title: Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach

abstract: This paper develops theoretical results regarding noisy 1-bit compressed sensing and sparse binomial regression. We show that a single convex program gives an accurate estimate of the signal, or coefficient vector, for both of these models. We demonstrate that an s-sparse signal in R^n can be accurately estimated from m = O(slog(n/s)) single-bit measurements using a simple convex program. This remains true even if each measurement bit is flipped with probability nearly 1/2. Worst-case (adversarial) noise can also be accounted for, and uniform results that hold for all sparse inputs are derived as well. In the terminology of sparse logistic regression, we show that O(slog(n/s)) Bernoulli trials are sufficient to estimate a coefficient vector in R^n which is approximately s-sparse. Moreover, the same convex program works for virtually all generalized linear models, in which the link function may be unknown. To our knowledge, these are the first results that tie together the theory of sparse logistic regression to 1-bit compressed sensing. Our results apply to general signal structures aside from sparsity; one only needs to know the size of the set K where signals reside. The size is given by the mean width of K, a computable quantity whose square serves as a robust extension of the dimension.


title: 3D shock wave visualization on unstructured grids

abstract: A critical issue in understanding high speed flows is the study of shock waves. The paper summarizes our research on techniques for the detection and visualization of shock waves occuring in simulations of three dimensional flows on unstructured grids. Detection algorithms based on Mach number, density gradient and directional derivatives are compared using a data set from calculations of a transonic flow with a weak double shock around an airfoil. Both surface and volume rendering techniques are used to display the shocks. The issues in this research area are very much like those occurring in medical imaging. Since the data themselves (in this case the results of the fluid dynamics simulation) are intrinsically low resolution and noisy, properly extracting and visualizing the shock is very difficult. In this environment, blurry, low resolution techniques, like the splatting volume rendering, seem to do rather well. More complex schemes, using sophisticated numerical shock detectors coupled with polygon rendering, produce visually sharper shocks, but also introduce "graphics artifacts," which complicate understanding of the flow physics. On the other hand, visualization results produced with techniques like splatting are, in effect, relying more on the human visual system to compensate for limited resolution in the simulation.


title: Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors

abstract: This paper introduces a simple yet effective method to improve visual word based image retrieval. Our method is based on an analysis of the k-reciprocal nearest neighbor structure in the image space. At query time the information obtained from this process is used to treat different parts of the ranked retrieval list with different distance measures. This leads effectively to a re-ranking of retrieved images. As we will show, this has two benefits: first, using different similarity measures for different parts of the ranked list allows for compensation of the “curse of dimensionality”. Second, it allows for dealing with the uneven distribution of images in the data space. Dealing with both challenges has very beneficial effect on retrieval accuracy. Furthermore, a major part of the process happens offline, so it does not affect speed at retrieval time. Finally, the method operates on the bag-of-words level only, thus it could be combined with any additional measures on e.g. either descriptor level or feature geometry making room for further improvement. We evaluate our approach on common object retrieval benchmarks and demonstrate a significant improvement over standard bag-of-words retrieval.


title: Developing a community definition and teaching modules for computational thinking: accomplishments and challenges

abstract: The NSF-funded Northwest Distributed Computer Science Department (NW-DCSD) project brings together 24 multi-disciplinary faculty from 19 diverse colleges and universities in an effort to change the face of computing in the Pacific Northwest region of the United States. We offer an innovative and inclusive vision of computing in the 21st century and foster opportunities for multi-disciplinary and inter-institutional computing and computer science education collaborations. Over the project's first two years, this community has created 9 engaging, easy-to-use learning modules that teach various levels of computational thinking to two different audiences: non-computer science and computer science undergraduate students. This paper presents the development of a community definition of computational thinking, the learning modules, initial findings, unanticipated challenges, and next steps.


title: A Ka-Band Waveguide Magic-T With Coplanar Arms Using Ridge-Waveguide Transition

abstract: A Ka-band waveguide magic-T with coplanar arms is presented in this letter. By using an E-plane power divider and a ridge-waveguide transition, the four arms of the magic-T are placed in the same plane, which greatly simplifies the assembly. Compared to the previous method utilizing a microstrip-to-waveguide transition, the structure proposed in this letter achieved a higher power-handling capability because of all-metal elements. Over the frequency band of 28 to 36 GHz, the measured return loss of the input port and the isolation between the opposite ports are greater than 20 dB, indicating good characteristics of the proposed structure.


title: A hybrid context aware system for tourist guidance based on collaborative filtering

abstract: In the area of ambient intelligence there is a need to address user needs according with context features. Recently, the synergy between context aware computing and collaborative filtering is leading to enhance recommender systems with capabilities always nearer to user needs. Specifically, in the domain of tourism it is useful to proactively suggest right sets of attractive locations, events and so on. This work defines a context aware recommender system aimed at suggesting pertinent points of interest (POIs) to tourists. In particular, the approach is strongly based on the synergy between soft computing and data mining techniques. The general framework integrates user profiles, history of social networking and POIs data. Then by defining collaborative filtering approach on the history meaningful POIs are extracted. Indeed, soft computing techniques are mainly applied in order to support activity of unsupervised users and POIs classification. On the other hand, data mining techniques are exploited in order to extract rules able to associate user profile and context features with an eligible set of recommendable POIs. Experimental results show performance in terms of recommendations accuracy.


title: Simplified spiking neural network architecture and STDP learning algorithm applied to image

abstract: Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning.


title: The Java Virtual Machine Specification

abstract: Preface. 1. Introduction. A Bit of History. The Java Virtual Machine. Summary of Chapters. Notation. 2. Java Programming Language Concepts. Unicode. Identifiers. Literals. Types and Values. Primitive Types and Values. Operators on Integral Values. Floating-Point Types, Value Sets, and Values. Operators on Floating-Point Values. Operators on boolean Values. Reference Types, Objects, and Reference Values. The Class Object. The Class String. Operators on Objects. Variables. Initial Values of Variables. Variables Have Types, Objects Have Classes. Conversions and Promotions. Identity Conversions. Widening Primitive Conversions. Narrowing Primitive Conversions. Widening Reference Conversions. Narrowing Reference Conversions. Value Set Conversion. Assignment Conversion. Method Invocation Conversion. Casting Conversion. Numeric Promotion. Names and Packages. Names. Packages. Members. Package Members. The Members of a Class Type. The Members of an Interface Type. The Members of an Array Type. Qualified Names and Access Control. Fully Qualified Names. Classes. Class Names. Class Modifiers. Superclasses and Subclasses. The Class Members. Fields. Field Modifiers. Initialization of Fields. Methods. Formal Parameters. Method Signature. Method Modifiers. Static Initializers. Constructors. Constructor Modifiers. Interfaces. Interface Modifiers. Superinterfaces. Interface Members. Interface (Constant) Fields. Interface (Abstract) Methods. Overriding, Inheritance, and Overloading in Interfaces. Nested Classes and Interfaces. Arrays. Array Types. Array Variables. Array Creation. Array Access. Exceptions. The Causes of Exceptions. Handling an Exception. The Exception Hierarchy. The Classes Exception and RuntimeException. Execution. Virtual Machine Start-up. Loading. Linking: Verification, Preparation, and Resolution. Initialization. Detailed Initialization Procedure. Creation of New Class Instances. Finalization of Class Instances. Unloading of Classes and Interfaces. Virtual Machine Exit. FP-strict Expressions. Threads. 3. The Structure of the Java Virtual Machine. The class File Format. Data Types. Primitive Types and Values. Integral Types and Values. Floating-Point Types, Value Sets, and Values. The returnAddress Type and Values. The boolean Type. Reference Types and Values. Runtime Data Areas. The pc Register. Java Virtual Machine Stacks. Heap. Method Area. Runtime Constant Pool. Native Method Stacks. Frames. Local Variables. Operand Stacks. Dynamic Linking. Normal Method Invocation Completion. Abrupt Method Invocation Completion. Additional Information. Representation of Objects. Floating-Point Arithmetic. Java Virtual Machine Floating-Point Arithmetic and IEEE 754. Floating-Point Modes. Value Set Conversion. Specially Named Initialization Methods. Exceptions. Instruction Set Summary. Types and the Java Virtual Machine. Load and Store Instructions. Arithmetic Instructions. Type Conversion Instructions. Object Creation and Manipulation. Operand Stack Management Instructions. Control Transfer Instructions. Method Invocation and Return Instructions. Throwing Exceptions. Implementing finally. Synchronization. Class Libraries. Public Design, Private Implementation. 4. The class File Format. The ClassFile Structure. The Internal Form of Fully Qualified Class and Interface Names. Descriptors. Grammar Notation. Field Descriptors. Method Descriptors. The Constant Pool. The CONSTANT_Class_info Structure. The CONSTANT_Fieldref_info, CONSTANT_Methodref_info, and CONSTANT_InterfaceMethodref_info Structures. The CONSTANT_String_info Structure. The CONSTANT_Integer_info and CONSTANT_Float_info Structures. The CONSTANT_Long_info and CONSTANT_Double_info Structures. The CONSTANT_NameAndType_info Structure. The CONSTANT_Utf8_info Structure. Fields. Methods. Attributes. Defining and Naming New Attributes. The ConstantValue Attribute. The Code Attribute. The Exceptions Attribute. The InnerClasses Attribute. The Synthetic Attribute. The SourceFile Attribute. The LineNumberTable Attribute. The LocalVariableTable Attribute. The Deprecated Attribute. Constraints on Java Virtual Machine Code. Static Constraints. Structural Constraints. Verification of class Files. The Verification Process. The Bytecode Verifier. Values of Types long and double. Instance Initialization Methods and Newly Created Objects. Exception Handlers. Exceptions and finally. Limitations of the Java Virtual Machine. 5. Loading, Linking, and Initializing. The Runtime Constant Pool. Virtual Machine Start-up. Creation and Loading. Loading Using the Bootstrap Class Loader. Loading Using a User-defined Class Loader. Creating Array Classes. Loading Constraints. Deriving a Class from a class File Representation. Linking. Verification. Preparation. Resolution. Access Control. Initialization. Binding Native Method Implementations. 6. The Java Virtual Machine Instruction Set. Assumptions: The Meaning of "Must." Reserved Opcodes. Virtual Machine Errors. Format of Instruction Descriptions. 7. Compiling for the Java Virtual Machine. Format of Examples. Use of Constants, Local Variables, and Control Constructs. Arithmetic. Accessing the Runtime Constant Pool. More Control Examples. Receiving Arguments. Invoking Methods. Working with Class Instances. Arrays. Compiling Switches. Operations on the Operand Stack. Throwing and Handling Exceptions. Compiling finally. Synchronization. Compiling Nested Classes and Interfaces. 8. Threads and Locks. Terminology and Framework. Execution Order and Consistency. Rules About Variables. Nonatomic Treatment of double and long Variables. Rules About Locks. Rules About the Interaction of Locks and Variables. Rules for volatile Variables. Prescient Store Operations. Discussion. Example: Possible Swap. Example: Out-of-Order Writes. Threads. Locks and Synchronization. Wait Sets and Notification. 9. Opcode Mnemonics by Opcode. Appendix: Summary of Clarifications and Amendments. Index. 0201432943T04062001


