title: System Design and Control of Anthropomorphic Walking Robot LOLA

abstract: This paper presents the 25-DOF full-size humanoid robot LOLA . Our goal is to realize fast and human-like walking. Furthermore, we want to increase the robot's autonomous, vision-guided walking capabilities. LOLA is characterized by a redundant kinematic configuration, an extremely lightweight design, joint actuators with brushless motors and an electronics architecture using decentralized joint control. Special emphasis was put on an improved mass distribution to achieve good dynamic performance. Center of mass trajectories are calculated in real-time from footstep locations using a spline collocation method. Reference trajectories are modified by a stabilizing control system based on hybrid force/position control with an inner joint position control loop.


title: Cooperative Inverse Reinforcement Learning

abstract: For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partial-information game with two agents, human and robot; both are rewarded according to the human's reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.


title: Unruptured Cerebral Aneurysms Do Not Shrink When They Rupture: Multicenter Collaborative Aneurysm Study Group

abstract: BACKGROUND: The International Study of Intracranial Aneurysms found that for patients with no previous history of subarachnoid hemorrhage, small (< 7 mm) anterior circulation and posterior circulation aneurysms had a 0% and 2.5% risk of subarachnoid hemorrhage over 5 years, respectively. OBJECTIVE: To determine whether cerebral aneurysms shrink with rupture. METHODS: The clinical databases of 7 sites were screened for patients with imaging of cerebral aneurysms before and after rupture. Inclusion criteria included documented subarachnoid hemorrhage by imaging or lumbar puncture and intracranial imaging before and after cerebral aneurysm rupture. The patients were evaluated for aneurysm maximal height, maximal width, neck diameter, and other measurement parameters. Only a change of ≥ 2 mm was considered a true change. RESULTS: Data on 13 patients who met inclusion criteria were collected. The median age was 60, and 11 of the 13 patients (84.6%) were female. Only 5 patients had posterior circulation aneurysms. None of the aneurysms had a significant decrease in size. One aneurysm decreased by 1.8 mm in maximum size after rupture (7.7%). Six aneurysms had an increase in maximum size of at least 2 mm after rupture (46.2%) with a mean increase of 3.5 mm (± 0.5 mm). CONCLUSION: Unruptured aneurysms do not shrink when they rupture. The large percentage of ruptured small aneurysms in previous studies were likely small before they ruptured.


title: A broadband dual-polarization base station antenna element with a coupling feed

abstract: This paper presents a broadband, dual-polarized base station antenna element by reasonably designing two orthogonal symmetrical dipole, four loading cylinders, balun, feed patches, specific shape reflector and plastic fasteners. Coupling feed is adopted to avoid the direct connection between the feed cables and the dipoles. The antenna element matches well in the frequency range of 1.7–2.7 GHz and the return loss (RL) S11 25dB) and stable half-power beam width (HPBW) with 65±5° are also achieved. The proposed antenna element covers the whole long term evolution (LTE) band and is backward compatible with 3G and 2G bands.


title: Strategies and Principles of Distributed Machine Learning on Big Data

abstract: The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics thereupon. In order to run ML algorithms at such scales, on a distributed cluster with 10s to 1000s of machines, it is often the case that significant engineering efforts are required --- and one might fairly ask if such engineering truly falls within the domain of ML research or not. Taking the view that Big ML systems can benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of Big ML systems and architectures, with the goal of understanding how to make them efficient, generally-applicable, and supported with convergence and scaling guarantees. They concern four key questions which traditionally receive little attention in ML research: How to distribute an ML program over a cluster? How to bridge ML computation with inter-machine communication? How to perform such communication? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and grow the area that lies between ML and systems.


title: Perceived racism and mental health among Black American adults: A meta-analytic review.

abstract: The literature indicates that perceived racism tends to be associated with adverse psychological and physiological outcomes; however, findings in this area are not yet conclusive. In this meta-analysis, we systematically reviewed 66 studies (total sample size of 18,140 across studies), published between January 1996 and April 2011, on the associations between racism and mental health among Black Americans. Using a random-effects model, we found a positive association between perceived racism and psychological distress (r .20). We found a moderation effect for psychological outcomes, with anxiety, depression, and other psychiatric symptoms having a significantly stronger association than quality of life indicators. We did not detect moderation effects for type of racism scale, measurement precision, sample type, or type of publication. Implications for research and practice are discussed.


title: Haptic interaction in robot-assisted endoscopic surgery: a sensorized end-effector

abstract: Conventional endoscopic surgery has some drawbacks that can be addressed by using robots. The robotic systems used for surgery are still in their infancy. A major deficiency is the lack of haptic feedback to the surgeon. In this paper, the benefits of haptic feedback in robot-assisted surgery are discussed. A novel robotic end-effector is then described that meets the requirements of endoscopic surgery and is sensorized for force/ torque feedback. The endoscopic end-effector is capable of non-invasively measuring its interaction with tissue in all the degrees of freedom available during endoscopic manipulation. It is also capable of remotely actuating a tip and measuring its interaction with the environment without using any sensors on the jaws. The sensorized end-effector can be used as the last arm of a surgical robot to incorporate haptic feedback and/or to evaluate skills and learning curves of residents and surgeons in endoscopic surgery.


title: Comparative Study of Data Mining Tools

abstract: Today the rapid development of information technology and adoption of its several applications has created the revolution in business and various fields significantly. The growing interest in business using electronics and technology has brought vital improvement in data mining field also, since it's an important part of data accessibility. Data mining and it's applications can be viewed as one of the emerging and promising technological developments that provide efficient means to access various types of data and information available worldwide. Not only this, these applications also aids in decision making. A better understanding of these applications helps in aking choice among all available application and tools. The paper gives the comprehensive and theoretical analysis of six open source data mining tools. The study describes the technical specification, features, and specialization for each selected tool along with its applications. By employing the study the choice and selection of tools can be made easy.


title: The Neural Substrate of Human Empathy: Effects of Perspective-taking and Cognitive Appraisal

abstract: Whether observation of distress in others leads to empathic concern and altruistic motivation, or to personal distress and egoistic motivation, seems to depend upon the capacity for self-other differentiation and cognitive appraisal. In this experiment, behavioral measures and event-related functional magnetic resonance imaging were used to investigate the effects of perspective-taking and cognitive appraisal while participants observed the facial expression of pain resulting from medical treatment. Video clips showing the faces of patients were presented either with the instruction to imagine the feelings of the patient (“imagine other”) or to imagine oneself to be in the patient's situation (“imagine self”). Cognitive appraisal was manipulated by providing information that the medical treatment had or had not been successful. Behavioral measures demonstrated that perspective-taking and treatment effectiveness instructions affected participants' affective responses to the observed pain. Hemodynamic changes were detected in the insular cortices, anterior medial cingulate cortex (aMCC), amygdala, and in visual areas including the fusiform gyrus. Graded responses related to the perspective-taking instructions were observed in middle insula, aMCC, medial and lateral premotor areas, and selectively in left and right parietal cortices. Treatment effectiveness resulted in signal changes in the perigenual anterior cingulate cortex, in the ventromedial orbito-frontal cortex, in the right lateral middle frontal gyrus, and in the cerebellum. These findings support the view that humans' responses to the pain of others can be modulated by cognitive and motivational processes, which influence whether observing a conspecific in need of help will result in empathic concern, an important instigator for helping behavior.


title: An accurate infrared hand geometry and vein pattern based authentication system

abstract: An acquisition system is designed to simultaneously acquire palm dorsal vein and infrared (IR) hand geometry.A novel quality parameter is proposed for palm dorsal vein.A quality based variational approach is proposed for vein extraction that can handle hair, skin texture and variable width veins.Several IR hand features are extracted accurately even in absence of texture and consolidated.The proposed system exhibits better performance than state of the art systems and unimodal systems. Display Omitted In this paper, hand dorsal images acquired under infrared light are used to design an accurate personal authentication system. Each of the image is segmented into palm dorsal and fingers which are subsequently used to extract palm dorsal veins and infrared hand geometry features respectively. A new quality estimation algorithm is proposed to estimate the quality of palm dorsal which assigns low values to the pixels containing hair or skin texture. Palm dorsal is enhanced using filtering. For vein extraction, information provided by the enhanced image and the vein quality is consolidated using a variational approach. The proposed vein extraction can handle the issues of hair, skin texture and variable width veins so as to extract the genuine veins accurately. Several post processing techniques are introduced in this paper for accurate feature extraction of infrared hand geometry features. Matching scores are obtained by matching palm dorsal veins and infrared hand geometry features. These are eventually fused for authentication. For performance evaluation, a database of 1500 hand images acquired from 300 different hands is created. Experimental results demonstrate the superiority of the proposed system over existing systems.


