title: Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Networks

abstract: Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. We also propose a multi-task learning framework for reconstructing audio and visual signals at the output layer. Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then, fused into a joint net-work to generate enhanced speech (the primary task) and reconstructed image (the secondary task) at the output layer. It is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five instrumental criteria. Results show that the AVDCNN model yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual in-formation into the SE process. In addition, the AVDCNN model also outperforms an existing audio-visual SE model, confirming its capability of effectively combining audio and visual information in SE.


title: Empirical Evaluation of Smartphone Augmented Reality Browsers in an Urban Tourism Destination Context

abstract: Today, exposure to new and unfamiliar environments is a necessary part of daily life. Effective communication of location-based information through location-based services has become a key concern for cartographers, geographers, human-computer interaction and professional designers alike. Recently, much attention was directed towards Augmented Reality (AR) interfaces. Current research, however, focuses primarily on computer vision and tracking, or investigates the needs of urban residents, already familiar with their environment. Adopting a user-centred design approach, this paper reports findings from an empirical mobile study investigating how tourists acquire knowledge about an unfamiliar urban environment through AR browsers. Qualitative and quantitative data was used in the development of a framework that shifts the perspective towards a more thorough understanding of the overall design space for such interfaces. The authors analysis provides a frame of reference for the design and evaluation of mobile AR interfaces. The authors demonstrate the application of the framework with respect to optimization of current design of AR.


title: Industry 4.0 key research topics: A bibliometric review

abstract: The digitalization phenomenon and the growing adoption of innovative technologies is favoring the transformation of organizations into Cyber Physical Systems. The transition towards this new organizational system is often labeled as Industry 4.0. The attention to this topic is increasing lately, even if heterogeneous denominations and approaches are adopted to addresses this phenomenon. This article aims to shed some light between twelve different approaches of industry 4.0 described in literature. Conducting a bibliometric review, the paper identifies the key topics related to Industry 4.0 by the literature available.


title: Mise en Place: Unsupervised Interpretation of Instructional Recipes

abstract: We present an unsupervised hard EM approach to automatically mapping instructional recipes to action graphs, which define what actions should be performed on which objects and in what order. Recovering such structures can be challenging, due to unique properties of procedural language where, for example, verbal arguments are commonly elided when they can be inferred from context and disambiguation often requires world knowledge. Our probabilistic model incorporates aspects of procedural semantics and world knowledge, such as likely locations and selectional preferences for different actions. Experiments with cooking recipes demonstrate the ability to recover high quality action graphs, outperforming a strong sequential baseline by 8 points in F1, while also discovering general-purpose knowledge about cooking.


Discovering Process Models from Incomplete Event Logs using Conjoint Occurrence Classes


title: A machine learning framework for programming by example

abstract: Learning programs is a timely and interesting challenge. In Programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, by learning weights that relate textual features describing the provided input-output examples to plausible sub-components of a program. This generic learning framework lets us address problems beyond the scope of earlier PBE systems. Experiments on a prototype implementation show that learning improves search and ranking on a variety of text processing tasks found on help forums.


title: Magnesium in major depression

abstract: There are contradictory data regarding the levels of magnesium in patients with major depression (MD) and how antidepressants influence their concentration. Our results show erythrocyte magnesium in patients with MD (44.39 ± 2.7 mg/L vs 59.1 ± 3.2 mg/L in control group, p 23) was a moderate decrease in plasmatic magnesium observed (17.7 ± 1.5 mg/L vs 22.9 ± 3.3 mg/L in control group). Therapy with antidepressants from different groups and with different mechanisms of action, such as amytriptiline (25 mg x 3/day per os, 4 weeks) and sertraline (50 mg x 3/day per os, 4 weeks) leads to a significant increase of magnesium concentration in erythrocytes (57.6 ± 4.5 mg/L after amytriptiline, respectively 56.9 ± 3.2 mg/L after sertraline, p < 0.05 vs before therapy). At the same time, in patients with MD, plasmatic levels of zinc were significantly decreased before therapy and increased after treatment with amytriptiline and sertraline (0.68 ± 0.09 mg/L before treatment vs 0.9 ± 0.07 after amytriptiline). There is a positive correlation between concentrations of magnesium in erythrocytes and the clinical evolution of patients with MD. We consider that increasing intracellular concentration is a component of the antidepressant mechanism of sertraline and amytriptiline and maybe of other antidepressants. Anhedonia and autolytic tendencies are important elements of MD symptomatology. We tested the influence of MgCl 2 0.2 mM/kg/day on a reward system using conditioned place preference (Panlab) in rats. Our data show a moderate stimulation of the reward system by magnesium (290.6 ± 27 s time spent in a conditioned compartment before magnesium treatment and 363.3 ± 16 s after magnesium treatment) that reflects a stimulation of the reward system (RS). We consider that a magnesium-induced stimulation of the RS is an important issue for treating anhedonia in patients with MD. An increase of intracellular magnesium may be part of the mechanism of action of antidepressants.