title: Progressive compression for lossless transmission of triangle meshes

abstract: Lossless transmission of 3D meshes is a very challenging and timely problem for many applications, ranging from collaborative design to engineering. Additionally, frequent delays in transmissions call for progressive transmission in order for the end user to receive useful successive refinements of the final mesh. In this paper, we present a novel, fully progressive encoding approach for lossless transmission of triangle meshes with a very fine granularity. A new valence-driven decimating conquest, combined with patch tiling and an original strategic retriangulation is used to maintain the regularity of valence. We demonstrate that this technique leads to good mesh quality, near-optimal connectivity encoding, and therefore a good rate-distortion ratio throughout the transmission. We also improve upon previous lossless geometry encoding by decorrelating the normal and tangential components of the surface. For typical meshes, our method compresses connectivity down to less than 3.7 bits per vertex, 40% better in average than the best methods previously reported [5, 18]; we further reduce the usual geometry bit rates by 20% in average by exploiting the smoothness of meshes. Concretely, our technique can reduce an ascii VRML 3D model down to 1.7% of its size for a 10-bit quantization (2.3% for a 12-bit quantization) while providing a very progressive reconstruction.


title: Iteratively Trained Interactive Segmentation

abstract: Deep learning requires large amounts of training data to be effective. For the task of object segmentation, manually labeling data is very expensive, and hence interactive methods are needed. Following recent approaches, we develop an interactive object segmentation system which uses user input in the form of clicks as the input to a convolutional network. While previous methods use heuristic click sampling strategies to emulate user clicks during training, we propose a new iterative training strategy. During training, we iteratively add clicks based on the errors of the currently predicted segmentation. We show that our iterative training strategy together with additional improvements to the network architecture results in improved results over the state-of-the-art.


title: Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots

abstract: Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biome- chanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of in- ternal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively com- bines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of 1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex loco- motive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles...


title: Open Information Extraction on Scientific Text: An Evaluation

abstract: Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems applying a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.


title: 2006) “Honestly, why are you driving a BMW

abstract: This paper proposes that people derive utility not only from goods or their attributes as in standard models, but also from their self-image as influenced by their own perception of their preferences. In a representative survey, most respondents considered their own concern for status when purchasing a car to be minor in comparison with the status concerns of others. Similarly, most individuals considered themselves to be more environmentally concerned than other people. These results are consistent with an extension of the conventional theory where an individual’s selfimage is added as an argument in the utility function.


title: An intelligent agent-assisted decision support system for family financial planning

abstract: The demand for family financial planning (FFP) services is growing dramatically as the financial market grows more complex and people become more aware of the importance of qualified financial guidance. To provide decision support for FFP-related decisions, we formulate a conceptual model for FFP by following Simon's decision-making process model and map our model to the generic FFP process. The design, development, and empirical investigation of an intelligent FFP system for supporting FFP decisions by utilizing intelligent agents and Web-services technology are presented.


title: Assessing Language through Computer Technology

abstract: In 1998 and 1999, three of the largest providers of educational tests introduced computer-based versions of proficiency tests for English as a foreign language. Around the same time, many institutions began to offer Web-based tests for particular language courses and classes. These two phenomena have greatly added to the momentum of work in computer-assisted testing and mean that assessment through computer technology is becoming a fact for language learners in educational settings and therefore for teachers and researchers. This book is the first to consider the theoretical, methodological and practical issues and their implications for language-teaching professionals wishing to engage with computer-assisted assessment. It overviews the work in the field, evaluates examples of assessment though computer technology, and provides language teachers and researchers with practical guidelines for implementation.


Introduction to Information Extraction Technology


title: DRAMA: An Architecture for Accelerated Processing Near Memory

abstract: Improving energy efficiency is crucial for both mobile and high-performance computing systems while a large fraction of total energy is consumed to transfer data between storage and processing units. Thus, reducing data transfers across the memory hierarchy of a processor (i.e., off-chip memory, on-chip caches, and register file) can greatly improve the energy efficiency. To this end, we propose an architecture, DRAMA, that 3D-stacks coarse-grain reconfigurable accelerators (CGRAs) atop off-chip DRAM devices. DRAMA does not require changes to the DRAM device architecture, apart from through-silicon vias (TSVs) that connect the DRAM device's internal I/O bus to the CGRA layer. We demonstrate that DRAMA can reduce the energy consumption to transfer data across the memory hierarchy by 66-95 percent while achieving speedups of up to 18× over a commodity processor.


title: A Numerical PDE Approach For Pricing Callable Bonds

abstract: Many debt issues contain an embedded call option that allows the issuer to redeem the bond at specified dates for a specified price. The issuer is typically required to provide advance notice of a decision to exercise this call option. The valuation of these contracts is an interesting numerical exercise because discontinuities may arise in the bond value or its derivative at call and/or notice dates. Recently, it has been suggested that finite difference methods cannot be used to price callable bonds requiring notice. Poor accuracy was attributed to discontinuities and difficulties in handling boundary conditions. As an alternative, a semi-analytical method using Green's functions for valuing callable bonds with notice was proposed. Unfortunately, the Green's function method is limited to special cases. Consequently, it is desirable to develop a more general approach. This is provided by using more advanced techniques such as flux limiters to obtain an accurate numerical partial differential equation method. Finally, in a typical pricing model an inappropriate financial condition is required in order to properly specify boundary conditions for the associated PDE. It is shown that a small perturbation of such a model is free from such artificial conditions.


title: Blockchains everywhere - a use-case of blockchains in the pharma supply-chain

abstract: Blockchains are on the top of the Gartner Hype Cycle 2016 and many start-ups are integrating blockchains into their technology portfolio. While blockchains have emerged in the context of financial applications, non-financial application areas are of interest as well. In this paper, modum.io is presented, a start-up that uses IoT (Internet of Things) sensor devices leveraging blockchain technology to assert data immutability and public accessibility of temperature records, while reducing operational costs in the pharmaceutical supply-chain. The medical industry has many complex and strict environmental control process (e.g., temperature and humidity) to ensure quality control and regulatory compliance over the transport of medical products. The sensor devices monitor the temperature of each parcel during the shipment to fully ensure GDP regulations. All data is transferred to the blockchain where a smart contract assesses against the product attributes. As modum.io is not the only non-financial start-up working with blockchains, a list of areas and other start-ups is provided that aim to reduce bureaucracy, distribute the infrastructure, and saving costs using blockchains.


