title: Polymeric nanoparticle-encapsulated curcumin ("nanocurcumin"): a novel strategy for human cancer therapy

abstract: BackgroundCurcumin, a yellow polyphenol extracted from the rhizome of turmeric (Curcuma longa), has potent anti-cancer properties as demonstrated in a plethora of human cancer cell line and animal carcinogenesis models. Nevertheless, widespread clinical application of this relatively efficacious agent in cancer and other diseases has been limited due to poor aqueous solubility, and consequently, minimal systemic bioavailability. Nanoparticle-based drug delivery approaches have the potential for rendering hydrophobic agents like curcumin dispersible in aqueous media, thus circumventing the pitfalls of poor solubility.ResultsWe have synthesized polymeric nanoparticle encapsulated formulation of curcumin – nanocurcumin – utilizing the micellar aggregates of cross-linked and random copolymers of N-isopropylacrylamide (NIPAAM), with N-vinyl-2-pyrrolidone (VP) and poly(ethyleneglycol)monoacrylate (PEG-A). Physico-chemical characterization of the polymeric nanoparticles by dynamic laser light scattering and transmission electron microscopy confirms a narrow size distribution in the 50 nm range. Nanocurcumin, unlike free curcumin, is readily dispersed in aqueous media. Nanocurcumin demonstrates comparable in vitro therapeutic efficacy to free curcumin against a panel of human pancreatic cancer cell lines, as assessed by cell viability and clonogenicity assays in soft agar. Further, nanocurcumin's mechanisms of action on pancreatic cancer cells mirror that of free curcumin, including induction of cellular apoptosis, blockade of nuclear factor kappa B (NFκB) activation, and downregulation of steady state levels of multiple pro-inflammatory cytokines (IL-6, IL-8, and TNFα).ConclusionNanocurcumin provides an opportunity to expand the clinical repertoire of this efficacious agent by enabling ready aqueous dispersion. Future studies utilizing nanocurcumin are warranted in pre-clinical in vivo models of cancer and other diseases that might benefit from the effects of curcumin.


title: Contextual Music Information Retrieval and Recommendation: State of the Art and Challenges

abstract: Abstract Increasing amount of online music content has opened new opportunities for implementing new effective information access services–commonly known as music recommender systems–that support music navigation, discovery, sharing, and formation of user communities. In the recent years a new research area of contextual (or situational) music recommendation and retrieval has emerged. The basic idea is to retrieve and suggest music depending on the user’s actual situation, for instance emotional state, or any other contextual conditions that might influence the user’s perception of music. Despite the high potential of such idea, the development of real-world applications that retrieve or recommend music depending on the user’s context is still in its early stages. This survey illustrates various tools and techniques that can be used for addressing the research challenges posed by context-aware music retrieval and recommendation. This survey covers a broad range of topics, starting from classical music information retrieval (MIR) and recommender system (RS) techniques, and then focusing on context-aware music applications as well as the newer trends of affective and social computing applied to the music domain.


title: Mobile learning and integration of mobile technologies in education

abstract: Mobile technologies have a huge potential to transform education provided these technologies are designed and implemented in such a way that they are relevant to the social and cultural context of learning. Clearly, the application, implementation, and design of mobile technology in the global educational context pose technological and socio-cultural challenges. Specifically, technology tools and applications that are familiar in developed nations may pose unique challenges in developing countries, so `one size fits all' or `one technology for all contexts' does not practically work. Therefore, the authors provide case studies focusing on the pedagogical benefits of mobile technologies when used as educational tools. Ideally, this article is intended to stimulate reflections on the subject of m-learning as well as the integration of mobile technologies in education.


title: TI: an efficient indexing mechanism for real-time search on tweets

abstract: Real-time search dictates that new contents be made available for search immediately following their creation. From the database perspective, this requirement may be quite easily met by creating an up-to-date index for the contents and measuring search quality by the time gap between insertion time and availability of the index. This approach, however, poses new challenges for micro-blogging systems where thousands of concurrent users may upload their micro-blogs or tweets simultaneously. Due to the high update and query loads, conventional approaches would either fail to index the huge amount of newly created contents in real time or fall short of providing a scalable indexing service. In this paper, we propose a tweet index called the TI (Tweet Index), an adaptive indexing scheme for microblogging systems such as Twitter. The intuition of the TI is to index the tweets that may appear as a search result with high probability and delay indexing some other tweets. This strategy significantly reduces the indexing cost without compromising the quality of the search results. In the TI, we also devise a new ranking scheme by combining the relationship between the users and tweets. We group tweets into topics and update the ranking of a topic dynamically. The experiments on a real Twitter dataset confirm the efficiency of the TI.


title: Can Structural Models Price Default Risk? Evidence from Bond and Credit Derivative Markets

abstract: Using a set of structural models, we evaluate the price of default protection for a sample of US corporations. In contrast to previous evidence from corporate bond data, credit default swap (CDS) premia are not systematically underestimated. In fact, one of our studied models has little difficulty on average in predicting their level. For robustness, we perform the same exercise for bond spreads by the same issuers on the same trading date. As expected, bond spreads relative to the treasury curve are systematically underestimated. This is not the case when the swap curve is used as a benchmark, suggesting that previously documented underestimation results may be sensitive to the choice of risk-free rate.


title: A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array

abstract: A multi-functional in-memory inference processor integrated circuit (IC) in a 65-nm CMOS process is presented. The prototype employs a deep in-memory architecture (DIMA), which enhances both energy efficiency and throughput over conventional digital architectures via simultaneous access of multiple rows of a standard 6T bitcell array (BCA) per precharge, and embedding column pitch-matched low-swing analog processing at the BCA periphery. In doing so, DIMA exploits the synergy between the dataflow of machine learning (ML) algorithms and the SRAM architecture to reduce the dominant energy cost due to data movement. The prototype IC incorporates a 16-kB SRAM array and supports four commonly used ML algorithms—the support vector machine, template matching, $k$ -nearest neighbor, and the matched filter. Silicon measured results demonstrate simultaneous gains (dot product mode) in energy efficiency of 10 $\times $ and in throughput of 5.3 $\times $ leading to a 53 $\times $ reduction in the energy-delay product with negligible ( $\le $ 1%) degradation in the decision-making accuracy, compared with the conventional 8-b fixed-point single-function digital implementations.


title: Learning motion manifolds with convolutional autoencoders

abstract: We present a technique for learning a manifold of human motion data using Convolutional Autoencoders. Our approach is capable of learning a manifold on the complete CMU database of human motion. This manifold can be treated as a prior probability distribution over human motion data, which has many applications in animation research, including projecting invalid or corrupt motion onto the manifold for removing error, computing similarity between motions using geodesic distance along the manifold, and interpolation of motion along the manifold for avoiding blending artefacts.


title: Bayesian binomial mixture model for collaborative prediction with non-random missing data

abstract: Collaborative prediction involves filling in missing entries of a user-item matrix to predict preferences of users based on their observed preferences. Most of existing models assume that the data is missing at random (MAR), which is often violated in recommender systems in practice. Incorrect assumption on missing data ignores the missing data mechanism, leading to biased inferences and prediction. In this paper we present a Bayesian binomial mixture model for collaborative prediction, where the generative process for data and missing data mechanism are jointly modeled to handle non-random missing data. Missing data mechanism is modeled by three factors, each of which is related to users, items, and rating values. Each factor is modeled by Bernoulli random variable, and the observation of rating value is determined by the Boolean OR operation of three binary variables. We develop computationally-efficient variational inference algorithms, where variational parameters have closed-form update rules and the computational complexity depends on the number of observed ratings, instead of the size of the rating data matrix. We also discuss implementation issues on hyperparameter tuning and estimation based on empirical Bayes. Experiments on Yahoo! Music and MovieLens datasets confirm the useful behavior of our model by demonstrating that: (1) it outperforms state-of-the-art methods in yielding higher predictive performance; (2) it finds meaningful solutions instead of undesirable boundary solutions; (3) it provides rating trend analysis on why ratings are observed.


title: Angle-of-arrival estimation for a rotating Digital Beamforming radar

abstract: In Digital Beamforming (DBF) radars, multiple beams are formed simultaneously at different azimuth and/or elevation angles to monitor large areas of interest. In order to estimate the location of a target in azimuth without specifically steering the beams at the target or using special techniques such as monopulse, amplitude comparison angle measurement can be used. Since the digital phased array beam shape is different at each azimuth, each beam is modeled precisely using polynomial curve fitting. Target location is found by comparing amplitudes in adjacent beams. This method assumes targets are seen as single points in the beam pattern, however this assumption no longer holds when the radar is rotating to cover 360 degrees. Extension of the amplitude comparison method for the rotating case is presented in this paper, along with experimental results.


