title: Open-Source Colorimeter

abstract: The high cost of what have historically been sophisticated research-related sensors and tools has limited their adoption to a relatively small group of well-funded researchers. This paper provides a methodology for applying an open-source approach to design and development of a colorimeter. A 3-D printable, open-source colorimeter utilizing only open-source hardware and software solutions and readily available discrete components is discussed and its performance compared to a commercial portable colorimeter. Performance is evaluated with commercial vials prepared for the closed reflux chemical oxygen demand (COD) method. This approach reduced the cost of reliable closed reflux COD by two orders of magnitude making it an economic alternative for the vast majority of potential users. The open-source colorimeter demonstrated good reproducibility and serves as a platform for further development and derivation of the design for other, similar purposes such as nephelometry. This approach promises unprecedented access to sophisticated instrumentation based on low-cost sensors by those most in need of it, under-developed and developing world laboratories.


title: All Reviews are Not Created Equal: The Disaggregate Impact of Reviews and Reviewers at Amazon.Com

abstract: Online product review networks help to transmit information that customers can use to evaluate product quality. The prior literature has found that, in the aggregate, better product reviews lead to higher sales. However, product review networks increasingly include an explicit social component that allows consumers to evaluate individual reviews based on the status of the reviewer and on the "helpfulness" of the review to the community. In this research, we extend this literature by analyzing the impact of reviews at a disaggregate level. We find that reviews that the community finds helpful have a stronger influence on consumers' purchase decisions than other reviews do. Moreover, these reviews have a stronger impact on less popular books than on more popular books, where consumers may be able to use other outside information sources to form an opinion of the product. Overall, our results suggest that the micro-level dynamics of community interactions are valuable in signaling quality over-and-above aggregate-level scores. One implication of this result is that the micro-level dynamics of reputation communities make it harder for self-interested parties to manipulate reviews versus an environment where consumers only have aggregate quality measures.


title: The Lasso Problem and Uniqueness

abstract: The lasso is a popular tool for sparse linear regression, especially for problems in which the number of variables p exceeds the number of observations n. But when p>n, the lasso criterion is not strictly convex, and hence it may not have a unique minimum. An important question is: when is the lasso solution well-defined (unique)? We review results from the literature, which show that if the predictor variables are drawn from a continuous probability distribution, then there is a unique lasso solution with probability one, regardless of the sizes of n and p. We also show that this result extends easily to $\ell_1$ penalized minimization problems over a wide range of loss functions. A second important question is: how can we deal with the case of non-uniqueness in lasso solutions? In light of the aforementioned result, this case really only arises when some of the predictor variables are discrete, or when some post-processing has been performed on continuous predictor measurements. Though we certainly cannot claim to provide a complete answer to such a broad question, we do present progress towards understanding some aspects of non-uniqueness. First, we extend the LARS algorithm for computing the lasso solution path to cover the non-unique case, so that this path algorithm works for any predictor matrix. Next, we derive a simple method for computing the component-wise uncertainty in lasso solutions of any given problem instance, based on linear programming. Finally, we review results from the literature on some of the unifying properties of lasso solutions, and also point out particular forms of solutions that have distinctive properties.


title: End to End Long Short Term Memory Networks for Non-Factoid Question Answering

abstract: Retrieving correct answers for non-factoid queries poses significant challenges for current answer retrieval methods. Methods either involve the laborious task of extracting numerous features or are ineffective for longer answers. We approach the task of non-factoid question answering using deep learning methods without the need of feature extraction. Neural networks are capable of learning complex relations based on relatively simple features which make them a prime candidate for relating non-factoid questions to their answers. In this paper, we show that end to end training with a Bidirectional Long Short Term Memory (BLSTM) network with a rank sensitive loss function results in significant performance improvements over previous approaches without the need for combining additional models.


title: Inferring activities from interactions with objects

abstract: A key aspect of pervasive computing is using computers and sensor networks to effectively and unobtrusively infer users' behavior in their environment. This includes inferring which activity users are performing, how they're performing it, and its current stage. Recognizing and recording activities of daily living is a significant problem in elder care. A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use. We propose an approach that addresses these challenges and shows promise in automating some types of ADL monitoring. Our key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution. So, we have developed Proactive Activity Toolkit (PROACT).


title: Wideband Flush-Mounted Surface Wave Antenna of Very Low Profile

abstract: This paper presents the design of a compact and wideband surface wave antenna that exhibits very low profile and can be flush-mounted on a conformal platform. The proposed antenna comprises a tapered grounded ceramic slab, a unidirectional surface wave launcher, and a tapered impedance transition. The ceramic slab is smoothly tapered to transform the guided surface wave into radiated space wave over a wide frequency range. The surface wave launcher employs a probe-fed parallel plate waveguide with a modified parabolic reflecting wall and a metallic post, which are critical for effectively transforming the cylindrical waves to a unidirectional plane wave within a broad frequency range. Simulated results show that the proposed surface wave antenna achieves a wide operation bandwidth from 6.1 to 18 GHz with a thickness of only ${\bf 0}.{\bf 12}{\lambda _0}$ at the center frequency. Acceptable gain values with stable and quasi end-fire radiation beams are obtained over the entire frequency band. A prototype of the proposed antenna is fabricated and tested. Measured results are in good agreement with simulated ones.


title: Open Set Domain Adaptation

abstract: When the training and the test data belong to different domains, the accuracy of an object classifier is significantly reduced. Therefore, several algorithms have been proposed in the last years to diminish the so called domain shift between datasets. However, all available evaluation protocols for domain adaptation describe a closed set recognition task, where both domains, namely source and target, contain exactly the same object classes. In this work, we also explore the field of domain adaptation in open sets, which is a more realistic scenario where only a few categories of interest are shared between source and target data. Therefore, we propose a method that fits in both closed and open set scenarios. The approach learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset. A thorough evaluation shows that our approach outperforms the state-of-the-art.


title: Sarcomatoid Renal Cell Carcinoma: Biologic Behavior, Prognosis, and Response to Combined Surgical Resection and Immunotherapy

abstract: PURPOSE: Sarcomatoid variants of renal cell carcinoma (RCC) are aggressive tumors that respond poorly to immunotherapy. We report the outcomes of 31 patients with sarcomatoid RCC treated with a combination of surgical resection and immunotherapy. PATIENTS AND METHODS: Patients were identified from the database of the University of California Los Angeles Kidney Cancer Program. We retrospectively reviewed the cases of 31 consecutive patients in whom sarcomatoid RCC was diagnosed between 1990 and 1997. Clinical stage, sites of metastasis, pathologic stage, and type of immunotherapy were abstracted from the medical records. The primary end point analyzed was overall survival, and a multivariate analysis was performed to distinguish any factors conferring an improved survivorship. RESULTS: Twenty-six percent of patients were male and 74% were female, and the median age was 59 years (range, 34 to 73 years). Length of follow-up ranged from 2 to 77 months (mean, 21.4 months). Twenty-eight patients (84%) had known...


title: Doubleclick Ad Exchange Auction

abstract: Display advertisements on the web are sold via ad exchanges that use real time auction. We describe the challenges of designing a suitable auction, and present a simple auction called the Optional Second Price (OSP) auction that is currently used in Doubleclick Ad Exchange.


title: Fast and Efficient Skin Detection for Facial Detection

abstract: In this paper, an efficient skin detection system is proposed. The algorithm is based on a very fast efficient pre-processing step utilizing the concept of ternary conversion in order to identify candidate windows and subsequently, a novel local two-stage diffusion method which has F-score accuracy of 0.5978 on SDD dataset. The pre-processing step has been proven to be useful to boost the speed of the system by eliminating 82% of an image in average. This is obtained by keeping the true positive rate above 98%. In addition, a novel segmentation algorithm is also designed to process candidate windows which is quantitatively and qualitatively proven to be very efficient in term of accuracy. The algorithm has been implemented in FPGA to obtain real-time processing speed. The system is designed fully pipeline and the inherent parallel structure of the algorithm is fully exploited to maximize the performance. The system is implemented on a Spartan-6 LXT45 Xilinx FPGA and it is capable of processing 98 frames of 640*480 24-bit color images per second.


title: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

abstract: Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods, self-supervised learning methods are proposed to learn general image and video features from large-scale unlabeled data without using any human-annotated labels. This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos. First, the motivation, general pipeline, and terminologies of this field are described. Then the common deep neural network architectures that used for self-supervised learning are summarized. Next, the main components and evaluation metrics of self-supervised learning methods are reviewed followed by the commonly used image and video datasets and the existing self-supervised visual feature learning methods. Finally, quantitative performance comparisons of the reviewed methods on benchmark datasets are summarized and discussed for both image and video feature learning. At last, this paper is concluded and lists a set of promising future directions for self-supervised visual feature learning.


title: Resolution Recommendation for Event Tickets in Service Management

abstract: In recent years, IT service providers have rapidly achieved an automated service delivery model. Software monitoring systems are designed to actively collect and signal event occurrences and, when necessary, automatically generate incident tickets. Repeating events generate similar tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. In this paper, we develop techniques to recommend appropriate resolution for incoming events by making use of similarities between the events and historical resolutions of similar events. Built on the traditional k-nearest neighbor algorithm (KNN), our proposed algorithms take into account false positives often generated by monitoring systems. An additional penalty is incorporated into the algorithms to control the number of misleading resolutions in the recommendation results. Moreover, as the effectiveness of the KNN heavily relies on the underlying similarity measurement, we proposed two other approaches to significantly improve our recommendation with respect to resolution relevance. One approach uses topic-level features to incorporate resolution information into the similarity measurement; the other uses metric learning to learn a more effective similarity measure. Extensive empirical evaluations on three ticket data sets demonstrate the effectiveness and efficiency of our proposed methods.