title: Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support

abstract: Objective: Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS). Materials and methods: We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data. Results: The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions. Conclusion: A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core components has been developed to facilitate the tasks of TCM knowledge discovery and CDS. We have conducted several OLAP and data mining tasks to explore the empirical knowledge from the TCM clinical data. The CDW platform would be a promising infrastructure to make full use of the TCM clinical data for scientific hypothesis generation, and promote the development of TCM from individualized empirical knowledge to large-scale evidence-based medicine.


title: Boosting Methods for Regression

abstract: In this paper we examine ensemble methods for regression that leverage or “boost” base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its strong theoretical guarantees. We present several gradient descent leveraging algorithms for regression and prove AdaBoost-style bounds on their sample errors using intuitive assumptions on the base learners. We bound the complexity of the regression functions produced in order to derive PAC-style bounds on their generalization errors. Experiments validate our theoretical results.


title: On lightweight mobile phone application certification

abstract: Users have begun downloading an increasingly large number of mobile phone applications in response to advancements in handsets and wireless networks. The increased number of applications results in a greater chance of installing Trojans and similar malware. In this paper, we propose the Kirin security service for Android, which performs lightweight certification of applications to mitigate malware at install time. Kirin certification uses security rules, which are templates designed to conservatively match undesirable properties in security configuration bundled with applications. We use a variant of security requirements engineering techniques to perform an in-depth security analysis of Android to produce a set of rules that match malware characteristics. In a sample of 311 of the most popular applications downloaded from the official Android Market, Kirin and our rules found 5 applications that implement dangerous functionality and therefore should be installed with extreme caution. Upon close inspection, another five applications asserted dangerous rights, but were within the scope of reasonable functional needs. These results indicate that security configuration bundled with Android applications provides practical means of detecting malware.


title: A New VPN Solution Based on Asymmetrical SSL Tunnels

abstract: Conventional SSL tunnel of SSL-based Virtual Private Network is symmetric, in which the data must be encrypted at one end and decrypted at the other end, or contrariwise for the reverse direction. Because all data flows of VPN are relayed by VPN server via SSL tunnels, those symmetric SSL tunnels cause a lot of computational load concentrated in VPN server, and make it the bottleneck of VPN. This paper proposes a cheap solution to eliminate that bottleneck for larger scale SSL VPNs: The VPN based on Asymmetric SSL Tunnels (AST). It is coupled with two algorithms: IP packet engrafting and UDP diffusing. In this solution, portion of computational load is distributed to disengaged internal application servers. Experiment shows that the overall throughput of VPN can be greatly improved by adopting AST solution.


title: The LilyPad Arduino: using computational textiles to investigate engagement, aesthetics, and diversity in computer science education

abstract: The advent of novel materials (such as conductive fibers) combined with accessible embedded computing platforms have made it possible to re-imagine the landscapes of fabric and electronic crafts--extending these landscapes with the creative range of electronic/computational textiles or e-textiles. This paper describes the LilyPad Arduino, a fabric-based construction kit that enables novices to design and build their own soft wearables and other textile artifacts. The kit consists of a microcontroller and an assortment of sensors and actuators in stitch-able packages; these elements can be sewn to cloth substrates and each other with conductive thread to build e-textiles. This paper will introduce the latest version of the kit; reflect on its affordances; present the results of our most recent user studies; and discuss possible directions for future work in the area of personalized e-textile design and its relation to technology education.


title: Learn-as-You-Go with Megh: Efficient Live Migration of Virtual Machines

abstract: We propose a reinforcement learning algorithm, Megh, for live migration of virtual machines that simultaneously reduces the cost of energy consumption and enhances the performance. Megh learns the uncertain dynamics of workloads as-it-goes. Megh uses a dimensionality reduction scheme to projectthe combinatorially explosive state-action space to a polynomial dimensional space. These schemes enable Megh to be scalable and to work in real-time. We experimentally validate that Megh is more cost-effective and time-efficient than the MadVM and MMT algorithms.


title: StereoBrush: interactive 2D to 3D conversion using discontinuous warps

abstract: We introduce a novel workflow for stereoscopic 2D to 3D conversion in which the user "paints" depth onto a 2D image via sparse scribbles, instantaneously receiving intuitive 3D feedback. This workflow is enabled by the introduction of a discontinuous warping technique that creates stereoscopic pairs from sparse, possibly erroneous user input. Our method assumes a piecewise continuous depth representation, preserving visual continuity in most areas, while creating sharp depth discontinuities at important object boundaries. As opposed to prior work that relies strictly on a per pixel depth map, our scribbles are processed as soft constraints in a global solve and operate entirely on image domain disparity, allowing for relaxed input requirements. This formulation also allows us to simultaneously compute a disparity-and-content-aware stretching of background areas to automatically fill disoccluded regions with valid stereo information. We tightly integrate all steps of stereo content conversion into a single optimization framework, which can then be solved on a GPU at interactive rates. The instant feedback received while painting depth allows even untrained users to quickly create compelling 3D scenes from single-view footage.


title: Use of kernel deep convex networks and end-to-end learning for spoken language understanding

abstract: We present our recent and ongoing work on applying deep learning techniques to spoken language understanding (SLU) problems. The previously developed deep convex network (DCN) is extended to its kernel version (K-DCN) where the number of hidden units in each DCN layer approaches infinity using the kernel trick. We report experimental results demonstrating dramatic error reduction achieved by the K-DCN over both the Boosting-based baseline and the DCN on a domain classification task of SLU, especially when a highly correlated set of features extracted from search query click logs are used. Not only can DCN and K-DCN be used as a domain or intent classifier for SLU, they can also be used as local, discriminative feature extractors for the slot filling task of SLU. The interface of K-DCN to slot filling systems via the softmax function is presented. Finally, we outline an end-to-end learning strategy for training the softmax parameters (and potentially all DCN and K-DCN parameters) where the learning objective can take any performance measure (e.g. the F-measure) for the full SLU system.


title: Prototype Design of Smart Home System using Internet of Things

abstract: Smart home control system can be integrated into an existing home appliances to reduce the need for human intervention, increase security and energy efficiency. However, it is still an open problem due to difficulties such as network distance, signal interference, not user friendly, increased cost and power consumption. This paper reviews various topics on smart home technologies including control system, smart home network, smart home appliance and sensor technologies for smart home. In this research, the proposed prototype of home automation allows users to remotely switch on or off any household appliance based on Internet of Things (IoT) with the enhancement of solar charger. The smartphone and/or tablet replaces the manual use of personal computer without the need for high additional cost. This prototype uses four types of sensors i.e. PIR sensor, temperature sensor, ultrasonic sensor and smoke gas sensor for automatic environmental control and intrusion detection.


title: Conditional BERT Contextual Augmentation

abstract: Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. Bidirectional Encoder Representations from Transformers (BERT) demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. We retrofit BERT to conditional BERT by introducing a new conditional masked language model (The term “conditional masked language model” appeared once in original BERT paper, which indicates context-conditional, is equivalent to term “masked language model”. In our paper, “conditional masked language model” indicates we apply extra label-conditional constraint to the “masked language model”.) task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain improvement.


title: Application-Specific Digital Forensics Investigative Model in Internet of Things (IoT)

abstract: Besides its enormous benefits to the industry and community the Internet of Things (IoT) has introduced unique security challenges to its enablers and adopters. As the trend in cybersecurity threats continue to grow, it is likely to influence IoT deployments. Therefore it is eminent that besides strengthening the security of IoT systems we develop effective digital forensics techniques that when breaches occur we can track the sources of attacks and bring perpetrators to the due process with reliable digital evidence. The biggest challenge in this regard is the heterogeneous nature of devices in IoT systems and lack of unified standards. In this paper we investigate digital forensics from IoT perspectives. We argue that besides traditional digital forensics practices it is important to have application-specific forensics in place to ensure collection of evidence in context of specific IoT applications. We consider top three IoT applications and introduce a model which deals with not just traditional forensics but is applicable in digital as well as application-specific forensics process. We believe that the proposed model will enable collection, examination, analysis and reporting of forensically sound evidence in an IoT application-specific digital forensics investigation.


