title: Design Research in the Technology of Information Systems: Truth or Dare

abstract: This essay develops the philosophical foundations for design research in the Technology of Information Systems (TIS). Traditional writings on philosophy of science cannot fully describe this mode of research, which dares to intervene and improve to realize alternative futures instead of explaining or interpreting the past to discover truth. Accordingly, in addition to philosophy of science, the essay draws on writings about the act of designing, philosophy of technology and the substantive (IS) discipline. I define design research in TIS as in(ter)vention in the representational world defined by the hierarchy of concerns following semiotics. The complementary nature of the representational (internal) and real (external) environments provides the basis to articulate the dual ontological and epistemological bases. Understanding design research in TIS in this manner suggests operational principles in the internal world as the form of knowledge created by design researchers, and artifacts that embody these are seen as situated instantiations of normative theories that affect the external phenomena of interest. Throughout the paper, multiple examples illustrate the arguments. Finally, I position the resulting ‘method’ for design research vis-a-vis existing research methods and argue for its legitimacy as a viable candidate for research in the IS discipline.


Music Playlist Generation based on Community Detection and Personalized PageRank Final Report CS 224 W : Social and Information Network Analysis Autumn 2015


title: Integrating BIM and GIS: Exploring the use of IFC space objects and boundaries

abstract: In GIS, understanding the layout of interior spaces has important applications in the analysis of energy efficiency, indoor navigation and atmospheric pollution. However, detailed models of building internals are usually held in engineering or architecture software, including Building Information Modelling (BIM) software such as Autodesk Revit, and are not easily understood by GIS in this format. Additionally, such models contain excessive detail, such as wall thickness, not required for GIS operations such as topological adjacency. This paper describes the process required to convert BIM data into GIS, addressing both conceptual modelling and data format differences.


title: Wire speed name lookup: a GPU-based approach

abstract: This paper studies the name lookup issue with longest prefix matching, which is widely used in URL filtering, content routing/switching, etc. Recently Content-Centric Networking (CCN) has been proposed as a clean slate future Internet architecture to naturally fit the content-centric property of today's Internet usage: instead of addressing end hosts, the Internet should operate based on the identity/name of contents. A core challenge and enabling technique in implementing CCN is exactly to perform name lookup for packet forwarding at wire speed. In CCN, routing tables can be orders of magnitude larger than current IP routing tables, and content names are much longer and more complex than IP addresses. In pursuit of conquering this challenge, we conduct an implementation-based case study on wire speed name lookup, exploiting GPU's massive parallel processing power. Extensive experiments demonstrate that our GPU-based name lookup engine can achieve 63.52M searches per second lookup throughput on large-scale name tables containing millions of name entries with a strict constraint of no more than the telecommunication level 100µs per-packet lookup latency. Our solution can be applied to contexts beyond CCN, such as search engines, content filtering, and intrusion prevention/ detection.


title: Detection of Malicious Portable Executables Using Evidence Combinational Theory with Fuzzy Hashing

abstract: Fuzzy hashing is a known technique that has been adopted to speed up malware analysis processes. However, Hashing has not been fully implemented for malware detection because it can easily be evaded by applying a simple obfuscation technique such as packing. This challenge has limited the usage of hashing to triaging of the samples based on the percentage of similarity between the known and unknown. In this paper, we explore the different ways fuzzy hashing can be used to detect similarities in a file by investigating particular hashes of interest. Each hashing method produces independent but related interesting results which are presented herein. We further investigate combination techniques that can be used to improve the detection rates in hashing methods. Two such evidence combination theory based methods are applied in this work in order propose a novel way of combining the results achieved from different hashing algorithms. This study focuses on file and section Ssdeep hashing, PeHash and Imphash techniques to calculate the similarity of the Portable Executable files. Our results show that the detection rates are improved when evidence combination techniques are used.


title: Demo: Data Analysis and Visualization in Bike-Sharing Systems

abstract: Public bike-sharing systems (BSS) are booming all around the world, providing a flexible trip mode for people in cities. However, the imbalance usage among stations is causing problems for both system operators and users. As a first step to address this challenge, a recent paper has proposed a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devised a traffic prediction mechanism on a per-station basis with sub-hour granularity. To better demonstrate the mobility model and prediction, we develop this data analysis and visualization system, not only presenting massive real-time prediction results but also serving as a platform for human mobility analysis.


title: RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density

abstract: Discovering clusters from a dataset with different shapes, density, and scales is a known challenging problem in data clustering. In this paper, we propose the RElative COre MErge (RECOME) clustering algorithm. The core of RECOME is a novel density measure, i.e., Relative $K$ nearest Neighbor Kernel Density (RNKD). RECOME identifies core objects with unit RNKD, and partitions non-core objects into atom clusters by successively following higher-density neighbor relations toward core objects. Core objects and their corresponding atom clusters are then merged through $\alpha$-reachable paths on a KNN graph. Furthermore, we discover that the number of clusters computed by RECOME is a step function of the $\alpha$ parameter with jump discontinuity on a small collection of values. A jump discontinuity discovery (JDD) method is proposed using a variant of the Dijkstra's algorithm. RECOME is evaluated on three synthetic datasets and six real datasets. Experimental results indicate that RECOME is able to discover clusters with different shapes, density and scales. It achieves better clustering results than established density-based clustering methods on real datasets. Moreover, JDD is shown to be effective to extract the jump discontinuity set of parameter $\alpha$ for all tested dataset, which can ease the task of data exploration and parameter tuning.


title: Fashion Marketing in Social Media: Using Instagram for Fashion Branding

abstract: Social media has become an important tool for the business of marketers. Increasing exposure and traffics are the main two benefits of social media marketing. Most marketers are using social media to develop loyal fans and gain marketplace intelligence. Marketers reported increased benefits across all categories since 2013 and trademarks increased the number of loyal fans and sales [1]. Therefore, 2013 was a significant year for social media. Feeling the power of Instagram may be one of the most interesting cases. Social media is an effective key for fashion brands as they allow them to communicate directly with their consumers, promote various events and initiatives, and build brand awareness. As the increasing use of visual info graphic and marketing practices in social media, trademarks has begun to show more interest in Instagram. There is also no language barriers in Instagram and provides visuals which are very crucial for fashion industry. The purpose of this study is to determine and contrast the content sharing types of 10 well-known fashion brands (5 Turkish brands and 5 international brands), and to explain their attitude in Instagram. Hence, the content of Instagram accounts of those brands were examined according to post type (photo/video), content type (9 elements), number of likes and reviews, photo type (amateur/professional), shooting place (studio/outdoor/shops/etc.), and brand comments on their posts. This study provides a snapshot of how fashion brands utilize Instagram in their efforts of marketing.


title: The HTM Spatial Pooler—A Neocortical Algorithm for Online Sparse Distributed Coding

abstract: Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.


Atherosclerosis The Road Ahead


title: Comparison of Radio Propagation Models for Long Term Evolution (LTE) Network

abstract: This paper concerns about the radio propagation models used for the upcoming 4th Generation (4G) of cellular networks known as Long Term Evolution (LTE). The radio wave propagation model or path loss model plays a very significant role in planning of any wireless communication systems. In this paper, a comparison is made between different proposed radio propagation models that would be used for LTE, like Stanford University Interim (SUI) model, Okumura model, Hata COST 231 model, COST Walfisch-Ikegami & Ericsson 9999 model. The comparison is made using different terrains e.g. urban, suburban and rural area.SUI model shows the lowest path lost in all the terrains while COST 231 Hata model illustrates highest path loss in urban area and COST Walfisch-Ikegami model has highest path loss for suburban and rural environments.


title: A keyword-topic model for contextual advertising

abstract: Contextual advertising is a type of online advertising in which the placement of commercial ads within a web page depends on the relevance of the ads to the page content. A common approach to determine relevance is to score the match between ads and the content of the viewed page, for example, by simple keyword or syntactic matching. However, because of the sparseness of advertising language and the lack of context, this approach often leads to the selection of irrelevant ads. In this paper, we propose using topic modeling to improve the relevance of retrieved ads. Unlike existing methods that directly model the content of an ad as a distribution over topics, the proposed method uses a keyword-topic model that associates each keyword provided by the advertiser with a multinomial distribution over topics. Then, an ad with multiple keywords is represented as a mixture of topic distributions associated with those keywords. We empirically evaluated the performance of the proposed method on a set of real ads and web pages. The results show that using the keyword-topic model gives improved accuracy over traditional keyword matching and a topic modeling methods that do not include information about keyword-topic association. Further, combining the keyword-topic model with other methods yields extra increase in ad recommendation accuracy.


