Query: Comparison of Approximate Methods for Handling Hyperparameters

Relevant passages:
Learning representations by back-propagating errors <=SEP=> We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.

Probabilistic Inference Using Markov Chain Monte Carlo Methods <=SEP=> Probabilistic inference is an attractive approach to uncertain reasoning and em pirical learning in arti cial intelligence Computational di culties arise however because probabilistic models with the necessary realism and exibility lead to com plex distributions over high dimensional spaces Related problems in other elds have been tackled using Monte Carlo methods based on sampling using Markov chains providing a rich array of techniques that can be applied to problems in arti cial intelligence The Metropolis algorithm has been used to solve di cult problems in statistical physics for over forty years and in the last few years the related method of Gibbs sampling has been applied to problems of statistical inference Concurrently an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well and has recently been uni ed with the Metropolis algorithm to produce the hybrid Monte Carlo method In computer science Markov chain sampling is the basis of the heuristic optimization technique of simulated annealing and has recently been used in randomized algorithms for approximate counting of large sets In this review I outline the role of probabilistic inference in arti cial intelligence present the theory of Markov chains and describe various Markov chain Monte Carlo algorithms along with a number of supporting techniques I try to present a comprehensive picture of the range of methods that have been developed including techniques from the varied literature that have not yet seen wide application in arti cial intelligence but which appear relevant As illustrative examples I use the problems of probabilistic inference in expert systems discovery of latent classes from data and Bayesian learning for neural networks

The Evidence Framework Applied to Classification Networks <=SEP=> Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalizing over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a "moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework successfully chooses the magnitude of weight decay terms, and ranks solutions found using different numbers of hidden units. Third, an information-based data selection criterion is derived and demonstrated within this framework.

Bayesian Learning for Neural Networks <=SEP=> 

Generalization by Weight-Elimination with Application to Forecasting <=SEP=> Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates.

A case study of user participation in the information systems development process <=SEP=> There are many in the information systems discipline who believe that user participation is necessary for successful systems development. However, it has been suggested that this belief is neither grounded in theory nor substantiated by research data. This may indicate that researchers have not addressed fully the underlying complexity of the concept. If so, this is indicative of a deficiency in understanding user participation in information systems development as it occurs in organizations. In order to enhance the extant understanding of participative information systems development, the present study adopts a qualitative, case-based approach to research so as to provide an in-depth description of the complex social nature of the phenomenon as manifested in one organization. The results of the study illustrate that a high degree of direct and indirect user participation did not guarantee the successful implementation and use of information systems in the organization studied. Such participatory development practices did, however, result in the development of systems that adequately captured user requirements and hence satisfied user informational needs. It was clear that despite the perceived negative impact, which the new systems would have on user work-related roles and activities, the existence of an organization-wide participative policy, and associated participative structures, coupled with a favorable organization culture, generated a participatory development climate that was conducive to the successful development of information systems, while not guaranteeing it. That said, the central conclusion of this study was that user dissatisfaction with developed systems centered on the poor management of change in the organization.

Spatial Data Mining Features between General Data Mining <=SEP=> Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.

Real-time human gesture grading based on OpenPose <=SEP=> In this paper, we presented a real-time 2D human gesture grading system from monocular images based on OpenPose, a library for real-time multi-person keypoint detection. After capturing 2D positions of a person's joints and skeleton wireframe of the body, the system computed the equation of motion trajectory for every joint. Similarity metric was defined as distance between motion trajectories of standard and real-time videos. A modifiable scoring formula was used for simulating the gesture grading scenario. Experimental results showed that the system worked efficiently with high real-time performance, low cost of equipment and strong robustness to the interference of noise.

