Query: Buried Object Detection from B-Scan Ground Penetrating Radar Data Using Faster-RCNN

Relevant passages:
Rapid Object Detection using a Boosted Cascade of Simple Features <=SEP=> This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

Training Support Vector Machines: an Application to Face Detection <=SEP=> Detection (To appear in the Proceedings of CVPR'97, June 17-19, 1997, Puerto Rico.) Edgar Osunay? Robert Freund? Federico Girosiy yCenter for Biological and Computational Learning and ?Operations Research Center Massachusetts Institute of Technology Cambridge, MA, 02139, U.S.A. Abstract We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classi ers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.

Boosting the margin: A new explanation for the effectiveness of voting methods <=SEP=> One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik’s support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.

Statistical Pattern Recognition <=SEP=> This course will provide an introduction to statistical pattern recognition. The lectures will focus on different techniques including methods for feature extraction, dimensionality reduction, data clustering and pattern classification. State-of-art approaches such as ensemble learning and sparse modelling will be introduced. Selected real-world applications will illustrate how the techniques are applied in practice.

Going deeper with convolutions <=SEP=> We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

The Evolution of Accuracy and Bias in Social Judgment <=SEP=> Humans are an intensely social species and therefore it is essential for our interpersonal judgments to be valid enough to help us to avoid enemies, form useful alliances and find suitable mates; flawed judgments can literally be fatal. An evolutionary perspective implies that humans ought to have developed sufficient skills at solving problems of interpersonal judgment, including gauging the personalities of others, to be useful for the basic tasks of survival and reproduction. Yet, the view to be derived from the large and influential bias-and-error literature of social psychology is decidedly different—the social mind seems riddled with fundamental design flaws. We will argue in this paper that flawed design is probably the least plausible explanation for the existence of so many errors. We present an evolutionarily-based taxonomy of known bias effects that distinguishes between biases that are trivial or even artifactual and lead virtually nowhere, and those that have interesting implications and deserve further study. Finally, we present an evolutionary perspective that suggests that the ubiquity, automaticity, and success of interpersonal judgment, among other considerations, presents the possibility of a universal Personality Judgment Instinct. Archeological evidence and behavioral patterns observed in extant hunter-gatherer groups indicate that the human species has been intensely social for a long time (e.g., Chagnon, 1983, Tooby & Devore, 1987). Human offspring have a remarkably extended period of juvenile dependency, which both requires and provides the skills for surviving in a complex social world (Hrdy, 1999). Humans evolved language and universal emotional expressions which serve the social purpose of discerning and influencing the thoughts of others (e. and humans will infer social intentions on the basis of minimal cues, as Heider and Simmel (1944) demonstrated in their classic experiment involving chasing triangles and evading circles. Recent work has shown that children above age 4 and adults in disparate cultures (Germans and Amazonian Indians) can categorize intentions—chasing, fighting, following, playing, and courting (for adults)— from no more than the motion patterns of computerized v-shaped arrowheads (Barrett, Todd, Miller, & Blythe, in press). Most notably, humans have a deeply-felt need for social inclusion. Deprivation of social contact produces anxiety, loneliness, and depression (Baumeister & Leary, 1995); indeed, as William James (1890) observed: " Solitary confinement is by many regarded as a mode of torture Accuracy and Bias-3 too cruel and unnatural for civilised countries to adopt. " Participants in laboratory studies who are left out of a face-to-face …

Pre-Prosthetic Orthodontic Implant for Management of Congenitally Unerupted Lateral Incisors – A Case Report <=SEP=> The maxillary lateral incisor is one of the most common congenitally missing teeth of the permanent dentition. With the advent of implants in the field of restorative dentistry, a stable and predictable fixed prosthetic replacement has become a reality, especially for young adult patients who suffer from congenital absence of teeth. The dual goals of establishment of functional stability as well as enhancement of esthetic outcomes are made achievable by the placement of implants. A multidisciplinary team approach involving the triad of orthodontist, periodontist and restorative dentist will ensure the successful completion of the integrated treatment approach in these patients. The present case report achieved successful implant based oral rehabilitation in a patient diagnosed with congenital absence of bilateral maxillary lateral incisors utilizing a preprosthetic orthodontic implant site preparation for the purpose of space gain.