title: Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July2006 Special Issue: Probabilistic models of cognition Bayesian theories of conditioning in a changing world

abstract: The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.


title: Effects of a Combined Resistance-Plyometric Training Program on Muscular Strength, Running Economy, and V[Combining Dot Above]O2peak in Division I Female Soccer Players

abstract: Resistance and plyometric training programs have demonstrated consistent improvements in running economy (RE) in trained and untrained adults in the absence of improvements in maximal oxygen consumption. The purpose of this study was to investigate the effect of a 10-week combined resistance-plyometric training program on the RE and V[Combining Dot Above]O2max in female soccer players. Fifteen Division 1A female soccer players (age 19.0 ± 0.7 years; height 1.67 ± 0.1 m; weight 61.7 ± 8.1 kg) performed a treadmill test for V[Combining Dot Above]O2max and RE at the end of a competitive season (PRE) and after a 10-week training program (POST). Isometric strength was measured in knee flexion and extension. Resistance training was conducted 2 d·wk-1 on nonconsecutive days; plyometric training was conducted separately on different nonconsecutive days. Eleven subjects were included in the PRE-POST analysis (age 19.0 ± 0.8 years; height 1.67 ± 0.5 m; weight 59.9 ± 6.7 kg). Descriptive statistics were compared using analysis of variance with repeated measures with a Bonferroni adjustment, and significance was set at p < 0.05. A significant increase occurred after training in the V[Combining Dot Above]O2peak (10.5%; p = 0.008), time to fatigue (6.9%; p = 0.017), and interpolated maximal speed (3.6%; p = 0.016), despite there being a decrease in the maximal respiratory exchange ratio (2.9%; p = 0.001). There was no significant change in the RE at 9 km·h-1; however, there was a significant decrease in the percentage of the V[Combining Dot Above]O2peak at 9 km·h-1 (-5.6%; p = 0.02). Maximal isometric strength of knee flexors and extensors did not change. The results suggest a plyometric-agility training program may increase the V[Combining Dot Above]O2peak in female soccer players; however, the effect on RE was equivocal.


title: Design of quadrifilar spiral antenna with integrated module for UHF RFID reader

abstract: In this paper, a quadrifilar spiral antenna (QSA) with an integrated module for UHF radio frequency identification (RFID) reader is presented. The proposed QSA consists of four spiral antennas with short stubs and a microstrip feed network. Also, the shielded module is integrated on the center of the ground inside the proposed QSA. In order to match the proposed QSA with the integrated module, we adopt a short stub connected from each spiral antenna to ground. Experimental result shows that the QSA of size 80 × 80 × 11.2 mm3 with the integrated module (40 × 40 × 3 mm3) has a peak gain of 3.5 dBic, an axial ratio under 2.5 dB and a 3-dB beamwidth of about 130o.


title: Procrastination and Cramming: How Adept Students Ace the System

abstract: Abstract Clear power differentials between teacher and pupil and the assignment of well-delineated tasks within specified time constraints characterize the academic system. Most university students are supremely adept at working within that structure. Many students who outwardly adapt to the system, however, engage in an intense and private ritual that comprises five aspects: calculated procrastination, preparatory anxiety, climactic cramming, nick-of-time deadline-making, and a secret, if often uncelebrated, victory. These adept students often find it difficult to admit others into their efficient program of academic survival. Although such behaviors are adaptive for school rhythms and expectations, these students may also try to impose them onto personal relationships, including those that are psychotherapeutic. The students' tendency to transfer their longstanding habits of procrastination and cramming to the workplace after graduation is less problematic. The argument is made that school rhythms and e...


title: Arc-swift: A Novel Transition System for Dependency Parsing

abstract: Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7--7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.


title: Semantic Space is a pervasive computing infrastructure that exploits Semantic Web technologies to support explicit representation , expressive querying , and flexible reasoning of contexts in smart spaces

abstract: Semantic Space is a pervasive computing infrastructure that exploits semantic Web technologies to support explicit representation, expressive querying, and flexible reasoning of contexts in smart spaces.


title: Novel Vector Control Method for Three-Stage Hybrid Cascaded Multilevel Inverter

abstract: A three-stage 18-level hybrid inverter circuit and its innovative control method have been presented. The three hybrid inverter stages are the high-, medium-, and low-voltage stages. The high-voltage stage is made of a three-phase conventional inverter to reduce dc source cost and losses. The medium- and low-voltage stages are made of three-level inverters constructed using cascaded H-bridge units. The novelty of the proposed algorithm is to avoid the undesirable high switching frequency for high- and medium-voltage stages despite the fact that the inverter's dc sources are selected to maximize the inverter levels by eliminating redundant voltage states. Switching algorithms of the high- and medium-voltage stages have been developed to assure fundamental switching frequency operation of the high-voltage stages and not more than few times this frequency for the medium-voltage stage. The low-voltage stage is controlled using SVM to achieve the reference voltage vector exactly and to set the order of dominant harmonics as desired. The realization of this control approach has been enabled by considering the vector space plane in the state selection rather than individual phase levels. The inverter has been constructed, and the control algorithm has been implemented. Test results show that the proposed algorithm achieves the claimed features, and all major hypotheses have been verified.


An Approach to Sentiment Analysis using Artificial Neural Network with Comparative Analysis of Different Techniques


title: A novel binary particle swarm optimization

abstract: Particle swarm optimization (PSO) as a novel computational intelligence technique, has succeeded in many continuous problems. But in discrete or binary version there are still some difficulties. In this paper a novel binary PSO is proposed. This algorithm proposes a new definition for the velocity vector of binary PSO. It will be shown that this algorithm is a better interpretation of continuous PSO into discrete PSO than the older versions. Also a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.


title: Children's Participation Patterns in Online Communities: An Analysis of Israeli Learners in the Scratch Online Community

abstract: Online participation and content contribution are pillars of the Internet revolution and are core activities for younger generations online. This study investigated participation patterns, users' contributions and gratification mechanisms, as well as the gender differences of Israeli learners in the Scratch online community. The findings showed that: (1) Participation patterns reveal two distinct participation types "project creators" and "social participators", suggesting different users' needs. (2) Community members gratified "project creators" and "social participators" for their investment – using several forms of community feedback. Gratification at the user level was given both to "project creators" and "social participators" – community members added them as friends. The majority of the variance associated with community feedback was explained by seven predictors. However, gratification at the project level was different for the two participation types - active "project creators" received less feedback on their projects, while active "social participators" received more. Project feedback positively correlated with social participation investment, but negatively correlated with project creation investment. A possible explanation is that community members primarily left feedback to their friends. (3) No gender differences were found in participation patterns or in project complexity, suggesting that Scratch provides similar opportunities to both genders in programming, learning, and participation.


title: DESIGN, IMPLEMENTATION AND PERFORMANCE OF ULTRA-WIDEBAND TEXTILE ANTENNA

abstract: Communication technology is increasingly pervading everyday life. The rapid progress in wireless communication besides the increasing interest in wearable antennas and electronics in civil, medical, sport wear and military domains promises to replace wired- communication networks in the near future in which antennas are in more important role. Recently, there has been growing interest in the antenna community to merge between wearable systems technology, Ultra-Wideband (UWB) technology and textile technology. All these together have resulted in demand for ∞exible fabric antennas, which can be easily attached to a piece of clothing. In this paper, three difierent structures of UWB antennas using clothing materials and suitable for wearable application were fabricated and presented. The substrate of the designed antennas was made from jeans textile material, while radiating element and ground plane are made out of copper tape. The operating frequency of all three designs is between 3GHz and 12GHz. Measured results are compared with simulations and good agreement was observed.


