Query: Personality Consistency in Dogs: A Meta-Analysis

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Development of personality in early and middle adulthood: set like plaster or persistent change? <=SEP=> Different theories make different predictions about how mean levels of personality traits change in adulthood. The biological view of the Five-factor theory proposes the plaster hypothesis: All personality traits stop changing by age 30. In contrast, contextualist perspectives propose that changes should be more varied and should persist throughout adulthood. This study compared these perspectives in a large (N = 132,515) sample of adults aged 21-60 who completed a Big Five personality measure on the Internet. Conscientiousness and Agreeableness increased throughout early and middle adulthood at varying rates; Neuroticism declined among women but did not change among men. The variety in patterns of change suggests that the Big Five traits are complex phenomena subject to a variety of developmental influences.

Nature over nurture: temperament, personality, and life span development. <=SEP=> Temperaments are often regarded as biologically based psychological tendencies with intrinsic paths of development. It is argued that this definition applies to the personality traits of the five-factor model. Evidence for the endogenous nature of traits is summarized from studies of behavior genetics, parent-child relations, personality structure, animal personality, and the longitudinal stability of individual differences. New evidence for intrinsic maturation is offered from analyses of NEO Five-Factor Inventory scores for men and women age 14 and over in German, British, Spanish, Czech, and Turkish samples (N = 5,085). These data support strong conceptual links to child temperament despite modest empirical associations. The intrinsic maturation of personality is complemented by the culturally conditioned development of characteristic adaptations that express personality; interventions in human development are best addressed to these.

A power primer. <=SEP=> One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

Gender differences in personality: a meta-analysis. <=SEP=> Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.

Personality and Performance at the Beginning of the New Millennium : What Do We Know and Where Do We Go Next ? <=SEP=> As we begin the new millennium, it is an appropriate time to examine what we have learned about personality-performance relationships over the past century and to embark on new directions for research. In this study we quantitatively summarize the results of 15 prior meta-analytic studies that have investigated the relationship between the Five Factor Model (FFM) personality traits and job performance. Results support the previous findings that conscientiousness is a valid predictor across performance measures in all occupations studied. Emotional stability was also found to be a generalizable predictor when overall work performance was the criterion, but its relationship to specific performance criteria and occupations was less consistent than was conscientiousness. Though the other three Big Five traits (extraversion, openness and agreeableness) did not predict overall work performance, they did predict success in specific occupations or relate to specific criteria. The studies upon which these results are based comprise most of the research that has been conducted on this topic in the past century. Consequently, we call for a moratorium on meta-analytic studies of the type reviewed in our study and recommend that researchers embark on a new research agenda designed to further our understanding of personalityperformance linkages.

3D printing bone models extracted from medical imaging data <=SEP=> Additive manufacturing in the form of 3D printing is becoming more common with the rise of low-cost hobby machines which makes research in the field of medicine more accessible. This article explores and experiments with methods of producing three dimensional replicas of body parts as education material from the medical imaging data of Computed Tomography and Magnetic Resonance Imaging.

A novel redis security extension for NoSQL database using authentication and encryption <=SEP=> Redis is a new generation NoSQL database. Redis in its simplest form is a key value pair based data system. It supports all the data structures like variables, Linked list, arrays, strings, and queues. However unlike the conventional databases, Redis does not provide enough security for the data. Anyone can get the value if the key is known because the data is stored in the form of key value pair. Therefore such a database is unsuitable for enterprise and most practical application data. In this paper, the work is carried out to add immense security to a Redis system using following: a) Authentication Service b) Encryption Services c) Security to persistent data d) Security to blob data (multimedia data for images). The Encryption algorithm plays a very important role in the field of Database Management System. Here we make use of AES algorithm because the AES algorithm consumes least Encryption and Decryption time in comparison with RSA and DES [12]. The principle of the work is that a separate Key is created in the database whose value is an encrypted data, encrypted by symmetric key cryptography using AES. This data contains all other key values being concatenated and encrypted. Once a query is generated, first the extraction of independent data entities are made, followed by decryption of data using symmetric key. We also design a UI system to demonstrate the capabilities of the system with and without the security implementation. The result of our work shows that adding the security extension does not increase the overhead by much in terms of system resources and latency. We also extend the key-value based system to be able to store binary image data which is also stored in the encrypted pattern.