Ten Lessons for Computer Science for All


title: Tagging Sentence Boundaries

abstract: In this paper we tackle sentence boundary disambiguation through a part-of-speech (POS) tagging framework. We describe necessary changes in text tokenization and the implementation of a POS tagger and provide results of an evaluation of this system on two corpora. We also describe an extension of the traditional POS tagging by combining it with the document-centered approach to proper name identification and abbreviation handling. This made the resulting system robust to domain and topic shifts.


title: Ketamine-Induced NMDA Receptor Hypofunction as a Model of Memory Impairment and Psychosis

abstract: N-methyl-D-aspartate (NMDA) glutamate receptor antagonists are reported to induce schizophrenia-like symptoms in humans, including cognitive impairments. Shortcomings of most previous investigations include failure to maintain steady-state infusion conditions, test multiple doses and/or measure antagonist plasma concentrations. This double-blind, placebo-controlled, randomized, within-subjects comparison of three fixed subanesthetic, steady-state doses of intravenous ketamine in healthy males (n = 15) demonstrated dose-dependent increases in Brief Psychiatric Rating Scale positive (F[3,42] = 21.84; p < 0.0001) and negative symptoms (F[3,42] = 2.89; p = 0.047), and Scale for the Assessment of Negative Symptoms (SANS) total scores (F[3,42] = 10.55; p < 0.0001). Ketamine also produced a robust dose-dependent decrease in verbal declarative memory performance (F[3,41] = 5.11; p = 0.004), and preliminary evidence for a similar dose-dependent decrease in nonverbal declarative memory, occurring at or below plasma concentrations producing other symptoms. Increasing NMDA receptor hypofunction is associated with early occurring memory impairments followed by other schizophrenia-like symptoms.


title: .NET Gadgeteer: A Platform for Custom Devices

abstract: .NET Gadgeteer is a new platform conceived to make it easier to design and build custom electronic devices and systems for a range of ubiquitous and mobile computing scenarios. It consists of three main elements: solder-less modular electronic hardware; object-oriented managed software libraries accessed using a high-level programming language and established development environment; and 3D design and construction tools designed to facilitate a great deal of control over the form factor of the resulting electronic devices. Each of these elements is designed to be accessible to a wide range of people with varying backgrounds and levels of experience and at the same time provide enough flexibility to allow experts to build relatively sophisticated devices and complex systems in less time than they are used to. In this paper we describe the .NET Gadgeteer system in detail for the first time, explaining a number of key design decisions and reporting on its use by new users and experts alike.


title: Drug discovery from medicinal plants.

abstract: Current research in drug discovery from medicinal plants involves a multifaceted approach combining botanical, phytochemical, biological, and molecular techniques. Medicinal plant drug discovery continues to provide new and important leads against various pharmacological targets including cancer, HIV/AIDS, Alzheimer's, malaria, and pain. Several natural product drugs of plant origin have either recently been introduced to the United States market, including arteether, galantamine, nitisinone, and tiotropium, or are currently involved in late-phase clinical trials. As part of our National Cooperative Drug Discovery Group (NCDDG) research project, numerous compounds from tropical rainforest plant species with potential anticancer activity have been identified. Our group has also isolated several compounds, mainly from edible plant species or plants used as dietary supplements, that may act as chemopreventive agents. Although drug discovery from medicinal plants continues to provide an important source of new drug leads, numerous challenges are encountered including the procurement of plant materials, the selection and implementation of appropriate high-throughput screening bioassays, and the scale-up of active compounds.


title: Socio-economic distance and spatial patterns in unemployment

abstract: This paper examines the spatial patterns of unemployment in Chicago between 1980 and 1990. We study unemployment clustering with respect to different social and economic distance metrics that reflect the structure of agents' social networks. Specifically, we use physical distance, travel time, and differences in ethnic and occupational distribution between locations. Our goal is to determine whether our estimates of spatial dependence are consistent with models in which agents' employment status is affected by information exchanged locally within their social networks. We present non-parametric estimates of correlation across Census tracts as a function of each distance metric as well as pairs of metrics, both for unemployment rate itself and after conditioning on a set of tract characteristics. Our results indicate that there is a strong positive and statistically significant degree of spatial dependence in the distribution of raw unemployment rates, for all our metrics. However, once we condition on a set of covariates, most of the spatial autocorrelation is eliminated, except for physical and occupational distance.


title: Comparison between experimental results and simulation of a fuel cell powered DC motor using PID controller

abstract: In this paper, the comparison between simulation and experimental results of a fuel cell that is powered a DC motor by using Proportional, Integral, and Derivative (PID) controller has been studied and tested. The experimental set-up is done at Memorial University Laboratory to compare and validate the experimental and simulation results. The hydrogen gas was generated in+ separate stage in order to feed the fuel cell. All the components are built in MATLAB/Simulink to compare them with the real experiment. Two different speed readings (300 and 400 rpm) were taken to examine the behavior of the system in both simulation and experiments. The simulation and experiment results were well matched and illustrated.


title: Looking outside of the Box: Object Detection and Localization with Multi-scale Patterns.

abstract: Detection and localization of objects at multiple scales often involves sliding a single scale template in order to score windows at different scales independently. Nonetheless, multi-scale visual information at a given image location is highly correlated. This fundamental insight allows us to generalize the traditional multi-scale sliding window technique by jointly considering image features at all scales in order to detect and localize objects. Two max-margin approaches are studied for learning the multi-scale templates and leveraging the highly structured multi-scale information which would have been ignored if a single-scale template was used. The multi-scale formulation is shown to significantly improve general detection performance (measured on the PASCAL VOC dataset). The experimental analysis shows the method to be effective with different visual features, both HOG and CNN. Surprisingly, for a given window in a specific scale, visual information from windows at the same image location but other scales (`out-of-scale' information) contains most of the discriminative information for detection.


title: Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods

abstract: Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learning. In theory, under certain conditions, this problem can be solved in polynomial time via a natural convex relaxation, known as Compressive Principal Component Pursuit (CPCP). However, all existing provable algorithms for CPCP suffer from superlinear per-iteration cost, which severely limits their applicability to large scale problems. In this paper, we propose provable, scalable and efficient methods to solve CPCP with (essentially) linear per-iteration cost. Our method combines classical ideas from Frank-Wolfe and proximal methods. In each iteration, we mainly exploit Frank-Wolfe to update the low-rank component with rank-one SVD and exploit the proximal step for the sparse term. Convergence results and implementation details are also discussed. We demonstrate the scalability of the proposed approach with promising numerical experiments on visual data.


title: Virtual machine migration in cloud data centers: a review, taxonomy, and open research issues

abstract: Virtualization efficiently manages the ever-increasing demand for storage, computing, and networking resources in large-scale Cloud Data Centers. Virtualization attains multifarious resource management objectives including proactive server maintenance, load balancing, pervasive service availability, power management, and fault tolerance by virtual machine (VM) migration. VM migration is a resource-intensive operation as it constantly requires adequate CPU cycles, memory capacity, system cache, and network bandwidth. Consequently, it adversely affects the performance of running applications and cannot be entirely overlooked in contemporary data centers, particularly when user SLA and critical business goals are to be met. The unavailability of a comprehensive survey on VM migration schemes that covers various VM migration aspects such as migration patterns, sequence, application performance, bandwidth optimization, and migration granularity has motivated this review of existing schemes. This paper reviews state-of-the-art live and non-live VM migration schemes. Through an extensive literature review, a detailed thematic taxonomy is proposed for the categorization of VM migration schemes. Critical aspects and related features of current VM migration schemes are inspected through detailed qualitative investigation. We extract significant parameters from existing literature to discuss the commonalities and variances among VM migration schemes. Finally, open research issues and challenges with VM migration that require further consideration to develop optimal VM migration schemes in Cloud Data Centers are briefly addressed.