Algorithmic Trading Strategy Based On Massive Data Mining


title: Placing surface mount components using coarse/fine positioning and vision

abstract: An experimental system that can accurately align and place SMDs (surface mount devices) on a PCB (printed circuit board) using a coarse/fine positioning component placement strategy and end-point sensing to measure the alignment error is described. Coarse positioning is done with an IBM 7576 robot, and fine positioning is done using a custom-designed precision micropositioning device attached to the end of the IBM 7576 robot. The end-point sensor is a single-camera vision system that by image analysis determines the alignment error of the SMD to the board. System performance was evaluated by placing SMDs of 100 leads with 0.63-mm (25 mil) lead spacing on a board. An alignment error of less than 12 mu m (0.5 mil) and 0.015 degrees was obtained independent of feeder and board position error or robot repeatability. The average cycle time was less than 10 seconds. >


Crop Classification with Multi-Temporal Satellite Imagery


title: Ultrasonic imaging front-end design for CMUT: A 3-level 30Vpp pulse-shaping pulser with improved efficiency and a noise-optimized receiver

abstract: A four-channel analog front-end (AFE) transceiver chip for medical ultrasound imaging is demonstrated. The high voltage transmitter uses a 3-level pulse-shaping technique to deliver over 50% more acoustic power for the same power dissipation, compared to traditional methods. The design requires minimum off-chip components and is scalable for more channels. The receiver is implemented with a transimpedance amplifier (TIA) topology and is optimized for noise, bandwidth and power dissipation. Based on both acoustic and electrical measurements, we demonstrate the Transmitter (Tx) efficiency improvement, Tx beamformation and the pulse-echo response, revealing the system's full functionality.


title: Detection of Mobile Botnet Using VPN

abstract: As most applications in wired networks become available on mobile devices and mobile networks are well integrated with Internet, Botnet becomes the most significant threat to mobile devices. We propose a mobile Botnet detection scheme that detects "pull" style C&C channel. Our network-based scheme detects Botnet by inspecting abnormal flow features of C&C traffic traveling through VPN which provides a shared path for both 3/4G and WiFi. Through the verification analysis under real Botnet attacks, we show that our proposed scheme provides high detection rate by using abnormal models as well as low FP rate by adding white list and signatures.


title: OpenRAVE : A Planning Architecture for Autonomous Robotics

abstract: One of the challenges in developing real-world autonomous robots is the need for integrating and rigorously testing high-level scripting, motion planning, perception, and control algorithms. For this purpose, we introduce an open-source cross-platform software architecture called OpenRAVE, the Open Robotics and Animation Virtual Environment. OpenRAVE is targeted for real-world autonomous robot applications, and includes a seamless integration of 3-D simulation, visualization, planning, scripting and control. A plugin architecture allows users to easily write custom controllers or extend functionality. With OpenRAVE plugins, any planning algorithm, robot controller, or sensing subsystem can be distributed and dynamically loaded at run-time, which frees developers from struggling with monolithic code-bases. Users of OpenRAVE can concentrate on the development of planning and scripting aspects of a problem without having to explicitly manage the details of robot kinematics and dynamics, collision detection, world updates, and robot control. The OpenRAVE architecture provides a flexible interface that can be used in conjunction with other popular robotics packages such as Player and ROS because it is focused on autonomous motion planning and high-level scripting rather than low-level control and message protocols. OpenRAVE also supports a powerful network scripting environment which makes it simple to control and monitor robots and change execution flow during run-time. One of the key advantages of open component architectures is that they enable the robotics research community to easily share and compare algorithms.


title: Virtual try-on of eyeglasses using 3D model of the head

abstract: This work presents a system for virtual try-on of eyeglasses using a 3D model of user's face and head. The 3D head model is reconstructed using only one image of the user. The 3D glasses model are then fitted onto this head model, and the user's head movement is tracked in real-time to rotate the 3D head model with glasses accordingly.


title: A Survey about Consensus Algorithms Used in Blockchain

abstract: Thanks to its potential in many applications, Blockchain has recently been nominated as one of the technologies exciting intense attention. Blockchain has solved the problem of changing the original low-trust centralized ledger held by a single third-party, to a high-trust decentralized form held by different entities, or in other words, verifying nodes. The key contribution of the work of Blockchain is the consensus algorithm, which decides how agreement is made to append a new block between all nodes in the verifying network. Blockchain algorithms can be categorized into two main groups. The first group is proof-based consensus, which requires the nodes joining the verifying network to show that they are more qualified than the others to do the appending work. The second group is voting-based consensus, which requires nodes in the network to exchange their results of verifying a new block or transaction, before making the final decision. In this paper, we present a review of the Blockchain consensus algorithms that have been researched and that are being applied in some well-known applications at this time.


title: Will NoSQL Databases Live Up to Their Promise?

abstract: Many organizations collect vast amounts of customer, scientific, sales, and other data for future analysis. Traditionally, most of these organizations have stored structured data in relational databases for subsequent access and analysis. However, a growing number of developers and users have begun turning to various types of nonrelational, now frequently called NoSQL-databases. Nonrelational databases, including hierarchical, graph, and object-oriented databases-have been around since the late 1960s. However, new types of NoSQL databases are being developed. And only now are they beginning to gain market traction. Different NoSQL databases take different approaches. What they have in common is that they're not relational. Their primary advantage is that, unlike relational databases, they handle unstructured data such as word-processing files, e-mail, multimedia, and social media efficiently. This paper discuss issues such as limitations, advantages, concerns and doubts regarding NoSQL databases.


title: Balanced Hashing and Efficient GPU Sparse General Matrix-Matrix Multiplication

abstract: General sparse matrix-matrix multiplication (SpGEMM) is a core component of many algorithms. A number of recent works have used high throughput graphics processing units (GPUs) to accelerate SpGEMM. However, exploiting the power of GPUs for SpGEMM requires addressing a number of challenges, including highly imbalanced workloads and large numbers of inefficient random global memory accesses. This paper presents a SpGEMM algorithm which uses several novel techniques to overcome these problems. We first propose two low cost methods to achieve perfect load balancing during the most expensive step in SpGEMM. Next, we show how to eliminate nearly all random global memory accesses using shared memory based hash tables. To optimize the performance of the hash tables, we propose a lightweight method to estimate the number of nonzeros in the output matrix. We compared our algorithm to the CUSP, CUSPARSE and the state-of-the-art BHSPARSE GPU SpGEMM algorithms, and show that it performs 5.6x, 2.4x and 1.5x better on average, and up to 11.8x, 9.5x and 2.5x better in the best case, respectively. Furthermore, we show that our algorithm performs especially well on highly imbalanced and unstructured matrices.


title: Cross-lingual Transfer of Named Entity Recognizers without Parallel Corpora

abstract: We propose an approach to cross-lingual named entity recognition model transfer without the use of parallel corpora. In addition to global de-lexicalized features, we introduce multilingual gazetteers that are generated using graph propagation, and cross-lingual word representation mappings without the use of parallel data. We target the e-commerce domain, which is challenging due to its unstructured and noisy nature. The experiments have shown that our approaches beat the strong MT baseline, where the English model is transferred to two languages: Spanish and Chinese.


title: Hybrid singular value thresholding for tensor completion

abstract: In this paper, we study the low-rank tensor completion problem, where a high-order tensor with missing entries is given and the goal is to complete the tensor. We propose to minimize a new convex objective function, based on log sum of exponentials of nuclear norms, that promotes the low-rankness of unfolding matrices of the completed tensor. We show for the first time that the proximal operator to this objective function is readily computable through a hybrid singular value thresholding scheme. This leads to a new solution to high-order (low-rank) tensor completion via convex relaxation. We show that this convex relaxation and the resulting solution are much more effective than existing tensor completion methods (including those also based on minimizing ranks of unfolding matrices). The hybrid singular value thresholding scheme can be applied to any problem where the goal is to minimize the maximum rank of a set of low-rank matrices.