title: CUDA Leaks: A Detailed Hack for CUDA and a (Partial) Fix

abstract: Graphics processing units (GPUs) are increasingly common on desktops, servers, and embedded platforms. In this article, we report on new security issues related to CUDA, which is the most widespread platform for GPU computing. In particular, details and proofs-of-concept are provided about novel vulnerabilities to which CUDA architectures are subject. We show how such vulnerabilities can be exploited to cause severe information leakage. As a case study, we experimentally show how to exploit one of these vulnerabilities on a GPU implementation of the AES encryption algorithm. Finally, we also suggest software patches and alternative approaches to tackle the presented vulnerabilities.


title: Using MongoDB to implement textbook management system instead of MySQL

abstract: With the development of internet Web2.0 technology, the traditional relational database is widely used in information management system. However, it is not effective, when we need to query a wide range of massive data, especially with multi-table join queries. Now, a kind of new technology emerged — NoSQL, which is non-relational database management system with format loose data storage, not support the join operation, the effective query capability etc advantages. This paper attempts to use NoSQL database to replace the relational database, applied to traditional information management systems, compare the two database technologies, give the key code of NoSQL implementation, and finally list the performance comparison of two schemes.


Variations in Protein Levels in Kansas Sorghum Grain1


title: Antecedents of customer satisfaction on the internet: an empirical study of online shopping

abstract: With the ever increasing popularity of electronic commerce, the evaluation of antecedents and of customer satisfaction have become very important for the cyber shopping store (CSS) and for researchers. The various models of customer satisfaction that researchers have provided so far are mostly based on the traditional business channels and thus may not be appropriate for CSSs. This research has employed case and survey methods to study the antecedents of customer satisfaction. Though case methods a research model with hypotheses is developed. And through survey methods, the relationships between antecedents and satisfaction are further examined and analyzed. We find five antecedents of customer satisfaction to be more appropriate for online shopping on the Internet. Among them homepage presentation is a new and unique antecedent which has not existed in traditional marketing.


title: A Practical Quantum Instruction Set Architecture

abstract: We introduce an abstract machine architecture for classical/quantum computations---including compilation---along with a quantum instruction language called Quil for explicitly writing these computations. With this formalism, we discuss concrete implementations of the machine and non-trivial algorithms targeting them. The introduction of this machine dovetails with ongoing development of quantum computing technology, and makes possible portable descriptions of recent classical/quantum algorithms.


title: Extractive text summarization by feature-based sentence extraction using rule-based concept

abstract: World Wide Web is a tremendous source of knowledge. Vast amount of information available over the internet has made the humans suffer from problem of information explosion. Therefore, a good mechanism is required to extract relevant information. This calls a need for an automatic and significant tool that converts lengthy documents into concise form by extracting relevant information from it. Automatic Text Summarizer serves as one of the best tool for interpreting lengthy textual content. It represents the shorter version of the original document by choosing most important part of text, thus generating its summary. It is classified into two categories: abstraction and extraction. This paper highlights on extractive approach. Main aim is to select best sentences by weighting them. We carried out our experiment on 15 documents from DUC 2002 dataset. Each test document was first pre-processed. Then, all the sentences were represented as attribute vector of features by calculating their scores. Rule-based method was proposed to select the best sentences. Results were compared with GSM summarizer and conclusion was drawn that best average recall, precision and f-measure values was obtained for Rule-Based Summarizer.


title: Guided growth for the treatment of limb length discrepancy: a comparative study of the three most commonly used surgical techniques.

abstract: The purpose of this study was to compare the safety and effectiveness of three mechanical devices (percutaneous transphyseal screws, tension band plates, and staples) for the correction of limb length discrepancies in growing children and adolescents. Thirty-nine consecutive patients treated with epiphysiodesis for limb length discrepancy were retrospectively reviewed. No significant difference was recorded between the three devices in postoperative limb length discrepancy or the rate of correction between the plating and stapling groups and between plating and percutaneous transphyseal screws groups. Epiphysiodesis for length discrepancies can be performed effectively using staples, tension band plates, or percutaneous transphyseal screws. J Pediatr Orthop B 22:311–317 c 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins.


title: Dietary Inflammatory Index and Odds of Colorectal Cancer in a Case- Control Study from Iran

abstract: Background: In Iran, colorectal cancer (CRC) is the third and fourth leading cause of cancer incidence among men ::: and women, respectively. Diet and inflammation have been suggested as important risk factors for CRC. We examined ::: the association between dietary inflammatory index (DII) scores and CRC in a case-control study conducted in Iran. ::: Methods: This study included 71 CRC cases and 142 controls hospitalized for acute non-neoplastic diseases. DII ::: scores were computed from dietary intake assessed by a previously validated food frequency questionnaire. Logistic ::: regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CI) adjusted for age, sex, ::: education, energy intake, exercise, body mass index (BMI), smoking, family history of cancer, and history of aspirin, ::: acetaminophen, and multivitamin use. Results: Subjects with higher DII scores (i.e., indicating a more pro-inflammatory ::: diet) had a higher odds of CRC with the DII being used as both a continuous variable (OR continuous = 2.20, 95% CI: ::: 1.22-3.87) and as a categorical variable (OR tertile 3 vs tertile1 = 2.47, 95%CI: 1.10-5.55). Conclusion: These results ::: indicate that a pro-inflammatory diet is associated with increased odds of CRC in this Iranian population.


title: Frequency Domain Acceleration of Convolutional Neural Networks on CPU-FPGA Shared Memory System

abstract: We present a novel mechanism to accelerate state-of-art Convolutional Neural Networks (CNNs) on CPU-FPGA platform with coherent shared memory. First, we exploit Fast Fourier Transform (FFT) and Overlap-and-Add (OaA) to reduce the computational requirements of the convolutional layer. We map the frequency domain algorithms onto a highly-parallel OaA-based 2D convolver design on the FPGA. Then, we propose a novel data layout in shared memory for efficient data communication between the CPU and the FPGA. To reduce the memory access latency and sustain peak performance of the FPGA, our design employs double buffering. To reduce the inter-layer data remapping latency, we exploit concurrent processing on the CPU and the FPGA. Our approach can be applied to any kernel size less than the chosen FFT size with appropriate zero-padding leading to acceleration of a wide range of CNN models. We exploit the data parallelism of OaA-based 2D convolver and task parallelism to scale the overall system performance. By using OaA, the number of floating point operations is reduced by 39.14% ~54.10% for the state-of-art CNNs. We implement VGG16, AlexNet and GoogLeNet on Intel QuickAssist QPI FPGA Platform. These designs sustain 123.48 GFLOPs/sec, 83.00 GFLOPs/sec and 96.60 GFLOPs/sec, respectively. Compared with the state-of-the-art AlexNet implementation, our design achieves 1.35x GFLOPs/sec improvement using 3.33x less multipliers and 1.1x less memory. Compared with the state-of-art VGG16 implementation, our design has 0.66x GFLOPs/sec using 3.48x less multipliers without impacting the classification accuracy. For GoogLeNet implementation, our design achieves 5.56x improvement in performance compared with 16 threads running on a 10 Core Intel Xeon Processor at 2.8 GHz.