Decibel: Isolation and Sharing in Disaggregated Rack-Scale Storage.


title: A Secure Communication Protocol for Drones and Smart Objects

abstract: In many envisioned drone-based applications, drones will communicate with many different smart objects, such as sensors and embedded devices. Securing such communications requires an effective and efficient encryption key establishment protocol. However, the design of such a protocol must take into account constrained resources of smart objects and the mobility of drones. In this paper, a secure communication protocol between drones and smart objects is presented. To support the required security functions, such as authenticated key agreement, non-repudiation, and user revocation, we propose an efficient Certificateless Signcryption Tag Key Encapsulation Mechanism (eCLSC-TKEM). eCLSC-TKEM reduces the time required to establish a shared key between a drone and a smart object by minimizing the computational overhead at the smart object. Also, our protocol improves drone's efficiency by utilizing dual channels which allows many smart objects to concurrently execute eCLSC-TKEM. We evaluate our protocol on commercially available devices, namely AR.Drone2.0 and TelosB, by using a parking management testbed. Our experimental results show that our protocol is much more efficient than other protocols.


title: Real-time Video Recommendation Exploration

abstract: Video recommendation has attracted growing attention in recent years. However, conventional techniques have limitations in real-time processing, accuracy or scalability for the large-scale video data. To address the deficiencies of current recommendation systems, we introduce some new techniques to provide real-time and accurate recommendations to users in the video recommendation system of Tencent Inc.. We develop a scalable online collaborative filtering algorithm based upon matrix factorization, with an adjustable updating strategy considering implicit feedback solution of different user actions. To select high-quality candidate videos for real-time top-N recommendation generation, we utilize additional factors like video type and time factor to compute similar videos. In addition, we propose the scalable implementation of our algorithm together with some optimizations to make the recommendations more efficient and accurate, including the demographic filtering and demographic training. To demonstrate the effectiveness and efficiency of our model, we conduct comprehensive experiments by collecting real data from Tencent Video. Furthermore, our video recommendation system is in production to provide recommendation services in Tencent Video, one of the largest video sites in China, and verifies its superiority in performance.


title: Isolate First, Then Share: a New OS Architecture for Datacenter Computing

abstract: This paper presents the "isolate first, then share" OS model in which the processor cores, memory, and devices are divided up between disparate OS instances and a new abstraction, subOS, is proposed to encapsulate an OS instance that can be created, destroyed, and resized on-the-fly. The intuition is that this avoids shared kernel states between applications, which in turn reduces performance loss caused by contention. We decompose the OS into the supervisor and several subOSes running at the same privilege level: a subOS directly manages physical resources, while the supervisor can create, destroy, resize a subOS on-the-fly. The supervisor and subOSes have few state sharing, but fast inter-subOS communication mechanisms are provided on demand. ::: We present the first implementation, RainForest, which supports unmodified Linux binaries. Our comprehensive evaluation shows RainForest outperforms Linux with four different kernels, LXC, and Xen in terms of worst-case and average performance most of time when running a large number of benchmarks. The source code is available soon.


title: 1-Bit unit-cell for transmitarray applications in Ka-band

abstract: We report here the design and performance of an active unit-cell for transmitarray applications in Ka-band. With the proposed structure based on two PIN diodes, the phase of the transmitted wave can be selected between 0° and 180° (i.e. 1 bit of phase quantization) states. The numerical results show a minimum insertion loss of 0.9 dB and 9–10% 2-dB transmission bandwidth. The proposed design can be used as a building block of reconfigurable transmit-arrays with electronic beam-steering and beam-forming capabilities.


title: Heart rate variability in chronic heart failure

abstract: The analysis of heart rate variability (HRV) provides information about autonomic cardiovascular control in healthy subjects. In the past 15 years, several articles have been published regarding HRV and chronic heart failure (CHF). The results of these papers substantially demonstrated that HRV is significantly different in CHF patients compared to controls. Moreover, some variables derived from HRV analysis showed significant independent prognostic capacity. In particular, the reduction of variance (expressed as SDNN) and low-frequency spectral component of HRV (ranging from 0.03 to 0.15 Hz) seem related to an increased mortality in CHF. Nevertheless, these variables are not yet considered in clinical practice. A better understanding of the physiopathological basis of the reported alterations of HRV in CHF patients is required in order to permit its use as a clinical tool for prognosis and tailored therapy in individual CHF patients.


title: Research Challenges for the Security of Control Systems

abstract: In this paper we attempt to answer two questions: (1) Why should we be interested in the security of control systems? And (2) What are the new and fundamentally different requirements and problems for the security of control systems? We also propose a new mathematical framework to analyze attacks against control systems. Within this framework we formulate specific research problems to (1) detect attacks, and (2) survive attacks.


title: Band pass filter design using half mode substrate integrated waveguide (HMSIW) with periodically loaded F-EBG structures

abstract: Band-pass filter design based on Half Mode Substrate Integrated Waveguide (HMSIW) has been presented in this paper. The PEC surface of the HMSIW has been etched with periodic F-shaped Electromagnetic Band Gap (EBG) structures to obtain the bandpass characteristics. Single and array of three similar elements are used which effectively serves the purpose of reduced the Insertion Loss within the transmission passband. The concept is validated with parametric variations of several important EBG parameters. The design confirms the luxury to obtain pass-band at other microwave bands by simple alterations of the dimensions of the inverted F shaped EBG elements. The filter finds applications in Ku band satellite and radar operations with advantages like convenient integration, compact size, low cost and ease in fabrication.


title: Natural terrain classification using three-dimensional ladar data for ground robot mobility

abstract: In recent years, much progress has been made in outdoor autonomous navigation. However, safe navigation is still a daunting challenge in terrain containing vegetation. In this paper, we f ocus on the segmentation of ladar data into three classes using local three-dimensional point cloud statistics. The classes are: ”scatter” to represent porous volumes such as grass and tree canopy, ”linear” to capture thin objects like wires or tree branches, and finally ”surface” to capture solid objects like ground surface, rocks or large trunks. We present the details of the proposed method, and the modifications we made to implement it on-board an autonom ous ground vehicle for real-time data processing. Finally, we present results produced from different sta tionary laser sensors and from field tests using an unmanned ground vehicle.


title: Evaluation of the Lateral Step-Down Test as a Clinical Assessment of Hip Musculature Strength

abstract: Gluteal musculature weakness is associated with lower extremity injury and greater frontal plane knee motion. A method for functionally evaluating hip abduction and external rotation strength by observing knee motion would be beneficial for clinicians. This study evaluated the association between hip abductor and external rotation strength and knee motion. Peak isometric and eccentric hip abduction and external rotation strength were assessed in 47 healthy participants before analysis of knee kinematics during a lateral step-down test. The relationships between each strength measure and frontal plane knee kinematics were evaluated using bivariate Pearson correlation coefficients. Frontal plane knee displacement was not associated with hip musculature strength. Frontal plane knee angle at peak knee flexion was not associated with exter nal rotation or eccentric abduction strength. Isometric abduction strength was negatively correlated with peak knee flexion. Hip musculature strength is not indicative of the magnitude of frontal plane knee motion during the lateral step-down test, and this test is not recommended for clinical assessment of hip muscle strength.


title: QMaxSAT: A Partial Max-SAT Solver

abstract: We present a partial Max-SAT solver QMaxSAT which uses CNF encoding of Boolean cardinality constraints. The old version 0.1 was obtained by adapting a CDCL based SAT solver MiniSat to manage cardinality constraints. It was placed first in the industrial subcategory and second in the crafted subcategory of partial Max-SAT category of the 2010 Max-SAT Evaluation. The new version 0.2 is obtained by modifying version 0.1 to decrease the number of clauses for the cardinality encoding. We compare the two versions by solving Max-SAT instances taken from the 2010 Max-SAT Evaluation.


title: SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis

abstract: Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, we developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis. SenticNet 2 is built by means of sentic computing, a new paradigm that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions. By providing the semantics and sentics (that is, the cognitive and affective information) associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect-sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing.


title: Quality Indicators for Learning Analytics

abstract: This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts from the field of learning analytics. The outcomes of this study are further extended with findings from a focused literature review.


title: Probing anomalous driving

abstract: We study the effects of driving a magnetically charged black brane solution of Einstein-Maxwell-Chern-Simons theory by a time dependent electric field. From a holographic perspective, we find that placing a sample in a background magnetic field and driving the system via a parallel electric field generates a charge current which may oscillate for long periods and (or) may exhibit non-Ohmic behavior. We discuss how these two effects manifest themselves in various types of quenches and in periodic driving of the sample.


title: IEEE 1609.4 DSRC multi-channel operations and its implications on vehicle safety communications

abstract: This paper provides an overview of IEEE 1609.4, a work in progress standard for multi-channel operations over the 5.9GHz Dedicated Short Range Communications (DSRC) spectrum. In the U.S., the DSRC spectrum is organized into several channels. IEEE 1609.4 defines a time-division scheme for DSRC radios to alternately switch within these channels to support different applications concurrently. We describe the main features of IEEE 1609.4 in detail and discuss the main concerns with the original protocol design. In particular, we focus on those issues that can have a significant impact on vehicle safety communications. While IEEE 1609.4 is currently being updated and revised, this paper is intended to contribute to the technical discussions, and to bring attention to the most relevant and critical issues. This paper also contains results from software simulations conducted to study vehicle safety communications under stressful but realistic conditions. These results confirm concerns for the currently proposed scheme and provide a motivation for updating and revising the standard.