MapReduce: Distributed Computing for Machine Learning


title: Rethinking the Future of Wireless Emergency Alerts: A Comprehensive Study of Technical and Conceptual Improvements

abstract: The Wireless Emergency Alerting (WEA) service is a standards-based transport and presentation channel used nationwide in the United States. The service can deliver short text warnings to wireless subscribers through a cell broadcast mechanism. For emergency situations in which a broadcast modality and a single, short text message are sufficient to convey information, the WEA service can be efficient and effective. However, the content to be delivered may necessitate more than a single, unchanging short message. In this research, we first examine the WEA service from the perspective of alert originators. We then use the insights gained to explore the efficacy of a range of potential extensions to the service. The extensions mainly address the importance of user context and the ability to create awareness through careful attention to the integrity of the vital information. We evaluated these extensions using a WEA emulation testbed in two public usability trials. We present an analysis of the broad range of improvements as a basis for further research into improving the service. We conclude that (1) precise geo-targeting augmented with location information and maps is an important aspect of capturing users' context, and (2) presenting information in a digested form can markedly improve the actionability and the accuracy of interpretation.


title: Multimedia mobile application e-commerce service satisfaction

abstract: Previous research on e-commerce through multimedia mobile applications primarily focused on the relationships between e-service factors and positive customer behavior. However, these studies did not consider the consumer purchasing decision process sufficiently to develop firms’ strategic priorities in the context of e-commerce services through multimedia mobile applications. To fill this gap, we conducted a choice-based conjoint analysis (CBCA) to identify the preferences for each attribute and the interaction effects between each pair of attributes related to the purchasing decision, and used an analytic hierarchy process (AHP) analysis to calculate the overall priorities for every sub-criterion and partial priorities for each main criterion. The main findings from the integrated CBCA and AHP are that consumers must be able to fulfill their most basic needs through multimedia mobile applications’ e-commerce service, e-commerce service providers using multimedia mobile applications should enhance their existing advantages to complement their shortcomings, and e-commerce service providers besides social commerce operators should change their business structures to adapt to the new e-commerce environment. We also discuss the main contributions and implications to improve e-commerce service satisfaction and strategic decision-making.


title: Boosted decision trees as an alternative to artificial neural networks for particle identification

abstract: The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.


title: Review: Interoperability evaluation models: A systematic review

abstract: Interoperability is defined as the ability for two (or more) systems or components to exchange information and to use the information that has been exchanged. There is increasing demand for interoperability between individual software systems. Developing an interoperability evaluation model between software and information systems is difficult, and becoming an important challenge. An interoperability evaluation model allows knowing the degree of interoperability, and lead to the improvement of interoperability. This paper describes the existing interoperability evaluation models, and performs a comparative analysis among their findings to determine the similarities and differences in their philosophy and implementation. This analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability evaluation model.


International Marketing Strategies For Global Competitiveness


title: S-band shorted patch antenna for inter pico satellite communications

abstract: In this paper we study and evaluate shorted patch and CPW-feed square slot antennas, both of which we deem suitable for use in inter pico satellite communications in terms of gain, bandwidth and size. We have simulated both antennas in the High Frequency Structure Simulator (HFSS). Our results show that the shorted patch antenna achieves higher gain; e.g., 4.8 dBi, wider impedance bandwidth; e.g., 3.9–11.1 GHz with Voltage Standing Wave Ratio (VSWR) ≤ 2 at resonance frequencies of 4.4, 6.9 and 10.1 GHz. In addition, the Quasi Newton method is used to shift the shorted patch antenna's operating frequency to 2.45 GHz (S-band). This thus enables its use in the unlicensed ISM band without critically affecting its radiation performance. The simulated results show an impedance bandwidth of 3650 MHz (2.05–5.7 GHz) with VSWR ≤ 2 at a resonance frequency of 2.45 GHz.


An Automatic Knowledge Graph Creation Framework from Natural Language Text


title: Digital signature based on PlayGamal algorithm

abstract: To keep the security and integrity of digital messages, can be applied cryptography method. Digital Signature is one of the vital digital data, so the secrecy must be maintained at the time of distribution and storage of data. This study aims to combine modern cryptographic methods with ElGamal traditional algorithm method is called PlayGamal Cipher Algorithm. Moreover. the method will be implement as application for digital signature. The proposed method will be compare with the existing El-Gamal to know the performance during encryption and decryption process.


title: Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes

abstract: Cognitive control supports flexible behavior by selecting actions that are consistent with our goals and appropriate for our environment. The prefrontal cortex (PFC) has an established role in cognitive control, and research on the functional organization of PFC promises to contribute to our understanding of the architecture of control. A recently popular hypothesis is that the rostro–caudal axis of PFC supports a control hierarchy whereby posterior-to-anterior PFC mediates progressively abstract, higher-order control. This review discusses evidence for a rostro–caudal gradient of function in PFC and the theories proposed to account for these results, including domain generality in working memory, relational complexity, the temporal organization of behavior and abstract representational hierarchy. Distinctions among these frameworks are considered as a basis for future research.


title: Predicting students marks in Hellenic Open University

abstract: The ability to provide assistance for a student at the appropriate level is invaluable in the learning process. Not only does it aids the student's learning process but also prevents problems, such as student frustration and floundering. Students' key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student's performance. The scope of this work compares some of the state of the art regression algorithms in the application domain of predicting students' marks. A number of experiments have been conducted with six algorithms, which were trained using datasets provided by the Hellenic Open University. Finally, a prototype version of software support tool for tutors has been constructed implementing the M5rules algorithm, which proved to be the most appropriate among the tested algorithms.


title: Using multi-dimensional dynamic time warping for TUG test instrumentation with inertial sensors

abstract: The Timed Up and Go (TUG) is a clinical test used widely to measure balance and mobility, e.g. in Parkinson's disease (PD). The test includes a sequence of functional activities, namely: sit-to-stand, 3-meters walk, 180° turn, walk back, another turn and sit on the chair. Meanwhile the stopwatch is used to score the test by measuring the time which the patients with PD need to perform the test. Here, the work presents an instrumented TUG using a wearable inertial sensor unit attached on the lower back of the person. The approach is used to automate the process of assessment compared with the manual evaluation by using visual observation and a stopwatch. The developed algorithm is based on the Dynamic Time Warping (DTW) for multi-dimensional time series and has been applied with the augmented feature for detection and duration assessment of turn state transitions, while a 1-dimensional DTW is used to detect the sit-to-stand and stand-to-sit phases. The feature set is a 3-dimensional vector which consists of the angular velocity, derived angle and features from Linear Discriminant Analysis (LDA). The algorithm was tested on 10 healthy individuals and 20 patients with PD (10 patients with early and late disease phases respectively). The test demonstrates that the developed technique can successfully extract the time information of the sit-to-stand, both turns and stand-to-sit transitions in the TUG test.


title: Weighted Naive Bayes classification algorithm based on particle swarm optimization

abstract: Naive Bayesian classification method is applied in many fields, but in the real world, its properties do not satisfy the assumption of independence. Harry Zhang and Shengli Sheng extended the naive Bayes into weighted naive Bayes[1]. This paper presents a Weighted Naive Bayes Classification Algorithm Based on PSO (particle swarm optimization, which was first proposed by Kenney and Eberhart[2]). This method makes use of automatic search function of PSO, while maintaining the integrity of each attribute of data. According to the characteristics of the data itself, this method improves the classification accuracy of Naive Bayes and avoids the loss of information. Through the experiment on UCI data sets, expected results were achieved. The experimental results showed that the method was feasible and effective.