Continuous Integration and Its Tools <=SEP=> Continuous integration has been around for a while now, but the habits it suggests are far from common practice. Automated builds, a thorough test suite, and committing to the mainline branch every day sound simple at first, but they require a responsible team to implement and constant care. What starts with improved tooling can be a catalyst for long-lasting change in your company's shipping culture. Continuous integration is more than a set of practices, it's a mindset that has one thing in mind: increasing customer value. The Web extra at http://youtu.be/tDl_cHfrJZo is an audio podcast of the Tools of the Trade column discusses how continuous integration is more than a set of practices, it's a mindset that has one thing in mind: increasing customer value.

The evaluation of children in the primary care setting when sexual abuse is suspected. <=SEP=> This clinical report updates a 2005 report from the American Academy of Pediatrics on the evaluation of sexual abuse in children. The medical assessment of suspected child sexual abuse should include obtaining a history, performing a physical examination, and obtaining appropriate laboratory tests. The role of the physician includes determining the need to report suspected sexual abuse; assessing the physical, emotional, and behavioral consequences of sexual abuse; providing information to parents about how to support their child; and coordinating with other professionals to provide comprehensive treatment and follow-up of children exposed to child sexual abuse.

Malaria Models with Spatial Effects <=SEP=> Malaria, a vector-borne infectious disease caused by the Plasmodium parasite, is still endemic in more than 100 countries in Africa, Southeast Asia, the Eastern Mediterranean, Western Pacific, Americas, and Europe. In 2010 there were about 219 million malaria cases, with an estimated 660,000 deaths, mostly children under 5 in sub-Saharan Africa (WHO 2012). The malaria parasite is transmitted to humans via the bites of infected female mosquitoes of the genus Anopheles. Mosquitoes can become infected when they feed on the blood of infected humans. Thus the infection goes back and forth between humans and mosquitoes. Mathematical modeling of malaria transmission has a long history. It has helped us to understand transmission mechanism, design and improve control measures, forecast disease outbreaks, etc. The so-called Ross–Macdonald model

Contextual Video Recommendation by Multimodal Relevance and User Feedback <=SEP=> With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online video services. This article presents a novel contextual video recommendation system, called VideoReach, based on multimodal content relevance and user feedback. We consider an online video usually consists of different modalities (i.e., visual and audio track, as well as associated texts such as query, keywords, and surrounding text). Therefore, the recommended videos should be relevant to current viewing in terms of multimodal relevance. We also consider that different parts of videos are with different degrees of interest to a user, as well as different features and modalities have different contributions to the overall relevance. As a result, the recommended videos should also be relevant to current users in terms of user feedback (i.e., user click-through). We then design a unified framework for VideoReach which can seamlessly integrate both multimodal relevance and user feedback by relevance feedback and attention fusion. VideoReach represents one of the first attempts toward contextual recommendation driven by video content and user click-through, without assuming a sufficient collection of user profiles available. We conducted experiments over a large-scale real-world video data and reported the effectiveness of VideoReach.

CONSTRAINTS AND APPROACHES FOR DISTRIBUTED SENSOR NETWORK SECURITY <=SEP=> Executive Summary Confidentiality, integrity, and authentication services are critical to preventing an adversary from compromising the security of a distributed sensor network. Key management is likewise critical to establishing the keys necessary to provide this protection. However, providing key management is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the sensor network environment. As part of the SensIT program, NAI Labs is addressing this problem by identifying and developing cryptographic protocols and mechanisms that efficiently provide key management security support services. This document describes our sensor network constraints and key management approaches research for FY 2000. As a first step, NAI Labs has researched battlefield sensor and sensor network technology and the unique communications environment in which it will be deployed. We have identified the requirements specific to our problem of providing key management for confidentiality and group-level authentication. We have also identified constraints, particularly energy consumption, that render this problem difficult. NAI Labs has developed novel key management protocols specifically designed for the distributed sensor network environment, including Identity-Based Symmetric Keying and Rich Uncle. We have analyzed both existing and NAI Labs-developed keying protocols for their suitability at satisfying identified requirements while overcoming battlefield energy constraints. Our research has focused heavily on key management energy consumption, evaluating protocols based on total system, average sensor node, and individual sensor node energy consumption. We examined a number of secret-key-based protocols, determining some to be suitable for sensor networks but all of the protocols have flexibility limitations. Secret-key-based protocols are generally energy-efficient, using encryption and hashing algorithms that consume relatively little energy. Security of secret-key-based protocols is generally determined by the granularity of established keys, which vary widely for the protocols described herein. During our examination of these protocols we noted that some of these protocols are not sufficiently flexible for use in battlefield sensor network, since they cannot efficiently handle unanticipated additions of sensor nodes to the network. Our Identity-Based Symmetric Keying protocol and the less efficient Symmetric Key Certificate Based Protocol are well suited for certain sensor networks, establishing granular keys while consuming relatively little energy. However, all of the secure secret-key-based protocols use special nodes that operate as Key Distribution Centers (or Translators). The sensor nodes communicate with these centers exchanging information as part of the key establishment process. Since these special nodes are expected to make up less than 1% of the sensor …