Partial Foot Amputation, Finite Element Analysis of Foot and Manufacturing of Prosthtic Sockets: A Review


title: A virtual electrical drive control laboratory: Neuro-fuzzy control of induction motors

abstract: Neural and fuzzy courses are widely offered at graduate and undergraduate level due to the successful applications of neural and fuzzy control to nonlinear and unmodeled dynamic systems, including electrical drives. However, teaching students a neuro-fuzzy controlled electrical drive in a laboratory environment is often difficult for schools with limited access to expensive equipment facilities. Therefore, computer simulations and dedicated software are needed to assist the students in visualizing the concepts and to provide graphical feedback during the learning process. In this article, an educational software is proposed for the neuro-fuzzy control of induction machine drives. The tool helps students learn the application of neuro-fuzzy control of electrical drives. The software has a flexible structure and graphical user interface. The neuro-fuzzy architecture, the motor and load parameters can be easily changed in the developed software. Neuro-fuzzy control performance of induction motors can be monitored graphically for various control structures and current controllers Comput Appl Eng Educ 14: 211–221, 2006; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20082


title: Complexation of Metal Ions of Higher Charge by the Highly Preorganized Tetradentate Ligand 2,9-Bis(hydroxymethyl)-1,10-Phenanthroline. A Crystallographic and Thermodynamic Study

abstract: The metal ion selectivity for M(III) (M = metal) ions exhibited by the highly preorganized ligand PDALC is investigated (PDALC = 2,9-bis(hydroxymethyl)-1,10-phenanthroline). The structures are reported of [Bi(PDALC)(H2O)2(ClO4)3]·H2O (1), monoclinic, P21/c, a = 12.8140(17), b = 19.242(3), c = 9.2917(12) A, β = 91.763(2)°, V = 2289.9(5) A3, Z = 4, R = 0.0428; [Th(PDALC)(NO3)4]·3H2O (2), monoclinic, P21/n, a = 7.876(3), b = 22.827(9), c = 12.324(5) A, β = 94.651(6)°, V = 2208.4(15) A3, Z = 4, R = 0.0669; [Cd(PDALC)2](ClO4)2 (3),), triclinic, P1, a = 7.5871(16), b = 13.884(3), c = 14.618(3) A, α = 74.081(2)°, β = 88.422(2)°, γ = 78.454(2)°, V = 1450.2(5) A3, Z = 2, R = 0.0267. The Bi in 1 is best regarded as 9-coordinate, with four short bonds to the PDALC, and two short bonds to the coordinated water molecules, with three long bonds to perchlorate oxygens. The Bi−N bonds at 2.35 A are by a considerable margin the shortest Bi−N bonds to 1,10-phenanthroline (phen) type ligands, which is suggested to be due t...


title: No Free Lunch Theorems for Search

abstract: We show that all algorithms that search for an extremum of a cost function perform exactly the same, when averaged over all possible cost functions. In particular, if algorithm A outperforms algorithm B on some cost functions, then loosely speaking there must exist exactly as many other functions where B outperforms A. Starting from this we analyze a number of the other a priori characteristics of the search problem, like its geometry and its information-theoretic aspects. This analysis allows us to derive mathematical benchmarks for assessing a particular search algorithm's performance. We also investigate minimax aspects of the search problem, the validity of using characteristics of a partial search over a cost function to predict future behavior of the search algorithm on that cost function, and time-varying cost functions. We conclude with some discussion of the justifiablility of biologically-inspired search methods.


title: Moving Java into mobile phones

abstract: As mobile technology matures, handheld-device vendors are looking for ways to make their products more functional, and Java is one approach they are turning to. This is particularly the case with smart cellular phones, which are using Java to help add new capabilities. In smart phones, Java functions as a layer between the operating system and the hardware, or runs parallel to the OS within a separate chip.


title: 0. Introduction. THE BASIC ELEMENTS OF ARGUMENT STRUCTURE

abstract: An elevator driving system comprising a three-phase induction motor including three-phase armature windings having a plurality of terminals and arranged for change-over between the double star connection mode and the delta connection mode, means for connecting said armature windings in the double star connection mode to a three-phase a.c. power supply when the elevator is driven for service operation, means for connecting said armature windings in the delta connection mode to the three-phase a.c. power supply when the elevator car is driven for maintenance operation, means for changing over the connection mode of said armature windings to the delta connection during application of the brake, and a controlled rectifier circuit for applying a d.c. voltage across selected two terminals among said terminals during the same braking period.


title: AONT-RS: Blending security and performance in dispersed storage systems

abstract: Dispersing files across multiple sites yields a variety of obvious benefits, such as availability, proximity and reliability. Less obviously, it enables security to be achieved without relying on encryption keys. Standard approaches to dispersal either achieve very high security with correspondingly high computational and storage costs, or low security with lower costs. In this paper, we describe a new dispersal scheme, called AONT-RS, which blends an All-Or-Nothing Transform with Reed-Solomon coding to achieve high security with low computational and storage costs. We evaluate this scheme both theoretically and as implemented with standard open source tools. AONTRS forms the backbone of a commercial dispersed storage system, which we briefly describe and then use as a further experimental testbed. We conclude with details of actual deployments.


title: The Perception of Stress and its Impact on Health in Poor Communities

abstract: With the increased understanding of the relationship between stress and disease, the role of stress in explaining persistent disparities in health outcomes has received growing attention. One body of research has focused on allostatic load—the “wear and tear” that results from chronic or excessive activation of the stress response. Other research has looked at the link between stress and health behaviors. In this study, we conducted 7 focus groups with a total of 56 people to understand how people living in Highbridge, South Bronx, New York, a low income community with poor health outcomes, perceive stress and its relationship to health. Focus group participants described a direct causal pathway between stress and poor health as well as an indirect pathway through health behaviors, including uncontrolled eating, sleep deprivation, substance abuse, smoking, violence and aggression, and withdrawal and inactivity. Participants articulated a number of theories about why stress leads to these unhealthy behaviors, including self-medication, adaptive behavior, discounting the future, depletion of willpower, and competing priorities. Their nuanced understanding of the link between stress and health elucidates the mechanisms and pathways by which stress may result in disparities in health outcomes and create challenges in changing health behaviors in poor communities like the South Bronx.


title: Minimum-Threshold Crowbar for a Fault-Ride-Through Grid-Code-Compliant DFIG Wind Turbine

abstract: Doubly fed induction generator (DFIG) technology is the dominant technology in the growing global market for wind power generation, due to the combination of variable-speed operation and a cost-effective partially rated power converter. However, the DFIG is sensitive to dips in supply voltage and without specific protection to “ride-through” grid faults, a DFIG risks damage to its power converter due to overcurrent and/or overvoltage. Conventional converter protection via a sustained period of rotor-crowbar closed circuit leads to poor power output and sustained suppression of the stator voltages. A new minimum-threshold rotor-crowbar method is presented in this paper, improving fault response by reducing crowbar application periods to 11-16 ms, successfully diverting transient overcurrents, and restoring good power control within 45 ms of both fault initiation and clearance, thus enabling the DFIG to meet grid-code fault-ride-through requirements. The new method is experimentally verified and evaluated using a 7.5-kW test facility.


title: Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation

abstract: The recent boom of AI has seen the emergence of many human-computer conversation systems such as Google Assistant, Microsoft Cortana, Amazon Echo and Apple Siri. We introduce and formalize the task of predicting questions in conversations, where the goal is to predict the new question that the user will ask, given the past conversational context. This task can be modeled as a "sequence matching" problem, where two sequences are given and the aim is to learn a model that maps any pair of sequences to a matching probability. Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have attracted immense research interests of information retrieval and natural language processing communities. In this paper, we first study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the effectiveness of neural models for this task is relatively unstudied. We further evaluate the neural matching models in the next question prediction task in conversations. We have used the publicly available Quora data and Ubuntu chat logs in our experiments. Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations. Experimental results show that neural matching models perform well for both tasks.


title: On the capacitated vehicle routing problem

abstract: We consider the Vehicle Routing Problem, in which a fixed fleet of delivery vehicles of uniform capacity must service known customer demands for a single commodity from a common depot at minimum transit cost. This difficult combinatorial problem contains both the Bin Packing Problem and the Traveling Salesman Problem (TSP) as special cases and conceptually lies at the intersection of these two well-studied problems. The capacity constraints of the integer programming formulation of this routing model provide the link between the underlying routing and packing structures. We describe a decomposition-based separation methodology for the capacity constraints that takes advantage of our ability to solve small instances of the TSP efficiently. Specifically, when standard procedures fail to separate a candidate point, we attempt to decompose it into a convex combination of TSP tours; if successful, the tours present in this decomposition are examined for violated capacity constraints; if not, the Farkas Theorem provides a hyperplane separating the point from the TSP polytope. We present some extensions of this basic concept and a general framework within which it can be applied to other combinatorial models. Computational results are given for an implementation within the parallel branch, cut, and price framework SYMPHONY.