The Fallacy of the Net Promoter Score : Customer Loyalty Predictive Model


title: A resonant based Marx generator

abstract: The configuration proposed in this paper aims to generate high voltage for pulsed power applications. The main idea is to charge two groups of capacitors in parallel through an inductor and take the advantage of resonant phenomena in charging each capacitor up to a double input voltage level. In each resonant half a cycle, one of those capacitor groups are charged, and finally the charged capacitors will be connected together in series and the summation of the capacitor voltages can be appeared at the output of the topology. This topology can be considered as a modified Marx generator which works based on the resonant concept. Simulation models of this converter have been investigated in Matlab/SIMULINK platform and the attained results fully satisfy the proper operation of the converter.


title: A Novel Consequent-Pole Hybrid Excited Vernier Machine

abstract: This paper proposes a novel consequent-pole hybrid excited vernier machine (CPHEVM) by combining hybrid excited concept and vernier structure, and the consequent-pole magnet arrangement is employed on outer rotor. The proposed machine can achieve high torque density at low speed and excellent flux-weakening capability at high speed, which are highly desirable for electric vehicle applications. The direct current field windings are embedded between modulating poles, which increase the space utilization ratio to enable flux regulation without decreasing torque capability. The configuration, operation principle, and design consideration of the proposed CPHEVM are described, respectively. Finite-element method is employed to analyze the electromagnetic performance of the selected CPHEVMs with various slot/pole combinations, including flux regulation capability, back-electromagnetic force, and torque characteristics with and without flux-weakening control.


title: Selecting appropriate support for marketing decisions

abstract: Though several forms of support systems for marketing decisions have been reported in the literature, the field of marketing decision support systems (MDSS) has not yet been formalized to the extent that appropriate computer-based support can be provided for the spectrum of decisions faced by today's marketing managers. Given the wide variety of marketing decisions, it becomes imperative that a systematic approach to the selection of support mechanism for diverse decision types be employed for effective decision making. This paper proposes a framework based on the structure of decisions and nature of decision input that permits classification of marketing decisions and outlines normative support mechanisms for each decision type. This approach offers savings for operational managers and senior executives through reduction in exploratory analysis effort, improved quality of decisions, and more effective allocation of resources for MDSS development.


title: New York Bight fault

abstract: High-resolution, single-channel and multichannel seismic-reflection profiles in the New York Bight provide 7 crossings of a 50-km-long fault that trends north-northeast for 30 km from its southern end, then bends northeast, and may continue northward beneath Long Island. Displacement, which is consistently down to the west, decreases upsection and suggests a growth fault. Dip of the fault is near vertical. Its maximum offset of 109 m is the largest offset at the base of the Coastal Plain known for any Cretaceous and younger fault along the East Coast. Seismic stratigraphic controls constrain motion on the fault between Late Cretaceous (95 m.y. B.P.) and middle Oligocene (30 m.y. B.P.). The evidence for Quaternary activity is ambiguous. The fault parallels a magnetic low to the east, interpreted as a buried Mesozoic rift basin from seismic-reflection data, and a gravity low to the west, interpreted as a structure within Paleozoic rocks from well data. Whether these structures control the location of, or movement on, the fault is not clear.


title: Enforcing Least Privilege Memory Views for Multithreaded Applications

abstract: Failing to properly isolate components in the same address space has resulted in a substantial amount of vulnerabilities. Enforcing the least privilege principle for memory accesses can selectively isolate software components to restrict attack surface and prevent unintended cross-component memory corruption. However, the boundaries and interactions between software components are hard to reason about and existing approaches have failed to stop attackers from exploiting vulnerabilities caused by poor isolation. We present the secure memory views (SMV) model: a practical and efficient model for secure and selective memory isolation in monolithic multithreaded applications. SMV is a third generation privilege separation technique that offers explicit access control of memory and allows concurrent threads within the same process to partially share or fully isolate their memory space in a controlled and parallel manner following application requirements. An evaluation of our prototype in the Linux kernel (TCB


title: From Traditional Data Warehouse To Real Time Data Warehouse

abstract: The Traditional data warehouse did not contain data as today. Hence, it is difficult to retrieve these data and treat them. Furthermore, its content is not updated, which may lead to bad decisions. Data are typically loaded from conventional operational systems. Given that today’s decisions, in the business world, are becoming more and more in real time. Thus, the system supporting these decisions is to be held. In this paper, we are interested in giving a survey on data warehousing starting from a traditional data warehouse to a real time data warehouse. This survey, focus firstly, on data warehouse architecture. Secondly, it details the changes in the Extract-Transform-Load process to deal with real time data warehousing. Thirdly, it sketches the integration data in the real time data warehouse. Finally, a comparative study concerning the real data warehouse approaching is also presented in this paper.


title: Transaction processing in a mobile computing environment with alternating client hosts

abstract: Previous research in mobile data management has been focused on scenarios where mobile clients replace fixed ones. However, not all potential application fields of mobile computing fit into this scenario. There exist scenarios where mobile clients will not replace but accompany fixed ones. In this paper, we introduce a real-world application scenario requiring the capability to process a transaction from alternating, potentially mobile client hosts. With this scenario in mind, we have developed three approaches for supporting multiple clients in combination with a so-called TXAgent (Transaction Processing Agent), which is an object in the fixed network which manages transactions on behalf of the client, and an approach to realise the client as a migrating object. Finally, we present a prototype implementation of the most promising approach.


title: Mutual Inductance of a Precise Rogowski Coil in Dependence of the Position of Primary Conductor

abstract: The Rogowski coil is a well-known current-to-voltage transducer. To use it for the high-accuracy measurement of AC current (at power supply frequency), all influencing quantities and their contribution should be recognized and analyzed. Thus, the partial influence due to the deviation from the central position of the primary conductor for a real sensor, with the nonhomogeneous density of secondary turns, is given in this paper. The measured deviation of mutual inductance in this case showed very good agreement with the theoretical prediction, which is promising for the expected application of such a sensor.


title: On the speedup of single-disk failure recovery in XOR-coded storage systems: Theory and practice

abstract: Modern storage systems stripe redundant data across multiple disks to provide availability guarantees against disk failures. One form of data redundancy is based on XOR-based erasure codes, which use only XOR operations for encoding and decoding. In addition to providing failure tolerance, a storage system must also provide fast failure recovery to avoid data unavailability. We consider the problem of speeding up the recovery of a single-disk failure for arbitrary XOR-based erasure codes. We address this problem from both theoretical and practical perspectives. We propose a replace recovery algorithm, which uses a hill-climbing technique to search for a fast recovery solution, such that the solution search can be completed within a short time period. We further implement our replace recovery algorithm atop a parallelized architecture to justify its practicality. We experiment our replace recovery algorithm and its parallelized implementation on a networked storage system testbed, and demonstrate that our replace recovery algorithm uses less recovery time than the conventional approach.


title: Using convolutional neural networks to explore the microbiome

abstract: The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.


title: Platform Embedded Slot Antenna Backed by Shielded Parallel Plate Resonator

abstract: A new cavity architecture for applications in design of resonator-backed slot antennas with a height less than λ/15 profile is presented. This architecture renders cavity heights as low as λ/130 and small lateral dimensions. Miniaturization of the supporting cavity resonator is achieved by meandering the passage from the bottom of the cavity resonator to the slot aperture. Unlike conventional cavity resonators, the proposed design operates in a TEM mode which allows for wider bandwidth operation and miniaturization of lateral dimensions. Ultrasonic consolidation technique is employed to fabricate the complicated cavity structure monolithically. Measurements verify the proposed antenna exhibits excellent gain and front-to-back ratio (FTBR). Wide-band mode of the antenna is achieved by using a carefully designed microstrip feed across the slot aperture which facilitates a fictitious short along the slot aperture. The antenna is then flush-mounted onto an arbitrarily built metallic platform and is shown to feature consistent impedance matching. The FTBR is found to improve when the antenna is embedded into the platform. The same antenna architecture is redesigned for VHF band operation using standard multi-layer PCB technology and consistent functionality is verified.


title: Decentralized adaptive control of uncertain systems with gain scheduled reference models

abstract: Control theoretic concepts for decentralized adaptive control of uncertain systems with gain scheduled reference model is developed. For each subsystem a single Lyapunov matrix is computed, using convex optimization tools, for multiple linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop gain scheduled reference subsystem. This approach guarantees stability of the closed-loop gain scheduled reference model. Then, decentralized adaptive state feedback control architecture is developed and its stability is proved. Specifically, the resulting closed-loop system is shown to have bounded solutions with bounded tracking error for all the subsystems. Simulation results for two different models of a turboshaft engine, including the nominal engine model and an engine model with a new core subsystem, illustrate the possibility of stable decentralized adaptive control of gas turbine engines with proper tracking performance.