title: A 0.6-V 800-MHz All-Digital Phase-Locked Loop With a Digital Supply Regulator

abstract: This paper proposes an ultra-low-voltage all-digital phase-locked loop (ADPLL) with a digital supply regulator (DSR). The DSR maintains an RMS jitter for a 280-MHz output signal of less than 0.55% when a 100-kHz to 100-MHz supply noise is produced on a digitally controlled oscillator (DCO). The DCO uses the two-step timing resolution of a digitally controlled varactor to achieve the high timing resolution. The proposed digital loop filter can reduce the area cost and critical path using the double-edge trigger technique. For a low supply voltage, the DCO and the time-to-digital converter use bulk-controlled techniques to increase the highest operating frequency and timing resolution, respectively. When the ADPLL output is 800 MHz at 0.6 V, the power consumption and core area are 656 μW and 0.02 mm2, respectively, in a 90-nm CMOS process.


title: Building Lifecycle Management System for Enhanced Closed Loop Collaboration

abstract: In the past few years, the architecture, engineering and construction (AEC) industry has carried out efforts to develop BIM (Building Information Modelling) facilitating tools and standards for enhanced collaborative working and information sharing. Lessons learnt from other industries and tools such as PLM (Product Lifecycle Management) – established tool in manufacturing to manage the engineering change process – revealed interesting potential to manage more efficiently the building design and construction processes. Nonetheless, one of the remaining challenges consists in closing the information loop between multiple building lifecycle phases, e.g. by capturing information from middle-of-life processes (i.e., use and maintenance) to re-use it in end-of-life processes (e.g., to guide disposal decision making). Our research addresses this lack of closed-loop system in the AEC industry by proposing an open and interoperable Web-based building lifecycle management system. This paper gives (i) an overview of the requirement engineering process that has been set up to integrate efforts, standards and directives of both the AEC and PLM industries, and (ii) first proofs-of-concept of our system implemented on two distinct campus.


APPOINTMENT SCHEDULING WITH OVERBOOKING TO MITIGATE PRODUCTIVITY LOSS FROM NO-SHOWS


title: Context-based entity description rule for entity resolution

abstract: In this paper, we consider the entity resolution(ER) problem, which is to identify objects referring to the same real-world entity. Prior work of ER involves expensive similarity comparison and clustering approaches. Additionally, the quality of entity resolution may be low due to insufficient information. To address these problems, by adopting context information of data objects, we present a novel framework of entity resolution, context-based entity description (CED), to make context information help entity resolution. In our framework, each entity is described by a set of CEDs. During entity resolution, objects are only compared with CEDs to determine its corresponding entity. Additionally, we propose efficient algorithms for CED discovery and CED-based entity resolution. We experimentally evaluated our CED-based ER algorithm on the real DBLP datasets, and the experimental results show that our algorithm can achieve both high precision and recall as well as outperform existing methods.


abstract: Spelling errors are introduced in text either during typing, or when the user does not know the correct phoneme or grapheme. If a language contains complex words like sandhi where two or more morphemes join based on some rules, spell checking becomes very tedious. In such situations, having a spell checker with sandhi splitter which alerts the user by flagging the errors and providing suggestions is very useful. A novel algorithm of sandhi splitting is proposed in this paper. The sandhi splitter can split about 7000 most common sandhi words in Kannada language used as test samples. The sandhi splitter was integrated with a Kannada spell checker and a mechanism for generating suggestions was added. A comprehensive, platform independent, standalone spell checker with sandhi splitter application software was thus developed and tested extensively for its efficiency and correctness. A comparative analysis of this spell checker with sandhi splitter was made and results concluded that the Kannada spell checker with sandhi splitter has an improved performance. It is twice as fast, 200 times more space efficient, and it is 90% accurate in case of complex nouns and 80% accurate for complex verbs. Such a spell checker with sandhi splitter will be of foremost significance in machine translation systems, voice processing, etc. This is the first sandhi splitter in Kannada and the advantage of the novel algorithm is that, it can be extended to all Indian languages.


title: Music and Stress

abstract: Myths about the power of music to regulate and change emotional states are found in each culture. Googling the words “music and stress” produces promises for different types of music specially designed to assist managing stress. Music therapists are familiar with using different techniques to reduce stress with different populations. This paper examines both the application of music to stress management and the neurological and physiological mechanisms of stress that can be modulated by music.


title: Design and application of a compact UWB Antipodal Vivaldi Antenna

abstract: This paper presents the design of a compact Antipodal Vivaldi Antenna (AVA) with good performance for ultra-wideband (UWB) applications. The miniaturized AVA — 40 × 40 × 0.8 mm, its balanced feed structure and its mechanical ground support are described in detail. The simulated results show that the proposed AVA covers a bandwidth ranging from 4.73 to more than 22 GHz with a peak gain above 5 dBi for the highest frequencies. A prototype was built and its return loss was measured. The designed antenna is utilized in a direction finding (DF) system based on the amplitude-comparison monopulse technique. The calibration curves and the corresponding residual errors of the proposed system are derived. An RMS residual error lower than 2.5° is obtained over a range of 40°.


title: Emotional speech comprehension in children and adolescents with autism spectrum disorders.

abstract: Abstract We examined the understanding of emotional speech by children and adolescents with autism spectrum disorders (ASD). We predicted that they would have difficulty understanding emotional speech, not because of an emotional prosody processing impairment but because of problems drawing appropriate inferences, especially in multiple-cue environments. Twenty-six children and adolescents with ASD and 26 typically developing controls performed a computerized task featuring emotional prosody, either embedded in a discrepant context or without any context at all. They must identify the speaker's feeling. When the prosody was the sole cue, participants with ASD performed just as well as controls, relying on this cue to infer the speaker's intention. When the prosody was embedded in a discrepant context, both ASD and TD participants exhibited a contextual bias and a negativity bias. However ASD participants relied less on the emotional prosody than the controls when it was positive. We discuss these findings with respect to executive function and intermodal processing. Learning outcomes: After reading this article, the reader should be able to (1) describe the ASD participants pragmatic impairments, (2) explain why ASD participants did not have an emotional prosody processing impairment, and (3) explain why ASD participants had difficulty inferring the speaker's intention from emotional prosody in a discrepant situation.


title: Budgeted Task Scheduling for Crowdsourced Knowledge Acquisition

abstract: Knowledge acquisition (e.g. through labeling) is one of the most successful applications in crowdsourcing. In practice, collecting as specific as possible knowledge via crowdsourcing is very useful since specific knowledge can be generalized easily if we have a knowledge base, but it is difficult to infer specific knowledge from general knowledge. Meanwhile, tasks for acquiring more specific knowledge can be more difficult for workers, thus need more answers to infer high-quality results. Given a limited budget, assigning workers to difficult tasks will be more effective for the goal of specific knowledge acquisition. However, existing crowdsourcing task scheduling cannot incorporate the specificity of workers' answers. In this paper, we present a new framework for task scheduling with the limited budget, targeting an effective solution to more specific knowledge acquisition. We propose novel criteria for evaluating the quality of specificity-dependent answers and result inference algorithms to aggregate more specific answers with budget constraints. We have implemented our framework with real crowdsourcing data and platform, and have achieved significant performance improvement compared with existing approaches.


Combining Rhinoplasty With Upper Lip–Lift Using a Single Incision Line


title: A single feed dual-band, linearly/circularly polarized cross-slot millimeter-wave antenna for future 5G networks

abstract: A low-profile, single-fed, dual-band, linearly/circularly polarized antenna for future 5G networks in the millimeter wave frequency regime is presented. The antenna comprises of two truncated-corner square patches. A cross-slot is etched on the inner patch. It is shown that this cross-slot generates additional resonances and lowers the axial ratio (AR) of the radiating structure. The proposed antenna is fed asymmetrically by a two-section microstrip line. The overall size of the antenna is only 8 mm × 8 mm. The proposed antenna operates at 28 GHz and 38 GHz. The antenna is linearly polarized in the lower band with an impedance bandwidth from 27.55 GHz to 28.15 GHz for |S11| < −10 dB. The antenna is circularly polarized in the upper band with an impedance bandwidth from 37.2 GHz to 38.3 GHz for |S11| < −10 dB and axial ratio (AR) bandwidth from 37.1 GHz to 38.1 GHz for AR < 3 dB. The antenna has a stable radiation pattern and a gain of greater than 6 dBi over both frequency bands.


title: Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques

abstract: Studentsâ€™ academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining studentsâ€™ performance during their period of studies in the institutions. The academic performance in this study is measured by their cumulative grade point average (CGPA) upon graduating. This study presents the work of data mining in predicting the drop out feature of students. This study applies decision tree technique to choose the best prediction and analysis. The list of students who are predicted as likely to drop out from college by data mining is then turned over to teachers and management for direct or indirect intervention.