title: Learning of Multimodal Representations With Random Walks on the Click Graph

abstract: In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. With the click data collected from the users’ searching behavior, existing approaches take either one-to-one paired data (text–image pairs) or ranking examples (text–query–image and/or image–query–text ranking lists) as training examples, which do not make full use of the click data, particularly the implicit connections among the data objects. In this paper, we treat the click data as a large click graph, in which vertices are images/text queries and edges indicate the clicks between an image and a query. We consider learning a multimodal representation from the perspective of encoding the explicit/implicit relevance relationship between the vertices in the click graph. By minimizing both the truncated random walk loss as well as the distance between the learned representation of vertices and their corresponding deep neural network output, the proposed model which is named multimodal random walk neural network (MRW-NN) can be applied to not only learn robust representation of the existing multimodal data in the click graph, but also deal with the unseen queries and images to support cross-modal retrieval. We evaluate the latent representation learned by MRW-NN on a public large-scale click log data set Clickture and further show that MRW-NN achieves much better cross-modal retrieval performance on the unseen queries/images than the other state-of-the-art methods.


title: Remote GPU-Accelerated Online Pre-processing of Raster Maps for Terrain Rendering

abstract: We present a distributed architecture for accelerated pre-processing of remote sensing data for immediate terrain visualization. Interactive 3D visualization approaches for large terrain datasets employ level of detail techniques that require a multi-resolution data representation. The high computational cost of constructing these representations is often not viewed as a major drawback, as it is considered an o-line pre-processing step. This prevents the application of existing methods in the case of changing data, which is becoming increasingly important for a multitude of applications where datasets are being generated, transmitted and must be visualized immediately, such as in disaster management. ::: Our system uses graphics processing units (GPUs) to accelerate the process of generating a multi-resolution representation, achieving sucient performance to enable on-line visualization on a front-end workstation communicating with a back-end cluster of machines equipped with GPUs. As a reference data structure, we use a quad tree decomposition of the so-called HEALPix sphere parameterization, which is well-suited for spherical terrain rendering. Our system correctly handles overlapping and unregistered mixed-resolution datasets. We demonstrate the ecacy of our approach by applying it to the surface of Mars using both the NASA Mars Orbiter Laser Altimeter and the ESA Mars Express High Resolution Stereo Camera datasets.


title: Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models

abstract: Named Entity Disambiguation NED is the task of disambiguating named entities in a natural language text by linking them to their corresponding entities in a knowledge base such as DBpedia, which are already recognized. It is an important step in transforming unstructured text into structured knowledge. Previous work on this task has proven a strong impact of graph-based methods such as PageRank on entity disambiguation. Other approaches rely on distributional similarity between an article and the textual description of a candidate entity. However, the combined impact of these different feature groups has not been explored to a sufficient extent. In this paper, we present a novel approach that exploits an undirected probabilistic model to combine different types of features for named entity disambiguation. Capitalizing on Markov Chain Monte Carlo sampling, our model is capable of exploiting complementary strengths between both graph-based and textual features. We analyze the impact of these features and their combination on named entity disambiguation. In an evaluation on the GERBIL benchmark, our model compares favourably to the current state-of-the-art in 8 out of 14 data sets.


title: Study of chemical-free trough-hole plating technology for prototyping of power converters

abstract: Chemical-free trough-hole plating process LPKF ProConduct® allows significantly decrease the time of rapid PCB prototyping; this technology is well tested by prototyping a wide spectrum of weak signal and digital electronics boards. Current work presents tests of LPKF ProConduct® technology on applicability for prototyping of power converter boards. This paper describes theory and experiment setup. The results show the specifics of the technology. Maximum safe current of vias, their thermal resistance and optimal diameter for high current boards are found. Recommendations for successful application of LPKF ProConduct® technology is given.


title: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling

abstract: We propose a novel tracking algorithm for the target of which geometric appearance changes drastically over time. To track it, we present a local patch-based appearance model and provide an efficient scheme to evolve the topology between local patches by on-line update. In the process of on-line update, the robustness of each patch in the model is estimated by a new method of measurement which analyzes the landscape of local mode of the patch. This patch can be moved, deleted or newly added, which gives more flexibility to the model. Additionally, we introduce the Basin Hopping Monte Carlo (BHMC) sampling method to our tracking problem to reduce the computational complexity and deal with the problem of getting trapped in local minima. The BHMC method makes it possible for our appearance model to consist of enough numbers of patches. Since BHMC uses the same local optimizer that is used in the appearance modeling, it can be efficiently integrated into our tracking framework. Experimental results show that our approach tracks the object whose geometric appearance is drastically changing, accurately and robustly.


title: 300mm low k wafer dicing saw study

abstract: With the farther shrink of the IC dimension, the low k material has been widely used to replace the traditional SiO/sub 2/ ILD in order to reduce the interconnect delay. The introduction of low k material into silicon imposed challenges on dicing saw process, ILD and metal layers peeling and its penetration into the sealing ring of the die during dicing saw are the most common defects. In this paper, the low k material structure and its impact on wafer dicing were elaborated. A practical dicing quality inspection matrix was developed to assess the cutting process variation. A 300mm CMOS 90nm dual damascene low k wafer was chosen as a test vehicle to develop robust low k dicing saw process. The critical factors (dicing blade, index speed, spindle speed, cut in depth, test pattern in the saw street...) affecting cutting quality were studied and optimized. The selected C90 dual damascene low k device passed package reliability test with the optimized low k dicing saw recipe and process. The further improvement and solutions in eliminating the low k dicing saw peeling from both wafer fab and packaging assembly were also explored.


title: Adaptive Congestion Control for Unpredictable Cellular Networks

abstract: Legacy congestion controls including TCP and its variants are known to perform poorly over cellular networks due to highly variable capacities over short time scales, self-inflicted packet delays, and packet losses unrelated to congestion. To cope with these challenges, we present Verus, an end-to-end congestion control protocol that uses delay measurements to react quickly to the capacity changes in cellular networks without explicitly attempting to predict the cellular channel dynamics. The key idea of Verus is to continuously learn a delay profile that captures the relationship between end-to-end packet delay and outstanding window size over short epochs and uses this relationship to increment or decrement the window size based on the observed short-term packet delay variations. While the delay-based control is primarily for congestion avoidance, Verus uses standard TCP features including multiplicative decrease upon packet loss and slow start. Through a combination of simulations, empirical evaluations using cellular network traces, and real-world evaluations against standard TCP flavors and state of the art protocols like Sprout, we show that Verus outperforms these protocols in cellular channels. In comparison to TCP Cubic, Verus achieves an order of magnitude (> 10x) reduction in delay over 3G and LTE networks while achieving comparable throughput (sometimes marginally higher). In comparison to Sprout, Verus achieves up to 30% higher throughput in rapidly changing cellular networks.


title: Canopus: A Scalable and Massively Parallel Consensus Protocol

abstract: Achieving consensus among a set of distributed entities (or participants) is a fundamental problem at the heart of many distributed systems. A critical problem with most consensus protocols is that they do not scale well. As the number of participants trying to achieve consensus increases, increasing network traffic can quickly overwhelm the network from topology-oblivious broadcasts, or a central coordinator for centralized consensus protocols. Thus, either achieving strong consensus is restricted to a handful of participants, or developers must resort to weaker models of consensus. We propose Canopus, a highly-parallel consensus protocol that is 'plug-compatible' with ZooKeeper, which exploits modern data center network topology, parallelism, and consensus semantics to achieve scalability with respect to the number of participants and throughput (i.e., the number of key-value reads/writes per second). In our prototype implementation, compared to EPaxos and ZooKeeper, Canopus increases throughput by more than 4x and 16x respectively for read-heavy workloads.


title: Object-oriented design: A goal-driven and pattern-based approach

abstract: In recent years, the influences of design patterns on software quality have attracted increasing attention in the area of software engineering, as design patterns encapsulate valuable knowledge to resolve design problems, and more importantly to improve the design quality. One of the key challenges in object-oriented design is how to apply appropriate design patterns during the system development. In this paper, design pattern is analyzed from different perspectives to see how it can facilitate design activities, handle non-functional requirement, solve design problems and resolve design conflicts. Based on the analysis, various kinds of applicability of design patterns are explored and integrated with a goal-driven approach to guiding developers to construct the object-oriented design model in a systematic manner. There are three benefits to the proposed approach: making it easy to meet requirements, helping resolve design conflicts, and facilitating improvement of the design quality.