Efficient and accurate nearest neighbor and closest pair search in high-dimensional space <=SEP=> Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results.
 Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders of magnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results.
 As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial reduction in the space and running time. In our experiments, our technique was faster: (i) than distance browsing (a well-known method for solving the problem exactly) by several orders of magnitude, and (ii) than D-shift (an approximate approach with theoretical guarantees in low-dimensional space) by one order of magnitude, and at the same time, outputs better results.

Adversarial Examples for Generative Models <=SEP=> We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.

Exploring hacker assets in underground forums <=SEP=> Many large companies today face the risk of data breaches via malicious software, compromising their business. These types of attacks are usually executed using hacker assets. Researching hacker assets within underground communities can help identify the tools which may be used in a cyberattack, provide knowledge on how to implement and use such assets and assist in organizing tools in a manner conducive to ethical reuse and education. This study aims to understand the functions and characteristics of assets in hacker forums by applying classification and topic modeling techniques. This research contributes to hacker literature by gaining a deeper understanding of hacker assets in well-known forums and organizing them in a fashion conducive to educational reuse. Additionally, companies can apply our framework to forums of their choosing to extract their assets and appropriate functions.

Analysis of circulating tumor DNA to monitor metastatic breast cancer. <=SEP=> BACKGROUND
The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer.


METHODS
We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points.


RESULTS
Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%).


CONCLUSIONS
This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).

Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation <=SEP=> This paper addresses the problem of weakly supervised semantic image segmentation. Our goal is to label every pixel in a new image, given only image-level object labels associated with training images. Our problem statement differs from common semantic segmentation, where pixel-wise annotations are typically assumed available in training. We specify a novel deep architecture which fuses three distinct computation processes toward semantic segmentation &#x2013; namely, (i) the bottom-up computation of neural activations in a CNN for the image-level prediction of object classes, (ii) the top-down estimation of conditional likelihoods of the CNNs activations given the predicted objects, resulting in probabilistic attention maps per object class, and (iii) the lateral attention-message passing from neighboring neurons at the same CNN layer. The fusion of (i)-(iii) is realized via a conditional random field as recurrent network aimed at generating a smooth and boundary-preserving segmentation. Unlike existing work, we formulate a unified end-to-end learning of all components of our deep architecture. Evaluation on the benchmark PASCAL VOC 2012 dataset demonstrates that we outperform reasonable weakly supervised baselines and state-of-the-art approaches.

Skipping Refinement <=SEP=> We introduce skipping refinement, a new notion of correctness for reasoning about optimized reactive systems. Reasoning about reactive systems using refinement involves defining an abstract, highlevel specification system and a concrete, low-level implementation system. One then shows that every behavior allowed by the implementation is also allowed by the specification. Due to the difference in abstraction levels, it is often the case that the implementation requires many steps to match one step of the specification, hence, it is quite useful for refinement to directly account for stuttering. Some optimized implementations, however, can actually take multiple specification steps at once. For example, a memory controller can buffer the commands to the memory and at a later time simultaneously update multiple memory locations, thereby skipping several observable states of the abstract specification, which only updates one memory location at a time. We introduce skipping simulation refinement and provide a sound and complete characterization consisting of “local” proof rules that are amenable to mechanization and automated verification. We present case studies that highlight the applicability of skipping refinement: a JVM-inspired stack machine, a simple memory controller and a scalar to vector compiler transformation. Our experimental results demonstrate that current model-checking and automated theorem proving tools have difficultly automatically analyzing these systems using existing notions of correctness, but they can analyze the systems if we use skipping refinement.