title: CHRONOLOGICAL ANALYSIS OF THE ELECTRONIC WORD- OF-MOUTH EFFECT OF FOUR SOCIAL MEDIA CHANNELS ON MOVIE SALES: COMPARING TWITTER, YAHOO!MOVIES, YOUTUBE, AND BLOGS

abstract: Based on Rogers’s innovation diffusion model, we investigate how electronic word-of-mouth (eWOM) through different types of social media impacts movie sales across the different phases of movie screening. We collected eWOM information on movies from February to October 2012 from Twitter, Yahoo!Movies, YouTube, and blogs on a daily basis. The results indicate that Twitter is relatively influential on movie revenue in the initial stage of opening because of its mass media characteristic. On the other hand, Yahoo!Movies and blogs are relatively influential on movie revenue in the late stages of opening because of their interpersonal communication characteristics. Since YouTube contains both characteristics of mass media and interpersonal communication, we determine that there is no difference in the impact of YouTube on movie revenue between the initial and late stages of opening.


title: Toward meaningful engagement: a framework for design and research of gamified information systems

abstract: Gamification, an emerging idea for using game design elements and principles to make everyday tasks more engaging, is permeating many different types of information systems. Excitement surrounding gamification results from its many potential organizational benefits. However, few research and design guidelines exist regarding gamified information systems. We therefore write this commentary to call upon information systems scholars to investigate the design and use of gamified information systems from a variety of disciplinary perspectives and theories, including behavioral economics, psychology, social psychology, information systems, etc. We first explicate the idea of gamified information systems, provide real-world examples of successful and unsuccessful systems, and, based on a synthesis of the available literature, present a taxonomy of gamification design elements. We then develop a framework for research and design: its main theme is to create meaningful engagement for users; that is, gamified information systems should be designed to address the dual goals of instrumental and experiential outcomes. Using this framework, we develop a set of design principles and research questions, using a running case to illustrate some of our ideas. We conclude with a summary of opportunities for IS researchers to extend our knowledge of gamified information systems, and, at the same time, advance existing theories.


title: Semi-supervised Clustering in Attributed Heterogeneous Information Networks

abstract: A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects' relationships. In many applications, such as social networks and RDF-based knowledge bases, information can be modeled as HINs. To enrich its information content, objects (as represented by nodes) in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects' similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem. We conduct extensive experiments comparing SCHAIN with other state-of-the-art clustering algorithms and show that SCHAIN outperforms the others in clustering quality.


Prediction and Classification Using String Matching


title: Discriminative acoustic word embeddings: Tecurrent neural network-based approaches

abstract: Acoustic word embeddings --- fixed-dimensional vector representations of variable-length spoken word segments --- have begun to be considered for tasks such as speech recognition and query-by-example search. Such embeddings can be learned discriminatively so that they are similar for speech segments corresponding to the same word, while being dissimilar for segments corresponding to different words. Recent work has found that acoustic word embeddings can outperform dynamic time warping on query-by-example search and related word discrimination tasks. However, the space of embedding models and training approaches is still relatively unexplored. In this paper we present new discriminative embedding models based on recurrent neural networks (RNNs). We consider training losses that have been successful in prior work, in particular a cross entropy loss for word classification and a contrastive loss that explicitly aims to separate same-word and different-word pairs in a "Siamese network" training setting. We find that both classifier-based and Siamese RNN embeddings improve over previously reported results on a word discrimination task, with Siamese RNNs outperforming classification models. In addition, we present analyses of the learned embeddings and the effects of variables such as dimensionality and network structure.


title: Computer vision based segregation of carrot and curry leaf plants with weed identification in carrot field

abstract: Image processing has a major role in providing machine vision. One of the major objectives of a farmer is to enhance productivity at any cost. So weed removal in the crop area which is a labour intensive and expensive job is carried out by spraying concentrated chemicals throughout the field, which is hazardous to the environment. An alternative technique is patch spraying of herbicides. This will help in preserving the nutrient content of the soil to a great extent as well as in reducing the environmental pollution. In this paper distinction of two types of crops, carrot and curry leaf and weed identification in carrot field is done using the images. In future this algorithm can be implemented in mobile robots for patch spraying of herbicides. Also robotic arm can be incorporated so that the weeds can be plucked away and thereby an eco friendly farming can be adopted.


Learning Robot Skill Embeddings


title: Whatever Happened to Formal Methods for Security

abstract: We asked 7 experts 7 questions to find out what has occurred recently in terms of applying formal methods (FM) to security-centric, cyber problems. We are continually reminded of the 1996 paper by Tony Hoare "How did Software Get So Reliable Without Proof?" [1] In that vein, how did we get so insecure with proof? Given daily press announcements concerning new malware, data breaches, and privacy loss, is FM still relevant or was it ever? Our experts answered with unique personal insights. We were curious as to whether this successful methodology in "safety-critical" has succeeded as well for today's "build it, hack it, patch it" mindset. Our experts were John McLean (Naval Research Labs), Paul Black (National Institute of Standards and Technology), Karl Levitt (University of California at Davis), Joseph Williams (CloudEconomist.Com), Connie Heitmeyer (Naval Research Labs), Eugene Spafford (Purdue University), and Joseph Kiniry (Galois, Inc.). The questions and responses follow.


title: Quantitative Risk Management Concepts , Techniques and Tools

abstract: Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives.


title: Self-Aligned AlGaN/GaN FinFETs

abstract: We have demonstrated highly scaled, self-aligned AlGaN/GaN fin-shaped field-effect transistors (FinFETs), which were fabricated using e-beam lithography and a regrown n+ GaN ohmic process with a sacrificial dummy gate. Our devices were very aggressively scaled, with fin widths, gate length, and source drain spacing as small as 50, 60, and 200nm, respectively. DC characteristics, when normalized to active device periphery (number of fins times fin width), showed peak transconductance of 1.5 mS/ $\mu \text{m}$ and ON resistance of $390~\Omega $ - $\mu \text{m}$ . Enhancement mode device operation was observed for fin widths below 100 nm.


title: Smart probabilistic fingerprinting for WiFi-based indoor positioning with mobile devices

abstract: Different positioning schemes are based on the probability p ( o | l ) to have an observation vector o at a Reference Point (RP) l , based on Gaussian probabilities. This paper presents an approach to speed-up the p ( o | l ) computation without any approximation. The consequent positioning scheme is called Smart P-FP. The comparison between Traditional (without any p ( o | l ) acceleration) and Smart P-FP is performed over different smartphones. The saved energy is about 90% for a large number of Access Points (APs) but is significant even with few APs: more than 86% with 3 APs. The proposed p ( o | l ) computation is beneficial to any p ( o | l ) -based positioning scheme.


title: The Dynamic Distribution of Small-Tail Han Sheep Microbiota across Different Intestinal Segments

abstract: Abstract: The sheep intestinal tract is characterized by a diverse microbial ecosystem that is vital for the host to digest diet material. The importance of gut microbiota (GM) of animals has also been widely acknowledged because of its pivotal roles in the health and well-being of animals. However, there are no relevant studies on GM of small-tail Han sheep, a superior mutton variety domestic in China. In this study, the structure and distribution of gut microflora were studied by high-throughput sequencing technology. Results showed a significant difference between jejunum and cecum, jejunum and rectum. Meanwhile, the cecum and rectum not only display higher species richness but also exhibit higher similarity of the bacterial diversity than that of the jejunum based on the results of abundance-based coverage estimator (ACE), Chao1, and Shannon indexes. Firmicutes and Bacteroidetes were the predominant phyla in cecum and rectum, while higher relative abundances of Firmicutes and Cyanobacteria were observed in jejunum. At the genus level, Bacteroidetes, Ruminococcus, Lactobacillus, Flavonifractor and Clostridium were the dominant genera in the cecum and rectum. An obvious dynamic distribution of Lactobacillus is continuously decreasing from the jejunum to the cecum, then to the rectum, whereas the result of Bacteroides is completely inverse. In addition, this study also found many kinds of bacteria associated with the production of volatile fatty acids (VFA) colonized in the large intestine. This study is the first to investigate the distribution of intestinal flora in small-tail Han sheep. The findings provide an important indication for diagnosis and treatment of intestinal diseases in small-tail Han sheep, as well as offer a direction for the development of intestinal microecological preparations.