Voice Quality Characterization of IETF Opus Codec.


title: Attack Planning in the Real World

abstract: Assessing network security is a complex and difficult task. Attack graphs have been proposed as a tool to help network administrators understand the potential weaknesses of their network. However, a problem has not yet been addressed by previous work on this subject; namely, how to actually execute and validate the attack paths resulting from the analysis of the attack graph. In this paper we present a complete PDDL representation of an attack model, and an implementation that integrates a planner into a penetration testing tool. This allows to automatically generate attack paths for penetration testing scenarios, and to validate these attacks by executing the corresponding actions -including exploits- against the real target network. We present an algorithm for transforming the information present in the penetration testing tool to the planning domain, and show how the scalability issues of attack graphs can be solved using current planners. We include an analysis of the performance of our solution, showing how our model scales to medium-sized networks and the number of actions available in current penetration testing tools.


title: Contour Boxplots: A Method for Characterizing Uncertainty in Feature Sets from Simulation Ensembles

abstract: Ensembles of numerical simulations are used in a variety of applications, such as meteorology or computational solid mechanics, in order to quantify the uncertainty or possible error in a model or simulation. Deriving robust statistics and visualizing the variability of an ensemble is a challenging task and is usually accomplished through direct visualization of ensemble members or by providing aggregate representations such as an average or pointwise probabilities. In many cases, the interesting quantities in a simulation are not dense fields, but are sets of features that are often represented as thresholds on physical or derived quantities. In this paper, we introduce a generalization of boxplots, called contour boxplots, for visualization and exploration of ensembles of contours or level sets of functions. Conventional boxplots have been widely used as an exploratory or communicative tool for data analysis, and they typically show the median, mean, confidence intervals, and outliers of a population. The proposed contour boxplots are a generalization of functional boxplots, which build on the notion of data depth. Data depth approximates the extent to which a particular sample is centrally located within its density function. This produces a center-outward ordering that gives rise to the statistical quantities that are essential to boxplots. Here we present a generalization of functional data depth to contours and demonstrate methods for displaying the resulting boxplots for two-dimensional simulation data in weather forecasting and computational fluid dynamics.


title: A 2.2/2.7fJ/conversion-step 10/12b 40kS/s SAR ADC with Data-Driven Noise Reduction

abstract: Low-power sensor applications e.g. for environmental monitoring, bio-potential recording, and wireless autonomous sensor networks require highly power-efficient ADCs, typically with resolutions of at least 10b. SAR ADCs are generally beneficial in terms of power efficiency. However, the most power-efficient designs currently lack the required accuracy for these applications [1, 2], as they are limited to 9b ENOB. Other designs that have sufficient accuracy (10b) are limited to power efficiencies above 10fJ/conv-step [3]. The aim of this work is to increase the accuracy of highly efficient SAR ADCs beyond 10b, while further improving the efficiency to 2.2fJ/conv-step. To do so, this work introduces a Data-Driven Noise-Reduction method to efficiently suppress comparator noise, applies a segmented capacitive DAC with 250aF unit elements for better efficiency and accuracy, and implements a self-oscillating comparator to locally generate the internally required oversampled clock.


title: Creating a universal SNP and small indel variant caller with deep neural networks

abstract: Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome1 by calling genetic variants present in an individual using billions of short, errorful sequence reads2. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome3,4. Here we show that a deep convolutional neural network5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.


title: Multilingual use of twitter: social networks and language choice

abstract: Understanding the language ecology within an online social network could inform the design of socio-technical systems that effectively enable information diffusion and collective action across language borders. This dissertation work proposes to explore the language choices of bilingual and multilingual users in the microblogging site Twitter in relation to their social network and types of information shared. At the same time, it aims at identifying the characteristics that could enable cross-language information flows. The methodology will follow a recently proposed paradigm of Web content analysis that includes social network analysis.


title: Automated creation of a forms-based database query interface

abstract: Forms-based query interfaces are widely used to access databases today. The design of a forms-based interface is often a key step in the deployment of a database. Each form in such an interface is capable of expressing only a very limited range of queries. Ideally, the set of forms as a whole must be able to express all possible queries that any user may have. Creating an interface that approaches this ideal is surprisingly hard. In this paper, we seek to maximize the ability of a forms-based interface to support queries a user may ask, while bounding both the number of forms and the complexity of any one form. Given a database schema and content we present an automated technique to generate a good set of forms that meet the above desiderata. While a careful analysis of real or expected query workloads are useful in designing the interface, these query sets are often unavailable or hard to obtain prior to the database even being deployed. Hence generating a good set of forms just using the database itself is a challenging yet important problem. Our experimental analysis shows that our techniques can create a reasonable set of forms, one that can express 60--90% of user queries, without any input from the database administrator. Human experts, without support from software such as ours, are often unable to support as high a fraction of user queries.


title: Distributed active transformer-a new power-combining and impedance-transformation technique

abstract: In this paper, we compare the performance of the newly introduced distributed active transformer (DAT) structure to that of conventional on-chip impedance-transformations methods. Their fundamental power-efficiency limitations in the design of high-power fully integrated amplifiers in standard silicon process technologies are analyzed. The DAT is demonstrated to be an efficient impedance-transformation and power-combining method, which combines several low-voltage push-pull amplifiers in series by magnetic coupling. To demonstrate the validity of the new concept, a 2.4-GHz 1.9-W 2-V fully integrated power-amplifier achieving a power-added efficiency of 41% with 50-/spl Omega/ input and output matching has been fabricated using 0.35-/spl mu/m CMOS transistors.


title: Temperature-Robust LC3 Passive LED Drivers With Low THD, High Efficiency and PF, and Long Life

abstract: New passive LED drivers that can reduce the total harmonic distortion (THD) significantly by LC parallel resonance are proposed. Using an inductor and three capacitors, called LC3, novel characteristics, such as high power efficiency and power factor (PF) with extremely long life time are achieved. The proposed LED drivers have a temperature-robust characteristic, because their power is hardly changed by temperature. By selecting the number of LEDs in series $n_{s}$ appropriately, the LED power variation caused by temperature change in LED can be zero. For the universal use of the proposed LED drivers in various countries with different frequencies, circuit configurations applicable to 50/60 Hz are proposed. To analyze the LED power and PF of the proposed LED driver, the phasor transformation was, for the first time, applied to nonlinear diode rectifier modeling. Although the proposed LED driver is a nonlinear switching circuit, the proposed analyses matched well with simulation and experimental results. A prototype LED driver showed a very high power efficiency of 95.2% at 70 W, meeting PF and THD regulations for source voltage variation of ±6% of 220 V, even when a reasonably small number of filters were used.


Data domain description using support vectors


title: Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

abstract: Prognostics focuses on predicting the future performance of a system, specifically the time at which the system no long performs its desired functionality, its time to failure. As an important aspect of prognostics, remaining useful life (RUL) prediction estimates the remaining usable life of a system, which is essential for maintenance decision making and contingency mitigation. A significant amount of research has been reported in the literature to develop prognostics models that are able to predict a system's RUL. These models can be broadly categorized into experience-based models, date-driven models, and physics-based models. However, due to system complexity, data availability, and application constraints, there is no universally accepted best model to estimate RUL. The review part of this paper specifically focused on the development of hybrid prognostics approaches, attempting to leverage the advantages of combining the prognostics models in the aforementioned different categories for RUL prediction. The hybrid approaches reported in the literature were systematically classified by the combination and interfaces of various types of prognostics models. In the case study part, a hybrid prognostics method was proposed and applied to a battery degradation case to show the potential benefit of the hybrid prognostics approach.


title: Towards Modelling Variation in Music as Foundation for Similarity

abstract: This paper investigates the concept of variation in music from the perspective of music similarity. Music similarity is a central concept in Music Information Retrieval (MIR), however there exists no comprehensive approach to music similarity yet. As a consequence, MIR faces the challenge on how to relate musical features to the experience of similarity by listeners. Musicologists and studies in music cognition have argued that variation in music leads to the experience of similarity. In this paper we review the concept of variation from three different research strands: MIR, Musicology, and cognitive Science. We show that all of these disciplines have contributed insights to the study of variation that are important for modelling variation as a foundation for similarity. We introduce research steps that need to be taken to model variation as a base for music similarity estimation within a computational approach.


title: 32nm 1-D regular pitch SRAM bitcell design for interference-assisted lithography

abstract: As optical lithography advances into the 45nm technology node and beyond, new manufacturing-aware design requirements have emerged. We address layout design for interference-assisted lithography (IAL), a double exposure method that combines maskless interference lithography (IL) and projection lithography (PL); cf. hybrid optical maskless lithography (HOMA) in [2] and [3]. Since IL can generate dense but regular pitch patterns, a key challenge to deployment of IAL is the conversion of existing designs to regular-linewidth, regular-pitch layouts. In this paper, we propose new 1-D regular pitch SRAM bitcell layouts which are amenable to IAL. We evaluate the feasibility of our bitcell designs via lithography simulations and circuit simulations, and confirm that the proposed bitcells can be successfully printed by IAL and that their electrical characteristics are comparable to those of existing bitcells.


title: A Framework for an Adaptive Intrusion Detection System using Bayesian Network

abstract: The goal of a network-based intrusion detection system (IDS) is to identify malicious behavior that targets a network and its resources. Intrusion detection parameters are numerous and in many cases they present uncertain and imprecise causal relationships which can affect attack types. A Bayesian Network (BN) is known as graphical modeling tool used to model decision problems containing uncertainty. In this paper, a BN is used to build automatic intrusion detection system based on signature recognition. The goal is to recognize signatures of known attacks, match the observed behavior with those known signatures, and signal intrusion when there is a match. A major difficulty of this system is that intrusions signatures change over the time and the system must be retrained. An IDS must be able to adapt to these changes. The goal of this paper is to provide a framework for an adaptive intrusion detection system that uses Bayesian network.


title: A comprehensive introduction to label noise

abstract: In classification, it is often difficult or expensive to obtain completely accurate and reliable labels. Indeed, labels may be polluted by label noise, due to e.g. insufficient information, expert mistakes, and encoding errors. The problem is that errors in training labels that are not properly handled may deteriorate the accuracy of subsequent predictions, among other effects. Many works have been devoted to label noise and this paper provides a concise and comprehensive introduction to this research topic. In particular, it reviews the types of label noise, their consequences and a number of state of the art approaches to deal with label noise.