title: gDLS: A Scalable Solution to the Generalized Pose and Scale Problem

abstract: In this work, we present a scalable least-squares solution for computing a seven degree-of-freedom similarity transform. Our method utilizes the generalized camera model to compute relative rotation, translation, and scale from four or more 2D-3D correspondences. In particular, structure and motion estimations from monocular cameras lack scale without specific calibration. As such, our methods have applications in loop closure in visual odometry and registering multiple structure from motion reconstructions where scale must be recovered. We formulate the generalized pose and scale problem as a minimization of a least squares cost function and solve this minimization without iterations or initialization. Additionally, we obtain all minima of the cost function. The order of the polynomial system that we solve is independent of the number of points, allowing our overall approach to scale favorably. We evaluate our method experimentally on synthetic and real datasets and demonstrate that our methods produce higher accuracy similarity transform solutions than existing methods.


title: Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI)

abstract: A brain-computer interface (BCI) based on functional magnetic resonance imaging (fMRI) records noninvasively activity of the entire brain with a high spatial resolution. We present a fMRI-based BCI which performs data processing and feedback of the hemodynamic brain activity within 1.3 s. Using this technique, differential feedback and self-regulation is feasible as exemplified by the supplementary motor area (SMA) and parahippocampal place area (PPA). Technical and experimental aspects are discussed with respect to neurofeedback. The methodology now allows for studying behavioral effects and strategies of local self-regulation in healthy and diseased subjects.


title: Affordance detection for task-specific grasping using deep learning

abstract: In this paper we utilize the notion of affordances to model relations between task, object and a grasp to address the problem of task-specific robotic grasping. We use convolutional neural networks for encoding and detecting object affordances, class and orientation, which we utilize to formulate grasp constraints. Our approach applies to previously unseen objects from a fixed set of classes and facilitates reasoning about which tasks an object affords and how to grasp it for that task. We evaluate affordance detection on full-view and partial-view synthetic data and compute task-specific grasps for objects that belong to ten different classes and afford five different tasks. We demonstrate the feasibility of our approach by employing an optimization-based grasp planner to compute task-specific grasps.


title: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames

abstract: We extend Convolutional Neural Networks (CNNs) on flat and regular domains (e.g. 2D images) to curved surfaces embedded in 3D Euclidean space that are discretized as irregular meshes and widely used to represent geometric data in Computer Vision and Graphics. We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces with the canonical parallel transport that preserves metric. For computation, we rely on a parallel N-direction frame field on the surface that minimizes field variation and therefore is as compatible as possible to and approximates the parallel transport. On the tangent spaces equipped with parallel frames, the computation of surface convolution becomes standard routine. The frames have rotational symmetry which we disambiguate by constructing the covering space of surface induced by the parallel frames and grouping the feature maps into N sets accordingly; convolution is computed on the N branches of the cover space with respective feature maps while the kernel weights are shared. To handle irregular points of a discrete mesh while sharing kernel weights, we make the convolution semi-discrete, i.e. the convolution kernels are polynomial functions, and their convolution with discrete surface points becomes sampling and weighted summation. Pooling and unpooling operations are computed along a mesh hierarchy built through simplification. The presented surface CNNs allow effective deep learning on meshes. We show that for tasks of classification, segmentation and non-rigid registration, surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features.


GPU performance prediction using parametrized models


title: A data locality optimizing algorithm

abstract: This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix transformations.The algorithm has been implemented in the SUIF (Stanford University Intermediate Format) compiler, and is successful in optimizing codes such as matrix multiplication, successive over-relaxation (SOR), LU decomposition without pivoting, and Givens QR factorization. Performance evaluation indicates that locality optimization is especially crucial for scaling up the performance of parallel code.


title: Fears of compassion: Development of three

abstract: Objectives. There is increasing evidence that helping people develop compassion for themselves and others has powerful impacts on negative affect and promotes positive affect. However, clinical observations suggest that some individuals, particularly those high in self-criticism, can find self-compassion and receiving compassion difficult and can be fearful of it. This study therefore developed measures of fear of: compassion for others, compassion from others, and compassion for self. We also explored the relationship of these fears with established compassion for self and compassion for others measures, self-criticism, attachment styles, and depression, anxiety, and stress. ::: ::: Method. Students (N= 222) and therapists (N= 53) completed measures of fears of compassion, self-compassion, compassion for others, self-criticism, adult attachment, and psychopathology. ::: ::: Results. Fear of compassion for self was linked to fear of compassion from others, and both were associated with self-coldness, self-criticism, insecure attachment, and depression, anxiety, and stress. In a multiple regression, self-criticism was the only significant predictor of depression. ::: ::: Conclusion. This study suggests the importance of exploring how and why some people may actively resist engaging in compassionate experiences or behaviours and be fearful of affiliative emotions in general. This has important implications for therapeutic interventions and the therapeutic relationship because affiliative emotions are major regulators of threat-based emotions.


title: A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching

abstract: The problem of describing images through natural language has gained importance in the computer vision community. Solutions to image description have either focused on a top-down approach of generating language through combinations of object detections and language models or bottom-up propagation of keyword tags from training images to test images through probabilistic or nearest neighbor techniques. In contrast, describing videos with natural language is a less studied problem. In this paper, we combine ideas from the bottom-up and top-down approaches to image description and propose a method for video description that captures the most relevant contents of a video in a natural language description. We propose a hybrid system consisting of a low level multimodal latent topic model for initial keyword annotation, a middle level of concept detectors and a high level module to produce final lingual descriptions. We compare the results of our system to human descriptions in both short and long forms on two datasets, and demonstrate that final system output has greater agreement with the human descriptions than any single level.


title: Multi-task Learning with Gradient Guided Policy Specialization

abstract: We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization training. During the joint training stage, a neural network policy is trained with minimal information to disambiguate the motor skills. This forces the policy to learn a common representation of the different tasks. Then, during the specialization training stage we selectively split the weights of the policy based on a per-weight metric that measures the disagreement among the multiple tasks. By splitting part of the control policy, it can be further trained to specialize to each task. To update the control policy during learning, we use Trust Region Policy Optimization with Generalized Advantage Function (TRPOGAE). We propose a modification to the gradient update stage of TRPO to better accommodate multi-task learning scenarios. We evaluate our approach on three continuous motor skill learning problems in simulation: 1) a locomotion task where three single legged robots with considerable difference in shape and size are trained to hop forward, 2) a manipulation task where three robot manipulators with different sizes and joint types are trained to reach different locations in 3D space, and 3) locomotion of a two-legged robot, whose range of motion of one leg is constrained in different ways. We compare our training method to three baselines. The first baseline uses only joint training for the policy, the second trains independent policies for each task, and the last randomly selects weights to split. We show that our approach learns more efficiently than each of the baseline methods.


title: A raspberry Pi controlled cloud based air and sound pollution monitoring system with temperature and humidity sensing

abstract: In recent day scenario, the incessant increase in air and sound pollution prove to be an alarming problem. It has become mandatory to control and appropriately monitor the situation so that the required steps to curb the situation can be undertaken. In this project, an IOT-based method to monitor the Air Quality Index and the Noise Intensity of a region, have been proposed. The recommended technology comprises of four modules namely, the Air Quality Index Monitoring Module, the Sound Intensity Detection Module, the Cloud-based Monitoring Module and the Anomaly Notification Module. Firstly, the Air Quality Index is measured considering the presence of the five criteria air pollutants. Then the sound intensity is detected using respective sensor. After that, the Cloud-based Monitoring Module ensures the process of acquiring the data with the help of Wi-fi-module present in Raspberry Pi which fulfils the objective of analysis of information on a periodical basis. Finally, the Anomaly Notification Module alerts the user in case of an undesired condition.


title: Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks

abstract: We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.