title: A genetic screen identifies an LKB1–MARK signalling axis controlling the Hippo–YAP pathway

abstract: The Hippo-YAP pathway is an emerging signalling cascade involved in the regulation of stem cell activity and organ size. To identify components of this pathway, we performed an RNAi-based kinome screen in human cells. Our screen identified several kinases not previously associated with Hippo signalling that control multiple cellular processes. One of the hits, LKB1, is a common tumour suppressor whose mechanism of action is only partially understood. We demonstrate that LKB1 acts through its substrates of the microtubule affinity-regulating kinase family to regulate the localization of the polarity determinant Scribble and the activity of the core Hippo kinases. Our data also indicate that YAP is functionally important for the tumour suppressive effects of LKB1. Our results identify a signalling axis that links YAP activation with LKB1 mutations, and have implications for the treatment of LKB1-mutant human malignancies. In addition, our findings provide insight into upstream signals of the Hippo-YAP signalling cascade.


title: The attentive brain: insights from developmental cognitive neuroscience

abstract: Visual attention functions as a filter to select environmental information for learning and memory, making it the first step in the eventual cascade of thought and action systems. Here, we review studies of typical and atypical visual attention development and explain how they offer insights into the mechanisms of adult visual attention. We detail interactions between visual processing and visual attention, as well as the contribution of visual attention to memory. Finally, we discuss genetic mechanisms underlying attention disorders and how attention may be modified by training.


title: Controlled Lagrangians and the stabilization of mechanical systems II: Potential shaping

abstract: For pt.I, see ibid., vol.45, p.2253-70 (2000). We extend the method of controlled Lagrangians (CL) to include potential shaping, which achieves complete state-space asymptotic stabilization of mechanical systems. The CL method deals with mechanical systems with symmetry and provides symmetry-preserving kinetic shaping and feedback-controlled dissipation for state-space stabilization in all but the symmetry variables. Potential shaping complements the kinetic shaping by breaking symmetry and stabilizing the remaining state variables. The approach also extends the method of controlled Lagrangians to include a class of mechanical systems without symmetry such as the inverted pendulum on a cart that travels along an incline.


Frequency analysis of nonlinear dynamical systems and the control of vibration


title: A FRACTAL DIMENSION STANDPOINT TO THE CLUSTER VALIDATION PROBLEM

abstract: The article proposes a new standpoint to the cluster validation problem based on a fractal dimension cluster quality model. The suggested method uses the fractal property to describe cluster geometrical configuration. This notion is applied for further exploration of cluster validity, assuming that its low variability, calculated via different samples, can indicate stable parti- tions. In the framework of this model, the goodness of a partition is char- acterized by the quality of mixing two random samples within the partition's clusters. It is implicitly assumed that the quality of a cluster is reflected by its fractal dimensionality estimated via different samples. Valid results are ob- tained by repeating these calculations on sufficiently largeamount of samples drawn. Hence, empirical distributions of the absolute values of the fractal di- mension differences are constructed. The distribution most concentrated at the origin is proposed to indicate the true number of clusters. Numerical experi- ments are presented for various datasets.


title: Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition

abstract: Abstract Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other natural language processing tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we propose a new model which combines data-driven deep learning approaches and knowledge-driven dictionary approaches. Specifically, we incorporate dictionaries into deep neural networks. In addition, two different architectures that extend the bi-directional long short-term memory neural network and five different feature representation schemes are also proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.


title: Relation Extraction Using Multi-Encoder LSTM Network on a Distant Supervised Dataset

abstract: Relation extraction techniques are used to find potential relational facts in textual content. Relation Extraction systems require huge amount of training data to model semantics of sentences and identify relations. Distant supervision, often used to construct training data for relation extraction, produces noisy alignments that can hurt the performance of relation extraction systems. To this end, we propose a simple, yet effective, technique to automatically compute confidence levels of alignments. We compute the confidence values of automatically labeled content using co-occurrence statistics of relations and dependency patterns of aligned sentences. Thereafter, we propose a novel multi-encoder bidirectional Long Short Term Memory (LSTM) model to identify relations in a given sentence. We use different features (words, part-of-speech (POS) tags and dependency paths) in each encoder and concatenate the hidden states of all the encoders to predict the relations. Our experiments show that a multi-encoder network can handle different features together to predict relations more accurately (~9% improvement over a single encoder model). We also conduct visualization experiments to show that our model learns intermediate representations effectively to identify relations in sentences.


title: Non-Intrusive Load Monitoring: A Review and Outlook

abstract: With the roll-out of smart meters the importance of effective non-intrusive load monitoring (NILM) techniques has risen rapidly. NILM estimates the power consumption of individual devices given their aggregate consumption. In this way, the combined consumption must only be monitored at a single, central point in the household, providing various advantages such as reduced cost for metering equipment. In this paper we discuss the fundamental building-blocks of NILM, first giving a taxonomy of appliance models and device signatures and then explaining common supervised and unsupervised learning methods. Furthermore, we outline a fundamental algorithm that tackles the task of NILM. Subsequently, this paper reviews recent research that has brought novel insight to the field and more effective techniques. Finally, we formulate future challenges in the domain of NILM and smart meters.


title: The research and implementation of metadata cache backup technology based on CEPH file system

abstract: Based on research and analysis of the Ceph file system, the log cache backup scheme is proposed. The log cache backup scheme reduces metadata access delays by caching logs and reducing the time of storing logs to server clusters. In order to prevent the loss of cached data in metadata servers, a cached data backup scheme is proposed. Compared to the metadata management subsystem of the Ceph, the log cache backup scheme can effectively improve the access performance of metadata. Finally, based on open source codes of the Ceph file system, the log cache backup scheme is implemented. Compared to the performance of the Ceph metadata management subsystem, experiment results show that performance improvements of the log cache backup scheme are up to 11.5%.


Tramadol Challenge for Relief of Intractable Central Poststroke Pain


title: Rebanking CCGbank for Improved NP Interpretation

abstract: Once released, treebanks tend to remain unchanged despite any shortcomings in their depth of linguistic analysis or coverage of specific phenomena. Instead, separate resources are created to address such problems. In this paper we show how to improve the quality of a treebank, by integrating resources and implementing improved analyses for specific constructions. ::: ::: We demonstrate this rebanking process by creating an updated version of CCG-bank that includes the predicate-argument structure of both verbs and nouns, base-NP brackets, verb-particle constructions, and restrictive and non-restrictive nominal modifiers; and evaluate the impact of these changes on a statistical parser.


Blockchains and the Logic of Accountability: Keynote Address


Optimization of Machining Parameters and Material Selection during Drilling of Alluminium Alloys – Review


title: Pedestrian Path Forecasting in Crowd: A Deep Spatio-Temporal Perspective

abstract: Predicting the walking path of a pedestrian in crowds is a pivotal step towards understanding his/her behavior. This is one of the recently emerging tasks in computer vision scarcely addressed to date. In this paper, we put forth a deep spatio-temporal learning-forecasting approach, which is composed of two modules. First, displacement information from pedestrians' walking history is extracted and fed into a convolutional layer in order to learn the undergoing motion patterns and produce high-level representations. Second, unlike the mainstream literature which learns the temporal or the spatial dynamics among the pedestrians separately, we propose to embed both components into a single framework via a Long-Short Term Memory based architecture that takes as input the previously extracted high-level motion cues and outputs the potential future walking routes of all pedestrians in one shot. We evaluate our approach on three large benchmark datasets, and show that it introduces large margin improvements with respect to recent works in the literature, both in short and long-term forecasting scenarios.


title: A Fast Set Intersection Algorithm for Sorted Sequences

abstract: This paper introduces a simple intersection algorithm for two sorted sequences that is fast on average. It is related to the multiple searching problem and to merging. We present the worst and average case analysis, showing that in the former, the complexity nicely adapts to the smallest list size. In the later case, it performs less comparisons than the total number of elements on both inputs when n = αm (α > 1). Finally, we show its application to fast query processing in Web search engines, where large intersections, or differences, must be performed fast.


title: Sparse coding of sensory inputs

abstract: Several theoretical, computational, and experimental studies suggest that neurons encode sensory information using a small number of active neurons at any given point in time. This strategy, referred to as ‘sparse coding’, could possibly confer several advantages. First, it allows for increased storage capacity in associative memories; second, it makes the structure in natural signals explicit; third, it represents complex data in a way that is easier to read out at subsequent levels of processing; and fourth, it saves energy. Recent physiological recordings from sensory neurons have indicated that sparse coding could be a ubiquitous strategy employed in several different modalities across different organisms.


title: Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values

abstract: Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdependent, linked instances is now common and the dependencies in such data can be exploited to improve predictive performance. Collective inference is one approach to exploit relational correlation patterns and significantly reduce classification error. However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous. %target values has not got that attention in terms of collective inference. In this work, we investigate how to combine these two paradigms together to improve regression in relational domains. Specifically, we propose a boosting algorithm for learning a collective inference model that predicts a continuous target variable. In the algorithm, we learn a basic relational model, collectively infer the target values, and then iteratively learn relational models to predict the residuals. We evaluate our proposed algorithm on a real network dataset and show that it outperforms alternative boosting methods. However, our investigation also revealed that the relational features interact together to produce better predictions.