title: The Malevolent Side of Revenge Porn Proclivity: Dark Personality Traits and Sexist Ideology

abstract: This paper presents a novel study, exploring a form of technology facilitated sexual violence TFSV known as revenge porn. Despite its emerging prevalence, little is known about the characteristics of revenge porn perpetrators. In the current study, a revenge porn proclivity scale was devised to examine participants' behavioural propensity to engage in revenge porn. One hundred adults, aged 18-54, were recruited online from a community sample. The correlational relationship between revenge porn proclivity and the self-reported endorsement of the Dark Triad, sadism, and ambivalent sexism was examined. Additional proclivity subscales of revenge porn enjoyment and revenge porn approval were also created. The study's main findings revealed a positive correlation between a greater behavioural propensity to engage in revenge porn and higher levels of the Dark Triad and ambivalent sexism. Moreover, endorsement of psychopathy was found to be the only Dark Triad trait that independently predicted revenge porn proclivity. The results suggest that perpetrators of revenge porn may have distinct personality profiles. Limitations and directions for future research are discussed.


title: Activity classification using realistic data from wearable sensors

abstract: Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network


title: Multi-step Reinforcement Learning: A Unifying Algorithm

abstract: Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter $\lambda$. Currently, there are a multitude of algorithms that can be used to perform TD control, including Sarsa, $Q$-learning, and Expected Sarsa. These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance. Each of these algorithms is seemingly distinct, and no one dominates the others for all problems. In this paper, we study a new multi-step action-value algorithm called $Q(\sigma)$ which unifies and generalizes these existing algorithms, while subsuming them as special cases. A new parameter, $\sigma$, is introduced to allow the degree of sampling performed by the algorithm at each step during its backup to be continuously varied, with Sarsa existing at one extreme (full sampling), and Expected Sarsa existing at the other (pure expectation). $Q(\sigma)$ is generally applicable to both on- and off-policy learning, but in this work we focus on experiments in the on-policy case. Our results show that an intermediate value of $\sigma$, which results in a mixture of the existing algorithms, performs better than either extreme. The mixture can also be varied dynamically which can result in even greater performance.


title: Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior

abstract: Online review sites provide increasingly important sources of information in tourism product purchases. We tested experimentally how source, content style, and peripheral credibility cues in online postings influence four consumer beliefs, and how those in turn influence attitudes and purchase intentions for an eco-resort. We compared tourists' posts to managers' posts, containing vague versus specific content, and with or without peripheral certification logos. First, we tested effects of tourists' beliefs about utility, trustworthiness, quality and corporate social responsibility on attitude toward the resort and purchase intentions. Second, we tested the role of source, content, and certification on the beliefs. The interactions are complex, but broadly tourists treat specific information posted by customers as most useful and trustworthy. Their purchase intentions are influenced principally by their overall attitude toward the resort and their beliefs in its corporate social responsibility.


title: How Does Free Riding on Customer Service Affect Competition

abstract: The free-riding problem occurs if the presales activities needed to sell a product can be conducted separately from the actual sale of the product. Intuitively, free riding should hurt the retailer that provides that service, but the author shows analytically that free riding benefits not only the free-riding retailer, but also the retailer that provides the service when customers are heterogeneous in terms of their opportunity costs for shopping. The service-providing retailer has a postservice advantage, because customers who have resolved their matching uncertainty through sales service incur zero marginal shopping cost if they purchase from the service-providing retailer rather than the free-riding retailer. Moreover, allowing free riding gives the free rider less incentive to compete with the service provider on price, because many customers eventually will switch to it due to their own free riding. In turn, this induced soft strategic response enables the service provider to charge a higher price and enjoy the strictly positive profit that otherwise would have been wiped away by head-to-head price competition. Therefore, allowing free riding can be regarded as a necessary mechanism that prevents an aggressive response from another retailer and reduces the intensity of price competition.


title: Spectral analysis of the vegetative background in the NIR and SWIR spectral range

abstract: Reflectance spectra of vegetative background areas are measured and their variation is analyzed. It is shown that the ::: variation of different samples is significantly larger than the accuracy of the measurement. Furthermore the difference of ::: diverse measurement procedures is discussed.


title: Query strategy for sequential ontology debugging

abstract: Debugging is an important prerequisite for the wide-spread application of ontologies, especially in areas that rely upon everyday users to create and maintain knowledge bases, such as the Semantic Web. Most recent approaches use diagnosis methods to identify sources of inconsistency. However, in most debugging cases these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit probabilities of typical user errors to formulate information theoretic concepts for query selection. Our evaluation showed that the suggested method reduces the number of required observations compared to myopic strategies.


title: Sustained attention performance during sleep deprivation: evidence of state instability

abstract: Nathaniel Kleitman was the first to observe that sleep deprivation in humans did not eliminate the ability to perform neurobehavioral functions, but it did make it difficult to maintain stable performance for more than a few minutes. To investigate variability in performance as a function of sleep deprivation, n = 13 subjects were tested every 2 hours on a 10-minute, sustained-attention, psychomotor vigilance task (PVT) throughout 88 hours of total sleep deprivation (TSD condition), and compared to a control group of n = 15 subjects who were permitted a 2-hour nap every 12 hours (NAP condition) throughout the 88-hour period. PVT reaction time means and standard deviations increased markedly among subjects and within each individual subject in the TSD condition relative to the NAP condition. TSD subjects also had increasingly greater performance variability as a function of time on task after 18 hours of wakefulness. During sleep deprivation, variability in PVT performance reflected a combination of normal timely responses, errors of omission (i.e., lapses), and errors of commission (i.e., responding when no stimulus was present). Errors of omission and errors of commission were highly intercorrelated across deprivation in the TSD condition (r = 0.85, p = 0.0001), suggesting that performance instability is more likely to include compensatory effort than a lack of motivation. The marked increases in PVT performance variability as sleep loss continued supports the "state instability" hypothesis, which posits that performance during sleep deprivation is increasingly variable due to the influence of sleep initiating mechanisms on the endogenous capacity to maintain attention and alertness, thereby creating an unstable state that fluctuates within seconds and that cannot be characterized as either fully awake or asleep.


title: An extended banker's algorithm for deadlock avoidance

abstract: We describe a natural extension of the banker's algorithm (D.W. Dijkstra, 1968) for deadlock avoidance in operating systems. Representing the control flow of each process as a rooted tree of nodes corresponding to resource requests and releases, we propose a quadratic-time algorithm which decomposes each flow graph into a nested family of regions, such that all allocated resources are released before the control leaves a region. Also, information on the maximum resource claims for each of the regions can be extracted prior to process execution. By inserting operating system calls when entering a new region for each process at runtime, and applying the original banker's algorithm for deadlock avoidance, this method has the potential to achieve better resource utilization because information on the "localized approximate maximum claims" is used for testing system safety.


title: Taming memory related performance pitfalls in linux Cgroups

abstract: Linux kernel feature of Cgroups (Control Groups) is being increasingly adopted for running applications in multi-tenanted environments. Many projects (e.g., Docker) rely on cgroups to isolate resources such as CPU and memory. It is critical to ensure high performance for such deployments. At LinkedIn, we have been using Cgroups and investigated its performance. This work presents our findings about memory-related performance issues of cgroups during certain scenarios. These issues can significantly affect the performance of the applications running in cgroups. Specifically, (1) memory is not reserved for cgroups (as with virtual machines); (2) both anonymous memory and page cache are part of memory limit and the former can evict the latter; (3) OS can steal page cache from any cgroups; (4) OS can swap any cgroups. We provide a set of recommendations for addressing these issues.


title: Forensic readiness of smart buildings: Preconditions for subsequent cybersecurity tests

abstract: Smart grid and smart buildings are seamlessly interacting together to improve energy usage. Smart buildings accept excess of energy during periods when renewable energy generation is cheap and overflowing. Then, the electric energy is kept for future use and returned to the smart grid when needed. Many advantages exist for smart cities which are focused on the incorporation of smart buildings. The control of smart buildings is supported by dramatically increased embedded systems. This approach is becoming cost-effective and the systems will be almost maintenance-free, involving some or no individuals. Users will also have the ability to simply update settings using their smartphones and other smart devices. However, even with above mentioned advantages, some disadvantages, including cybersecurity risk, are present and therefore, protective measures for a smart world are needed. The consequences of not incorporating cybersecurity and forensic readiness in a smart world could have serious impact if cyber-attack was successfully launched. In addition, a broad Knowledge Management (KM) is needed to obtain increasingly better understanding and handling of cybersecurity issues of smart sensors and other extensively configurable devices. This paper addresses the importance of forensic readiness, digital evidence preservation and interpretation, and knowledge management in a smart world. The approaches we recommend can be further used for subsequent smart (as model-based) cybersecurity tests.


title: Tag recommendations based on tensor dimensionality reduction

abstract: Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.