SPICE Models to Analyze Radiated and Conducted Susceptibilities of Shielded Coaxial Cables <=SEP=> This paper presents simulation program with integrated circuit emphasis (SPICE) models for the analyses of the radiated and conducted susceptibilities of shielded coaxial cables. Derived from the transmission-line equations of shielded cables, these models can be directly used for the time-domain and frequency-domain analyses, and connected to nonlinear and time-varying loads with the models already available in SPICE. In the proposed models, the usual discretization of shielded cables is not needed, and both the transfer impedance and admittance have been taken into account. Results obtained with these new models are in a good agreement with those obtained by using other approaches.

Network navigability in the social Internet of Things <=SEP=> The Internet of Things is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner. However, in the resulting network, every object will still have to manage a large number of friends, slowing down the search of the services. In this work, we intend to address this issue by analyzing possible strategies to drive the objects to select the appropriate links for the benefit of overall network navigability.

Requirements Elicitation for an Inter-Organizational Business Intelligence System for Small and Medium Retail Enterprises <=SEP=> Business Intelligence (BI) is on everyone's lips nowadays, since it provides businesses with the possibility to analyze their business practices and improve them. However, Small and Medium Enterprises (SME) often cannot leverage the positive effects of BI because of missing resources like personnel, knowledge, or money. Since SME pose a major form of business organization, this fact has to be overcome. As the retail industry is a substantial part of the SME branch, we propose an inter-organizational approach for a BI system for retail SME, which allows them to collaboratively collect data and perform analysis task. The aim of our ongoing research effort is the development of such a system following the Design Science Research Methodology. Within this article, the status quo of current BI practices in SME in the retail industry is analyzed through qualitative interviews with ten SME managers. Afterwards, adoption and success factors of BI systems and Inter-organizational Information Systems are worked out in a comprehensive structured literature review. Based on the status quo and the adoption and success factors, first requirements for the acceptance of an inter-organizational BI system are identified and validated in another round of qualitative interviews. This leads to nine functional requirements and three non-functional requirements, which can be used for designing and implementing an inter-organizational BI system for SME in the following research efforts.

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions <=SEP=> Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.

Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex <=SEP=> IN the hippocampus and neocortex, high-frequency (tetanic) stimu-lation of an afferent pathway leads to long-term potentiation (LTP) of synaptic transmission1–5. In the hippocampus it has recently been shown that long-term depression (LTD) of excitatory transmission can also be induced by certain combinations of synaptic activation6,7. In most hippocampal8 and all neocortical pathways4,9–11 studied so far, the induction of LTP requires the activation of JV-methyl-D-aspartate (NMDA) receptorgated conductances. Here we report that LTD can occur in neurons of slices of the rat visual cortex and that the same tetanic stimulation can induce either LTP or LTD depending on the level of depolarization of the postsynaptic neuron. By applying intracellular current injections or pharmacological disinhibition to modify the depolarizing response of the postsynaptic neuron to tetanic stimulation, we show that the mechanisms of induction of LTD and LTP are both postsynaptic. LTD is obtained if postsynaptic depolarization exceeds a critical level but remains below a threshold related to NMDA receptorgated conductances, whereas LTP is induced if this second threshold is reached.

Semi-Supervised Text Classification Using EM <=SEP=> For several decades, statisticians have advocated using a c ombination of labeled and unlabeled data to train classifiers by estimating parameters o f a generative model through iterative Expectation-Maximization (EM) techniques. Thi s chapter explores the effectiveness of this approach when applied to the domain of text class ification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This model is an ext remely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classifi cat on with generative models. First, despite the simplistic representation, some text do mains have a high positive correlation between generative model probability and classifica tion accuracy. In these domains, a straightforward application of EM with the naive Bayes tex t model works well. Second, some text domains do not have this correlation. Here we can ad opt a more expressive and appropriate generative model that does have a positive c orrelation. In these domains, semi-supervised learning again improves classification ac curacy. Finally, EM suffers from the problem of local maxima, especially in high dimension do mains such as text classification. We demonstrate that deterministic annealing, a varia nt of EM, can help overcome the problem of local maxima and increase classification accurac y further when the generative model is appropriate.