Evidences for the Anti-panic Actions of Cannabidiol <=SEP=> BACKGROUND
Panic disorder (PD) is a disabling psychiatry condition that affects approximately 5% of the worldwide population. Currently, long-term selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for PD; however, the common side-effect profiles and drug interactions may provoke patients to abandon the treatment, leading to PD symptoms relapse. Cannabidiol (CBD) is the major non-psychotomimetic constituent of the Cannabis sativa plant with antianxiety properties that has been suggested as an alternative for treating anxiety disorders. The aim of the present review was to discuss the effects and mechanisms involved in the putative anti-panic effects of CBD.


METHODS
electronic database was used as source of the studies selected selected based on the studies found by crossing the following keywords: cannabidiol and panic disorder; canabidiol and anxiety, cannabidiol and 5-HT1A receptor).


RESULTS
In the present review, we included both experimental laboratory animal and human studies that have investigated the putative anti-panic properties of CBD. Taken together, the studies assessed clearly suggest an anxiolytic-like effect of CBD in both animal models and healthy volunteers.


CONCLUSIONS
CBD seems to be a promising drug for the treatment of PD. However, novel clinical trials involving patients with the PD diagnosis are clearly needed to clarify the specific mechanism of action of CBD and the safe and ideal therapeutic doses of this compound.

Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV <=SEP=> Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g . road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.

Twitter games: how successful spammers pick targets <=SEP=> Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network.
 We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73% of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by Thomas et al., spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in Thomas et al.'s work. Specifically, we identify four major strategies used by 2/3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution.

Structure Based User Identification across Social Networks <=SEP=> Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.

libtissue - implementing innate immunity <=SEP=> In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients <=SEP=> AIM
To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use.


METHODS
In total, 11 AC methods were evaluated: two vendor-implemented (MR-ACDIXON and MR-ACUTE), five based on template/atlas information (MR-ACSEGBONE (Koesters et al., 2016), MR-ACONTARIO (Anazodo et al., 2014), MR-ACBOSTON (Izquierdo-Garcia et al., 2014), MR-ACUCL (Burgos et al., 2014), and MR-ACMAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-ACMLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-ACMUNICH (Cabello et al., 2015), MR-ACCAR-RiDR (Juttukonda et al., 2015), and MR-ACRESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [18F]FDG (210), [11C]PiB (51), and [18F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis.


RESULTS
The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-ACDIXON (-11.3±3.5)%, MR-ACUTE (-5.7±2.0)%, MR-ACONTARIO (-4.3±3.6)%, MR-ACMUNICH (3.7±2.1)%, MR-ACMLAA (-1.9±2.6)%, MR-ACSEGBONE (-1.7±3.6)%, MR-ACUCL (0.8±1.2)%, MR-ACCAR-RiDR (-0.4±1.9)%, MR-ACMAXPROB (-0.4±1.6)%, MR-ACBOSTON (-0.3±1.8)%, and MR-ACRESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-ACCAR-RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir.