title: Monolithic three-stage 6–18GHz high power amplifier with distributed interstage in GaN technology

abstract: This paper reports on a three-stage broadband amplifier architecture suitable for wide bandwidth applications such as electronic warfare systems. The proposed topology comprises a distributed active power splitter to function as an interstage driver amplifier, whereas the pre-driver and power amplifier stages are reactively-matched. As a result, purely resistive interstage impedances are obtained and therefore the proposed architecture allows wider bandwidth operation as compared to the conventional reactively-matched multistage topology. A 6GHz to 18GHz three-stage high gain and high power amplifier MMIC is designed and realized in order to demonstrate the capabilities of the concept. The MMIC is based on a 0.25μm gate length AlGaN/GaN HEMT microstrip transmission line technology. It shows a measured small-signal gain of (25 ± 3)over the entire frequency range. The saturated output power is higher than 25W at the center frequency in pulsed operation.


title: Transport and Entrapment of Particles in Continuous Casting of Steel

abstract: The entrapment of inclusions, bubbles, slag, and other particles into solidified steel products is a critically-important quality concern. During continuous casting, particles may enter the mold with the steel flowing through the submerged nozzle. In addition, mold slag may be entrained from the top surface. A computational model has been developed to simulate the transport and entrapment of particles from both of these sources. The model first computes transient turbulent flow in the mold region using Large Eddy Simulation (LES), with the sub-grid-scale (SGS) k model. Next, the transport and capture of over 30,000 particles are simulated using a Lagrangian approach to track the trajectories. A new criterion was developed to model particle pushing and capture by a dendritic interface and was incorporated into the particle transport model. Particles smaller than the primary dendrite arm spacing are entrapped if they enter the boundary layer region and touch the solidifying steel shell. Larger particles are entrapped only if they remain stable while the shell grows around them. The new criterion models this by considering a balance of ten different forces which act on a particle in the boundary layer region, including the bulk hydrodynamic forces (lift, pressure gradient, stress gradient, Basset, and added mass forces), transverse drag force, (caused by fluid flow across the dendrite interface), gravity (buoyancy) force, and the forces acting at the interface (Van der Waals interfacial force, lubrication drag force, and surface energy gradient force). The criterion was validated by reproducing experimental results in two different systems. Finally, the model was used to predict the entrapment distributions, removal rates, and fractions of different sized particles in a straight-walled thin slab caster. Although more large particles are safely removed than small ones, the capture rate as defects is still very high.


title: Interacting Conceptual Spaces I : Grammatical Composition of Concepts

abstract: The categorical compositional approach to meaning has been successfully applied in natural language processing, outperforming other models in mainstream empirical language processing tasks. We show how this approach can be generalized to conceptual space models of cognition. In order to do this, first we introduce the category of convex relations as a new setting for categorical compositional semantics, emphasizing the convex structure important to conceptual space applications. We then show how to construct conceptual spaces for various types such as nouns, adjectives and verbs. Finally we show by means of examples how concepts can be systematically combined to establish the meanings of composite phrases from the meanings of their constituent parts. This provides the mathematical underpinnings of a new compositional approach to cognition.


title: Cannabidiol , a Cannabis sativa constituent , as an antipsychotic drug

abstract: A high dose of delta9-tetrahydrocannabinol, the main Cannabis sativa (cannabis) component, induces anxiety and psychotic-like symptoms in healthy volunteers. These effects of delta9-tetrahydrocannabinol are significantly reduced by cannabidiol (CBD), a cannabis constituent which is devoid of the typical effects of the plant. This observation led us to suspect that CBD could have anxiolytic and/or antipsychotic actions. Studies in animal models and in healthy volunteers clearly suggest an anxiolytic-like effect of CBD. The antipsychotic-like properties of CBD have been investigated in animal models using behavioral and neurochemical techniques which suggested that CBD has a pharmacological profile similar to that of atypical antipsychotic drugs. The results of two studies on healthy volunteers using perception of binocular depth inversion and ketamine-induced psychotic symptoms supported the proposal of the antipsychotic-like properties of CBD. In addition, open case reports of schizophrenic patients treated with CBD and a preliminary report of a controlled clinical trial comparing CBD with an atypical antipsychotic drug have confirmed that this cannabinoid can be a safe and well-tolerated alternative treatment for schizophrenia. Future studies of CBD in other psychotic conditions such as bipolar disorder and comparative studies of its antipsychotic effects with those produced by clozapine in schizophrenic patients are clearly indicated.


title: Riffle: optimized shuffle service for large-scale data analytics

abstract: The rapidly growing size of data and complexity of analytics present new challenges for large-scale data processing systems. Modern systems keep data partitions in memory for pipelined operators, and persist data across stages with wide dependencies on disks for fault tolerance. While processing can often scale well by splitting jobs into smaller tasks for better parallelism, all-to-all data transfer---called shuffle operations---become the scaling bottleneck when running many small tasks in multi-stage data analytics jobs. Our key observation is that this bottleneck is due to the superlinear increase in disk I/O operations as data volume increases. We present Riffle, an optimized shuffle service for big-data analytics frameworks that significantly improves I/O efficiency and scales to process petabytes of data. To do so, Riffle efficiently merges fragmented intermediate shuffle files into larger block files, and thus converts small, random disk I/O requests into large, sequential ones. Riffle further improves performance and fault tolerance by mixing both merged and unmerged block files to minimize merge operation overhead. Using Riffle, Facebook production jobs on Spark clusters with over 1,000 executors experience up to a 10x reduction in the number of shuffle I/O requests and 40% improvement in the end-to-end job completion time.


title: A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data

abstract: We developed a highly automated three-dimensionally based method for the segmentation of bone in volumetric computed tomography (CT) datasets. The multistep approach starts with three-dimensional (3-D) region-growing using local adaptive thresholds followed by procedures to correct for remaining boundary discontinuities and a subsequent anatomically oriented boundary adjustment using local values of cortical bone density. We describe the details of our approach and show applications in the proximal femur, the knee, and the skull. The accuracy of the determination of geometrical parameters was analyzed using CT scans of the semi-anthropomorphic European spine phantom. Depending on the settings of the segmentation parameters cortical thickness could be determined with an accuracy corresponding to the side length of 1 to 2.5 voxels. The impact of noise on the segmentation was investigated by artificially adding noise to the CT data. An increase in noise by factors of two and five changed cortical thickness corresponding to the side length of one voxel. Intraoperator and interoperator precision was analyzed by repeated analysis of nine pelvic CT scans. Precision errors were smaller than 1% for trabecular and total volumes and smaller than 2% for cortical thickness. Intraoperator and interoperator precision errors were not significantly different. Our segmentation approach shows: 1) high accuracy and precision and is 2) robust to noise, 3) insensitive to user-defined thresholds, 4) highly automated and fast, and 5) easy to initialize.


title: Basic personality dimensions and alcohol consumption in young adults

abstract: Basic personality dimensions are relevant factors in the development of alcohol consumption. The main aim of this research was to evaluate the associations among some of the most important biodispositional models of personality, and to explore its role in the non-pathological alcohol consumption in young adults. In this study, the personality of 539 college students (283 women) was assessed using four questionnaires: EPQ-RS, SPSRQ-S, NEO-FFI, and TCI. Alcohol consumption was assessed with the AIS scale. Factor analyses of the different scales showed four broad factors labelled Negative Emotionality, Disagreeable Disinhibition, Unconscientious Disinhibition, and Positive Emotionality. The openness to experience scale was not well represented in the factor solution and was excluded from the factor analysis. This dimension was studied independently in relation to alcohol use. Disagreeable Disinhibition predicted alcohol consumption both during the week and at the weekend, whereas Unconscientious Disinhibition was associated with non-pathological alcohol drinking at weekends. These results were interpreted according to processes associated with impulsivity that facilitate alcohol use in young adults.


title: Stiffness Control With Shape Memory Polymer in Underactuated Robotic Origamis

abstract: Underactuated systems offer compact design with easy actuation and control but at the cost of limited stable configurations and reduced dexterity compared to the directly driven and fully actuated systems. Here, we propose a compact origami-based design in which we can modulate the material stiffness of the joints and thereby control the stable configurations and the overall stiffness in an underactuated robot. The robotic origami, robogami, design uses multiple functional layers in nominally two-dimensional robots to achieve the desired functionality. To control the stiffness of the structure, we adjust the elastic modulus of a shape memory polymer using an embedded customized stretchable heater. We study the actuation of a robogami finger with three joints and determine its stable configurations and contact forces at different stiffness settings. We monitor the configuration of the finger using feedback from customized curvature sensors embedded in each joint. A scaled down version of the design is used in a two-fingered gripper and different grasp modes are achieved by activating different sets of joints.