Bert: a scheduler for best-effort and realtime paths <=SEP=> We describe a new algorithm, called BERT, that can be used to schedule both best effort and realtime tasks on a multimedia workstation. BERT exploits two innovations. First, it is based on the virtual clock algorithm originally developed to schedule bandwidth on packet switches. Because this algorithm maintains a relationship between virtual time and real time, our algorithm allows us to simultaneously factor realtime deadlines into the scheduling decision, and to allocate some fraction of the processor to best effort tasks. Second, BERT includes a mechanism that allows one task to steal cycles from another. This mechanism is valuable for two reasons: it allows us to distinguish among tasks based on their importance, and it results in a robust algorithm that is not overly sensitive to the task having made a perfect reservation.

Similarity and Locality Based Indexing for High Performance Data Deduplication <=SEP=> Data deduplication has gained increasing attention and popularity as a space-efficient approach in backup storage systems. One of the main challenges for centralized data deduplication is the scalability of fingerprint-index search. In this paper, we propose SiLo, a near-exact and scalable deduplication system that effectively and complementarily exploits similarity and locality of data streams to achieve high duplicate elimination, throughput, and well balanced load at extremely low RAM overhead. The main idea behind SiLo is to expose and exploit more similarity by grouping strongly correlated small files into a segment and segmenting large files, and to leverage the locality in the data stream by grouping contiguous segments into blocks to capture similar and duplicate data missed by the probabilistic similarity detection. SiLo also employs a locality based stateless routing algorithm to parallelize and distribute data blocks to multiple backup nodes. By judiciously enhancing similarity through the exploitation of locality and vice versa, SiLo is able to significantly reduce RAM usage for index-lookup, achieve the near-exact efficiency of duplicate elimination, maintain a high deduplication throughput, and obtain load balance among backup nodes.

Functional System and Areal Organization of a Highly Sampled Individual Human Brain <=SEP=> Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

Image processing on multinode hadoop cluster <=SEP=> In the past few years the data produced all over the internet has increased with an exponential rate. The storage costs have been rising immensely. But, in the field of Computer Science, the introduction of new technologies has resulted in reduction of storage costs. This led to a rampant rise in the data generation rates. This huge amount of data that is so vast and complex such that classical methods are insufficient for processing is termed as ‘Big Data’. There are various tools in Hadoop to analyze the textual data such as Pig, HBase, etc. But the data present on the Internet as well as the Social Networking sites comprises of unstructured media. The maximum spectrum of the media files is covered by Image data. The major concern is not about the storage of images, but the processing of images being generated with the speed of light. Every day around 350 million pictures are being uploaded on social network. Until now, over 200 billion photos have been uploaded only on Facebook. This accounts to an average of around 200 photos per user. This whole amount of data generated round the globe can be classified into three formats-Structured, Semi-Structured, and Unstructured. The Image data contains not only the pictures but also the data defining those pictures such as the resolution, source of the image, capture device, etc. To fetch all this information in a structured format HIPI (Hadoop Image Processing Interface) tools are used. In this paper image processing is performed on a MultiNode Hadoop Cluster and its performance is measured.

A Fast Similarity Join Algorithm Using Graphics Processing Units <=SEP=> A similarity join operation A BOWTIEepsiv B takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data <=SEP=> Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source–destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events.