title: What Scientists Who Study Emotion Agree About

abstract: In recent years, the field of emotion has grown enormously—recently, nearly 250 scientists were identified who are studying emotion. In this article, I report a survey of the field, which revealed high agreement about the evidence regarding the nature of emotion, supporting some of both Darwin’s and Wundt’s 19th century proposals. Topics where disagreements remain were also exposed.


title: Impedance Control of a Rotary Series Elastic Actuator for Knee Rehabilitation

abstract: Abstract This paper deals with impedance control of a rotary Series Elastic Actuator designed to assist in flexion/extension of the knee joint during physical therapy. The device includes a DC motor, a worm gear and a customized torsion spring. Since the elastic element is the most important component in the actuator design, a finite element analysis is used to meet the specific requirements of knee assistance. The proposed impedance control scheme comprises three cascade controllers: an inner-loop PI velocity controller, a PI torque controller, and an outer-loop PD position controller. The impedance controller's performance is evaluated through the Frequency Response Function analysis, and experimental results demonstrate the ability of the system to reproduce impedance behavior within an amplitude and frequency range compatible for gait assistance.


title: A lens-coupled 210–270 GHz circularly polarized FMCW radar transceiver module in SiGe technology

abstract: A 240 GHz homodyne circularly polarized SiGe FMCW radar transceiver module with a single on-chip antenna is presented. The transceiver chip is implemented in a low-cost 0.13-µm SiGe HBT technology version with cutoff frequencies ƒT/ƒmax of 300/450 GHz. The transmit block comprises a wideband ×16 frequency multiplier chain, a 4-stage PA, while the receive block consists of a 3-stage PA acting as a preamplifier with high linearity, a fundamental down-conversion mixer, and a 4-stage PA to drive the mixer. A Gysel power splitter, a coupled line quadrature hybrid coupler, and a dual polarization on-chip antenna are added on-chip to realize a fully integrated radar transceiver. All building blocks are operated differentially. The use of a single antenna in the circularly polarized radar transceiver leads to compact size and high system SNR. The measured peak radiated power from the lens-packaged radar module is +5dBm and the SSB noise figure is 21 dB. The characterization bandwidth of the radar transceiver is 54 GHz around the center frequency of 240 GHz, and the simulated Tx-to-Rx leakage is below −23 dB. After system calibration the range resolution of 2.75mm is achieved, which exactly corresponds to the theoretical BW-limited resolution value.


title: Emerging Technologies and Research Challenges for 5G Wireless Networks

abstract: As the take-up of Long Term Evolution (LTE)/4G cellular accelerates, there is increasing interest in technologies that will define the next generation (5G) telecommunication standard. This article identifies several emerging technologies which will change and define the future generations of telecommunication standards. Some of these technologies are already making their way into standards such as 3GPP LTE, while others are still in development. Additionally, we will look at some of the research problems that these new technologies pose.


title: Powering aerial surveillance drones

abstract: The presented article analyzes, adopts and develops new solutions with regard to the aerial electric supply solutions for operational surveillance drones. This article proposes a number of innovative new usages and original solutions for continuously operating surveillance drones on a predefined path or around a predefined perimeter and, afterwards, steps are discussed which have to be followed to provide ingredients and end-to-end systems in order to transform this project into reality i.e. the aerial electric supply of surveillance drones.


title: Evolutionary Programming in Electromagnetic Optimization: A Review

abstract: In this paper, we review recent advances in evolutionary programming (EP) and its implementation in various antenna, microwave, frequency selective surfaces and RF circuit optimization problems. EP, unlike the other two paradigms of evolutionary computational techniques, namely, genetic algorithms (GA) and evolution strategies (ES), uses a mutation only variation operator where adaptive and/or self-adaptive techniques exist, or can easily be designed, for adapting the parameters of mutation operator during the evolution process. We present the so-called meta-EP and design of its mutation operator, using Gaussian, Cauchy and Poisson mutations as well as a hybrid of these mutations, for continuous, discrete and mixed parameter electromagnetic optimization problems. In addition, an efficient hybrid use of EP, gradient search methods and cluster analysis, as well as a novel hybrid EP-GA algorithm are discussed. Examples presented include optimizations of corrugated horn antennas, multilayered stacked microstrip antennas, Yagi-Uda arrays, partially reflective surfaces, dielectric filters, meander-line polarizer and RF duplexers


title: Exploiting bit-level parallelism in GPGPUs: A case study on KeeLoq exhaustive key search attack