title: Treatment with Glucagon-Like Peptide-1 Agonist Exendin-4 in a Patient with Hypothalamic Obesity Secondary to Intracranial Tumor

abstract: Background/Aims: Patients with hypothalamic tumors frequently experience severe obesity, and its treatment with diet, exercise, and/or pharmacologic treatment has


Design Principles for Industrie 4 . 0 Scenarios


title: A Unified Coding Framework for Distributed Computing with Straggling Servers

abstract: We propose a unified coded framework for distributed computing with straggling servers, by introducing a tradeoff between "latency of computation" and "load of communication" for some linear computation tasks. We show that the coded scheme of [1]-[3] that repeats the intermediate computations to create coded multicasting opportunities to reduce communication load, and the coded scheme of [4], [5] that generates redundant intermediate computations to combat against straggling servers can be viewed as special instances of the proposed framework, by considering two extremes of this tradeoff: minimizing either the load of communication or the latency of computation individually. Furthermore, the latency-load tradeoff achieved by the proposed coded framework allows to systematically operate at any point on that tradeoff to perform distributed computing tasks. We also prove an information-theoretic lower bound on the latency-load tradeoff, which is shown to be within a constant multiplicative gap from the achieved tradeoff at the two end points.


title: Plague in South African rodents 1972–1981

abstract: Abstract Sera from 3012 rodents of 24 species captured in South Africa during the period 1972–1981 were tested for antibody to the Fraction 1 antigen of Yersinia pestis by passive haemagglutination. Antibodies were found in seven (0·23%) rodents of three species. These were Desmodillus auricularis and Tatera brantsii in the northern Cape Province and Rhabdomys pumilio in the eastern Cape Province. Rodents were found positive in 1972, 1974, 1975 and 1979, indicating that plague continues to circulate in rodent populations apparently without causing human cases. The significance of the results is discussed in relation to plague outbreaks in neighbouring countries and to the 1982 outbreak in South Africa.


Classification and Regression using SAS


Transfer Deep Reinforcement Learning in 3 D Environments : An Empirical Study


title: Fusion of aerial lidar and images for road segmentation with deep CNN

abstract: In this paper we attempt to fuse both airborne Lidar and high resolution images (0.5 feet per pixel) to identify road networks in a large geographic region. We perform pixel-wise segmentation to classify each pixel as road or non-road based on color and depth features in a larger neighborhood context. This constitutes a bimodal setting because the RGB pixels represent the color space and the depth values come from three dimensional Lidar readings. We present multiple strategies for fusing Lidar and images. We describe a cost-effective, modular, deep convolution network design, TriSeg which gives better IoU metric for the aerial road segmentation problem than the state of the art RGB only architectures. We report on many other architectures as well as release our dataset for further research: https://bitbucket.org/biswas/fusion_lidar_images.


title: Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming

abstract: In this paper, we introduce a new stochastic approximation (SA) type algorithm, namely the randomized stochastic gradient (RSG) method, for solving an important class of nonlinear (possibly nonconvex) stochastic programming (SP) problems. We establish the complexity of this method for computing an approximate stationary point of a nonlinear programming problem. We also show that this method possesses a nearly optimal rate of convergence if the problem is convex. We discuss a variant of the algorithm which consists of applying a post-optimization phase to evaluate a short list of solutions generated by several independent runs of the RSG method, and show that such modification allows to improve significantly the large-deviation properties of the algorithm. These methods are then specialized for solving a class of simulation-based optimization problems in which only stochastic zeroth-order information is available.


title: Compact $S$ -/$Ka$-Band CMOS Quadrature Hybrids With High Phase Balance Based on Multilayer Transformer Over-Coupling Technique

abstract: This paper presents compact CMOS quadrature hybrids by using the transformer over-coupling technique to eliminate significant phase error in the presence of low-Q CMOS components. The technique includes the inductive and capacitive couplings, where the former is realized by employing a tightly inductive-coupled transformer and the latter by an additional capacitor across the transformer winding. Their phase balance effects are investigated and the design methodology is presented. The measurement results show that the designed 24-GHz CMOS quadrature hybrid has excellent phase balance within plusmn0.6deg and amplitude balance less than plusmn 0.3 dB over a 16% fractional bandwidth with extremely compact size of 0.05 mm2. For the 2.4-GHz hybrid monolithic microwave integrated circuit, it has measured phase balance of plusmn0.8deg and amplitude balance of plusmn 0.3 dB over a 10% fractional bandwidth with a chip area of 0.1 mm2 .


title: Model-based validation of QoS properties of biomedical sensor networks

abstract: A Biomedical Sensor Network (BSN) is a small-size sensor network for medical applications, that may contain tens of sensor nodes. In this paper, we present a formal model for BSNs using timed automata, where the sensor nodes communicate using the Chipcon CC2420 transceiver (developed by Texas Instruments) according to the IEEE 802.15.4 standard. Based on the model, we have used UPPAAL to validate and tune the temporal configuration parameters of a BSN in order to meet desired QoS requirements on network connectivity, packet delivery ratio and end-to-end delay. The network studied allows dynamic reconfigurations of the network topology due to the temporally switching of sensor nodes to power-down mode for energy-saving or their physical movements. Both the simulator and model-checker of UPPAAL are used to analyze the average-case and worst-case behaviors. To enhance the scalability of the tool, we have implemented a (new text-based) version of the UPPAAL simulator optimized for exploring symbolic traces of automata containing large data structures such as matrices. Our experiments show that even though the main feature of the tool is model checking, it is also a promising and competitive tool for efficient simulation and parameter tuning. The simulator scales well; it can easily handle up to 50 nodes in our experiments. The model checker installed on a notebook can also deal with networks with 5 up to 16 nodes within minutes depending on the properties checked; these are BSNs of reasonable size for medical applications. Finally, to study the accuracy of our model and analysis results, we compare simulation results by UPPAAL for two medical scenarios with traditional simulation techniques using OMNeT++, one of the most used simulation tools for wireless sensor networks. The comparison shows that our analysis results coincide with the simulation results by OMNeT++ in most cases although there are some differences caused the simplified wireless channel model in UPPAAL.


title: Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric

abstract: We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.


title: WQO is decidable for factorial languages

abstract: A language is factorial if it is closed under taking factors, i.e. contiguous subwords. Every factorial language can be described by an antidictionary, i.e. a minimal set of forbidden factors. We show that the problem of deciding whether a factorial language given by a finite antidictionary is well-quasi-ordered under the factor containment relation can be solved in polynomial time. We also discuss possible ways to extend our solution to permutations and graphs.


title: Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases.

abstract: Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.


title: QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

abstract: This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a risk-adjusted Markov Decision Process for a discrete-time version of the classical Black-Scholes-Merton (BSM) model, where the option price is an optimal Q-function. Pricing is done by learning to dynamically optimize risk-adjusted returns for an option replicating portfolio, as in the Markowitz portfolio theory. Using Q-Learning and related methods, once created in a parametric setting, the model is able to go model-free and learn to price and hedge an option directly from data generated from a dynamic replicating portfolio which is rebalanced at discrete times. If the world is according to BSM, our risk-averse Q-Learner converges, given enough training data, to the true BSM price and hedge ratio of the option in the continuous time limit, even if hedges applied at the stage of data generation are completely random (i.e. it can learn the BSM model itself, too!), because Q-Learning is an off-policy algorithm. If the world is different from a BSM world, the Q-Learner will find it out as well, because Q-Learning is a model-free algorithm. For finite time steps, the Q-Learner is able to efficiently calculate both the optimal hedge and optimal price for the option directly from trading data, and without an explicit model of the world. This suggests that RL may provide efficient data-driven and model-free methods for optimal pricing and hedging of options, once we depart from the academic continuous-time limit, and vice versa, option pricing methods developed in Mathematical Finance may be viewed as special cases of model-based Reinforcement Learning. Our model only needs basic linear algebra (plus Monte Carlo simulation, if we work with synthetic data).