A 1.6Gbps Digital Clock and Data Recovery Circuit <=SEP=> A digital clock and data recovery circuit employs simple 3-level digital-to-analog converters to interface the digital loop filter to the voltage controlled oscillator and achieves low jitter performance. Test chip fabricated in a 0.13mum CMOS process achieves BER < 10-12 , plusmn1500ppm lock-in range, plusmn2500ppm tracking range, recovered clock jitter of 8.9ps rms and consumes 12mW power from a single-pin 1.2V supply, while operating at 1.6Gbps

Deep Convolutional Networks as shallow Gaussian Processes <=SEP=> We show that the output of a (residual) convolutional neural network (CNN) with an appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike “deep kernels”, has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GPs with a comparable number of parameters. 1

From cyber-physical systems to Industry 4.0: make future manufacturing become possible <=SEP=> Recently, ‘Industry 4.0’ has become the supporting force in the manufacturing field. From CPS to Industry 4.0, they are leading a new round of industrial revolution. To meet the fourth industrial revolution, USA, European countries, Japan, South Korea and China have presented different versions of the so-called ‘Industry 4.0’ strategies respectively, which are different from each other in the content, but their common goal is to achieve manufacturing structural adjustment and restructuring and upgrading, to seize a place for future manufacturing, to provide users with better service. What is more, we summarised the five major trends of the future manufacturing. Then, we introduce some enabling technologies about Industry 4.0, among them, CPS plays a key role in the Industry 4.0 factory. Based on the customer to business (C2B) mode, we regard everything as a service, and propose a structure of Industry 4.0. Finally, the automotive industry as an example, we make a brief introduction about an Industry 4.0 case. [Received 15 December 2015; Revised 30 May 2016; Accepted 30 May 2016]

Line-start synchronous reluctance motors: Design guidelines and testing via active inertia emulation <=SEP=> Line-Start Synchronous Reluctance (LS-SyR) Motors are a viable solution to reach IE4 efficiency class with the frame size of induction motors having standard efficiency. The proposed paper reviews the guidelines for the design of LS-SyR machines and introduces a new technique for testing their capability of load synchronization. Design wise, the efficiency at synchronous speed and the load pull-in capability must be traded off. The key design principles of LS-SyR machines are reviewed in this sense, and two manufacturing solutions are proposed for comparison. A new experimental method for the evaluation of the pull-in characteristic is proposed. The test fixture consists of a torque-controlled ac drive load, equipped with a shaft accelerometer. The load applied to the line-start machine under test is augmented with a component proportional to the acceleration measurement, to emulate additional inertia.

Toward De-Anonymizing Bitcoin by Mapping Users Location <=SEP=> The Bitcoin system (https://bitcoin.org) is a pseudo-anonymous currency that can dissociate a user from any real-world identity. In that context, a successful breach of the virtual and physical divide represents a signficant aw in the Bit-coin system [1]. In this project we demonstrate how to glean information about the real-world users behind Bitcoin transactions. We analyze publicly available data about the cryptocurrency. In particular, we focus on determining information about a Bitcoin user's physical location by examining that user's spending habits.

Twitter spam detection based on deep learning <=SEP=> Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