Structural Models of Corporate Bond Pricing: An Empirical Analysis


title: Load Disaggregation Based on Aided Linear Integer Programming

abstract: Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.


title: Fast Iterative Kernel PCA

abstract: We introduce two methods to improve convergence of the Kernel Hebbian Algorithm (KHA) for iterative kernel PCA. KHA has a scalar gain parameter which is either held constant or decreased as 1/t, leading to slow convergence. Our KHA/et algorithm accelerates KHA by incorporating the reciprocal of the current estimated eigenvalues as a gain vector. We then derive and apply Stochastic Meta-Descent (SMD) to KHA/et; this further speeds convergence by performing gain adaptation in RKHS. Experimental results for kernel PCA and spectral clustering of USPS digits as well as motion capture and image de-noising problems confirm that our methods converge substantially faster than conventional KHA.


title: Publishing Graph Degree Distribution with Node Differential Privacy

abstract: Graph data publishing under node-differential privacy (node-DP) is challenging due to the huge sensitivity of queries. However, since a node in graph data oftentimes represents a person, node-DP is necessary to achieve personal data protection. In this paper, we investigate the problem of publishing the degree distribution of a graph under node-DP by exploring the projection approach to reduce the sensitivity. We propose two approaches based on aggregation and cumulative histogram to publish the degree distribution. The experiments demonstrate that our approaches greatly reduce the error of approximating the true degree distribution and have significant improvement over existing works. We also present the introspective analysis for understanding the factors of publishing the degree distribution with node-DP.


title: An Architecture for Fast and Accurate Control of Shape Memory Alloy Actuators

abstract: This paper presents a new control architecture for fast, accurate force control of antagonistic pairs of shape memory alloy wires. The main components are: a differential-mode controller which controls the output force, an anti-slack mechanism, a rapid-heating mechanism and an anti-overload mechanism. The closed-loop response is fast and accurate, even in the presence of large external motion disturbances. There is no sign of limit cycles, and the performance is unaffected by large load inertias. This paper also presents an architecture for position control, in which a position feedback loop is added to the force control architecture. Experimental results show force control accuracies as high as 1 mN in a 13 N range, force output rates as high as 50 N s21, and highly accurate position control with steady-state errors below the resolution of the position encoder.


title: Multiobjective cuckoo search for design optimization

abstract: Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.


title: A novel matrix based isolated three phase AC-DC converter with reduced switching losses

abstract: This paper presents a matrix based isolated three phase AC-DC converter suitable for aircraft application. A three -phase to single-phase (3×1) matrix topology with novel modulation scheme named as Switched Rectifier Inverter (SRI) is introduced for direct line frequency AC to high frequency AC conversion which in effect, eliminates the requirement of bulky DC link capacitor and therefore, provides high power density essential for the aircraft systems. The high frequency AC output of the matrix (3×1) converter is processed by high frequency transformer followed by rectifier to obtain isolated DC output voltage. The operation of the proposed converter with SRI modulation scheme is explained. Moreover, the input power quality under the proposed modulation scheme is discussed for wide load range. Comprehensive simulation is carried out in PSIM for an example converter of input 120 V (rms), 400 Hz AC to 400 V DC with 8 kW power.


title: Mereotopology: a theory of parts and boundaries

abstract: Abstract The paper is a contribution to formal ontology. It seeks to use topological means in order to derive ontological laws pertaining to the boundaries and interiors of wholes, to relations of contact and connectedness, to the concepts of surface, point, neighbourhood, and so on. The basis of the theory is mereology, the formal theory of part and whole, a theory which is shown to have a number of advantages, for ontological purposes, over standard treatments of topology in set-theoretic terms. One central goal of the paper is to provide a rigorous formulation of Brentano's thesis to the effect that a boundary can exist as a matter of necessity only as part of a whole of higher dimension of which it is the boundary. It concludes with a brief survey of current applications of mereotopology in areas such as natural-language analysis, geographic information systems, machine vision, naive physics, and database and knowledge engineering.


title: Detecting anomalous behavior of PLC using semi-supervised machine learning

abstract: Industrial Control System (ICS) is used to monitor and control critical infrastructures. Programmable logic controllers (PLCs) are major components of ICS, which are used to form automation system. It is important to protect PLCs from any attacks and undesired incidents. However, it is not easy to apply traditional tools and techniques to PLCs for security protection and forensics because of its unique architectures. Semi-supervised machine learning algorithm, One-class Support Vector Machine (OCSVM), has been applied successfully to many anomaly detection problems. This paper proposes a novel methodology to detect anomalous events of PLC by using OCSVM. The methodology was applied to a simulated traffic light control system to illustrate its effectiveness and accuracy. Our results show that high accuracy of identification of anomalous PLC operations is obtained which can help investigators to perform PLC forensics efficiently and effectively.


title: Deep Feature Learning for Graphs

abstract: This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that generalize across-networks and therefore useful for graph-based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable features, and is space-efficient (by learning sparse feature vectors). In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of $\mathcal{O}(|E|)$, and scalable for large networks via an efficient parallel implementation. Compared with the state-of-the-art method, DeepGL is (1) effective for across-network transfer learning tasks and attributed graph representation learning, (2) space-efficient requiring up to 6x less memory, (3) fast with up to 182x speedup in runtime performance, and (4) accurate with an average improvement of 20% or more on many learning tasks.


title: An introduction to network address shuffling

abstract: Moving Target Defense (MTD) has been proposed as a new revolutionary technology to alter the asymmetric situation between attacks and defenses. Network address shuffling is an important branch of the MTD technology. However, there is no systematic introduction to network address shuffling. In this paper, we present a brief introduction to the research achievements of network address shuffling according to two shuffling patterns which are identified and defined by us. We then summarize and analyze the supporting techniques and related features for each network address shuffling technique mentioned in this paper. What's more, the key issues to implement an effective network address shuffling mechanism are discussed, with the expectation of invigorating subsequent research.


title: Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study

abstract: By means of data mining techniques, we can exploit furtive and precious information through medicine data bases. Because of huge amount of this information, study and analyses are too difficult. We want some methods to exploring through data and extract valuable information which can be used in the future similar cases. One of these cases is accouchement. The mechanism of accouchement is a natural and spontaneous process without the need to any intervention. In some conditions, maybe mother, baby or both of them are in hazard and need help and support. This help is provided by Caesarian Section which saves mother and baby. Nevertheless, we need to know when we should use surgery. This study explains utilization of medical data mining in determination of medical operation methods. We render this with accumulating 80 pregnant women information. The results show that decision tree algorithm designed for this case study generates correct prediction for more than 86.25% tests cases


title: N–H Stretching Modes of Adenosine Monomer in Solution Studied by Ultrafast Nonlinear Infrared Spectroscopy and Ab Initio Calculations

abstract: The N–H stretching vibrations of adenine, one of the building blocks of DNA, are studied by combining infrared absorption and nonlinear two-dimensional infrared spectroscopy with ab initio calculations. We determine diagonal and off-diagonal anharmonicities of N–H stretching vibrations in chemically modified adenosine monomer dissolved in chloroform. For the single-quantum excitation manifold, the normal mode picture with symmetric and asymmetric NH2 stretching vibrations is fully appropriate. For the two-quantum excitation manifold, however, the interplay between intermode coupling and frequency shifts due to a large diagonal anharmonicity leads to a situation where strong mixing does not occur. We compare our findings with previously reported values obtained on overtone spectroscopy of coupled hydrogen stretching oscillators.


title: RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation

abstract: In multi-class indoor semantic segmentation using RGB-D data, it has been shown that incorporating depth feature into RGB feature is helpful to improve segmentation accuracy. However, previous studies have not fully exploited the potentials of multi-modal feature fusion, e.g., simply concatenating RGB and depth features or averaging RGB and depth score maps. To learn the optimal fusion of multimodal features, this paper presents a novel network that extends the core idea of residual learning to RGB-D semantic segmentation. Our network effectively captures multilevel RGB-D CNN features by including multi-modal feature fusion blocks and multi-level feature refinement blocks. Feature fusion blocks learn residual RGB and depth features and their combinations to fully exploit the complementary characteristics of RGB and depth data. Feature refinement blocks learn the combination of fused features from multiple levels to enable high-resolution prediction. Our network can efficiently train discriminative multi-level features from each modality end-to-end by taking full advantage of skip-connections. Our comprehensive experiments demonstrate that the proposed architecture achieves the state-of-the-art accuracy on two challenging RGB-D indoor datasets, NYUDv2 and SUN RGB-D.