Fossil chrysophycean cyst flora of Racze Lake, Wolin Island (Poland) in relation to paleoenvironmental conditions <=SEP=> The study presents stratigraphic distribution of fossil chrysophycean cysts in the bottom sediments of Racze Lake (Poland). Thirty morphotypes are described, most of them for the first time. The description of the cyst includes SEM microphotographs. The long-term relationship between the lake's conditions and the occurrence of characteristic morphotypes of chrysophycean cysts is discussed.

Design and implementation of a 120A resonant inverter for induction furnace <=SEP=> This paper presents the design and implementation of a series resonant inverter operating at high frequency for induction heating applications. The main advantage of this work is focused in applying power electronics, particularly a series resonant inverter based on an IGBT's full bridge and a series RLC Load matched with a high frequency coupling transformer. The series resonant inverter is designed to deliver a current load up to 120A with a one phase AC source. Due to the inherent high speed operation and high efficiency of switching converters, other advantages of the induction furnace presented in this paper are that it require less time to start melting, and require less physical space than other traditional furnaces.

Is Verbal Irony Special? <=SEP=> The way we speak can reveal much about what we intend to communicate, but the words we use often only indirectly relate to the meanings we wish to convey. Verbal irony is a commonly studied form of indirect speech in which a speaker produces an explicit evaluative utterance that implicates an unstated, opposing evaluation. Producing and understanding ironic language, as well as many other types of indirect speech, requires the ability to recognize mental states in others, sometimes described as a capacity for metarepresentation. This article aims to connect common elements between the major theoretical approaches to verbal irony to recent psycholinguistic, developmental, and neuropsychological research demonstrating the necessity for metarepresentation in the effective use of verbal irony in social interaction. Here I will argue that verbal irony is one emergent, strategic possibility given the interface between people’s ability to infer mental states, and use language. Rather than think of ironic communication as a specialized cognitive ability, I will claim that it arises from the same set of abilities that underlie a wide range of inferential communicative behaviors. Language interaction involves a complex interplay of many cognitive abilities. Theorists across many disciplines struggle with questions of how to carve up these abilities, and whether they can be carved up in the first place (e.g., Christiansen and Chater 2008). Among the most difficult questions to address are those that involve how people recognize intentions in others’ behavior, and how that is achieved through language in the context of many other sources of information. The way we speak can reveal much about what we intend to communicate, but the words we use are often quite different from the meanings we wish to convey. People often speak indirectly for a variety of strategic reasons, and these strategies rely intrinsically on social cognition. One well-researched example of this is the phenomenon of verbal irony – a type of indirect speech in which a speaker produces an explicit evaluative utterance that implicates an unstated, opposing evaluation. As described below, this trope has been traditionally defined in rather vague terms, but has generated a great deal of research as a phenomenon in need of special theoretical explanation. Here I will argue that verbal irony is one emergent, strategic possibility given the interface between people’s ability to infer the mental states of others, and use language. Rather than think of ironic communication as a unique skill or specialized cognitive ability, I suggest that it arises from the same set of abilities that underlie a wide range of inferential communicative behaviors. The study of verbal irony production and comprehension has been a multidisciplinary effort lacking extensive interdisciplinary exchange. There have been various taxonomies and definitions of what constitutes irony, both verbal and situational – a struggling enterprise to say the least (Colston and Gibbs 2007). The traditional notion of verbal irony dates back at least to the Roman rhetorician Quintilian, who described irony as a kind of allegory expressing the opposite of what one means, often for mocking Language and Linguistics Compass 6/11 (2012): 673–685, 10.1002/lnc3.364 a 2012 The Author Language and Linguistics Compass a 2012 Blackwell Publishing Ltd communicative effects. Some of the assumptions of this early categorization have carried over into modern psycholinguistic research without proper consideration of underlying communicative functions (Kreuz 2000). Many scholars have linked verbal irony to situational irony – particularly in how both forms seem to involve, at some level, a contradiction between what is expected, and what occurs. While verbal irony often points to unexpected or unfortunate outcomes in people’s actions (Kumon-Nakamura, Glucksberg, and Brown 1995; Pexman 2008), it does not do so necessarily. At present, most contemporary researchers of figurative language and inferential communication have settled on some version of the basic claim that verbal irony is a class of indirect language use where explicit sentence meanings are conceptually contradictory to a network of related implied propositions. Many other tropes, however, are