Evaluation of Physico-Chemical and Antimicrobial Studies of Delonix Regia and Piliostigma Thonningii Seed Oils


title: 'Silk Road', the virtual drug marketplace: a single case study of user experiences.

abstract: BACKGROUND ::: The online promotion of 'drug shopping' and user information networks is of increasing public health and law enforcement concern. An online drug marketplace called 'Silk Road' has been operating on the 'Deep Web' since February 2011 and was designed to revolutionise contemporary drug consumerism. ::: ::: ::: METHODS ::: A single case study approach explored a 'Silk Road' user's motives for online drug purchasing, experiences of accessing and using the website, drug information sourcing, decision making and purchasing, outcomes and settings for use, and perspectives around security. The participant was recruited following a lengthy relationship building phase on the 'Silk Road' chat forum. ::: ::: ::: RESULTS ::: The male participant described his motives, experiences of purchasing processes and drugs used from 'Silk Road'. Consumer experiences on 'Silk Road' were described as 'euphoric' due to the wide choice of drugs available, relatively easy once navigating the Tor Browser (encryption software) and using 'Bitcoins' for transactions, and perceived as safer than negotiating illicit drug markets. Online researching of drug outcomes, particularly for new psychoactive substances was reported. Relationships between vendors and consumers were described as based on cyber levels of trust and professionalism, and supported by 'stealth modes', user feedback and resolution modes. The reality of his drug use was described as covert and solitary with psychonautic characteristics, which contrasted with his membership, participation and feelings of safety within the 'Silk Road' community. ::: ::: ::: CONCLUSION ::: 'Silk Road' as online drug marketplace presents an interesting displacement away from 'traditional' online and street sources of drug supply. Member support and harm reduction ethos within this virtual community maximises consumer decision-making and positive drug experiences, and minimises potential harms and consumer perceived risks. Future research is necessary to explore experiences and backgrounds of other users.


title: Frequency Tracking Control for a Capacitor-Charging Parallel Resonant Converter with Phase-Locked Loop

abstract: This study investigates a phase-locked loop (PLL) controlled parallel resonant converter (PRC) for a pulse power capacitor charging application. The dynamic nature of the capacitor charging is such that it causes a shift in the resonant frequency of the PRC. Using the proposed control method, the PRC can be optimized to operate with its maximum power capability and guarantee ZVS operation, even when the input voltage and resonant tank parameters vary. The detailed implementation of the PLL controller, as well as the determination of dead-time and leading time, is presented in this paper. Simulation and experimental results verify the performance of the proposed control method.


title: RFID Transponders

abstract: The objective of this article is to review radio frequency identification (RFID) technology, its developments on RFID transponders, design and operating principles, so that end users can benefit from knowing which transponder meets their requirements. In this article, RFID system definition, RFID transponder architecture and RFID transponder classification based on a comprehensive literature review on the field of research are presented. Detailed descriptions of these tags are also presented, as well as an in-house developed semiactive tag in a compact package.


title: Machine Learning for Medical Imaging

abstract: Machine learning is a powerful technique for recognizing patterns on medical images; however, it must be used with caution because it can be misused if the strengths and weaknesses of this technology are not understood.


title: On the Choice-Based Linear Programming Model for Network Revenue Management

abstract: Gallego et al. [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. CORC Technical Report TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York.] recently proposed a choice-based deterministic linear programming model (CDLP) for network revenue management (RM) that parallels the widely used deterministic linear programming (DLP) model. While they focused on analyzing “flexible products”---a situation in which the provider has the flexibility of using a collection of products (e.g., different flight times and/or itineraries) to serve the same market demand (e.g., an origin-destination connection)---their approach has broader implications for understanding choice-based RM on a network. In this paper, we explore the implications in detail. Specifically, we characterize optimal offer sets (sets of available network products) by extending to the network case a notion of “efficiency” developed by Talluri and van Ryzin [Talluri, K. T., G. J. van Ryzin. 2004. Revenue management under a general discrete choice model of consumer behavior. Management Sci.50 15--33.] for the single-leg, choice-based RM problem. We show that, asymptotically, as demand and capacity are scaled up, only these efficient sets are used in an optimal policy. This analysis suggests that efficiency is a potentially useful approach for identifying “good” offer sets on networks, as it is in the case of single-leg problems. Second, we propose a practical decomposition heuristic for converting the static CDLP solution into a dynamic control policy. The heuristic is quite similar to the familiar displacement-adjusted virtual nesting (DAVN) approximation used in traditional network RM, and it significantly improves on the performance of the static LP solution. We illustrate the heuristic on several numerical examples.


title: Adaptive mobile activity recognition system with evolving data streams

abstract: Mobile activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich mobile phones. Supervised learning with static models has been applied pervasively for mobile activity recognition. In this paper, we propose a novel phone-based dynamic recognition framework with evolving data streams for activity recognition. The novel framework incorporates incremental and active learning for real-time recognition and adaptation in streaming settings. While stream evolves, we refine, enhance and personalise the learning model in order to accommodate the natural drift in a given data stream. Extensive experimental results using real activity recognition data have evidenced that the novel dynamic approach shows improved performance of recognising activities especially across different users.


title: Transformational leadership influence on organizational performance through organizational learning and innovation

abstract: This study analyzes the influences of transformational leadership on organizational performance through the dynamic capabilities of organizational learning and innovation. Although these indirect interrelations are very important for improving organizational performance, previous research has not usually explored them. The study confirms these influences empirically, basing the analysis on a sample of 168 Spanish firms. The results reveal that (1) transformational leadership influences organizational performance positively through organizational learning and innovation; (2) organizational learning influences organizational performance positively, both directly and indirectly through organizational innovation; (3) organizational innovation influences organizational performance positively.


title: A smart web service based on the context of things

abstract: Combining the Semantic Web and the Ubiquitous Web, Web 3.0 is for things. The Semantic Web enables human knowledge to be machine-readable and the Ubiquitous Web allows Web services to serve any thing, forming a bridge between the virtual world and the real world. By using context, Web services can become smarter—that is, aware of the target things' or applications' physical environments, or situations and respond proactively and intelligently. Existing methods for implementing context-aware Web services on Web 2.0 mainly enumerate different implementations corresponding to different attribute values of the context, in order to improve the Quality of Services (QoS). However, things in the physical world are extremely diverse, which poses new problems for Web services: it is difficult to unify the context of things and to implement a flexible smart Web service for things. This article proposes a novel smart Web service based on the context of things, which is implemented using a REpresentational State Transfer for Things (Thing-REST) style, to tackle the two problems. In a smart Web service, the user's description (semantic context) and sensor reports (sensing context) are two channels for acquiring the context of things which are then employed by ontology services to make the context of things machine-readable. With guidance of domain knowledge services, event detection services can analyze things' needs particularly, well through the context of things. We then propose a Thing-REST style to manage the context of things and user context, and to mashup Web services through three structures (i.e., chain, select, and merge) to implement smart Web services. A smart plant watering-service application demonstrates the effectiveness of our method.


title: McDonald's restaurants and neighborhood deprivation in Scotland and England.

abstract: BACKGROUND ::: Features of the local fast food environment have been hypothesized to contribute to the greater prevalence of obesity in deprived neighborhoods. However, few studies have investigated whether fast food outlets are more likely to be found in poorer areas, and those that have are local case studies. In this paper, using national-level data, we examine the association between neighborhood deprivation and the density of McDonald's restaurants in small census areas (neighborhoods) in Scotland and England. ::: ::: ::: METHODS ::: Data on population, deprivation, and the location of McDonald's Restaurants were obtained for 38,987 small areas in Scotland and England (6505 "data zones" in Scotland, and 32,482 "super output areas" in England) in January 2005. Measures of McDonald's restaurants per 1000 people for each area were calculated, and areas were divided into quintiles of deprivation. Associations between neighborhood deprivation and outlet density were examined during February 2005, using one-way analysis of variance in Scotland, England, and both countries combined. ::: ::: ::: RESULTS ::: Statistically significant positive associations were found between neighborhood deprivation and the mean number of McDonald's outlets per 1000 people for Scotland (p<0.001), England (p<0.001), and both countries combined (p<0.001). These associations were broadly linear with greater mean numbers of outlets per 1000 people occurring as deprivation levels increased. ::: ::: ::: CONCLUSIONS ::: Observed associations between presence or absence of fast food outlets and neighborhood deprivation may provide support for environmental explanations for the higher prevalence of obesity in poor neighborhoods.