Bad News Bearers: The Negative Tilt of the Financial Press


title: Allais, Ellsberg, and Preferences for

abstract: Two of the most well-known regularities observed in preferences under risk and uncertainty are ambiguity aversion and the Allais paradox. We study the behavior of an agent who can display both tendencies simultaneously. We introduce a novel notion of preference for hedging that applies to both objective lotteries and uncertain acts. We show that this axiom, together with other standard ones, is equivalent to a representation in which the agent 1) evaluates ambiguity using multiple priors, as in the model of Gilboa and Schmeidler [1989] and 2) evaluates objective lotteries by distorting probabilities, as in the Rank Dependent Utility model, but using the worst from a set of distortions. We show that a preference for hedging is not sufficient to guarantee Ellsberg-like behavior if the agent violates Expected Utility for objective lotteries; we provide a novel axiom that characterizes this case, linking the distortions for objective and subjective bets


title: Cognitive Task Analysis

abstract: This chapter presents an overview of the current state of cognitive task analysis (CTA) in research and practice. CTA uses a variety of interview and observation strategies to capture a description of the explicit and implicit knowledge that experts use to perform complex tasks. The captured knowledge is most often transferred to training or the development of expert systems. The first section presents descriptions of a variety of CTA techniques, their common characteristics, and the typical strategies used to elicit knowledge from experts and other sources. The second section describes research on the impact of CTA and synthesizes a number of studies and reviews pertinent to issues underlying knowledge elicitation. In the third section, we discuss the integration of CTA with training design. Finally, in the fourth section, we present a number of recommendations for future research and conclude with general comments.


abstract: An ultra low voltage rail-to-rail DTMOS voltage follower is presented. The circuit is developed based on a complementary source follower with a common-source output stage. The circuit is designed using a 0.13 μm CMOS technology and SPICE is used to verify the circuit performance. The voltage follower can drive ± 0.25 V to the 500 Ω with the total harmonic distortion (THD) of 0.4% at the operating frequency of 1 MHz. The bandwidth and power dissipation are 288 MHz and 103 μW, respectively.


title: A first-class approach to genericity

abstract: This paper describes how to add first-class generic types---including mixins---to strongly-typed OO languages with nominal subtyping such as Java and C#. A generic type system is "first-class" if generic types can appear in any context where conventional types can appear. In this context, a mixin is simply a generic class that extends one of its type parameters, e.g., a class C<T> that extends T. Although mixins of this form are widely used in Cpp (via templates), they are clumsy and error-prone because Cpp treats mixins as macros, forcing each mixin instantiation to be separately compiled and type-checked. The abstraction embodied in a mixin is never separately analyzed.Our formulation of mixins using first-class genericity accommodates sound local (class-by-class) type checking. A mixin can be fully type-checked given symbol tables for each of the classes that it directly references---the same context in which Java performs incremental class compilation. To our knowledge, no previous formal analysis of first-class genericity in languages with nominal type systems has been conducted, which is surprising because nominal subtyping has become predominant in mainstream object-oriented programming languages.What makes our treatment of first-class genericity particularly interesting and important is the fact that it can be added to the existing Java language without any change to the underlying Java Virtual Machine. Moreover, the extension is backward compatible with legacy Java source and class files. Although our discussion of a practical implementation strategy focuses on Java, the same implementation techniques could be applied to other object-oriented languages such as C# or Eiffel that support incremental compilation, dynamic class loading, and nominal subtyping.


title: Describing the Internet of Things with an ontology: The SusCity project case study

abstract: The Internet of Things comprises a network of physical objects, like sensors and actuators, collecting and exchanging data. Given the importance of the information exchanged in these environments, the communication infrastructure becomes a critical point that needs to be managed optimally, while providing high-performance levels to end users. To guarantee the correct interaction between the different procedures intended for the optimization of the communication infrastructure, a standard and flexible representation of the data related to the network is necessary. Many of the services could be monitored through the web, thus using standard Web languages with a rich expressive power, such as the languages used in the Semantic Web, would allow for the reification of interoperable descriptions. This paper presents an ontology for the Internet of Things infrastructure tailored to the needs of Smart Cities. Furthermore, different kinds of evaluations were performed to corroborate the correctness of this ontology, including potential infrastructure optimization objectives like low latency and high resilience.


title: (Guest Editors) Modeling Visual Attention in VR: Measuring the Accuracy of Predicted Scanpaths

abstract: Dynamic human vision is an important contributing factor to the design of perceptually-based Virtual Reality. A common strategy relies on either an implicit assumption or explicit measurement of gaze direction. Given the spatial location of foveal vision, computational resources are directed at enhancing the foveated region in realtime. To obtain an explicit gaze measurement, an eye tracker may be used. In the absence of an eye tracker, a computational model of visual attention may be substituted to predict visually salient features. The fidelity of the resultant real-time system hinges on the agreement between predicted and actual regions foveated by the human. The contributions of this paper are the development and evaluation of a novel method for the comparison of human and artificial scanpaths recorded in VR. The novelty of the present approach is the application of previous accuracy measures to scanpath comparison in VR where analysis is complicated by head movements and dynamic imagery. An attentional model previously used for view-dependent enhancement of Virtual Reality is evaluated. Analysis shows that the correlation between human and artificial scanpaths is much lower than expected. Recommendations are made for improvements to the model to foster closer correspondence to human attentional patterns in VR.


title: Fuzzy optimization for supply chain planning under supply, demand and process uncertainties

abstract: In today's global marketplace, individual firms do not compete as independent entities rather as an integral part of a supply chain. This paper proposes a fuzzy mathematical programming model for supply chain planning which considers supply, demand and process uncertainties. The model has been formulated as a fuzzy mixed-integer linear programming model where data are ill-known and modelled by triangular fuzzy numbers. The fuzzy model provides the decision maker with alternative decision plans for different degrees of satisfaction. This proposal is tested by using data from a real automobile supply chain.


title: On Statistical Tests for Randomness Included in the NIST SP800-22 Test Suite and Based on the Binomial Distribution

abstract: In this paper we review some statistical tests included in the NIST SP 800-22 suite, which is a collection of tests for the evaluation of both true-random (physical) and pseudorandom (algorithmic) number generators for cryptographic applications. The output of these tests is the so-called p-value which is a random variable whose distribution converges to the uniform distribution in the interval [0,1] when testing an increasing number of samples from an ideal generator. Here, we compute the exact non-asymptotic distribution of p-values produced by few of the tests in the suite, and propose some computation-friendly approximations. This allows us to explain why intensive testing produces false-positives with a probability much higher than the expected one when considering asymptotic distribution instead of the true one. We also propose a new approximation for the Spectral Test reference distribution, which is more coherent with experimental results.


title: Practitioners' Perspectives on Security in Agile Development

abstract: Agile methods are widely employed to develop high-quality software, but theoretical analyses argue that agile methods are inadequate for security-critical projects. However, most agile-developed software today needs to satisfy baseline security requirements, so that we need to focus on how to achieve this this level for typical agile projects. In this paper, we provide insights from the practitioner's perspective on security in agile development and report on exploratory, qualitative findings from interviews. Our findings extend the theoretical prior work and suggest to focus on adequate customer involvement, developer security awareness and expertise, and continuously improving the development process for security.


title: What do healthcare workers in elderly care know about occupational health and safety? An explorative survey

abstract: Background ::: Demographic changes will lead to a growing demand for healthy, motivated healthcare workers (HCW) in the years ahead. Along with well-targeted prevention, knowledge of occupational health and safety and infection precaution is essential for a healthy working life. In this context back-friendly working methods and protection from infectious diseases are necessary in elderly care.


title: Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization

abstract: Conversion prediction and click prediction are two important and intertwined problems in display advertising, but existing approaches usually look at them in isolation. In this paper, we aim to predict the conversion response of users by jointly examining the past purchase behavior and the click response behavior. Additionally, we model the temporal dynamics between the click response and purchase activity into a unified framework. In particular, a novel matrix factorization approach named dynamic collective matrix factorization (DCMF) is proposed to address this problem. Our model considers temporal dynamics of post-click conversions and also takes advantages of the side information of users, advertisements, and items. Experiments on a real-world marketing dataset show that our model achieves significant improvements over several baselines.