not obviously distinct (e.g., parody, pretense, double entendre, litotes, etc). One common thread between many theoretical and empirical approaches to verbal irony is the distinction language users must make between layers of meaning. This could be the difference between actual and attributed beliefs (Kreuz and Glucksberg 1989; Sperber and Wilson 1986, 1995), real and imagined discourse acts (Clark and Gerrig 1984), relevant or inappropriate to a particular context (Attardo 2000), what is negated and what is implicated (Giora 1995), or failed expectations and attitudes toward those failures (Kumon-Nakamura, Glucksberg, and Brown 1995). Speakers implicitly highlight the contrasts between these different levels of meaning, and in doing so communicate an attitude toward an attributed belief (Sperber and Wilson 1986, 1995). This attitudinal dissociation is typically considered a hallmark of ironic communication, and necessarily requires an implicit understanding of others’ mental states. Consider the following exchange: John: I got another virus on my laptop. Mary: Aren’t you glad you didn’t switch to Mac? In this example John and Mary are discussing John’s computer problems. John reports that he got a virus on his computer – not his first. Mary asks an ironic rhetorical question expressing her negative opinion of Windows PCs. The literal question is in particular opposition to the implied evaluation – the essence of irony. In this example, the irony is largely contained in a single lexical opposition – ‘‘glad’’ instead of, for example, ‘‘sad,’’ but is also communicated by the rhetorical aspect of the question. The question form is one format speakers use to express ironic intentions, but ironic forms can manifest themselves in various ways linguistically, and in a variety of media including television, music, and visual art (Bryant 2011; El Refaie 2005; Ettema and Glasser 1994; Nuolijarvi and Tiittula 2010; Scott 2003). Figurative language devices such as verbal irony can be quite powerful communicative tools. The sentences we speak are often produced in a manner that will allow our audience to derive certain unstated meanings, and the way we put them is often particularly economical because of our heavy reliance on others’ inferential abilities. These meanings are not just efficient, but unique to indirect communication (Gibbs 2000a). The combination of affect directed toward some attributed belief, along with a direct utterance, makes ironic meanings difficult, if not impossible, to precisely capture. But this is due to inferential processes associated with mindreading and linguistic communication, not some distinct process of irony understanding. Similarly, it is difficult to capture, in linguistic terms, many kinds of inferences regarding others’ intentions that people generate in social contexts. In our example, Mary, of course, communicates much more than a negative opinion of PCs. For instance, she implicitly reminds John of previous interactions, some being debates, when their differences of opinion about computer platforms were discussed (i.e., 674 Gregory A. Bryant a 2012 The Author Language and Linguistics Compass 6/11 (2012): 673–685, 10.1002/lnc3.364 Language and Linguistics Compass a 2012 Blackwell Publishing Ltd she echoes previous opinions expressed, perhaps even through literal quotations) (Kreuz and Glucksberg 1989; Sperber and Wilson 1986, 1995). In doing so she reiterates her claim that John’s decision was a mistake, and that he would regret it. By using an ironic rhetorical question, Mary efficiently conveys a complex set of implied meanings that are relevant to John. The irony is not necessarily obvious to an outsider, as is the case with most occurrences of irony in everyday conversations (Gibbs 2000b), but John understands Mary immediately, and Mary knew he would. In the process, Mary potentially reduces John’s perception that she is being critical, and makes him laugh (while actually criticizing him) (Dews and Winner 1995). Alternatively, Mary might be accentuating her criticism by highlighting a contrast between John’s current situation and his earlier stated preference (Colston 1997). Speakers attempt to fulfill particular communicative goals by using indirect language such as verbal irony, and different forms can result in quite different emotional reactions – we should expect it to be used strategically and variably (Bryant 2011; Bryant and Fox Tree 2005; Leggitt and Gibbs 2000). Scholars have suggested many functions for irony in discourse, including speakers’ attempts to be humorous, create solidarity, appear clever, increase memorability, save face, be polite, or alter the valence of an attack or praise (Gibbs 1994; Roberts and Kreuz 1994; Toplak and Katz 2000). The pragmatic uses of ironic speech are far-reaching – ‘‘what can’t irony do?’’ might be a better question. One reason that irony can fulfill so many discourse goals is that includable tokens in the category need only satisfy a couple conditions; thus, an enormous variety of speech acts can qualify. Burgers, van Mulken, and Schellens (2011) recently proposed a procedure for identifying ironies in discourse, and for a basic definition, they looked at the commonalities between different contemporary theories. In their view, verbal irony involves an expressed evaluative utterance that implies an opposing evaluative appraisal. This implied meaning does not need to be the opposite of the stated meaning, but just differ in a scalar manner. For instance, an understatement might still be negative, but less negative than the intended meaning (e.g., when asked about a personal experience with a

Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks <=SEP=> Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods. & 2014 Elsevier Ltd. All rights reserved.

Comparison of Measurement-Based Call Admission Control Algorithms for Controlled-Load Service <=SEP=> We compare the performance of four admission control algorithms—one parameter-based and three measurementbased—for controlled-load service. The parameter-based admission control ensures that the sum of reserved resources is bounded by capacity. The three measurementbased algorithms are based on measured bandwidth, acceptance region [9], and equivalent bandwidth [7]. We use simulationon several network scenarios to evaluate the link utilization and adherence to service commitment achieved by these four algorithms.

Sharpness Enhancement and Super-Resolution of Around-View Monitor Images <=SEP=> In the wide-angle (WA) images embedded in an around-view monitor system, the subject(s) in the peripheral region is normally small and has little information. Furthermore, since the outer region suffers from the non-uniform blur phenomenon and artifact caused by the inherent optical characteristic of WA lenses, its visual quality tends to deteriorate. According to our experiments, conventional image enhancement techniques rarely improve the degraded visual quality of the outer region of WA images. In order to solve the above-mentioned problem, this paper proposes a joint sharpness enhancement (SE) and super-resolution (SR) algorithm which can improve the sharpness and resolution of WA images together. The proposed SE algorithm improves the sharpness of the deteriorated WA images by exploiting self-similarity. Also, the proposed SR algorithm generates super-resolved images by using high-resolution information which is classified according to the extended local binary pattern-based classifier and learned on a pattern basis. Experimental results show that the proposed scheme effectively improves the sharpness and resolution of the input deteriorated WA images. Even in terms of quantitative metrics such as just noticeable blur, structural similarity, and peak signal-to-noise ratio. Finally, the proposed scheme guarantees real-time processing such that it achieves 720p video at 29 Hz on a low cost GPU platform.

Understanding the Potential of Interpreter-based Optimizations for Python <=SEP=> The increasing popularity of scripting languages as general purpose programming environments calls for more efficient execution. Most of these languages, such as Python, Ruby, PHP, and JavaScript are interpreted. Interpretation is a natural implementation given the dynamic nature of these languages and interpreter portability has facilitated wide-spread use. In this work, we analyze the performance of CPython, a commonly used Python interpreter, to identify major sources of overhead. Based on our findings, we investigate the efficiency of a number of optimizations and explore the design options and trade-offs involved.