title: Software architecture and requirements: A systematic literature review

abstract: Research in software architecture (SA) can be seen in two perspectives: a traditional and modern one. Software architecture is represented using graphical diagrams (Models, Frameworks) of the system, Architecture Description Languages, Ontology. The main focus of this paper is to know about the software architecture and to find out the strength of evidence in empirical work reported within literature. The outcome of this systematic literature review will be useful for researchers and practitioners and this SLR also includes widely used tools, models, and techniques used in SA; software architecture challenges/Issues widely reported; the SA areas which are under major consideration; the SA areas that require sufficient attention. In the end we also provide information model regarding requirements and architecture, which handles portability and contextual issues regarding platform dependency. Main purpose of information model is to facilitate practitioners for achieving traceability between requirements and architecture.


title: THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system

abstract: In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user’s private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.


title: Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis

abstract: In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-the-art results on these benchmark datasets.


title: P.Z.: Ontology alignment for linked open data

abstract: The Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.


title: A Probabilistic Approach to Spectral Graph Matching

abstract: Spectral Matching (SM) is a computationally efficient approach to approximate the solution of pairwise matching problems that are np-hard. In this paper, we present a probabilistic interpretation of spectral matching schemes and derive a novel Probabilistic Matching (PM) scheme that is shown to outperform previous approaches. We show that spectral matching can be interpreted as a Maximum Likelihood (ML) estimate of the assignment probabilities and that the Graduated Assignment (GA) algorithm can be cast as a Maximum a Posteriori (MAP) estimator. Based on this analysis, we derive a ranking scheme for spectral matchings based on their reliability, and propose a novel iterative probabilistic matching algorithm that relaxes some of the implicit assumptions used in prior works. We experimentally show our approaches to outperform previous schemes when applied to exhaustive synthetic tests as well as the analysis of real image sequences.


title: The Future of Pharmaceutical Manufacturing Sciences

abstract: The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed.


title: Tumor angiogenesis: molecular pathways and therapeutic targets

abstract: As angiogenesis is essential for tumor growth and metastasis, controlling tumor-associated angiogenesis is a promising tactic in limiting cancer progression. The tumor microenvironment comprises numerous signaling molecules and pathways that influence the angiogenic response. Understanding how these components functionally interact as angiogenic stimuli or as repressors and how mechanisms of resistance arise is required for the identification of new therapeutic strategies. Achieving a durable and efficient antiangiogenic response will require approaches to simultaneously or sequentially target multiple aspects of the tumor microenvironment.


title: An experiment on an ontology-based support approach for process modeling

abstract: Context: Recent research discusses the use of ontologies, dictionaries and thesaurus as a means to improve activity labels of process models. However, the trade-off between quality improvement and extra effort is still an open question. It is suspected that ontology-based support could require additional effort for the modeler.Objective: In this paper, we investigate to which degree ontology-based support potentially increases the effort of modeling. We develop a theoretical perspective grounded in cognitive psychology, which leads us to the definition of three design principles for appropriate ontology-based support. The objective is to evaluate the design principles through empirical experimentation.Method: We tested the effect of presenting relevant content from the ontology to the modeler by means of a quantitative analysis. We performed controlled experiments using a prototype, which generates a simplified and context-aware visual representation of the ontology. It logs every action of the process modeler for analysis. The experiment refers to novice modelers and was performed as between-subject design with vs. without ontology-based support. It was carried out with two different samples.Results: Part of the effort-related variables we measured showed significant statistical difference between the group with and without ontology-based support. Overall, for the collected data, the ontology support achieved good results.Conclusion: We conclude that it is feasible to provide ontology-based support to the modeler in order to improve process modeling without strongly compromising time consumption and cognitive effort.


title: Predictions not commands: active inference in the motor system

abstract: The descending projections from motor cortex share many features with top-down or backward connections in visual cortex; for example, corticospinal projections originate in infragranular layers, are highly divergent and (along with descending cortico-cortical projections) target cells expressing NMDA receptors. This is somewhat paradoxical because backward modulatory characteristics would not be expected of driving motor command signals. We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz’s ideas about perception; namely, that perception is inference on the causes of visual sensations. We explain behaviour in terms of inference on the causes of proprioceptive sensations. This explanation appeals to active inference, in which higher cortical levels send descending proprioceptive predictions, rather than motor commands. This process mirrors perceptual inference in sensory cortex, where descending connections convey predictions, while ascending connections convey prediction errors. The anatomical substrate of this recurrent message passing is a hierarchical system consisting of functionally asymmetric driving (ascending) and modulatory (descending) connections: an arrangement that we show is almost exactly recapitulated in the motor system, in terms of its laminar, topographic and physiological characteristics. This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular.


title: Z3: An Efficient SMT Solver

abstract: Satisfiability Modulo Theories (SMT) problem is a decision problem for logical first order formulas with respect to combinations of background theories such as: arithmetic, bit-vectors, arrays, and uninterpreted functions. Z3 is a new and efficient SMT Solver freely available from Microsoft Research. It is used in various software verification and analysis applications.


title: A 3GHz VCO suitable for MIPI M-PHY serial interface

abstract: This paper proposes a VCO which is based on a pseudo-differential ring oscillator scheme and it is suitable for multi-Gbps serial interfaces. The ring oscillator topology is based on two CMOS inverters loaded by a simple negative resistance realized with pMOS transistors. The circuit performance is compliant with the MIPI Alliance M-PHY standard which is the most updated high-speed serial interface technology. The circuit is designed and simulated in a 65nm CMOS process with 1.2 V supply voltage. The frequency tuning range was 1.9 to 3.5 GHz with an almost constant K VCO equal to 5.7 GHz/V, approximately, while the current consumption was 6.4 mA at 3 GHz operating frequency and the phase noise was −94 dBc/Hz at 1 MHz frequency offset.


title: International evidence-based recommendations for point-of-care lung ultrasound

abstract: Background ::: The purpose of this study is to provide evidence-based and expert consensus recommendations for lung ultrasound with focus on emergency and critical care settings.


title: Seminal Quality Prediction Using Clustering-Based Decision Forests

abstract: Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.


title: "Bridge": Enhanced Signed Directed Network Embedding

abstract: Signed directed networks with positive or negative links convey rich information such as like or dislike, trust or distrust. Existing work of sign prediction mainly focuses on triangles (triadic nodes) motivated by balance theory to predict positive and negative links. However, real-world signed directed networks can contain a good number of "bridge'' edges which, by definition, are not included in any triangles. Such edges are ignored in previous work, but may play an important role in signed directed network analysis.%Such edges serve as fundamental building blocks and may play an important role in signed network analysis. In this paper, we investigate the problem of learning representations for signed directed networks. We present a novel deep learning approach to incorporating two social-psychologic theories, balance and status theories, to model both triangles and "bridge'' edges in a complementary manner. The proposed framework learns effective embeddings for nodes and edges which can be applied to diverse tasks such as sign prediction and node ranking. Experimental results on three real-world datasets of signed directed social networks verify the essential role of "bridge" edges in signed directed network analysis by achieving the state-of-the-art performance.


title: n-metrics for multiple graph alignment.

abstract: The work of Ioannidis et al. 2018 introduces a family of distances between two graphs that provides tractable graph alignment strategies. Importantly, the alignment scores produced by this family satisfy the properties of metrics, which is very useful in several learning tasks. In this paper, we generalize this work to compare n graphs by introducing a family of distances, which is an n-metric, i.e., an extension of a metric to n elements that includes a generalization of the triangle inequality. Our new family of distances, includes the ones in the work of Ioannidis et al. 2018 as a special case, and can produce tractable alignments between multiple graphs.


title: A mathematics retrieval system for formulae in layout presentations

abstract: The semantics of mathematical formulae depend on their spatial structure, and they usually exist in layout presentations such as PDF, LaTeX, and Presentation MathML, which challenges previous text index and retrieval methods. This paper proposes an innovative mathematics retrieval system along with the novel algorithms, which enables efficient formula index and retrieval from both webpages and PDF documents. Unlike prior studies, which require users to manually input formula markup language as query, the new system enables users to "copy" formula queries directly from PDF documents. Furthermore, by using a novel indexing and matching model, the system is aimed at searching for similar mathematical formulae based on both textual and spatial similarities. A hierarchical generalization technique is proposed to generate sub-trees from the semi-operator tree of formulae and support substructure match and fuzzy match. Experiments based on massive Wikipedia and CiteSeer repositories show that the new system along with novel algorithms, comparing with two representative mathematics retrieval systems, provides more efficient mathematical formula index and retrieval, while simplifying user query input for PDF documents.