Formal semantics and automated analysis of BPMN process models


title: Customer satisfaction in virtual environments: A study of online investing

abstract: Many firms are moving to make virtual interfaces their primary, or even sole, points of customer contact. In this environment, some traditional service quality dimensions that determine customer satisfaction, such as the physical appearance of facilities, employees, and equipment, and employees' responsiveness and empathy are unobservable. In contrast, trust may play a central role here in enhancing customer satisfaction. We model trust as an endogenously formed entity that ultimately impacts customer satisfaction, and we elucidate the linkages between trust and other factors related to the performance of the online service provider and to the service environment. The model is validated using two samples--one comprising 225 online investors of a large online broker, and the other comprising 203 members of the American Association of Individual Investors (AAII). The findings suggest that perceived trustworthiness of an online broker is a significant antecedent to investors' satisfaction, and that perceived environmental security and perceived operational competence impact the formation of trust. The results have important managerial implications.


title: A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System Under Partial Shading Conditions

abstract: This paper presents a maximum power point tracking (MPPT) design for a photovoltaic (PV) system using a grey wolf optimization (GWO) technique. The GWO is a new optimization method which overcomes the limitations such as lower tracking efficiency, steady-state oscillations, and transients as encountered in perturb and observe (P&O) and improved PSO (IPSO) techniques. The problem of tracking the global peak (GP) of a PV array under partial shading conditions (PSCs) is attempted employing the GWO-based MPPT technique. The proposed scheme is studied for a PV array under PSCs which exhibits multiple peaks and its tracking performance is compared with that of two MPPT algorithms, namely P&O-MPPT and IPSO-MPPT. The proposed GWO-MPPT algorithm is implemented on a PV system using MATLAB/SIMULINK. Furthermore, an experimental setup is developed to verify the efficacy of the proposed system. From the obtained simulation and experimental results, it is observed that the proposed MPPT algorithm outperforms both P&O and IPSO MPPTs.


title: New findings and concepts about the G-spot in normal and absent vagina: precautions possibly needed for preservation of the G-spot and sexuality during surgery.

abstract: AIM ::: The aim of this study was to extend the clinicohistological study to involve the whole normal and absent vagina for confirming the presence of the G-spot and its relation to the surrounding organs and sexuality and to identify certain precautions for its preservation during surgery. ::: ::: ::: MATERIAL AND METHODS ::: This study was a descriptive randomized prospective study conducted at Kasr El Aini School of Medicine, Cairo University, Egypt. The G-spot was examined in 1500 women, 500 of them having vaginal and vulval surgery done for gynecological reasons. The G-spot was examined for its clinical and histological features and for determining the effect of surgery on its state and function. ::: ::: ::: RESULTS ::: The G-spot was found to be present in all women. It was a localized spot in 58% and diffuse in 42% of cases. Associated ejaculation was reported in all cases of the localized type and in 24.5% of the diffuse types. Clinical examination was found to be associated with certain local response in 52.7% of the local types. The G-spot was also found to be connected to the hymen in 100%, the urethra in 52.7%, the vulva in 82.2% and the cervix in 10.8% of cases. The mean of the sex scores and sexuality were significantly decreased in surgery involving the G-spot area. Recorded figures were 93.6 ± 3.4 and 88.2 ± 3.3 before and after surgery, respectively. The corresponding figures in the cases having a general spot were 86.4 ± 4.4 and 84.5 ± 2.4, respectively. The G-spot was found in cases of absent vagina to be localized in 59%, generalized in 28.2% and absent in 12.8% of cases. ::: ::: ::: CONCLUSIONS ::: The G-spot is actually present in all women. It is originally related to the lower urinary tract and it is connected to different parts of the genital tract. It may be localized or generalized. Its integrity is essential for obtaining normal physiological sexuality. Surgery may affect the integrity of the G-spot, so surgical precautions must be carried out to maintain the integrity of this spot and the patient's sexuality.


title: Mutation analysis of CLPTM 1 and PVRL 1 genes in patients with non-syndromic clefts of lip, alveolus and palate.

abstract: Summary Introduction Non-syndromic cleft lip with or without cleft palate (CL/P), is one of the most common birth defects, but its aetiology is largely unknown. The aim of this study was to determine the sequence changes of the Cleft Lip and Palate Transmembrane Protein 1 (CLPTM 1) and Poliovirus Receptor Related 1 (PVRL 1) genes in patients with non-syndromic complete clefts of lip, alveolus and palate and to correlate these findings with clinical features. Patients and methods 25 patients were analysed (14 male and 11 female, aged 4–10 years) of European descent (9 patients with right, 9 with left and 7 patients with bilateral CLAP) and 25 controls, respectively. Exons 2–14 of the CLPTM1 and exons 1–6 of the PVRL1 gene were analysed by a direct sequencing method using DNA extracted from whole blood. Results A novel in frame Glu441-Gly442 ins Glu mutation of the PVRL 1 gene in combination with novel exon mutations Gly331Gly, Ala88Ala, Pro309Pro and intron change IVS7-10G/A of the CLPTM 1 gene were found in 9 patients. The Glu441-Gly442 ins Glu mutation and the intron change IVS7-10G/A were not detected in 25 controls. Conclusion These results suggest that a simultaneous occurrence of PVRL1 and CLPTM 1 gene mutations in cleft patients does not correlate with the type of cleft (left, right, bilateral) or the gender of the patients. If a combination of the intron change IVS7-10G/A, exon changes Gly331Gly, Ala88Ala and Pro309Pro of the CLMPT 1 gene and Glu441-Gly442 ins Glu mutation of the PVRL 1 gene could be a genetic factor for non-syndromic clefts of the primary and the secondary palates, it is important to investigate more patients and controls.


title: A Minimax Algorithm Better Than Alpha-beta?: No and Yes

abstract: This paper has three main contributions to our understanding of fixed-depth minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*, finally transforming it into a practical algorithm. In effect, we show that SSS* = alpha-beta + ransposition tables. The crucial step is the realization that transposition tables contain so-called solution trees, structures that are used in best-first search algorithms like SSS*. Having created a practical version, we present performance measurements with tournament game-playing programs for three different minimax games, yielding results that contradict a number of publications. (B) Based on the insights gained in our attempts at understanding SSS*, we present a framework that facilitates the construction of several best-first fixed- depth game-tree search algorithms, known and new. The framework is based on depth-first null-window Alpha-Beta search, enhanced with storage to allow for the refining of previous search results. It focuses attention on the essential differences between algorithms. (C) We present a new instance of the framework, MTD(f). It is well-suited for use with iterative deepening, and performs better than algorithms that are currently used in most state-of-the-art game-playing programs. We provide experimental evidence to explain why MTD(f) performs better than the other fixed-depth minimax algorithms.


title: Technical Section: CGV-An interactive graph visualization system

abstract: Previous work on graph visualization has yielded a wealth of efficient graph analysis algorithms and expressive visual mappings. To support the visual exploration of graph structures, a high degree of interactivity is required as well. We present a fully implemented graph visualization system, called CGV (Coordinated Graph Visualization), whose particular emphasis is on interaction. The system incorporates several interactive views that address different aspects of graph visualization. To support different visualization tasks, view ensembles can be created dynamically with the help of a flexible docking framework. Several novel techniques, including enhanced dynamic filtering, graph lenses, and edge-based navigation are presented. The main graph canvas interactions are augmented with several visual cues, among which the infinite grid and the radar view are novel. CGV provides a history mechanism that allows for undo/redo of interaction. CGV is a general system with potential application in many scenarios. It has been designed as a dual-use system that can run as a stand-alone application or as an applet in a web browser. CGV has been used to evaluate graph clustering results, to navigate topological structures of neuronal systems, and to perform analysis of some time-varying graphs.


title: A Deep Organic Re-reading of Alvar Aalto’s Design Approach

abstract: The conceptual framework of ’organic architecture’ is the most common theory used in analysing Alvar Aalto’s life’s work. Actually, it could not be considered a real theory, but a quite fuzzy concept due to its many miscellaneous interpretations. Aalto himself talked about organic architecture without never explicating properly what it means. In this respect, more research should be done. For example, Aalto’s regional plans deserve to be analysed from this point of view. Perhaps the most favourable case is the Kokemaenjoki valley regional plan, for it offers new keys for interpretation due to its emphasis on textual representation. The other key used in this study is Goethe’s philosophy of science. Many writers have noticed its resemblance to Aalto’s thinking and approach. Unfortunately the argumentation is too often superficial, although more thorough processing seems to evoke new ideas on Aalto’s design approach, as well as on organic architecture on a more general level. Surprisingly these ideas appear quite topical in our digitalizing world.