A Fast Parallel Algorithm for Thinning Digital Patterns <=SEP=> A fast parallel thinning algorithm is proposed in this paper. It consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deleting the north-west boundary points and the south-east corner points. End points and pixel connectivity are preserved. Each pattern is thinned down to a "skeleton" of unitary thickness. Experimental results show that this method is very effective.

Lifetime study for a poly fuse in a 0.35 /spl mu/m polycide CMOS process <=SEP=> Poly fuses are used as the base element for one time programmable cells in a standard CMOS process. Using a defined programming current, the resistance of the poly fuse increases irreversibly over several orders of magnitude. The goal of this study is to show that a poly fuse has a sufficient life time stability to be used as a storage element even in high reliability circuits. This paper shows the drift of the resistance of a poly fuse over the whole range of programming currents for a standard polycide 0.35 /spl mu/m CMOS process. The poly fuse for the selected process is build using two different layers, which gives a special performance in terms of programming current.

The relative success of alternative approaches to strategic information systems planning: an empirical analysis <=SEP=> Strategic information systems planning (SISP) is an exercise or ongoing activity that enables organisations to develop priorities for information systems development. It has been suggested that the ‘SISP approach’, a combination of method, process and implementation, is the most complete way of describing SISP activity. Based upon questionnaire responses from 267 IT Directors, four distinct approaches to SISP have been derived using cluster analysis. A comparison of these four approaches with five approaches of Earl, M.J., 1993. Experiences in SISP, MIS Quarterly, (March), 1–24, indicates that three bear strong similarities to the ‘organisational’, ‘business-led’, and ‘administrative’ approaches, whilst the fourth cluster is related to both Earl’s ‘method-driven’ and ‘technological’ approaches. An analysis of the relationship between SISP approach and SISP success demonstrates that the ‘organisational approach’ is significantly more successful than the other three approaches. q 1999 Elsevier Science B.V. All rights reserved.

Classroom Re-design to Facilitate Student Learning : A Case Study of Changes to a University Classroom <=SEP=> This case study examines the physical aspects of a particular university classroom, and what affect specific changes to the classroom had on the perceptions of students, instructors and observers regarding the room as an effective learning space. We compare survey and focus group data collected from students taking courses in the classroom prior to changes to the physical environment with comparable data from students taking courses in the same classroom after specific changes had been made. Immediately following changes to the classroom, notable increases were observed in reported perceptions of student satisfaction with the physical environment, including perceptions of the classroom as a more effective and engaging learning space. Similar perceptions of improvement as a teaching-learning space were reported by instructors and observers. However, subsequent follow-up data collection and analyses suggested little if any sustained increase in perceptions of efficacy of the room as a learning space; indeed, most reported variables returned to baseline levels. The implications of these findings and their relevance to classroom design nevertheless may provide insight regarding the manner in which physical space might support or even enhance teaching and learning. Keywords: learning spaces, active learning, classrooms, teaching and learning environment, classroom design For a number of years there has been an on-going pedagogical shift in higher education away from a traditional content delivery model of instruction to more active models of learning in which students play more involved and interactive roles within the classroom (Cornell, 2002; Brown, 2006). This movement has been coupled with the recognition that the traditional university classroom, with its unidirectional design and tiered, fixed theatre-like seating, is insufficient to accommodate what have increasingly become more varied teaching and learning practices. This growing realization that as the nature of teaching and learning evolves so too must teaching and learning spaces has, in recent years, resulted in a heightened interest among scholars in the examination of classroom space and, more specifically, the inquiry into the connection between classroom design and pedagogy and learning (Brooks, 2011). This study presents findings from an on-going research project examining classroom spaces on a small university campus. The present case study, which is one component of the larger project, compares perceptions of a specific classroom as an effective learning space before and after relatively extensive changes were made to the physical environment of the room. These changes were guided by feedback from students and instructors as well as by existing literature on innovative classroom designs. In particular, we were interested in assessing if these changes 1	  University	  of	  Lethbridge,	  Lethbridge,	  Alberta,	  Canada

Improvements to BM25 and Language Models Examined <=SEP=> Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°Δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.