title: On Modeling Low-Power Wireless Protocols Based on Synchronous Packet Transmissions

abstract: Mathematical models play a pivotal role in understanding and designing advanced low-power wireless systems. However, the distributed and uncoordinated operation of traditional multi-hop low-power wireless protocols greatly complicates their accurate modeling. This is mainly because these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pair wise link-based transmissions (LT). ST improve the one-hop packet reliability to an extent that efficient multi-hop protocols with little network state are feasible. This paper studies whether ST also enable simple yet accurate modeling of these protocols. Our contribution to this end is two-fold. First, we show, through experiments on a 139-node test bed, that characterizing packet receptions and losses as a sequence of independent and identically distributed (i.i.d.) Bernoulli trials-a common assumption in protocol modeling but often illegitimate for LT-is largely valid for ST. We then show how this finding simplifies the modeling of a recent ST-based protocol, by deriving (i) sufficient conditions for probabilistic guarantees on the end-to-end packet reliability, and (ii) a Markovian model to estimate the long-term energy consumption. Validation using test bed experiments confirms that our simple models are also highly accurate, for example, the model error in energy against real measurements is 0.25%, a figure never reported before in the related literature.


title: Diabetic retinopathy detection using deep convolutional neural networks

abstract: Diabetic retinopathy is when damage occurs to the retina due to diabetes, which affects up to 80 percent of all patients who have had diabetes for 10 years or more. The expertise and equipment required are often lacking in areas where diabetic retinopathy detection is most needed. Most of the work in the field of diabetic retinopathy has been based on disease detection or manual extraction of features, but this paper aims at automatic diagnosis of the disease into its different stages using deep learning. This paper presents the design and implementation of GPU accelerated deep convolutional neural networks to automatically diagnose and thereby classify high-resolution retinal images into 5 stages of the disease based on severity. The single model accuracy of the convolutional neural networks presented in this paper is 0.386 on a quadratic weighted kappa metric and ensembling of three such similar models resulted in a score of 0.3996.


title: Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods

abstract: Recommender systems are useful in reducing information overload and improving decision making. Utility-based recommender systems provide recommendations based on the computation of the utility of each item for the user. Some utility-elicitation methods have been developed on the basis of multi-attribute utility theory (MAUT) to represent a decision maker's complete preference. This study investigates whether these utility-based techniques outperform the traditional content-based technique for online recommendations. A laboratory experiment was conducted in two e-commerce contexts to compare the decomposed and holistic utility-based methods, simple multi-attribute rating technique exploiting ranks (SMARTER) and radial basis function network (RBFN), with the content-based method vector space model (VSM) in terms of recommendation accuracy, time expense, and user perceptions. The results demonstrate that the performances of utility-based methods depend on recommendation contexts. Furthermore, this study proposes guidelines for choosing appropriate recommendation methods in different contexts.


title: Mediterranean Dietary Patterns and Cardiovascular Health

abstract: The Mediterranean dietary pattern has been linked with reduced cardiovascular disease incidence and mortality. Components of the Mediterranean diet associated with better cardiovascular health include low consumption of meat and meat products, moderate consumption of ethanol (mostly from wine), and high consumption of vegetables, fruits, nuts, legumes, fish, and olive oil. Increasing evidence indicates that the synergy among these components results in beneficial changes in intermediate pathways of cardiometabolic risk, such as lipids, insulin sensitivity, oxidative stress, inflammation, and vasoreactivity. As a result, consumption of a Mediterranean dietary pattern favorably affects numerous cardiovascular disease risk factors, such as dyslipidemia, hypertension, metabolic syndrome, and diabetes. Moreover, strong evidence links this dietary pattern with reduced cardiovascular disease incidence, reoccurrence, and mortality. This review evaluates the current evidence behind the cardioprotective effects of ...


title: Characterizing privacy leakage of public WiFi networks for users on travel

abstract: Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. However, most users are neglecting the privacy threats because currently there is no way for them to know to what extent their privacy is revealed. In this paper, we examine the privacy leakage in public hotspots from activities such as domain name querying, web browsing, search engine querying and online advertising. We discover that, from these activities multiple categories of user privacy can be leaked, such as identity privacy, location privacy, financial privacy, social privacy and personal privacy. We have collected real data from 20 airport datasets in four countries and discover that the privacy leakage can be up to 68%, which means two thirds of users on travel leak their private information while accessing the Internet at airports. Our results indicate that users are not fully aware of the privacy leakage they can encounter in the wireless environment, especially in public WiFi networks. This fact can urge network service providers and website designers to improve their service by developing better privacy preserving mechanisms.


title: Mediating and Moderating Processes in the Relation between Maltreatment and Psychopathology: Mother-Child Relationship Quality and Emotion Regulation

abstract: The present study investigated underlying processes of the effect of maltreatment on psychopathology (i.e., internalizing and externalizing problems) in a group of 111 maltreated and 110 nonmaltreated 7–10 year-old children (60% boys). We tested the moderating and/or mediating roles of emotion regulation and the mother-child relationship quality (pattern of relatedness) using Structural Equation Modeling. Emotion regulation, but not the pattern of relatedness, mediated the relation between maltreatment and psychopathology. This mediation was moderated by the pattern of relatedness: For the group of children with an insecure pattern of relatedness, maltreatment was related to lower levels of emotion regulation, which was predictive of higher levels of internalizing and externalizing symptomatology. In contrast, for the secure relatedness group, there was no mediation by emotion regulation since the impact of maltreatment on emotion regulation was not significant. Implications of the mediating role of emotion regulation and the buffering role of the mother-child relationship quality were discussed.


title: Automated visual inspection system for wood defect classification using computational intelligence techniques

abstract: This article presents improvements in the segmentation module, feature extraction module, and the classification module of a low-cost automated visual inspection (AVI) system for wood defect classification. One of the major drawbacks in the low-cost AVI system was the erroneous segmentation of clear wood regions as defects, which then introduces confusion in the classification module. To reduce this problem, we use the fuzzy min-max neural network for image segmentation (FMMIS). The FMMIS method grows boxes from a set of seed pixels, yielding ideally the minimum bounded rectangle for each defect present in the wood board image. Additional features with texture information are considered for the feature extraction module, and multi-class support vector machines are compared with multilayer perceptron neural networks in the classification module. Results using the FMMIS, additional features, and a pairwise classification support vector machine on a 550 test wood image set containing 11 defect categories show 91% of correct classification, which is significantly better than the original 75% of the low-cost AVI system. The use of computational intelligence techniques improved significantly the overall performance of the proposed automated visual inspection system for wood boards.


title: Deep Learning in the EEG Diagnosis of Alzheimer’s Disease

abstract: EEG (electroencephalogram) has a lot of advantages compared to other methods in the analysis of Alzheimer’s disease such as diagnosing Alzheimer’s disease in an early stage. Traditional EEG analysis method needs a lot of artificial works such as calculating coherence between different pair of electrodes. In our work we applied deep learning network in the analysis of EEG data of Alzheimer’s disease to fully use the advantage of the unsupervised feature learning. We studied EEG based deep learning on 15 clinically diagnosed Alzheimer’s disease patients and 15 healthy people. Each person has 16 electrodes. The time domain EEG data of each electrode is cut into 40 data units according to the data size in a period. In our work we first train the deep learning network with 25 data units on each electrode separately and then test with 15 data units to get the accuracy on each electrode. Finally we will combine the learning results on 16 electrodes and train them with SVM and get a final result. We report a 92 % accuracy after combining 16 electrodes of each person. In order to improve the deep learning model on Alzheimer’s disease with the upcoming new data, we use incremental learning to make full use of the existing data while decrease the expenses on memory space and computing time by replacing the exising data with new data. We report a 0.5 % improvement in accuracy with incremental learning.


title: Psilocybin occasioned mystical-type experiences: immediate and persisting dose-related effects

abstract: Rationale ::: This dose-effect study extends previous observations showing that psilocybin can occasion mystical-type experiences having persisting positive effects on attitudes, mood, and behavior.