title: Full-scale investigation of several jet-engine noise-reduction nozzles

abstract: A number of noise-suppression nozzles were tested on full-scale engines. In general, these nozzles achieved noise reduction by the mixing interference of adjacent jets, that is, by using multiple-slot-nozzles. Several of the nozzles achieved reductions in sound power of approximately 5 decibels (nearly 70 percent) with small thrust losses (approx. 1 percent). The maximum sound-pressure level was reduced by as much as 18 decibels in particular frequency bands. Some of the nozzles showed considerable spatial asymmetry; that is, the sound field was not rotationally symmetrical. A method of calculating the limiting frequency effected by such nozzles is presented. Furthermore data are shown that appear to indicate that further reductions in sound power will not be easily achieved from nozzles using mixing interference as a means of noise suppression.


title: A pilot phase-II prospective study to test the 'efficacy' and tolerability of a penile-extender device in the treatment of 'short penis'.

abstract: OBJECTIVE ::: To assess a commonly marketed brand of penile extender, the Andro-Penis(R) (Andromedical, Madrid, Spain), widely used devices which aim to increase penile size, in a phase II single-arm study powered to detect significant changes in penile size, as despite their widespread use, there is little scientific evidence to support their potential clinical utility in the treatment of patients with inadequate penile dimensions. ::: ::: ::: PATIENTS AND METHODS ::: Fifteen patients were required to test the efficacy of the device, assuming an effect size of >0.8. Eligible patients were counselled how to use the penile extender for at least 4 h/day for 6 months. Penile dimensions were measured at baseline and after 1, 3, 6 and 12 months (end of study). The erectile function (EF) domain of the International Index of EF was administered at baseline and at the end of the study. Treatment satisfaction was assessed using an institutional unvalidated five-item questionnaire. ::: ::: ::: RESULTS ::: After 6 months the mean gain in length was significant, meeting the goals of the effect size, at 2.3 and 1.7 cm for the flaccid and stretched penis, respectively. No significant changes in penile girth were detected. The EF domain scores improved significantly at the end of study. Treatment satisfaction scores were consistent with acceptable to good improvement in all items, except for penile girth, where the score was either 'no change' or 'mild improvement'. ::: ::: ::: CONCLUSIONS ::: Penile extenders should be regarded as a minimally invasive and effective treatment option to elongate the penile shaft in patients seeking treatment for a short penis.


title: Application of deep learning and geo-knowledge bases to scene understanding

abstract: Humans can easily perform tasks that use vision and language jointly, such as describing a scene and answering questions about objects in the scene and how they are related. Image captioning and visual question & answer are two popular research tasks that have emerged from advances in deep learning and the availability of datasets that specifically address these problems. However recent work has shown that deep learning based solutions to these tasks are just as brittle as solutions for only vision or only natural language tasks. Image captioning is vulnerable to adversarial perturbations; novel objects, which are not described in training data, and contextual biases in training data can degrade performance in surprising ways. For these reasons, it is important to find ways in which general-purpose knowledge can guide connectionist models. We investigate challenges to integrate existing ontologies and knowledge bases with deep learning solutions, and possible approaches for overcoming such challenges. We focus on geo-referenced data such as geo-tagged images and videos that capture outdoor scenery. Geo-knowledge bases are domain specific knowledge bases that contain concepts and relations that describe geographic objects. This work proposes to increase the robustness of automatic scene description and inference by leveraging geo-knowledge bases along with the strengths of deep learning for visual object detection and classification.


title: Cellular and WiFi Co-design for 5G User Equipment

abstract: Motivated by providing solutions to design challenges of coexisting cellular and WiFi for future 5G application scenarios, this paper, first, conducts an in-depth investigation of current technological trends of 5G from user equipment (UE) design perspective, and then presents a cost-effective cellular-WiFi design methodology based on the new distributed phased array MIMO (DPA-MIMO) architecture for practical 5G UE devices as an example. Furthermore, additional 5G cellular-WiFi application scenarios and co-operation details within 5G heterogeneous networks are unveiled on top of the said cellular-WiFi co-enabled 5G UE design.


title: Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion

abstract: This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. During adaptation, we perform LM fusion transfer, where an external LM is integrated into the decoder network of the attention-based S2S model in the whole adaptation stage, to effectively incorporate linguistic context of the target language. We also investigate various seed models for transfer learning. Experimental evaluations using the IARPA BABEL data set show that LM fusion transfer improves performances on all target five languages compared with simple transfer learning when the external text data is available. Our final system drastically reduces the performance gap from the hybrid systems.


title: Cross and Learn: Cross-Modal Self-Supervision

abstract: In this paper we present a self-supervised method for representation learning utilizing two different modalities. Based on the observation that cross-modal information has a high semantic meaning we propose a method to effectively exploit this signal. For our approach we utilize video data since it is available on a large scale and provides easily accessible modalities given by RGB and optical flow. We demonstrate state-of-the-art performance on highly contested action recognition datasets in the context of self-supervised learning. We show that our feature representation also transfers to other tasks and conduct extensive ablation studies to validate our core contributions.


title: Spoofing and Detection The paper discusses numerous defense strategies ranging from special signal processing within a traditional GNSS receiver to employing advanced encryption-based techniques on GNSS measurements

abstract: Global navigation satellite signals can be spoofed by false signals, but special receivers can provide defenses against such attacks. The development of good spoofing defenses requires an understanding of the possible attack modes of a spoofer and the properties of those modes that can be exploited for defense purposes. Sets of attack methods and defense methods are described in detail. An attack/defense matrix is developed that documents which defense techniques are effective against the various attack techniques. Recommendations are generated to improve the offerings of commercial off-the-shelf receivers from the current situation, a complete lack of spoofing defenses, to a situation in which various levels of defense are present, some that add significant security for relatively little additional cost and others that add more security at costs that start to become appreciable.


title: Video Captioning via Hierarchical Reinforcement Learning

abstract: Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.


CREB1-driven expression of miR-320a promotes mitophagy by down-regulating VDAC1 expression during serum starvation in cervical cancer cells


R OBUSTNESS OF CLASSIFIERS TO UNIVERSAL PERTURBATIONS : A GEOMETRIC PERSPECTIVE


title: Toward Continual Learning for Conversational Agents

abstract: While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they typically require an enormous amount of data. Previous studies adopted a hybrid approach with knowledge-based components to abstract out domain-specific things or to augment data to cover more diverse patterns. On the contrary, we propose to directly address the problem using the recent development in the space of continual learning for neural models. Specifically, we adopt a domain-independent neural conversational model and introduce a novel neural continual learning algorithm that allows the conversational agent to accumulate skills across different tasks in a data-efficient way. To the best of our knowledge, this is the first work that applies continual learning for conversation systems. We verified the efficacy of our method through a conversational skill transfer from synthetic dialogs or human-human dialogs to human-computer conversations in a customer support domain.


title: Modeling and Control of a Articulated Robot Arm with Embedded Joint Actuators

abstract: Nowadays human-cooperative robot (HCR) become to be used in the industry and cost-effective manufacturing of it is required. The UR5 robot uses only three type of common-structured rotating joint actuators, which differs from size and power. So this robot become famous cost-effective HCR. The embedded rotating joint actuator for UR5-type HCR is known as a "smart actuator", because only connection of them with links becomes a 6-DOF robot manipulator. Recent development of networked motor drive technology with EtherCAT communication makes this connection and operation of robot manipulator easy. The analysis about forward and inverse kinematics for UR5 is found in many literatures. With help of these materials, the dynamic simulation about a smart actuator for UR5 robot is done in this paper. The co-simulation of motor drive and manipulator dynamics is done with Matlab / Simulink and Simscape Multibody. The results shows that the developed smart actuator is good to substitute the joint actuator of UR5 robot.


title: Planar Filtering Ultra-Wideband (UWB) Antenna With Shorting Pins

abstract: A class of UWB filter-antenna is proposed. The proposed filter-antenna is compact, high inband selectivity and insensitive to ground plane size. A via-fed monopole antenna is proposed with tapered shape between the radiator and ground plane. This tapered shape supports two resonant modes to provide wideband matching. Four shorting pins are added to this antenna to improve the selectivity of the filter-antenna performance. Furthermore, a shunt LC-resonator is used to generate the notched-band in the UWB band without extra circuit size. To verify the predicted results, two prototype filter-antennas with and without notched-band are fabricated and measured on a low-cost two-side printed circuit board. The measured results exhibit a good selectivity, the measured return loss is below - 12.5 dB within the desired passband and the size is compact with overall dimension of 28.5×28×0.8 mm3.


title: Graph Neural Networks with Generated Parameters for Relation Extraction

abstract: Recently, progress has been made towards improving relational reasoning in machine learning field. Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning. In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction. In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs. We verify GP-GNNs in relation extraction from text. Experimental results on a human-annotated dataset and two distantly supervised datasets show that our model achieves significant improvements compared to baselines. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.