A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration <=SEP=> We generalize the alternating minimization algorithm recently proposed in [32] to efficiently solve a general, edge-preserving, variational model for recovering multichannel images degraded by withinand cross-channel blurs, as well as additive Gaussian noise. This general model allows the use of localized weights and higher-order derivatives in regularization, and includes a multichannel extension of total variation (MTV) regularization as a special case. In the MTV case, we show that the model can be derived from an extended half-quadratic transform of Geman and Yang [14]. For color images with three channels and when applied to the MTV model (either locally weighted or not), the per-iteration computational complexity of this algorithm is dominated by nine fast Fourier transforms. We establish strong convergence results for the algorithm including finite convergence for some variables and fast q-linear convergence for the others. Numerical results on various types of blurs are presented to demonstrate the performance of our algorithm compared to that of the MATLAB deblurring functions. We also present experimental results on regularization models using weighted MTV and higher-order derivatives to demonstrate improvements in image quality provided by these models over the plain MTV model.

Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models <=SEP=> The Kullback Leibler (KL) divergence is a widely used tool in statistics and pattern recognition. The KL divergence between two Gaussian mixture models (GMMs) is frequently needed in the fields of speech and image recognition. Unfortunately the KL divergence between two GMMs is not analytically tractable, nor does any efficient computational algorithm exist. Some techniques cope with this problem by replacing the KL divergence with other functions that can be computed efficiently. We introduce two new methods, the variational approximation and the variational upper bound, and compare them to existing methods. We discuss seven different techniques in total and weigh the benefits of each one against the others. To conclude we evaluate the performance of each one through numerical experiments.

Interprocedural Semantic Change-Impact Analysis using Equivalence Relations <=SEP=> Change-impact analysis (CIA) is the task of determining the set of program elements impacted by a program change. Precise CIA has great potential to avoid expensive t sting and code reviews for (parts of) changes that are refactoring s (semantics-preserving). Existing CIA is imprecise becaus e it is coarse-grained, deals with only few refactoring patterns,or is unaware of the change semantics. We formalize the notion of change impact in terms of the trace semantics of two program versions. We show how to leverage equivalence relations to make dataflow-based CIA aware of th e change semantics, thereby improving precision in the prese nce of semantics-preserving changes. We propose an anytime algorithm that allows applying costly equivalence relation inference incrementally to refine the set of impacted statements. We ha ve implemented a prototype in SYM DIFF , and evaluated it on 322 real-world changes from open-source projects and benchmar k programs used by prior research. The evaluation results sho w an average 35% improvement in the size of the set of impacted statements compared to standard dataflow-based techniques .

Dual Learning based Multi-Objective Pairwise Ranking <=SEP=> There are many different recommendation tasks in our real life. The item ranking task is ranking a set of items based on users’ preferences. The user ranking task is referred to as user retrieval, find the potential users and recommend items to them. We find every recommendation task has it’s own dual task, e.g., Recommend an item to a user versus the user is supposed to become the item’s potential interested user. In this paper, we propose a novel dual learning based ranking framework with the overall aim of learning users’ preferences over items and learning items’ preferences over users by minimizing a pairwise ranking loss. We generate effective feedback signal from close loop formed by dual task. Through a reinforcement learning process, we can iteratively update these two tasks’ models to catch relations between the item recommendation task and the user retrieval task. The experiments show that our method outperforms classical approaches for multi-object recommendation tasks, especially for user retrieval tasks.

Business Analytics in (a) Blink <=SEP=> The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two IBM accelerator products generally available since March 2011. We are now working on the next generation of Blink, which will significantly expand the “sweet spot” of the Blink technology to much larger, disk-based warehouses and allow Blink to “own” the data, rather