AS DISCRETE LATENT VARIABLES


title: PARADOXES, DUALITIES AND DILEMMAS: UNDERSTANDING SUSTAINABILITY IN HRM

abstract: The paper contributes to the emerging literature linking sustainability as a concept to problems researched in HRM literature. Sustainability is often equated with social responsibility. However, emphasizing mainly moral or ethical values neglects that sustainability can also be economically rational. This conceptual paper discusses how the notion of sustainability has developed and emerged in HRM literature. A typology of sustainability concepts in HRM is presented to advance theorizing in the field of Sustainable HRM. The concepts of paradox, duality, and dilemma are reviewed to contribute to understanding the emergence of sustainability in HRM. It is argued in this paper that sustainability can be applied as a concept to cope with the tensions of short- vs. long-term HRM and to make sense of paradoxes, dualities, and dilemmas. Furthermore, it is emphasized that the dualities cannot be reconciled when sustainability is interpreted in a way that leads to ignorance of one of the values or logics. Implications for further research and modest suggestions for managerial practice are derived.


title: Cascaded pose regression

abstract: We present a fast and accurate algorithm for computing the 2D pose of objects in images called cascaded pose regression (CPR). CPR progressively refines a loosely specified initial guess, where each refinement is carried out by a different regressor. Each regressor performs simple image measurements that are dependent on the output of the previous regressors; the entire system is automatically learned from human annotated training examples. CPR is not restricted to rigid transformations: ‘pose’ is any parameterized variation of the object's appearance such as the degrees of freedom of deformable and articulated objects. We compare CPR against both standard regression techniques and human performance (computed from redundant human annotations). Experiments on three diverse datasets (mice, faces, fish) suggest CPR is fast (2–3ms per pose estimate), accurate (approaching human performance), and easy to train from small amounts of labeled data.


title: A Comparative Study for Single Image Blind Deblurring

abstract: Numerous single image blind deblurring algorithms have been proposed to restore latent sharp images under camera motion. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real blurred images. It is thus unclear how these algorithms would perform on images acquired "in the wild" and how we could gauge the progress in the field. In this paper, we aim to bridge this gap. We present the first comprehensive perceptual study and analysis of single image blind deblurring using real-world blurred images. First, we collect a dataset of real blurred images and a dataset of synthetically blurred images. Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms. Second, we systematically analyze subject preferences, including the level of agreement, significance tests of score differences, and rationales for preferring one method over another. Third, we study the correlation between human subjective scores and several full-reference and noreference image quality metrics. Our evaluation and analysis indicate the performance gap between synthetically blurred images and real blurred image and sheds light on future research in single image blind deblurring.


title: Bags of Local Convolutional Features for Scalable Instance Search

abstract: This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an assignment map, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the BoW representation based on local CNN features for instance retrieval, achieving competitive performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.


title: Rethinking floating point for deep learning

abstract: Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relative to this baseline; it remains energy inefficient, and word size reduction yields drastic loss in needed dynamic range. We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson's posit format. With no network retraining, and drop-in replacement of all math and float32 parameters via round-to-nearest-even only, this open-sourced 8-bit log float is within 0.9% top-1 and 0.2% top-5 accuracy of the original float32 ResNet-50 CNN model on ImageNet. Unlike int8 quantization, it is still a general purpose floating point arithmetic, interpretable out-of-the-box. Our 8/38-bit log float multiply-add is synthesized and power profiled at 28 nm at 0.96x the power and 1.12x the area of 8/32-bit integer multiply-add. In 16 bits, our log float multiply-add is 0.59x the power and 0.68x the area of IEEE 754 float16 fused multiply-add, maintaining the same signficand precision and dynamic range, proving useful for training ASICs as well.


title: Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification

abstract: High-resolution remote sensing (HRRS) scene classification plays an important role in numerous applications. During the past few decades, a lot of remarkable efforts have been made to develop various methods for HRRS scene classification. In this paper, focusing on the problems of complex context relationship and large differences of object scale in HRRS scene images, we propose a deep CNN-based scene classification method, which not only enables to enhance the ability of spatial representation, but adaptively recalibrates channel-wise feature responses to suppress useless feature channels. We evaluated the proposed method on a publicly large-scale dataset with several state-of-the-art convolutional neural network (CNN) models. The experimental results demonstrate that the proposed method is effective to extract high-level category features for HRRS scene classification.


title: Are GSM Phones THE Solution for Localization?

abstract: In this paper, we argue that localization solution based on cellular phone technology, specifically GSM phones, is a sufficient and attractive option in terms of coverage and accuracy for a wide range of indoor, outdoor, and place-based location-aware applications. We present preliminary results that indicate that GSM-based localization systems have the potential to detect the places that people visit in their everyday lives, and can achieve median localization accuracies of 5 and 75 meters for indoor and outdoor environments, respectively


title: Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection

abstract: Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low-resource languages, Malagasy and Kinyarwanda.


title: Identity Management Frameworks for Cloud

abstract: Cloud computing is a new trend of computing paradigm that provides a set of scalable resources on demand. However, it also being a target of cyber attacks and creates risk for data privacy and protection. An Identity Management System (IDM) supports the management of multiple digital identities for authentication and authorization. This paper reviews various identity management frameworks that help making Cloud environment more secure. General Terms Identity management, Service provider, Identity provider, Cloud computing.


title: Mode‐coupling theory of the dynamics of polymer liquids: Qualitative predictions for flexible chain and ring melts

abstract: The qualitative predictions of the mode–mode‐coupling (MMC) theory developed in the preceding paper are determined for various transport properties and time correlation functions. The degree of polymerization N dependence of the self‐diffusion constant D of long flexible chain and rigid rod melts are found to be in agreement with the reptation/tube model scaling predictions. Ideal ring polymer liquids also follow a D∝N−2 law, but for collapsed non‐Gaussian rings a stronger power law dependence is obtained. The viscoelastic properties of chain melts are derived from a linear generalized Langevin equation (GLE), which at long times consists of the usual Rouse terms plus a chain length and internal normal mode‐dependent frictional contribution.The latter novel term gives rise to of order N slow internal modes, and hence a plateau modulus, and a shear viscosity and recoverable compliance which scale as N3 and N0, respectively. All the predictions are derived from the MMC dynamic memory function by neglecting ...


title: Positive Definite Kernels in Machine Learning

abstract: This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions $\{k(x,\cdot),x\in\mathcal{X}\}$ associated with a kernel $k$ defined on a space $\mathcal{X}$. We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain data-types such as images, graphs or speech segments.


title: A nearest-neighbor chain based approach to skew estimation in document images

abstract: A nearest-neighbor chain (NNC) based approach is proposed in this paper to develop a skew estimation method with a high accuracy and with language-independent capability. Size restriction is introduced to the detection of nearest-neighbors (NN). Then NNCs are extracted from the adjacent NN pairs, in which the slopes of the NNCs with a largest possible number of components are computed to give the skew angle of document image. Experimental results on various types of documents containing different linguistic scripts and diverse layouts show that the proposed approach has achieved an improved accuracy for estimating document image skew angle and has an advantage of being language independent.


title: Online Discovery of Gathering Patterns over Trajectories

abstract: The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviors can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modeling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. In this work, we first develop a set of novel techniques to tackle the challenge of efficient discovery of gathering patterns on archived trajectory dataset. Afterwards, since trajectory databases are inherently dynamic in many real-world scenarios such as traffic monitoring, fleet management and battlefield surveillance, we further propose an online discovery solution by applying a series of optimization schemes, which can keep track of gathering patterns while new trajectory data arrive. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.


title: Adolescent health needs. II: Utilization of health care by adolescents.

abstract: In a random, mailed survey, 730 adolescents supplied information on their patterns and preferences for health care. The majority of respondents reported that they usually receive medical care from a private physician. Girls consulted physicians more frequently than boys, but there was no significant variation by age or social group. As the age of the respondent increased, the proportion visiting pediatricians decreased. When asked what they liked and disliked about their physician, respondents placed great emphasis upon the personal characteristics of the practitioner. Long waiting periods were the most common complaint. A significant proportion of adolescents with health concerns do not consult a health professional, even in cases where efficacious treatment may be available. Nervous/emotional problems, obesity, and birth control are concerns of adolescents which should be discussed with health professionals.


title: HTSeq—a Python framework to work with high-throughput sequencing data

abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de


title: IT doesn't matter

abstract: As information technology has grown in power and ubiquity, companies have come to view it as ever more critical to their success; their heavy spending on hardware and software clearly reflects that assumption. Chief executives routinely talk about information technology's strategic value, about how they can use IT to gain a competitive edge. But scarcity, not ubiquity, makes a business resource truly strategic--and allows companies to use it for a sustained competitive advantage. You only gain an edge over rivals by doing something that they can't. IT is the latest in a series of broadly adopted technologies--think of the railroad or the electric generator--that have reshaped industry over the past two centuries. For a brief time, as they were being built into the infrastructure of commerce, these technologies created powerful opportunities for forward-looking companies. But as their availability increased and their costs decreased, they became commodity inputs. From a strategic standpoint, they became invisible; they no longer mattered. that's exactly what's happening to IT, and the implications are profound. In this article, HBR's editor-at-large Nicholas Carr suggests that IT management should, frankly, become boring. It should focus on reducing risks, not increasing opportunities. For example, companies need to pay more attention to ensuring network and data security. Even more important, they need to manage IT costs more aggressively. IT may not help you gain a strategic advantage, but it could easily put you at a cost disadvantage. If, like many executives, you've begun to take a more defensive posture toward IT, spending more frugally and thinking more pragmatically, you're already on the right course. The challenge will be to maintain that discipline when the business cycle strengthens.