title: The Body Appreciation Scale-2: Item refinement and psychometric evaluation

abstract: Abstract Considered a positive body image measure, the 13-item Body Appreciation Scale (BAS; Avalos, Tylka, & Wood-Barcalow, 2005 ) assesses individuals’ acceptance of, favorable opinions toward, and respect for their bodies. While the BAS has accrued psychometric support, we improved it by rewording certain BAS items (to eliminate sex-specific versions and body dissatisfaction-based language) and developing additional items based on positive body image research. In three studies, we examined the reworded, newly developed, and retained items to determine their psychometric properties among college and online community (Amazon Mechanical Turk) samples of 820 women and 767 men. After exploratory factor analysis, we retained 10 items (five original BAS items). Confirmatory factor analysis upheld the BAS-2's unidimensionality and invariance across sex and sample type. Its internal consistency, test–retest reliability, and construct (convergent, incremental, and discriminant) validity were supported. The BAS-2 is a psychometrically sound positive body image measure applicable for research and clinical settings.


title: Tensor theta norms and low rank recovery

abstract: We study extensions of compressive sensing and low rank matrix recovery to the recovery of tensors of low rank from incomplete linear information. While the reconstruction of low rank matrices via nuclear norm minimization is rather well-understand by now, almost no theory is available so far for the extension to higher order tensors due to various theoret- ical and computational difficulties arising for tensor decompositions. In fact, nuclear norm minimization for matrix recovery is a tractable convex relaxation approach, but the extension of the nuclear norm to tensors is NP-hard to compute. In this article, we introduce convex relaxations of the tensor nuclear norm which are computable in polynomial time via semidef- inite programming. Our approach is based on theta bodies, a concept from computational algebraic geometry which is similar to the one of the better known Lasserre relaxations. We introduce polynomial ideals which are generated by the second order minors corresponding to different matricizations of the tensor (where the tensor entries are treated as variables) such that the nuclear norm ball is the convex hull of the algebraic variety of the ideal. The theta body of order k for such an ideal generates a new norm which we call thek-norm. We show that in the matrix case, these norms reduce to the standard nuclear norm. For tensors of order three or higher however, we indeed obtain new norms. By providing the Grobner basis for the ideals, we explicitly give semidefinite programs for the computation of thek-norm and for the minimization of thek-norm under an affine constraint. Finally, numerical experiments for order three tensor recovery via �1-norm minimization suggest that our approach successfully reconstructs tensors of low rank from incomplete linear (random) measurements.


title: Measurement of signal-to-noise ratio in dysphonic voices by image processing of spectrograms

abstract: Abstract The measurement of glottal noise was investigated in human and synthesized dysphonic voices by means of two-dimensional (2D) speech processing. A prime objective was the reduction of measurement sensitivities to fundamental frequency (f0) tracking errors and phonatory aperiodicities. An available fingerprint image enhancement algorithm was used for signal-to-noise measurement in narrow band spectrographic images. This spectrographic signal-to-noise ratio estimation method (S2NR) creates binary masks, mainly based on the orientation field of the partials, to separate energy in regions with strong harmonics from energy in noisy areas. Synthesized vowels with additive noise were used to calibrate the algorithm, validate the calibration, and systematically evaluate its dependence on f0, shimmer (cycle-to-cycle amplitude perturbation), and jitter (cycle-to-cycle f0 perturbation). In synthesized voices with known signal-to-noise ratios in the 5–40 dB range, S2NR estimates were, on average, accurate within ±3.2 dB and robust to variations in f0 (120 Hz or 220 Hz), jitter (0–3%), and shimmer (0–30%). In human /a/ produced by dysphonic speakers, S2NR values and perceptual ratings of breathiness revealed a non-linear but monotonic decay of S2NR with increased breathiness. Comparison between S2NR and related acoustic measurements indicated similar behaviors regarding the relationship with breathiness and immunity to shimmer, but the other methods had marked influence of jitter. Overall, the S2NR method did not rely on accurate f0 estimation, was robust to vocal perturbations and largely independent of vowel type, having also potential application in running speech.


title: Semantics-based program verifiers for all languages

abstract: We present a language-independent verification framework that can be instantiated with an operational semantics to automatically generate a program verifier. The framework treats both the operational semantics and the program correctness specifications as reachability rules between matching logic patterns, and uses the sound and relatively complete reachability logic proof system to prove the specifications using the semantics. We instantiate the framework with the semantics of one academic language, KernelC, as well as with three recent semantics of real-world languages, C, Java, and JavaScript, developed independently of our verification infrastructure. We evaluate our approach empirically and show that the generated program verifiers can check automatically the full functional correctness of challenging heap-manipulating programs implementing operations on list and tree data structures, like AVL trees. This is the first approach that can turn the operational semantics of real-world languages into correct-by-construction automatic verifiers.


title: Twitter User Geolocation Using a Unified Text and Network Prediction Model

abstract: We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with two enhancements:(1) the removal of"celebrity"nodes to increase location homophily and boost tractability; and (2) he incorporation of text-based geolocation priors for test users. Experiments over three Twitter benchmark datasets achieve state-of-the-art results, and demonstrate the effectiveness of the enhancements.


title: Cashflow Risk, Systematic Earnings Revisions, and the Cross-Section of Stock Returns

abstract: The returns of stocks are partially driven by changes in their expected cashflow. Using revisions in analyst earnings forecasts, we construct an analyst earnings beta that measures the covariance between the cashflow innovations of an asset and those of the market. A higher analyst earnings beta implies greater sensitivity to marketwide revisions in expected cashflow, and therefore higher systematic risk. Our analyst earnings beta captures exposure to macroeconomic fluctuations and has a positive risk premium that provides a partial explanation for the value premium, size premium, and long-term return reversals. From 1984 to 2005, 55.1% of the return variation across book-to-market, size, and long-term return reversal portfolios is captured by their analyst earnings betas.


title: Microfocused Ultrasound for Skin Tightening.

abstract: Microfocused ultrasound (MFU) generates tissue heating, which can be used for facial skin tightening as well as other areas of skin laxity. Unlike radiofrequency energy, MFU can penetrate deep into tissue without heating the superficial skin structures, allowing for higher treatment temperatures. Depending on patient factors and treatment protocols, 60% to 90% of patients can expect to see facial improvement 6 months after treatment.


title: Learning the Parameters of Determinantal Point Process Kernels

abstract: Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in applications where diversity is desired. While DPPs have many appealing properties, learning the parameters of a DPP is difficult, as the likelihood is non-convex and is infeasible to compute in many scenarios. Here we propose Bayesian methods for learning the DPP kernel parameters. These methods are applicable in large-scale discrete and continuous DPP settings, even when the likelihood can only be bounded. We demonstrate the utility of our DPP learning methods in studying the progression of diabetic neuropathy based on the spatial distribution of nerve fibers, and in studying human perception of diversity in images.


title: Mapping the Landscape of Human-Level Artificial General Intelligence

abstract: We present the broad outlines of a roadmap toward human-level artificial general intelligence (henceforth, AGI). We begin by discussing AGI in general, adopting a pragmatic goal for its attainment and a necessary foundation of characteristics and requirements. An initial capability landscape will be presented, drawing on major themes from developmental psychology and illuminated by mathematical, physiological and information processing perspectives. The challenge of identifying appropriate tasks and environments for measuring AGI will be addressed, and seven scenarios will be presented as milestones suggesting a roadmap across the AGI landscape along with directions for future research and collaboration.


title: QoS Aware Service Clustering to Bootstrap the Web Service Selection

abstract: Web services have been extended to give value-added customized services to users through service composition. Service Composition consists of four stages: planning, discovery, selection, and execution. Web service discovery and selection are becoming challenging and time-consuming tasks due to large number of Web services available on the internet. Organizing Web services into clusters of similar services to aid the pruning of the query space is one of the solutions for the issues. Web services can be clustered into similar groups by considering functional attributes to increase the performance of the service discovery. In selection stage, algorithm has to select a single service among the large number of functional equal services by considering the Quality of Service (QoS) values. In this paper, we propose QoS aware service clustering approach to increase the performance of the service selection. Here, service clusters are created using functionality as the first factor, then QoS properties being considered as secondary factors. Our approach considers QoS profit values to compute the QoS similarity. In this paper, we apply a spatial clustering technique called the Spherical Associated Keyword Space which is projected clustering result from a three-dimensional sphere to a two dimensional (2D) spherical surface for 2D visualization. Empirical study of our approach has proved the effectiveness of clustering results.