title: Nasari : Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities

abstract: Owing to the need for a deep understanding of linguistic items, semantic representation is considered to be one of the fundamental components of several applications in Natural Language Processing and Artificial Intelligence. As a result, semantic representation has been one of the prominent research areas in lexical semantics over the past decades. However, due mainly to the lack of large sense-annotated corpora, most existing representation techniques are limited to the lexical level and thus cannot be effectively applied to individual word senses. In this paper we put forward a novel multilingual vector representation, called Nasari, which not only enables accurate representation of word senses in different languages, but it also provides two main advantages over existing approaches: (1) high coverage, including both concepts and named entities, (2) comparability across languages and linguistic levels (i.e., words, senses and concepts), thanks to the representation of linguistic items in a single unified semantic space and in a joint embedded space, respectively. Moreover, our representations are flexible, can be applied to multiple applications and are freely available at http://lcl.uniroma1.it/nasari/. As evaluation benchmark, we opted for four different tasks, namely, word similarity, sense clustering, domain labeling, and Word Sense Disambiguation, for each of which we report state-of-the-art performance on several standard datasets across different languages.


title: Two-Party Attribute-Based Key Agreement Protocol with Constant-Size Ciphertext and Key

abstract: Based on mutual authentication, the session key is established for communication nodes on the open network. In order to satisfy fine-grained access control for cloud storage, the two-party attribute-based key agreement protocol (TP-AB-KA) was proposed. However, the existing TP-AB-KA protocol is high in the cost of computation and communication and is not unfit for application in a mobile cloud setting because mobile devices are generally resource constrained. To solve the above issue, we propose a TP-AB-KA protocol with constant-size ciphertext and key. Our TP-AB-KA protocol is provable security in the standard model. The concrete proof is given under the augmented multisequence of exponents' decisional Diffie-Hellman (aMSE-DDH) hypothesis in the attribute-based BJM model (AB-BJM). Compared with the existing TP-AB-KA protocols, the computation cost and communication cost of our protocol are largely reduced.


A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network


title: Single Patch Antenna With Monopulse Patterns

abstract: A dual probe-fed single microstrip patch antenna for the monopulse tracking applications is proposed. The monopulse antenna, consisting of a slot-embedded patch, two symmetric probe feeds and a 180° directional coupler is fabricated in a normal PCB process with a size of only $0.63\lambda \times 0.69\lambda $ . The sum or difference patterns are achieved with out-of-phase or in-phase feeding mechanism. A prototype shows that the measured −10-dB impedance bandwidth is 345MHz (2.11GHz-2.455GHz), and the measured port-isolation is higher than 15dB at the frequency band 2.12GHz-2.50GHz. The measured sum gain and difference null at boresight are 7.54dBi and −32.7dBi, respectively.


title: Problematic Internet use, loneliness and dating anxiety among young adult university students

abstract: The Internet, an important modern means of obtaining information and establishing communication with others, has become an increasingly essential element of human life. Although Internet use makes life easier, it can become problematic in the event of non-functional use. Debate and research into whether Internet addiction is a cause or an effect are continuing. This study investigates problematic Internet use among young adult university students and examines correlation between problematic Internet use and loneliness and dating anxiety. University student Internet use patterns are also investigated. The study was conducted among 493 students from the Karadeniz Technical University Fatih Faculty of Education. The Online Cognition Scale, Dating Anxiety Scale, Loneliness Scale and Personal Information Questionnaire were employed in the collection of data. Pearson correlation analysis, the t-test, one-way analysis of variance and chi-square test were used for data analysis. The Pearson correlation analysis results reveal a significant positive correlation between problematic Internet use and loneliness (r = 0.194, p < 0.001), communication anxiety (r = 0.15, p < 0.001), unpopularity anxiety (r = 0.174, p < 0.001) and physiological symptoms (r = 0.125, p < 0.001) dating anxiety sub-scales. One-way analysis of variance was used to examine whether problematic Internet use varies according to length of Internet use, and a significantly high level of problematic Internet use was observed among those going online for more than 5 h a day compared to other users (F = 14.327, p < 0.001). Chi-square results reveal a significant association between length of Internet use and how students feel when they do not go online (chi-square = 116.543, p < 0.001). The t-test was used to determine whether there was a significant difference in levels of Internet use according to gender, and levels of problematic Internet use were significantly higher among male students than females (t = 4.046, p < 0.001).


title: Machine Learning, Neural and Statistical Classification

abstract: Survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning.


title: Generalizing locomotion style to new animals with inverse optimal regression

abstract: We present a technique for analyzing a set of animal gaits to predict the gait of a new animal from its shape alone. This method works on a wide range of bipeds and quadrupeds, and adapts the motion style to the size and shape of the animal. We achieve this by combining inverse optimization with sparse data interpolation. Starting with a set of reference walking gaits extracted from sagittal plane video footage, we first use inverse optimization to learn physically motivated parameters describing the style of each of these gaits. Given a new animal, we estimate the parameters describing its gait with sparse data interpolation, then solve a forward optimization problem to synthesize the final gait. To improve the realism of the results, we introduce a novel algorithm called joint inverse optimization which learns coherent patterns in motion style from a database of example animal-gait pairs. We quantify the predictive performance of our model by comparing its synthesized gaits to ground truth walking motions for a range of different animals. We also apply our method to the prediction of gaits for dinosaurs and other extinct creatures.


title: Conceptual Spaces: The Geometry of Thought

abstract: Within cognitive science, two approaches currently dominate the problem of modeling representations. The symbolic approach views cognition as computation involving symbolic manipulation. Connectionism, a special case of associationism, models associations using artificial neuron networks. Peter Gardenfors offers his theory of conceptual representations as a bridge between the symbolic and connectionist approaches. Symbolic representation is particularly weak at modeling concept learning, which is paramount for understanding many cognitive phenomena. Concept learning is closely tied to the notion of similarity, which is also poorly served by the symbolic approach. Gardenfors's theory of conceptual spaces presents a framework for representing information on the conceptual level. A conceptual space is built up from geometrical structures based on a number of quality dimensions. The main applications of the theory are on the constructive side of cognitive science: as a constructive model the theory can be applied to the development of artificial systems capable of solving cognitive tasks. Gardenfors also shows how conceptual spaces can serve as an explanatory framework for a number of empirical theories, in particular those concerning concept formation, induction, and semantics. His aim is to present a coherent research program that can be used as a basis for more detailed investigations. (Less)


title: SiGe BiCMOS and eWLB packaging technologies for automotive radar solutions

abstract: In this paper the evolution of silicon based automotive radar for the 76-81 GHz range is described. Starting with SiGe bare-die chips in 2009, today packaged MMICs are available for low-cost radar applications. Future SiGe BiCMOS technology will enable highly integrated single chip radars with superior performance at lower power consumption. This will pave the way for automotive radar safety for everyone and will be an important step towards autonomous driving.


title: Noxim: An open, extensible and cycle-accurate network on chip simulator

abstract: Emerging on-chip communication technologies like wireless Networks-on-Chip (WiNoCs) have been proposed as candidate solutions for addressing the scalability limitations of conventional multi-hop NoC architectures. In a WiNoC, a subset of network nodes are equipped with a wireless interface which allows them long-range communication in a single hop. This paper presents Noxim, an open, configurable, extendible, cycle-accurate NoC simulator developed in SystemC which allows to analyze the performance and power figures of both conventional wired NoC and emerging WiNoC architectures.


title: Healthcare via Cell Phones: A Systematic Review

abstract: Empirical evidence on cell phone and text messaging used in health intervention was evaluated. A review of 25 previously reported studies of 38,060 participants, covering 12 clinical specialties such as diabetes, was conducted. Significant improvements were observed in the majority of the studies reviewed. The results of this review imply that the use of cell phones and short message services (SMS) offers valuable tools in improving healthcare outcomes.


title: Advances in Brief Estrogen Receptor Status in Breast Cancer Is Associated with Remarkably Distinct Gene Expression Patterns 1

abstract: To investigate the phenotype associated with estrogen receptor α (ER) expression in breast carcinoma, gene expression profiles of 58 node-negative breast carcinomas discordant for ER status were determined using DNA microarray technology. Using artificial neural networks as well as standard hierarchical clustering techniques, the tumors could be classified according to ER status, and a list of genes which discriminate tumors according to ER status was generated. The artificial neural networks could accurately predict ER status even when excluding top discriminator genes, including ER itself. By reference to the serial analysis of gene expression database, we found that only a small proportion of the 100 most important ER discriminator genes were also regulated by estradiol in MCF-7 cells. The results provide evidence that ER+ and ER− tumors display remarkably different gene-expression phenotypes not solely explained by differences in estrogen responsiveness.


title: The consume - create spectrum: balancing convenience and computational thinking in stem learning

abstract: Future school science standards, such as the Next Generation Science Standards (NGSS), emphasize the integration of simulation and modeling activities in the classroom environment. The extremes of these activities have two vastly different implementations. On one hand, a teacher can have students experiment on a pre-made simulation associated with the material. On the other hand, students can use, for example, an end-user programming tool to create the simulation from scratch. This allows students to not only experiment on, but also, to model the real world phenomenon being studied- a key component of computational thinking. However, the greater amount of time necessary for student authoring of simulations can make such an approach infeasible in the classroom environment. This paper presents a spectrum of strategies for integrating simulations into class- rooms emphasizing our research at the Scalable Game Design Lab, University of Colorado Boulder as well as research from other entities. Starting at consuming simulations and adding more user interaction and authoring elements begins to provide a gentle slope from consumption towards simulation creation. Results indicate that many of these strategies are quite effective.