title: Performance Improvement of Multi-Stacked CMOS mm-Wave Power Amplifiers Based on Negative Capacitance Phase Compensation

abstract: This paper proposes a method for performance improvement of multi-stacked CMOS millimeter-wave power amplifiers based on negative capacitance phase detuning, which can be generalized to other phase detuning techniques. It not only yields better inter-stage matching between stacks, but also offers the advantage of relaxed inter-stage phase rotation due to transistor parasitics. A $\times1.6$ output power/gain and 7% efficiency improvement is observed as a result of the proposed technique. This is achieved because phase misalignment between stacks is minimized. The method is evaluated using simulations based on 28GHz PA in 45nm CMOS technology.


title: P1-08-01: Survival in Metastatic Breast Cancer (MBC): No Evidence for Improved Survival Following Distant Recurrence after Adjuvant Chemotherapy.

abstract: Population-based studies have suggested improved survival for patients diagnosed with MBC in recent years, presumably due to the availability of new and more effective therapies (Chia et al. Cancer 2007; Dawood et al. JCO, 2008). The objective of this analysis was to determine if survival improved for patients who participated in Eastern Cooperative Oncology Group (ECOG) adjuvant trials and later developed MBC. Methods: Adjuvant trials coordinated by the ECOG that accrued patients between 1978 and 2002 were reviewed (n=12), which included followup until 2010. Cytotoxic and biologic agents approved for MBC during this time included paclitaxel (1994), capecitabine and trastuzumab (1998), docetaxel and gemcitabine (2004), lapatinib and ixabepilone (2007), and bevacizumab (2008). Survival following distant recurrence was estimated for 4 time periods ranging from 6–10 years, and adjusted for baseline covariates in a Cox proportional hazards model. Because distant relapse free interval (DRFI) was the covariate most strongly associated with survival after recurrence, and the potential for “gap time” bias this could introduce, logrank tests for other covariates and estimates of effects were computed stratified on DRFI (0-3, >3-6, > 6 years). HER2 status was not routinely available and thus not included. Results: The 12 trials included 14,752 patients (93% received adjuvant chemotherapy); 3711 (25.2%) developed distant recurrence. Median survival after distant recurrence was 20 months; the estimated 5 and 10-year survival rates were 16.3% and 6.1%, respectively. Median survival by time period is shown in the table, stratified by DRFI. Median survival did not significantly change over time by DRFI (≤3 years, p=0.15; >3 yr, p=0.57). In a Cox proportional hazards model, factors associated with inferior survival after adjusting for other covariates included shorter DRFI ( 6 vs. 9 vs. 0 nodes — HR 1.51, p Conclusions: In contrast to reports from population-based studies, we do not observe any improvement in survival over time for patients who develop distant recurrence after adjuvant chemotherapy. There remains a critical unmet need for new therapies for MBC, especially for those who recur after adjuvant chemotherapy. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-08-01.


title: A Potential Decline in Life Expectancy in the United States in the 21st Century

abstract: Forecasts of life expectancy are an important component of public policy that influence age-based entitlement programs such as Social Security and Medicare. Although the Social Security Administration recently raised its estimates of how long Americans are going to live in the 21st century, current trends in obesity in the United States suggest that these estimates may not be accurate. From our analysis of the effect of obesity on longevity, we conclude that the steady rise in life expectancy during the past two centuries may soon come to an end.


title: Design of an Image-Servo Mask Alignment System Using Dual CCDs with an XXY Stage

abstract: Mask alignment of photolithography technology is used in many applications, such as micro electro mechanical systems’ semiconductor process, printed circuits board, and flat panel display. As the dimensions of the product are getting smaller and smaller, the automatic mask alignment of photolithography is becoming more and more important. The traditional stacked XY-Θz stage is heavy and it has cumulative flatness errors due to its stacked assembly mechanism. The XXY stage has smaller cumulative error due to its coplanar design and it can move faster than the traditional XY-Θz stage. However, the relationship between the XXY stage’s movement and the commands of the three motors is difficult to compute, because the movements of the three motors on the same plane are coupling. Therefore, an artificial neural network is studied to establish a nonlinear mapping from the desired position and orientation of the stage to three motors’ commands. Further, this paper proposes an image-servo automatic mask alignment system, which consists of a coplanar XXY stage, dual GIGA-E CCDs with lens and a programmable automatic controller (PAC). Before preforming the compensation, a self-developed visual-servo provides the positioning information which is obtained from the image processing and pattern recognition according to the specified fiducial marks. To obtain better precision, two methods including the center of gravity method and the generalize Hough Transformation are studied to correct the shift positioning error.


title: Centimeter-Wave Concept for 5G Ultra-Dense Small Cells

abstract: Ultra-dense small cells are foreseen to play an essential role in the 5th generation (5G) of mobile radio access technology, which will be operating over different bands with respect to established systems. The natural step for exploring new spectrum is to look into the centimeter-wave bands as well as exploring millimeter-wave bands. This paper presents our vision on the technology components for a 5G centimeter-wave concept for ultra-dense small cells. Fundamental features such as optimized short frame structure, multi-antenna technologies, interference rejection, rank adaptation and dynamic scheduling of uplink/downlink transmission are discussed, along with the design of a novel flexible waveform and energy-saving enablers.


title: Real-time Multiple Abnormality Detection in Video Data

abstract: Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.


title: Selecting CNN features for online learning of 3D objects

abstract: We present a novel method for classifying 3D objects that is particularly tailored for the requirements in robotic applications. The major challenges here are the comparably small amount of available training data and the fact that often data is perceived in streams and not in fixed-size pools. Traditional state-of-the-art learning methods, however, require a large amount of training data, and their online learning capabilities are usually limited. Therefore, we propose a modality-specific selection of convolutional neural networks (CNN), pre-trained or fine-tuned, in combination with a classifier that is designed particularly for online learning from data streams, namely the Mondrian Forest (MF). We show that this combination of trained features obtained from a CNN can be improved further if a feature selection algorithm is applied. In our experiments, we use the resulting features both with a MF and a linear Support Vector Machine (SVM). With SVM we beat the state of the art on an RGB-D dataset, while with MF a strong result for active learning is achieved.


Sarcasm Detection : Building a Contextual Hierarchy.


title: An Evaluation of Shape Changes for Conveying Emotions

abstract: In this paper, we explore how shape changing interfaces might be used to communicate emotions. We present two studies, one that investigates which shapes users might create with a 2D flexible surface, and one that studies the efficacy of the resulting shapes in conveying a set of basic emotions. Results suggest that shape parameters are correlated to the positive or negative character of an emotion, while parameters related to movement are correlated with arousal level. In several cases, symbolic shape expressions based on clear visual metaphors were used. Results from our second experiment suggest participants were able to recognize emotions given a shape with a good accuracy within 28% of the dimensions of the Circumplex Model. We conclude that shape and shape changes of a 2D flexible surface indeed appear able to convey emotions in a way that is worthy of future exploration.


title: Integrating Prior Knowledge into Deep Learning

abstract: Deep learning allows to develop feature representations and train classification models in a fully integrated way. However, learning deep networks is quite hard and it improves over shallow architectures only if a large number of training data is available. Injecting prior knowledge into the learner is a principled way to reduce the amount of required training data, as the learner does not need to induce the knowledge from the data itself. In this paper we propose a general and principled way to integrate prior knowledge when training deep networks. Semantic Based Regularization (SBR) is used as underlying framework to represent the prior knowledge, expressed as a collection of first-order logic clauses (FOL), and where each task to be learned corresponds to a predicate in the knowledge base. The knowledge base correlates the tasks to be learned and it is translated into a set of constraints which are integrated into the learning process via backpropagation. The experimental results show how the integration of the prior knowledge boosts the accuracy of a state-of-the-art deep network on an image classification task.