The Effects of Feedback Interventions on Performance : A Historical Review , a Meta-Analysis , and a Preliminary Feedback Intervention Theory <=SEP=> the total number of papers may exceed 10,000. Nevertheless, cost consideration forced us to consider mostly published papers and technical reports in English. 4 Formula 4 in Seifert (1991) is in error—a multiplier of n, of cell size, is missing in the numerator. 5 Unfortunately, the technique of meta-analysis cannot be applied, at present time, to such effects because the distribution of dis based on a sampling of people, whereas the statistics of techniques such as ARIMA are based on the distribution of a sampling of observations in the time domain regardless of the size of the people sample involved (i.e., there is no way to compare a sample of 100 points in time with a sample of 100 people). That is, a sample of 100 points in time has the same degrees of freedom if it were based on an observation of 1 person or of 1,000 people. 258 KLUGER AND DENISI From the papers we reviewed, only 131 (5%) met the criteria for inclusion. We were concerned that, given the small percentage of usable papers, our conclusions might not fairly represent the larger body of relevant literature. Therefore, we analyzed all the major reasons to reject a paper from the meta-analysis, even though the decision to exclude a paper came at the first identification of a missing inclusion criterion. This analysis showed the presence of review articles, interventions of natural feedback removal, and papers that merely discuss feedback, which in turn suggests that the included studies represent 1015% of the empirical FI literature. However, this analysis also showed that approximately 37% of the papers we considered manipulated feedback without a control group and that 16% reported confounded treatments, that is, roughly two thirds of the empirical FI literature cannot shed light on the question of FI effects on performance—a fact that requires attention from future FI researchers. Of the usable 131 papers (see references with asterisks), 607 effect sizes were extracted. These effects were based on 12,652 participants and 23,663 observations (reflecting multiple observations per participant). The average sample size per effect was 39 participants. The distribution of the effect sizes is presented in Figure 1. The weighted mean (weighted by sample size) of this distribution is 0.41, suggesting that, on average, FI has a moderate positive effect on performance. However, over 38% of the effects were negative (see Figure 1). The weighted variance of this distribution is 0.97, whereas the estimate of the sampling error variance is only 0.09. A potential problem in meta-analyses is a violation of the assumption of independence. Such a violation occurs either when multiple observations are taken from the same study (Rosenthal, 1984) or when several papers are authored by the same person (Wolf, 1986). In the present investigation, there were 91 effects derived from the laboratory experiments reported by Mikulincer (e.g., 1988a, 1988b). This raises the possibility that the average effect size is biased, because his studies manipulated extreme negative FIs and used similar tasks. In fact, the weighted average d in Mikulincer's studies was —0.39; whereas in the remainder of the

Random telegraph noise (RTN) in scaled RRAM devices <=SEP=> The random telegraph noise (RTN) related read instability in resistive random access memory (RRAM) is evaluated by employing the RTN peak-to-peak (P-p) amplitude as a figure of merit (FoM). Variation of the FoM value over multiple set/reset cycles is found to follow the log-normal distribution. P-p decreases with the reduction of the read current, which allows scaling of the RRAM operating current. The RTN effect is attributed to the mechanism of activation/deactivation of the electron traps in (in HRS) or near (in LRS) the filament that affects the current through the RRAM device.

Scale Space Technique for Word Segmentation in Handwritten Documents <=SEP=> Indexing large archives of historical manuscripts, like the papers of George Washington, is required to allow rapid perusal by scholars and researchers who wish to consult the original manuscripts. Presently, such large archives are indexed manually. Since optical character recognition (OCR) works poorly with handwriting, a scheme based on matching word images called word spotting has been suggested previously for indexing such documents. The important steps in this scheme are segmentation of a document page into words and creation of lists containing instances of the same word by word image matching. We have developed a novel methodology for segmenting handwritten document images by analyzing the extent of “blobs” in a scale space representationof the image. We believe this is the first application of scale space to this problem. The algorithm has been applied to around 30 grey level images randomly picked from different sections of the George Washington corpus of 6,400 handwritten document images. An accuracy of 77 − 96 percent was observed with an average accuracy of around 87 percent. The algorithm works well in the presence of noise, shine through and other artifacts which may arise due aging and degradation of the page over a couple of centuries or through the man made processes of photocopying and scanning.

Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners <=SEP=> When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, usually planners are used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation of the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterized planner, showing that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which has 62 parameters and over 6.5 × 10 possible configurations. We demonstrate that by using this highly parameterized planning system in combination with the off-the-shelf, state-of-the-art automatic algorithm configuration procedure ParamILS, the planner can be specialized obtaining significantly improved performance.

Screen Printing of Highly Loaded Silver Inks on Plastic Substrates Using Silicon Stencils. <=SEP=> Screen printing is a potential technique for mass-production of printed electronics; however, improvement in printing resolution is needed for high integration and performance. In this study, screen printing of highly loaded silver ink (77 wt %) on polyimide films is studied using fine-scale silicon stencils with openings ranging from 5 to 50 μm wide. This approach enables printing of high-resolution silver lines with widths as small as 22 μm. The printed silver lines on polyimide exhibit good electrical properties with a resistivity of 5.5×10(-6) Ω cm and excellent bending tolerance for bending radii greater than 5 mm (tensile strains less than 0.75%).