title: Perlovsky, “Concurrent multi-target localization, data association, and navigation for a swarm of flying sensors

abstract: We are developing a probabilistic technique for performing multiple target detection and localization based on data from a swarm of flying sensors, for example to be mounted on a group of micro-UAVs (unmanned aerial vehicles). Swarms of sensors can facilitate detecting and discriminating low signal-to-clutter targets by allowing correlation between different sensor types and/or different aspect angles. However, for deployment of swarms to be feasible, UAVs must operate more autonomously. The current approach is designed to reduce the load on humans controlling UAVs by providing computerized interpretation of a set of images from multiple sensors. We consider a complex case in which target detection and localization are performed concurrently with sensor fusion, multi-target signature association, and improved UAV navigation. This method yields the bonus feature of estimating precise tracks for UAVs, which may be applicable for automatic collision avoidance. We cast the problem in a probabilistic framework known as modeling field theory (MFT), in which the pdf of the data is composed of a mixture of components, each conditional upon parameters including target positions as well as sensor kinematics. The most likely set of parameters is found by maximizing the log-likelihood function using an iterative approach related to expectation-maximization. In terms of computational complexity, this approach scales linearly with number of targets and sensors, which represents an improvement over most existing methods. Also, since data association is treated probabilistically, this method is not prone to catastrophic failure if data association is incorrect. Results from computer simulations are described which quantitatively show the advantages of increasing the number of sensors in the swarm, both in terms of clutter suppression and more accurate target localization.


title: Broadband suspended plate antenna for WiFi/WiMAX applications

abstract: This paper presents a high-gain broadband suspended plate antenna for indoor wireless access point applications. This antenna consists of two layers operating at two adjacent bands. The bottom plate is fed by a tapered down strip excited by a probe through an SMA RF connector. The top plate is shorted to a ground plane by a strip electromagnetically coupled with the feed-strip. The design is carried out using a commercial EM software package, and validated experimentally. The measured result shows that the antenna achieves a broad operational bandwidth of 66%, suitable for access points in WiFi (2.4-2.485 GHz) and WiMAX (2.3-2.7 GHz and the 3.4-3.6 GHz) systems (IEEE 802.11b/g and IEEE 802.16-2004/e). The measured antenna gain varies from 7.7-9.5 dBi across the frequency bands of interest. A parametric study of this antenna is also conducted.


title: Internet of Satellites (IoSat): Analysis of Network Models and Routing Protocol Requirements

abstract: The space segment has been evolved from monolithic to distributed satellite systems. One of these distributed systems is called the federated satellite system (FSS) which aims at establishing a win–win collaboration between satellites to improve their mission performance by using the unused on-board resources. The FSS concept requires sporadic and direct communications between satellites, using inter satellite links. However, this point-to-point communication is temporal and thus it can break existent federations. Therefore, the conception of a multi-hop scenario needs to be addressed. This is the goal of the Internet of satellites (IoSat) paradigm which, as opposed to a common backbone, proposes the creation of a network using a peer-to-peer architecture. In particular, the same satellites take part of the network by establishing intermediate collaborations to deploy a FSS. This paradigm supposes a major challenge in terms of network definition and routing protocol. Therefore, this paper not only details the IoSat paradigm, but it also analyses the different satellite network models. Furthermore, it evaluates the routing protocol candidates that could be used to implement the IoSat paradigm.


title: Active and passive distraction using a head-mounted display helmet: effects on cold pressor pain in children.

abstract: Objective: The current study tested the effectiveness of interactive versus passive distraction that was delivered via a virtual reality type head-mounted display helmet for children experiencing cold pressor pain. Design: Forty children, aged 5 to 13 years, underwent 1 or 2 baseline cold pressor trials followed by interactive distraction and passive distraction trials in counterbalanced order. Main Outcome Measures: Pain threshold and pain tolerance. Results: Children who experienced either passive or interactive distraction demonstrated significant improvements in both pain tolerance and pain threshold relative to their baseline scores. In contrast, children who underwent a second cold pressor trial without distraction showed no significant improvements in pain tolerance or threshold. Conclusion: Although both distraction conditions were effective, the interactive distraction condition was significantly more effective. Implications for the treatment of children's distress during painful medical procedures are discussed.


title: A Hierarchical Latent Structure for Variational Conversation Modeling

abstract: Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.


title: Technology and its impact in the classroom

abstract: Recent findings have shown that at-risk students in grades k-12 are being deprived of challenges and of the chance to use complex thinking skills. The purpose of this study is to determine the effect that the level of computer technology use in the classroom has on at-risk students' grades and attendance. A teacher technology survey is used to measure teacher use, student use and overall use of technology in the classrooms. The sample for this study consists of teachers from a Northwest Ohio high school. Results of the study indicate that teachers' technology use, students' technology use, and overall technology use have no significant positive effect on the grades and attendance of at-risk students. In addition the study finds that technology use is low among the teachers in the sample. These results suggest that for technology to be effective and make changes in at-risk students' grades and attendance, schools must be prepared for technology use in the classroom. Leaders need to develop a model that would include a shared vision, entire school community involvement, specific training for staff and time for the training, a full time technology director and time for the staff to communicate and share among peers for technology to be an effective tool in the classroom curriculum.


title: Visualizing LSTM decisions

abstract: Long Short-Term Memory (LSTM) recurrent neural networks are renowned for being uninterpretable"black boxes". In the medical domain where LSTMs have shown promise, this is specifically concerning because it is imperative to understand the decisions made by machine learning models in such acute situations. This study employs techniques used in the Convolutional Neural Network domain to elucidate the operations that LSTMs perform on time series. The visualization techniques include input saliency by means of occlusion and derivatives, class mode visualization, and temporal outputs. Moreover, we demonstrate that LSTMs appear to extract features similar to those extracted by wavelets. It was found that deriving the inputs for saliency is a poor approximation and occlusion is a better approach. Moreover, analyzing LSTMs on different sets of data provide novel interpretations.


title: Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

abstract: This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.


Afterword: Human Understanding and (Latin) American Interests--The Politics and Sensibilities of Geocultural Locations


title: HERMEVENT: a news collection for emerging-event detection

abstract: News portals and microblogging platforms have become people's medium of choice for breaking news and unexpected events, thanks to their ability to provide directions and useful information more timely and more effectively than official communication channels. This has caused a flourishing of research on event detection in social-media streams. However, this research is severely affected by the scarcity of publicly-available test collections, which are needed to build proper evaluation mechanisms. In this paper we introduce a new test collection for event detection, which we dub HERMEVENT. The dataset includes a large-scale dump of tweets and news articles from a list of major Italian newspapers, spanning a time interval of approximately 3 months in 2016/2017. From this dump we extracted a set of temporal graphs with different semantic and temporal granularity. To demonstrate the good quality of our data collection, we run two state-of-the-art algorithms that detect emerging events by extracting dense sub-graphs from a temporal graph. We conduct an editorial evaluation of the events discovered by the two algorithms on a set of 780 stories, achieving an accuracy of 75% in detecting real-world events. We make the text dump, the graphs and the editorial judgements freely available. We believe that this new dataset can be a really useful contribution to support research on event detection.


title: Multi-Core Program Optimization: Parallel Sorting Algorithms in Intel Cilk Plus

abstract: New performance leaps has been achieved with multiprogramming and multi-core systems. Present parallel programming techniques and environment needs significant changes in programs to accomplish parallelism and also constitute complex, confusing and error-prone constructs and rules. Intel Cilk Plus is a C based computing system that presents a straight forward and well-structured model for the development, verification and analysis of multicore and parallel programming. In this article, two programs are developed using Intel Cilk Plus. Two sequential sorting programs in C/C++ language are converted to multi-core programs in Intel Cilk Plus framework to achieve parallelism and better performance. Converted program in Cilk Plus is then checked for various conditions using tools of Cilk and after that, comparison of performance and speedup achieved over the single-core sequential program is discussed and reported.