title: Speech Impairment in Down Syndrome: A Review

abstract: Purpose This review summarizes research on disorders of speech production in Down syndrome (DS) for the purposes of informing clinical services and guiding future research. Method Review of the literature was based on searches using MEDLINE, Google Scholar, PsycINFO, and HighWire Press, as well as consideration of reference lists in retrieved documents (including online sources). Search terms emphasized functions related to voice, articulation, phonology, prosody, fluency, and intelligibility. Conclusions The following conclusions pertain to four major areas of review: voice, speech sounds, fluency and prosody, and intelligibility. The first major area is voice. Although a number of studies have reported on vocal abnormalities in DS, major questions remain about the nature and frequency of the phonatory disorder. Results of perceptual and acoustic studies have been mixed, making it difficult to draw firm conclusions or even to identify sensitive measures for future study. The second major area is speech s...


title: Correction of severe bimaxillary protrusion with first premolar extractions and total arch distalization with palatal anchorage plates.

abstract: Correction of a severe protrusive soft tissue profile without orthognathic surgery can be challenging. This case report describes the treatment of a young woman with a severe bimaxillary protrusion. Orthodontic treatment included extraction of her 4 first premolars and total arch distalization of both arches using a palatal plate appliance. The total treatment time was 24 months. Her occlusion and facial appearance were significantly improved.


title: An efficient and backwards-compatible transformation to ensure memory safety of C programs

abstract: Memory-related errors, such as buffer overflows and dangling pointers, remain one of the principal reasons for failures of C programs. As a result, a number of recent research efforts have focused on the problem of dynamic detection of memory errors in C programs. However, existing approaches suffer from one or more of the following problems: inability to detect all memory errors (e.g., Purify), requiring non-trivial modifications to existing C programs (e.g., Cyclone), changing the memory management model of C to use garbage collection (e.g., CCured), and excessive performance overheads. In this paper, we present a new approach that addresses these problems. Our approach operates via source code transformation and combines efficient data-structures with simple, localized optimizations to obtain good performance.


title: Structured Face Hallucination

abstract: The goal of face hallucination is to generate high-resolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms of facial components, contours and smooth regions. The image structure is maintained via matching gradients in the reconstructed high-resolution output. For facial components, we align input images to generate accurate exemplars and transfer the high-frequency details for preserving structural consistency. For contours, we learn statistical priors to generate salient structures in the high-resolution images. A patch matching method is utilized on the smooth regions where the image gradients are preserved. Experimental results demonstrate that the proposed algorithm generates hallucinated face images with favorable quality and adaptability.


title: Robust TiN HM process to overcome under etch issue for SAV scheme on 14nm node

abstract: For advance node such as 14nm technology and beyond, back end of line interconnect has implemented self-aligned via (SAV) schemes for better via-metal short process window [1]. A TiN metal hard mask (MHM) is used for the trench pattern definition, after which via lithography and partial via (PV) etch is performed where the TiN was opened. The via etch condition has very good selectivity so that via is formed in a self-aligned fashion by TiN HM [2]. It is indeed to have significant benefit of via to metal short [3]. However, one of the trade off in SAV scheme is the via under etch that whether or not via can well land on an opened oxide area during PV etch. To define the contact area between via resist hole and opened HM oxide as an effective area for via to open successfully. “Fig. 1” shows the effective area change during process variation due to Via alignment, Via photo resist CD variation (Via ADICD) and post hard mask etch CD variation (AMICD). “Fig. 2” shows the mechanism of this under etch failure mode and typical TEM image from 64nm metal pitch test vehicle. In this work, we try to enlarge the process window by an aggressive AMICD targeting in combining with a higher density TiN material to maintain profile.


title: A Simple Yet Efficient Accuracy-Configurable Adder Design

abstract: Approximate computing is a promising approach for low-power IC design and has recently received considerable research attention. To accommodate dynamic levels of approximation, a few accuracy-configurable adder (ACA) designs have been developed in the past. However, these designs tend to incur large area overheads as they rely on either redundant computing or complicated carry prediction. Some of these designs include error detection and correction circuitry, which further increase the area. In this paper, we investigate a simple ACA design that contains no redundancy or error detection/correction circuitry and uses very simple carry prediction. The simulation results show that our design dominates the latest previous work on accuracy-delay-power tradeoff while using 39% lower area. In the best case, the iso-delay power of our design is only 16% of accurate adder regardless of degradation in accuracy. One variant of this design provides finer-grained and larger tunability than that of the previous works. Moreover, we propose a delay-adaptive self-configuration technique to further improve the accuracy-delay-power tradeoff. The advantages of our method are confirmed by the applications in multiplication and discrete cosine transform computing.


title: Understanding the Role of Individual Innovativeness in the Acceptance of IT-Based Innovations: Comparative Analyses of Models and Measures

abstract: As new technological innovations are rapidly introduced and changed, identifying an individual characteristic that has a persistent effect on the acceptance decisions across multiple technologies is of substantial value for the successful implementation of information systems. Augmenting prior work on individual innovativeness within the context of information technology, we developed a new measure of adopter category innovativeness (ACI) and compared its effectiveness with the existing measure of personal innovativeness in IT (PIIT). Further, we examined two alternative models in which the role of individual innovativeness was theorized differently—either as a moderator of the effects the perceived innovation characteristics of usefulness, ease of use, and compatibility have on future use intention (moderator model) or as a direct determinant of the innovation characteristics (direct determinant model). To ensure the generalizability of the study findings, two field studies (N= 634) were conducted, each of which examined the two models (moderator and direct determinant) and measured individual innovativeness using the two measures (ACI and PIIT). Study 1 surveyed the online buying practices of 412 individuals, and Study 2 surveyed personal digital assistant adoption of 222 healthcare professionals. Across the markedly different adoption contexts, the study results consistently show that individual innovativeness is a direct determinant of the innovation characteristics, and the two measures share many commonalities. The new measure offers some additional utilities not found in the PIIT measure by allowing individuals to be directly classified and mapped into adopter categories. Implications are drawn for future research and practice.


title: Fuzzy clustering of time series data: A particle swarm optimization approach

abstract: With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because of different applications, the problem of clustering the time series data has become highly popular and many algorithms have been proposed in this field. Recently Swarm Intelligence (SI) as a family of nature inspired algorithms has gained huge popularity in the field of pattern recognition and clustering. In this paper, a technique for clustering time series data using a particle swarm optimization (PSO) approach has been proposed, and Pearson Correlation Coefficient as one of the most commonly-used distance measures for time series is considered. The proposed technique is able to find (near) optimal cluster centers during the clustering process. To reduce the dimensionality of the search space and improve the performance of the proposed method, a singular value decomposition (SVD) representation of cluster centers is considered. Experimental results over three popular data sets indicate the superiority of the proposed technique in comparing with fuzzy C-means and fuzzy K-medoids clustering techniques.


title: Deep Sequence-to-Sequence Neural Networks for Ionospheric Activity Map Prediction

abstract: The ability to predict the ionosphere activity is of interest for several applications such as satellite telecommunications or Global Navigation Satellite Systems (GNSS). A few studies have proposed models able to predict Total Electron Content (TEC) values of the ionosphere locally over measuring stations, but not worldwide for most of them. We propose a method using Deep Neural Networks (DNN) to predict a sequence of global TEC maps consecutive to an input sequence of past TEC maps, by combining Convolutional Neural Networks (CNNs) with convolutional Long Short-Term Memory (LSTM) networks. The numerical experiments show that the approach provides significant improvement over methods implemented for benchmarking and is competitive with state-of-the-art methods while providing global TEC predictions. The proposed architecture can be adapted to any sequence-to-sequence prediction problem.