CONCLUSIONS
All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.

A System for Video Surveillance and Monitoring <=SEP=> The Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation are developing a system for autonomous Video Surveillance and Monitoring. The technical objective is to use multiple, cooperative video sensors to provide continuous coverage of people and vehicles in cluttered environments. This paper presents an overview of the system and significant results achieved to date.

Web Accessibility Evaluation <=SEP=> Web accessibility evaluation is a broad field that combines different disciplines and skills. It encompasses technical aspects such as the assessment of conformance to standards and guidelines, as well as non-technical aspects such as the involvement of end-users during the evaluation process. Since Web accessibility is a qualitative and experiential measure rather than a quantitative and concrete property, the evaluation approaches need to include different techniques andmaintain flexibility and adaptability toward different situations. At the same time, evaluation approaches need to be robust and reliable so that they can be effective. This chapter explores some of the techniques and strategies to evaluate the accessibility of Web content for people with disabilities. It highlights some of the common approaches to carry out andmanage evaluation processes rather than list out individual steps for evaluating Web content. This chapter also provides an outlook to some of the future directions in which the field seems to be heading, and outlines some opportunities for research and

Phish Phinder: A Game Design Approach to Enhance User Confidence in Mitigating Phishing Attacks <=SEP=> Phishing is an especially challenging cyber security threat as it does not attack computer systems, but targets the user who works on that system by relying on the vulnerability of their decision-making ability. Phishing attacks can be used to gather sensitive information from victims and can have devastating impact if they are successful in deceiving the user. Several anti-phishing tools have been designed and implemented but they have been unable to solve the problem adequately. This failure is often due to security experts overlooking the human element and ignoring their fallibility in making trust decisions online. In this paper, we present Phish Phinder, a serious game designed to enhance the user’s confidence in mitigating phishing attacks by providing them with both conceptual and procedural knowledge about phishing. The user is trained through a series of gamified challenges, designed to educate them about important phishing related concepts, through an interactive user interface. Key elements of the game interface were identified through an empirical study with the aim of enhancing user interaction with the game. We also adopted several persuasive design principles while designing Phish Phinder to enhance phishing avoidance behaviour among users.

Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age <=SEP=> With modern computer architecture evolving, two problems conspire against the state-of-the-art approaches in parallel query execution: (i) to take advantage of many-cores, all query work must be distributed evenly among (soon) hundreds of threads in order to achieve good speedup, yet (ii) dividing the work evenly is difficult even with accurate data statistics due to the complexity of modern out-of-order cores. As a result, the existing approaches for plan-driven parallelism run into load balancing and context-switching bottlenecks, and therefore no longer scale. A third problem faced by many-core architectures is the decentralization of memory controllers, which leads to Non-Uniform Memory Access (NUMA). In response, we present the morsel-driven query execution framework, where scheduling becomes a fine-grained run-time task that is NUMA-aware. Morsel-driven query processing takes small fragments of input data (morsels) and schedules these to worker threads that run entire operator pipelines until the next pipeline breaker. The degree of parallelism is not baked into the plan but can elastically change during query execution, so the dispatcher can react to execution speed of different morsels but also adjust resources dynamically in response to newly arriving queries in the workload. Further, the dispatcher is aware of data locality of the NUMA-local morsels and operator state, such that the great majority of executions takes place on NUMA-local memory. Our evaluation on the TPC-H and SSB benchmarks shows extremely high absolute performance and an average speedup of over 30 with 32 cores.

Mean-shift Blob Tracking through Scale Space <=SEP=> The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing or updating scale while tracking blobs that are changing in size. We adapt Lindeberg’s theory of feature scale selection based on local maxima of differential scale-space filters to the problem of selecting kernel scale for mean-shift blob tracking. We show that a difference of Gaussian (DOG) mean-shift kernel enables efficient tracking of blobs through scale space. Using this kernel requires generalizing the mean-shift algorithm to handle images that contain negative sample weights.