title: Design and development of a DTMF controlled room cleaner robot with two path-following method

abstract: Automatic control system for robotics is getting popular day by day because of its incredible reliability and especially for the time optimization capability with performance. In this paper, the implementation of a room cleaning robot is presented which has been developed using the Arduino Uno platform with control ability by cell phone from any distance using DTMF technology. It is an electronic device with self-controlled obstacle avoidance capability along with a waste and dust cleaning system. Generally, a vacuum cleaner is an effective technology for household cleaning purpose, but it requires to be operated manually by any person. The main purpose of developing a DTMF-controlled room cleaner robot is to implement an automatic cleaning system which can be operated from any distance by using the cell phone. The robot has been built on microcontroller platform and utilized DTMF technology to develop remote controlling operation. Two effective smart paths following methods have been utilized by the robot for cleaning operation along with upgraded vacuum system with necessary filter and air suction system.


title: Personal cloud storage: Usage, performance and impact of terminals

abstract: Personal cloud storage services such as Dropbox and OneDrive are popular among Internet users. They help in sharing content and backing up data by relying on the cloud to store files. The rise of mobile terminals and the presence of new providers question whether the usage of cloud storage is evolving. This knowledge is essential to understand the workload these services need to handle, their performance, and implications. In this paper we present a comprehensive characterization of personal cloud storage services. Relying on traces collected for one month in an operational network, we show that users of each service present distinct behaviors. Dropbox is now threatened by competitors, with OneDrive and Google Drive reaching large market shares. However, the popularity of the latter services seems to be driven by their integration into Windows and Android. Indeed, around 50% of their users do not produce any workload. Considering performance, providers show distinct trade-offs, with bottlenecks that hardly allow users to fully exploit their access line bandwidth. Finally, usage of cloud services is now ordinary among mobile users, thanks to the automatic backup of pictures and media files.


title: Feasibility of monolithic and 3D-stacked DC-DC converters for microprocessors in 90nm technology generation

abstract: Rapidly increasing input current of microprocessors resulted in rising cost and motherboard real estate occupied by decoupling capacitors and power routing. We show by analysis that an on-die switching DC-DC converter is feasible for future microprocessor power delivery. The DC-DC converter can be fabricated in an existing CMOS process (90nm-180nm) with a back-end thin-film inductor module. We show that 85% efficiency and 10% output voltage droop can be achieved for 4:1, 3:1, and 2:1 conversion ratios, area overhead of 5% and no additional on-die decoupling capacitance. A 4:1 conversion results in 3.4x smaller input current and 6.8x smaller external decoupling.


title: Randomized clinical trial of tranexamic acid-free fibrin sealant during vascular surgical procedures.

abstract: BACKGROUND ::: This study evaluated the safety and haemostatic effectiveness of a fibrin sealant (EVICEL(™) Fibrin Sealant (Human)) during vascular surgery. ::: ::: ::: METHODS ::: This prospective randomized controlled trial compared the haemostatic effectiveness of fibrin sealant (75 patients) or manual compression (72) in polytetrafluoroethylene (PTFE) arterial anastomoses. The primary endpoint was the absence of bleeding at the anastomosis at 4 min after randomization. Secondary endpoints included haemostasis at 7 and 10 min, treatment failures and the incidence of complications potentially related to bleeding. Adverse events were recorded. ::: ::: ::: RESULTS ::: A higher percentage of patients who received fibrin sealant versus manual compression achieved haemostasis at 4 min (85 versus 39 per cent respectively; odds ratio 11·34, 95 per cent confidence interval 4·67 to 27·52; P < 0·001). Similarly, a higher percentage of patients who received fibrin sealant achieved haemostasis at 7 and 10 min (both P < 0·001). The incidence of treatment failure was lower in the fibrin sealant group (P < 0·001). The rate of complications potentially related to bleeding was similar (P = 0·426). Some 64 per cent of patients who received fibrin sealant experienced at least one adverse event, compared with 71 per cent who received manual compression. ::: ::: ::: CONCLUSION ::: This fibrin sealant was safe, and significantly shortened the time to haemostasis in vascular procedures using PTFE. ::: ::: ::: REGISTRATION NUMBER ::: NCT00154141 (http://www.clinicaltrials.gov).


abstract: PART OF THE NEW JONES & BARTLETT LEARNING INFORMATION SYSTEMS SECURITY & ASSURANCE SERIES The Second Edition of Security Strategies in Linux Platforms and Applications covers every major aspect of security on a Linux system. Written by an industry expert, this book is divided into three natural parts to illustrate key concepts in the field. It opens with a discussion of the risks, threats, and vulnerabilities associated with Linux as an operating system using current examples and cases. Part 2 discusses how to take advantage of the layers of security available to Linux--user and group options, filesystems, and security options for important services, as well as the security modules associated with AppArmor and SELinux. The book closes with a look at the use of both open source and proprietary tools when building a layered security strategy for Linux operating system environments. Using real-world examples and exercises, this useful resource incorporates hands-on activities to walk readers through the fundamentals of security strategies related to the Linux system. Instructor Materials for Security Strategies in Linux Platforms and Applications include: PowerPoint Lecture Slides Exam Questions Case Scenarios/Handouts About the Series This book is part of the Information Systems Security and Assurance Series from Jones and Bartlett Learning. Designed for courses and curriculums in IT Security, Cybersecurity, Information Assurance, and Information Systems Security, this series features a comprehensive, consistent treatment of the most current thinking and trends in this critical subject area. These titles deliver fundamental information-security principles packed with real-world applications and examples. Authored by Certified Information Systems Security Professionals (CISSPs), they deliver comprehensive information on all aspects of information security. Reviewed word for word by leading technical experts in the field, these books are not just current, but forward-thinkingputting you in the position to solve the cybersecurity challenges not just of today, but of tomorrow, as well.


title: A Holistic View of Data Warehousing in Education

abstract: Data warehousing (DW) is a widespread and essential practice in business organizations that support the data analytic and decision-making process. Despite the importance of DW in complex organizations, the adoption of a data warehouse (DWH) in education is apparently lower compared with other industries. To clarify this situation, this paper presents a systematic mapping that includes the study of empirical research papers from 2008 to 2018 on the topic of DW in education. For this paper, we applied a qualitative and quantitative approach based on a four-stage research method with the objective to have a holistic view of DWHs in education. After filtering and applying the proposed method, 34 relevant papers were identified and studied in detail. The study revealed interesting facts; for example, Kimball’s approach is the most applied methodology for DWH design in education. In addition, a mapping between this comprehensive collection of research papers covering educational DW and six dimensions of analysis (schema proposal, analysis of the user requirements, analysis of the business requirements, effectiveness, implementation, and data analysis) was performed. From this analysis, we discovered that the star schema is the most implemented approach. The purpose of the mapping was to explore and identify the priority areas of research and the research gaps within the academic community. These gaps are a source of opportunities to start new lines of research.


title: Achieving Consensus in Public Decision Making: Applying Interest-Based Problem Solving to the Challenges of Intergovernmental Collaboration

abstract: This article examines how interests can be used to guide collaborative public decision making in an intergovernmental setting. A brief overview of the governance challenge at the local level in the United States is provided first, highlighting the unorganized nature of the domain of interlocal relations. At the local level, the need for regional solutions to public policy problems has been a major challenge for policy makers over the past decade. Jurisdictional and locality-based interests have demanded a new set of intergovemmental management skills in interlocal relations. The interest-based problem-solving process is then delineated, focusing on the application of a collaborative approach in a regional, interlocal context. Interest-based problem solving was applied in a novel way to the problem domain of interlocal relations in a setting that includes four municipalities and 13 unincorporated areas. This article concludes with a discussion of the implications of using interest-based collaborative processes in regional public decision making


title: Sentiment Analysis of Twitter Data

abstract: We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the use of a tree kernel to obviate the need for tedious feature engineering. The new features (in conjunction with previously proposed features) and the tree kernel perform approximately at the same level, both outperforming the state-of-the-art baseline.