title: MuSQLE: Distributed SQL query execution over multiple engine environments

abstract: Multi-engine analytics has been gaining an increasing amount of attention from both the academic and the industrial community as it can successfully cope with the heterogeneity and complexity that the plethora of frameworks, technologies and requirements have brought forth. It is now common for a data analyst to combine data that resides on multiple and totally independent engines and perform complex analytics queries. Multi-engine solutions based on SQL can facilitate such efforts, as SQL is a popular standard that the majority of data-scientists understands. Existing solutions propose a middleware that centrally optimizes query execution for multiple engines. Yet, this approach requires manual integration of every primitive engine operator along with its cost model, rendering the process of adding new operators or engines highly inextensible. To address this issue we present MuSQLE, a system for SQL-based analytics over multi-engine environments. MuSQLE can efficiently utilize external SQL engines allowing for both intra and inter engine optimizations. Our framework adopts a novel API-based strategy. Instead of manual integration, MuSQLE specifies a generic API, used for the cost estimation and query execution, that needs to be implemented for each SQL engine endpoint. Our engine API is integrated with a state-of-the-art query optimizer, adding support for location-based, multi-engine query optimization and letting individual runtimes perform sub-query physical optimization. The derived multi-engine plans are executed using the Spark distributed execution framework. Our detailed experimental evaluation, integrating PostgreSQL, MemSQL and SparkSQL under MuSQLE, demonstrates its ability to accurately decide on the most suitable execution engine. MuSQLE can provide speedups of up to 1 order of magnitude for TPCH queries, leveraging different engines for the execution of individual query parts.


title: Powered wheelchair controlled by eye-tracking system

abstract: In this paper, we use the optical-type eye tracking system to control powered wheelchair. The useris eye movements are translated to screen position using the optical-type eye tracking system. The pupil-tracking goggles with a video CCD camera and a frame grabber analyzes a series of human pupil images when the user is gazing at the screen. A new calibration algorithm is then used to determine the direction of the eye gaze in real time. We design an interface with nine command zones to control powered wheelchair. The command at the calculated position of the gazed screen is then sent to move the powered wheelchair.


title: Is Over Practice Necessary? – Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining (submitted). Paper presented at the

abstract: This study examined the effectiveness of an educational data mining method --Learning Factors Analysis (LFA) --on improving the learning efficiency in the Cognitive Tutor curriculum. LFA uses a statistical model to predict how students perform in each practice of a knowledge component (KC), and identifies over-practiced or under-practiced KCs. By using the LFA findings on the Cognitive Tutor geometry curriculum, we optimized the curriculum with the goal of improving student learning efficiency. With a control group design, we analyzed the learning performance and the learning time of high school students participating in the Optimized Cognitive Tutor geometry curriculum. Results were compared to students participating in the traditional Cognitive Tutor geometry curriculum. Analyses indicated that students in the optimized condition saved a significant amount of time in the optimized curriculum units, compared with the time spent by the control group. There was no significant difference in the learning performance of the two groups in either an immediate post test or a two-week-later retention test. Findings support the use of this data mining technique to improve learning efficiency with other computer-tutor-based curricula.


title: Usable gestures for mobile interfaces: evaluating social acceptability

abstract: Gesture-based mobile interfaces require users to change the way they use technology in public settings. Since mobile phones are part of our public appearance, designers must integrate gestures that users perceive as acceptable for pub-lic use. This topic has received little attention in the litera-ture so far. The studies described in this paper begin to look at the social acceptability of a set of gestures with re-spect to location and audience in order to investigate possi-ble ways of measuring social acceptability. The results of the initial survey showed that location and audience had a significant impact on a user's willingness to perform ges-tures. These results were further examined through a user study where participants were asked to perform gestures in different settings (including a busy street) over repeated trials. The results of this work provide gesture design rec-ommendations as well as social acceptability evaluation guidelines.


title: The Caltech-UCSD Birds-200-2011 Dataset

abstract: CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localization annotations. All images are annotated with bounding boxes, part locations, and at- tribute labels. Images and annotations were filtered by mul- tiple users of Mechanical Turk. We introduce benchmarks and baseline experiments for multi-class categorization and part localization.


DIGITAL CALIBRATION OF SAR ADC


title: Code offload with least context migration in the mobile cloud

abstract: Mobile Cloud Computing (MCC) is of particular importance to address the contradiction between the increasing complexity of user applications and the limited lifespan of mobile device's battery, by offloading the computational workloads from local devices to the remote cloud. Current offloading schemes either require the programmer's annotations, which restricts its wide application; or transmits too much unnecessary data, resulting bandwidth and energy waste. In this paper, we propose a novel method-level offloading methodology to offload local computational workload with as least data transmission as possible. Our basic idea is to identify the contexts which are necessary to the method execution by parsing application binaries in advance and applying this parsing result to selectively migrate heap data while allowing successful method execution remotely. Our implementation of this design is built upon Dalvik Virtual Machine. Our experiments and evaluation against applications downloaded from Google Play show that our approach can save data transmission significantly comparing to existing schemes.


Software Maintenance from a Service Perspective


title: Pointing the Unknown Words

abstract: The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the deep learning models. We propose a novel way to deal with the rare and unseen words for the neural network models using attention. Our model uses two softmax layers in order to predict the next word in conditional language models: one predicts the location of a word in the source sentence, and the other predicts a word in the shortlist vocabulary. At each time-step, the decision of which softmax layer to use choose adaptively made by an MLP which is conditioned on the context.~We motivate our work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.~We observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset using our proposed model.


Resilience and Sustainable Development: Theory of resilience, systems thinking and adaptive governance


title: BCILAB: a platform for brain-computer interface development

abstract: Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human?computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.


title: Improved learning in a large-enrollment physics class.

abstract: We compared the amounts of learning achieved using two different instructional approaches under controlled conditions. We measured the learning of a specific set of topics and objectives when taught by 3 hours of traditional lecture given by an experienced highly rated instructor and 3 hours of instruction given by a trained but inexperienced instructor using instruction based on research in cognitive psychology and physics education. The comparison was made between two large sections (N = 267 and N = 271) of an introductory undergraduate physics course. We found increased student attendance, higher engagement, and more than twice the learning in the section taught using research-based instruction.


title: Current peak based device classification in NILM on a low-cost embedded platform using extra-trees

abstract: Non-Intrusive Load Monitoring (NILM) is a method for disaggregation of energy consumption of individual appliances in a household. This involves the classification of individual appliances, for which a number of electrical features in combination with machine learning algorithms have been used. Extraction of most of these features is a computationally demanding task, and use of complex machine learning algorithms further adds to this complexity. Although solutions to this problem exist, they tend to be expensive and unaffordable to consumers in developing countries. This necessitates a need for an inexpensive solution capable of running on low-cost embedded platforms. In this paper, the authors implement a machine learning approach on an embedded platform to address this problem using current-based features for device classification. The model was evaluated using the Building-Level fUlly-labeled dataset for Electricity Disaggregation (BLUED) which contains electrical measurements for a household in the US for one week. The classifier was trained on Raspberry Pi 3 in about 4 seconds and classification of an event was performed in under 400 ms, validating the feasibility of the classification model on such a platform.


title: The Feeney Amendment and the Continuing Rise of Prosecutorial Power to Plea Bargain

abstract: Congress has come close to a drive-by rewrite of sentencing law, and a sentencing revolution may still be in the works. On April 10, 2003, Congress passed the PROTECT bill (popularly known as Amber Alert), which creates a national notification system for child kidnappings. On March 26, while the bill was pending, the House of Representatives passed the Feeney Amendment to the bill. The original amendment was an unprecedented attempt by Congress to rewrite the Sentencing Guidelines by itself without the input or expertise of the Sentencing Commission. The House-Senate conference committee narrowed the amendment, limiting many of its changes to child pornography and child sex cases. The revised amendment nonetheless changes the Sentencing Guidelines substantially, and it instructs the Sentencing Commission to make many more changes within the next six months. The likely result is many fewer Guideline departures, less judicial discretion, and more prosecutorial control. The losers are defendants and judges, and the winners are prosecutors. Prosecutorial leverage to plea bargain will be at an all-time high, resulting in fewer trials, more bargains, and higher sentences. Judges used to check prosecutorial harshness, but now they are increasingly powerless unless prosecutors deign to grant leniency. Part I of this article surveys the scope of the Feeney Amendment, emphasizing the breadth of its changes and how far it reaches beyond child-sex cases. Part II explores how the Feeney Amendment shifts power from district judges to prosecutors. While Congress's desire to reduce sentencing departures is understandable, an evenhanded approach should regulate not only judicial leniency, but also prosecutor-initiated departures. The result is even more imbalance in plea-bargaining power. Part III considers Congress's directives to the Sentencing Commission. Congress has lost trust in the Commission, which led it to rewrite sentencing law on its own without awaiting the Commission's expertise. The result is a blunderbuss solution to the narrower problems caused by a subset of judges. I hope that the Commission will respond by constraining prosecutorial as well as judicial discretion. If however the Commission continues on the present course, it will skew the already lopsided balance of power even more.