Research on Chinese text classification based on Word2vec <=SEP=> The set of features which the traditional feature selection algorithm of chi-square selected is not complete. This causes the low performance for the final text classification. Therefore, this paper proposes a method. The method utilizes word2vec to generate word vector to improve feature selection algorithm of the chi square. The algorithm applies the word vector generated by word2vec to the process of the traditional feature selection and uses these words to supplement the set of features as appropriate. Ultimately, the set of features obtained by this method has better discriminatory power. Because, the feature words with the better discriminatory power has the strong ability of distinguishing categories as its semantically similar words. On this base, multiple experiments have been carried out in this paper. The experimental results show that the performance of text classification can increase after extension of feature words.

Deep Learning Advances on Different 3D Data Representations: A Survey <=SEP=> 3D data is a valuable asset in the field of computer vision as it provides rich information about the full geometry of sensed objects and scenes. With the recent availability of large 3D datasets and the increase in computational power, it is today possible to consider applying deep learning to learn specific tasks on 3D data such as segmentation, recognition and correspondence. Depending on the considered 3D data representation, different challenges may be foreseen in using existent deep learning architectures. In this paper, we provide a comprehensive overview of various 3D data representations highlighting the difference between Euclidean and non-Euclidean ones. We also discuss how deep learning methods are applied on each representation, analyzing the challenges to

INOCULANTS OF PLANT GROWTH-PROMOTING BACTERIA FOR USE IN AGRICULTURE <=SEP=> An assessment of the current state of bacterial inoculants for c ontemporary agriculture in developed and developing countries is critically evaluated from the point of view of their actual status and future use. Special emphasis is given to two new concepts of inoculation, as yet unavailable commercially: (i) synthetic inoculants under development for plant-growth promoting bacteria (PGPB) (Bashan and Holguin, 1998), and (ii) inoculation by groups of associated bacteria. This review contains: A brief historical overview of bacterial inoculants; the rationale for plant inoculation with emphasis on developing countries and semiarid agriculture, and the concept and application of mixed inoculant; discussion of microbial formulation including optimization of carrier-compound characteristics, types of existing carriers for in oculants, traditional formulations, future trends in formulations using unconventional materials, encapsulated synthetic formulations, macro and micro formulations of alginate, encapsulation of beneficial bacteria using other materials, regulation and contamination of commercial inoculants, and examples of modem commercial bacterial inoculants; and a consideration of time constraints and application methods for bacterial inoculants, commercial production, marketing, and the prospects of inoculants in modern agriculture. ® 1998 Elsevier Science Inc.

Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. <=SEP=> This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.

Enterprise Content Management - A Literature Review <=SEP=> Managing information and content on an enterprise-wide scale is challenging. Enterprise content management (ECM) can be considered as an integrated approach to information management. While this concept received much attention from practitioners, ECM research is still an emerging field of IS research. Most authors that deal with ECM claim that there is little scholarly literature available. After approximately one decade of ECM research, this paper provides an in-depth review of the body of academic research: the ECM domain, its evolution, and main topics are characterized. An established ECM research framework is adopted, refined, and explained with its associated elements and working definitions. On this basis, 68 articles are reviewed, classified, and concepts are derived. Prior research is synthesized and findings are integrated in a conceptcentric way. Further, implications for research and practice, including future trends, are drawn.

A New Mura Defect Inspection Way for TFT-LCD Using Level Set Method <=SEP=> Mura is a typical vision defect of LCD panel, appearing as local lightness variation with low contrast and blurry contour. This letter presents a new machine vision inspection way for Mura defect based on the level set method. First, a set of real Gabor filters are applied to eliminating the global textured backgrounds. Then, the level set method is employed for image segmentation with a new region-based active contours model, which is an improvement of the Chan-Vese's model so that it more suitable to the segmentation of Mura. Using some results from the level set based segmentation, the defects are quantified based on the SEMU method. Experiments show that the proposed method has better performance for Mura detection and quantification.