title: Characterization of early smartwatch apps

abstract: Wearable smart devices are already amongst us. Currently, smartwatches are one of the key drivers of the wearable technology and are being used by a large population of consumers. This paper takes a first look at this increasingly popular technology with a systematic characterization of the smartwatch app markets. We conduct a large scale analysis of three popular smartwatch app markets: Android Wear, Samsung, and Apple, and characterize more than 14,000 smartwatch apps in multiple aspects such as prices, number of developers and categories. Our analysis shows that approximately 41% and 30% of the apps in Android Wear and Samsung app markets are Personalization apps that provide watch faces. Further, we provide a generic taxonomy for apps on all three platforms based on their packaging and modes of communication, that allow us to investigate apps with respect to privacy and security. Finally, we study the privacy risks associated with the app usage by identifying third party trackers integrated into these apps and personal information leakage through network traffic analysis. We show that a higher percentage of Apple apps (62%) are connected to third party trackers compared to Samsung (36%) and Android Wear (46%).


title: Boundary Detection Using Double-Opponency and Spatial Sparseness Constraint

abstract: Brightness and color are two basic visual features integrated by the human visual system (HVS) to gain a better understanding of color natural scenes. Aiming to combine these two cues to maximize the reliability of boundary detection in natural scenes, we propose a new framework based on the color-opponent mechanisms of a certain type of color-sensitive double-opponent (DO) cells in the primary visual cortex (V1) of HVS. This type of DO cells has oriented receptive field with both chromatically and spatially opponent structure. The proposed framework is a feedforward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to V1. In addition, we employ the spatial sparseness constraint (SSC) of neural responses to further suppress the unwanted edges of texture elements. Experimental results show that the DO cells we modeled can flexibly capture both the structured chromatic and achromatic boundaries of salient objects in complex scenes when the cone inputs to DO cells are unbalanced. Meanwhile, the SSC operator further improves the performance by suppressing redundant texture edges. With competitive contour detection accuracy, the proposed model has the additional advantage of quite simple implementation with low computational cost.


Self-Concept and Self-Esteem in Adolescents


title: Cloudroid: A Cloud Framework for Transparent and QoS-Aware Robotic Computation Outsourcing

abstract: Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing the computation to the cloud. However, exploiting the potential of cloud resources in robotic software is difficult, because it involves complex code modification and extensive (re)configuration procedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. In this paper, we propose a transparent and QoS-aware software framework called Cloudroid for cloud robotic applications. This framework supports direct deployment of existing robotic software packages to the cloud, transparently transforming them into Internet-accessible cloud services. And with the automatically generated service stubs, robotic applications can outsource their computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintain the specific QoS property such as request response time, even in a highly dynamic and resource-competitive environment. We evaluated Cloudroid based on a group of typical robotic scenarios and a set of software packages widely adopted in real-world robot practices. Results show that robot's capability can be enhanced significantly without code modification and specific QoS objectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitude compared with the robot native setup.


title: Teaching Software Engineering Principles to K-12 Students: A MOOC on Scratch

abstract: In the last few years, many books, online puzzles, apps and games have been created to teach young children programming. However, most of these do not introduce children to broader concepts from software engineering, such as debugging and code quality issues like smells, duplication, refactoring and naming. To address this, we designed and ran an online introductory Scratch programming course in which we teach elementary programming concepts and software engineering concepts simultaneously. In total 2,220 children actively participated in our course in June and July of 2016, most of which (73%) between the ages of 7 and 11. In this paper we describe our course design and analyze the resulting data. More specifically, we investigate whether 1) students find programming concepts more difficult than software engineering concepts, 2) there are age-related differences in their performance, and 3) we can predict successful course completion. Our results show that there is no difference in students' scores between the programming concepts and the software engineering concepts, suggesting that it is indeed possible to teach these concepts to this age group. We also find that students over 12 years of age perform significantly better in questions related to operators and procedures. Finally, we identify the factors from the students' profile and their behaviour in the first week of the course that can be used to predict its successful completion.


title: Wideband stacked patch antenna array on LTCC for W-band

abstract: A design of a stacked microstrip patch antenna and a 16-element array on Ferro A6-M LTCC operating at W-band is presented. A parasitic patch is added on LTCC substrate to increase bandwidth. The stacked configuration is designed by using an equivalent circuit together with EM simulators. The manufactured antenna array is integrated with a WR-10-to-microstrip-line transition to facilitate RF testing. The S-parameters are measured on a probe station and radiation pattern testing is done using a WR-10 excitation in anechoic chamber. The array of size 9.3 mm × 9.3 mm × 0.4 mm has the measured maximum gain of 14.2 dBi at 84 GHz and gain higher than 11.2 dBi between 78.5 GHz and 98.5 GHz which results as −3-dB radiation bandwidth of 23%. The antenna efficiency of the array is 48% at the highest.


title: Wideband 3 dB Branch Line Coupler Based on $\lambda/4$ Open Circuited Coupled Lines

abstract: This letter introduces a new 3 dB branch line coupler for wideband applications. The wideband matching property of the coupler with four λ/4 open circuited coupled lines is briefly derived. The coupler with a suspended microstrip and microstrip line structures has been simulated with HFSS and fabricated. Measurement results show insertion loss better than 3.6 dB with a power imbalance of 0.5 dB; return loss and isolation characteristics both better than 20 dB over 49% fractional bandwidth.


title: Modeling and Field Oriented Control of Salient Pole Wound Field Synchronous Machine in Stator Flux Coordinates

abstract: This paper describes the field oriented control of a salient pole wound field synchronous machine in stator flux coordinates. The procedure for derivation of flux linkage equations along any general rotating axes including stator flux axes is given. The stator flux equations are used to identify the cross-coupling occurring between the axes due to saliency in the machine. The coupling terms are canceled as feedforward terms in the generation of references for current controllers to achieve good decoupling during transients. The design of current controller for stator-flux-oriented control is presented. This paper proposes the method of extending rotor flux closed loop observer for sensorless control of wound field synchronous machine. This paper also proposes a new sensorless control by using stator flux closed loop observer and estimation of torque angle using stator current components in stator flux coordinates. Detailed experimental results from a sensorless 15.8 hp salient pole wound field synchronous machine drive are presented to demonstrate the performance of the proposed control strategy from a low speed of 0.8 Hz to 50 Hz.


title: Productive Conversations from a Feminist Perspective

abstract: This chapter is written as a response to the ideas put forward in Scott Eacott’s text, Educational leadership relationally written from a feminist perspective. The response is centered on five main ideas that are discussed in the book: (1) that the relational approach provides a new way of thinking about educational administration; (2) that there exist tensions in current approaches to research due to the embodied and embedded nature of the researcher vis-a-vis the research object; (3) that there is much value in historicizing temporal trajectories and the socio-spatial contexts of research; (4) that interpretive description has the most to offer the study of the legitimation of social organizing; and; (5) that there does not exist enough attention to language and discourse within current educational administrative research. The paper concludes with suggestions for further intra-disciplinary dialogue that could deepen the sophistication of the relational approach over time.


title: A virtual reality testbed for brain-computer interface research

abstract: Virtual reality promisers to extend the realm of possible brain-computer interface (BCI) prototypes. Most of the work using electroencephalograph (EEG) signals in VR has focussed on brain-body actuated control, where biological signals from the body as well as the brain are used. The authors show that when subjects are allowed to move and act normally in an immersive virtual environment, cognitive evoked potential signals can still be obtained and used reliably. A single trial accuracy average of 85% for recognizing the differences between evoked potentials at red and yellow stop lights is presented and future directions discussed.


title: The Throughput of Packet Broadcasting Channels

abstract: Packet broadcasting is a form of data communications architecture which can combine the features of packet switching with those of broadcast channels for data communication networks. Much of the basic theory of packet broadcasting has been presented as a byproduct in a sequence of papers with a distinctly practical emphasis. In this paper we provide a unified presentation of packet broadcasting theory. In Section II we introduce the theory of packet broadcasting data networks. In Section III we provide some theoretical results dealing with the performance of a packet broadcasting network when the users of the network have a variety of data rates. In Section IV we deal with packet broadcasting networks distributed in space, and in Section V we derive some properties of power-limited packet broadcasting channels, showing that the throughput of such channels can approach that of equivalent point-to-point channels.