective benchmarking of state-of-the-art algorithms on sound source localization and tracking. The data corpus comprises six tasks ranging from the localization of a single static sound source with a static microphone array to the tracking of multiple moving speakers with a moving microphone array. It contains real-world multichannel audio recordings, obtained by hearing aids, microphones integrated in a robot head, a planar and a spherical microphone array in an enclosed acoustic environment, as well as positional information about the involved arrays and sound sources represented by moving human talkers or static loudspeakers.

Aspect Extraction with Automated Prior Knowledge Learning <=SEP=> Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.

Application Protocols and Wireless Communication for IoT: A Simulation Case Study Proposal <=SEP=> The current Internet of Things (IoT) solutions require support at different network layers, from higher level applications to lower level media-based support. This paper presents some of the main application requirements for IoT, characterizing architecture, Quality of Service (QoS) features, security mechanisms, discovery service resources and web integration options and the protocols that can be used to provide them (e.g. CoAP, XMPP, DDS, MQTT-SN, AMQP). As examples of lower-level requirements and protocols, several wireless network characteristics (e.g. ZigBee, Z-Wave, BLE, LoRaWAN, SigFox, IEEE 802.11af, NB-IoT) are presented. The variety of possible applications scenarios and the heterogeneity of enabling technologies combined with a large number of sensors and devices, suggests the need for simulation and modeling tactics to describe how the previous requirements can be met. As a potential solution, the creation of simulation models and the usage of the OMNET++ simulation tool to enable meaningful IoT simulation is discussed. The analysis of the behavior of IoT applications is proposed for two use cases: Wireless Sensor Networks (WSN) for home and industrial automation, and Low Power Wide Area (LPWA) networks for smart meters, smart buildings, and smart cities.

Integrating Surface and Abstract Features for Robust Cross-Domain Chinese Word Segmentation <=SEP=> Current character-based approaches are not robust for cross domain Chin ese word segmentation. In this paper, we alleviate this problem by deriving a novel enhanced ch aracterbased generative model with a new abstract aggregate candidate-feature, which indicates if th given candidate prefers the corresponding position-tag of the longest dictionary matching wo rd. Since the distribution of the proposed feature is invariant across domains, our m del thus possesses better generalization ability. Open tests on CIPS-SIGHAN-2010 show that the enhance d gen rative model achieves robust cross-domain performance for various OOV cov erage rates and obtains the best performance on three out of four domains. The enhanced gen erative model is then further integrated with a discriminative model which also utilizes dictionary information . This integrated model is shown to be either superior or comparable to all other models repo rted in the literatur e on every domain of this task.

Neural Networks and Graph Algorithms with Next-Generation Processors <=SEP=> The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.

An algorithm for treatment of the drooping nose. <=SEP=> UNLABELLED
Nasal tip ptosis ("drooping" or long nose) occurs when the tip of the nose is more caudal than what is deemed ideal. Intrinsic factors, such as elongated or caudally-rotated lower lateral cartilages, can lead to nasal tip ptosis. Extrinsic factors, such as elongated upper lateral cartilages or excessive caudal anterior septum and heavy nasal skin, can push the nasal tip caudally and lead to drooping of the nasal tip. The loss of maxillary or nasal spine support may enhance the potential for tip ptosis. In addition, a hyperactive depressor nasi septi could, as a result of continuous pull on the tip, result in tip ptosis. Aging or previous nasal procedures (such as the Goldman-type tip surgery) where the continuity of the lateral and medial crura of the lower lateral cartilages have been violated may cause a weakening of the tip-supporting mechanisms and de-rotation of the nasal tip. Correction of this deformity is challenging and rewarding; it can resolve both the cosmetic deformity and nasal obstruction symptoms related to this entity. The goal of this article is to present our current principles of diagnosis and treatment of nasal tip ptosis, as well as to introduce and algorithm of preferred methods and techniques for its reliable and stable correction.


RESULTS
Correction of the nasal tip ptosis requires accurate diagnosis, a recognition of the interplay between various anatomic components, specific strategy planning, and a correction of anatomic abnormalities.

Identification of extract method refactoring opportunities for the decomposition of methods <=SEP=> The extraction of a code fragment into a separate method is one of the most widely performed refactoring activities, since it allows the decomposition of large and complex methods and can be used in combination with other code transformations for fixing a variety of design problems. Despite the significance of Extract Method refactoring towards code quality improvement, there is limited support for the identification of code fragments with distinct functionality that could be extracted into new methods. The goal of our approach is to automatically identify Extract Method refactoring opportunities which are related with the complete computation of a given variable (complete computation slice) and the statements affecting the state of a given object (object state slice). Moreover, a set of rules regarding the preservation of existing odule decomposition dependences is proposed that exclude refactoring opportunities corresponding to slices whose extraction could possibly cause a change in program behavior. The proposed approach has been evaluated regarding its ability to capture slices of code implementing a distinct functionality, its ability to resolve existing design flaws, its impact on the cohesion of the decomposed and extracted methods, and its ability to preserve program behavior. Moreover, precision and recall have been computed employing the refactoring depe opportunities found by in

WHY PEOPLE STAY : USING JOB EMBEDDEDNESS TO PREDICT VOLUNTARY TURNOVER <=SEP=> A new construct, entitled job embeddedness, is introduced. Assessing factors from on and off the job, it includes an individual’s (a) links to other people, teams and groups, (b) perception of their fit with their job, organization and community and (c) what they say they would have to sacrifice if they left their job. A measure of job embeddedness is developed with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and voluntary turnover, and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives and job search. Implications for theory and practice are discussed.

Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture <=SEP=> Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks (Eedn) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.

Hopelessness Depression : A Theory-Based Subtype of Depression <=SEP=> We present a revision of the 1978 reformulated theory of helplessness and depression and call it the hopelessness theory of depression. Although the 1978 reformulation has generated a vast amount of empirical work on depression over the past 10 years and recently has been evaluated as a model of depression, we do not think that it presents a clearly articulated theory of depression. We build on the skeletal logic of the 1978 statement and (a) propose a hypothesized subtype of depression— hopelessness depression, (b) introduce hopelessness as a proximal sufficient cause of the symptoms of hopelessness depression, (c) deemphasize causal attributions because inferred negative consequences and inferred negative characteristics about the self are also postulated to contribute to the formation of hopelessness and, in turn, the symptoms of hopelessness depression, and (d) clarify the diathesisstress and causal mediation components implied, but not explicitly articulated, in the 1978 statement. We report promising findings for the hopelessness theory and outline the aspects that still need to be tested.

3D laser scan classification using web data and domain adaptation <=SEP=> Over the last years, object recognition has become a more and more active field of research in robotics. An important problem in object recognition is the need for sufficient labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google’s 3D Warehouse to train classifiers for 3D laser scans collected by a robot navigating through urban environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled 3D laser scans and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real laser scans.

SSS: a hybrid architecture applied to robot navigation <=SEP=> This paper describes a new three layer architecture, SSS, for robot control. It combines a servo-control layer, a Òsubsumption" layer, and a symbolic layer in a way that allows the advantages of each technique to be fully exploited. The key to this synergy is the interface between the individual subsystems. We describe how to build situation recognizers that bridge the gap between the servo and subsumption layers, and event detectors that link the subsumption and symbolic layers. The development of such a combined system is illustrated by a fully implemented indoor navigation example. The resulting real robot, called ÒTJ", is able automatically map office building environments and smoothly navigate through them at the rapid speed of 2.6 feet per second.

Deep Face Recognition: A Survey <=SEP=> Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.

Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing <=SEP=> Vocal perception is particularly important for understanding a speaker's emotional state and intentions because, unlike facial perception, it is relatively independent of speaker distance and viewing conditions. The idea, derived from brain lesion studies, that vocal emotional comprehension is a special domain of the right hemisphere has failed to receive consistent support from neuroimaging. This conflict can be reconciled if vocal emotional comprehension is viewed as a multi-step process with individual neural representations. This view reveals a processing chain that proceeds from the ventral auditory pathway to brain structures implicated in cognition and emotion. Thus, vocal emotional comprehension appears to be mediated by bilateral mechanisms anchored within sensory, cognitive and emotional processing systems.

Generating English from Abstract Meaning Representations <=SEP=> We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences. We treat AMR-to-English generation as phrase-based machine translation (PBMT). We introduce a method that learns to linearize tokens of AMR graphs into an English-like order. Our linearization reduces the amount of distortion in PBMT and increases generation quality. We report a Bleu score of 26.8 on the standard AMR/English test set.

A Novel Distributed SDN-Secured Architecture for the IoT <=SEP=> Due to their rapid evolution, mobile devices demand for more dynamic and flexible networking services. A major challenges of future mobile networks is the increased mobile traffic. With the recent upcoming technologies of network programmability like Software-Defined Network (SDN), it may be integrated to create a new communication platform for Internet of Things (IoT). In this work, we present how to determine the effectiveness of an approach to build a new secured network architecture based on SDN and clusters. Our proposed scheme is a starting point for some experiments providing perspective over SDN deployment in a cluster environment. With this aim in mind, we suggest a routing protocol that manages routing tasks over Cluster-SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system controlled by SDN. With our testbed, we are able to manage 500 things. We can analyze every OpenFlow messages and we have discovered that with a particular flow, the things can exchange information unlike the routing principle.

Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation <=SEP=> Sponsored search advertising is largely based on bidding on individual terms. The richness of natural languages permits web searchers to express their information needs in myriad ways. Advertisers have difficulty discovering all the terms that are relevant to their products or services. We examine the performance of logistic regression and collaborative filtering models on two different data sources to predict terms relevant to a set of seed terms describing an advertiser’s product or service.

Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean <=SEP=> Suppression of impulse noise in images is an important problem in image processing. In this paper, we propose a novel adaptive iterative fuzzy filter for denoising images corrupted by impulse noise. It operates in two stages-detection of noisy pixels with an adaptive fuzzy detector followed by denoising using a weighted mean filter on the “good” pixels in the filter window. Experimental results demonstrate the algorithm to be superior to state-of-the-art filters. The filter is also shown to be robust to very high levels of noise, retrieving meaningful detail at noise levels as high as 97%.

Ant colony optimization for routing and load-balancing: survey and new directions <=SEP=> Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field ofant colony optimization (ACO), models ofcollective intelligenceof ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigatingstagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include:evaporation, aging, pheromone smoothingand limiting, privileged pheromone layingandpheromone-heuristic control . The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNetand its extensions, as well as ASGAand SynthECA. Discussions on new directions include an ongoing work of the authors in applyingmultiple ant colony optimizationin load-balancing.