abstract: Graphic Processing Units (GPU) are increasingly popular in the field of high-performance computing for their ability to provide computational power for massively parallel problems at a reduced cost. However, the programming model exposed by the GPGPU software development tools is often insufficient to achieve full performance, and a major rethinking of algorithmic choices is needed. In this paper, we showcase such an effect on a case study drawn from the cryptography application domain. The pervasive use of cryptographic primitives in modern embedded systems is a growing trend. Small, efficient cryptosystems have been effectively employed to design and implement keyless password-based access control systems in various wireless authentication applications. The security margin provided by these lightweight ciphers should be accurately examined in light of the speed and area constraints imposed by the target environment. We present a re-design of the ASIC-oriented Keeloq implementation to perform efficient exhaustive key search attacks while fitting tightly the parallel programming model exposed by modern GPUs. Indeed, the bitslicing technique allows the intrinsic parallelism offered by word-oriented SIMD computations to be effectively exploited. Through proper adaptation of the algorithm implementation to a platform radically different from the one it was designed for, we achieved a ×40 speedup in the computation time with respect to a single-core CPU bruteforce attack, employing only consumer grade hardware. The outstanding speedup obtainable points to a significant weakening of the cipher security margin, since it proves that anyone with off-the-shelf hardware is able to circumvent the security measures in place.


title: Classifying and Recovering from Sensing Failures in Autonomous Mobile Robots

abstract: This paper presents a characterization of sensing failures in autonomous mobile robots, a methodology for classification and recovery, and a demonstration of this approach on a mobile robot performing landmark navigation. A sensing failure is any event leading to defective perception, including sensor malfunctions, software errors, environmental changes, and errant expectations. The approach demonstrated in this paper exploits the ability of the robot to interact with its environment to acquire additional information for classification (i.e., active perception). A Generate and Test strategy is used to generate hypotheses to explain the symptom resulting from the sensing failure. The recovery scheme replaces the affected sensing processes with an alternative logical sensor. The approach is implemented as the Sensor Fusion Effects Exception Handling (SFX-EH) architecture. The advantages of SFX-EN are that it requires only a partial causal model of sensing failure, the control scheme strives for a fast response, tests are constructed so as to prevent confounding from collaborating sensors which have also failed, and the logical sensor organization allows SFX-EH to be interfaced with the behavioral level of existing robot architectures.


title: Counterfactual Risk Minimization: Learning from Logged Bandit Feedback

abstract: We develop a learning principle and an efficient algorithm for batch learning from logged bandit feedback. This learning setting is ubiquitous in online systems (e.g., ad placement, web search, recommendation), where an algorithm makes a prediction (e.g., ad ranking) for a given input (e.g., query) and observes bandit feedback (e.g., user clicks on presented ads). We first address the counterfactual nature of the learning problem through propensity scoring. Next, we prove generalization error bounds that account for the variance of the propensity-weighted empirical risk estimator. These constructive bounds give rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM can be used to derive a new learning method -- called Policy Optimizer for Exponential Models (POEM) -- for learning stochastic linear rules for structured output prediction. We present a decomposition of the POEM objective that enables efficient stochastic gradient optimization. POEM is evaluated on several multi-label classification problems showing substantially improved robustness and generalization performance compared to the state-of-the-art.


title: Action Anticipation with RBF Kernelized Feature Mapping RNN

abstract: We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN. Our novel RNN architecture builds upon three effective principles of machine learning, namely parameter sharing, Radial Basis Function kernels and adversarial training. Using only some of the earliest frames of a video, the feature mapping RNN is able to generate future features with a fraction of the parameters needed in traditional RNN. By feeding these future features into a simple multi-layer perceptron facilitated with an RBF kernel layer, we are able to accurately predict the action in the video. In our experiments, we obtain 18% improvement on JHMDB-21 dataset, 6% on UCF101-24 and 13% improvement on UT-Interaction datasets over prior state-of-the-art for action anticipation.


title: Playing Doom with SLAM-Augmented Deep Reinforcement Learning

abstract: A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep Q-Learning Network (DQN), by adding details of objects and structural elements encountered, along with the agent's localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction.We evaluate the efficacy of our approach in Doom, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies.


title: Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments

abstract: In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using global nearest neighborhood (GNN) method. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.


title: Fog detection system based on computer vision techniques

abstract: In this document, a real-time fog detection system using an on-board low cost b&w camera, for a driving application, is presented. This system is based on two clues: estimation of the visibility distance, which is calculated from the camera projection equations and the blurring due to the fog. Because of the water particles floating in the air, sky light gets diffuse and, focus on the road zone, which is one of the darkest zones on the image. The apparent effect is that some part of the sky introduces in the road. Also in foggy scenes, the border strength is reduced in the upper part of the image. These two sources of information are used to make this system more robust. The final purpose of this system is to develop an automatic vision-based diagnostic system for warning ADAS of possible wrong working conditions. Some experimental results and the conclusions about this work are presented.


title: Designing Haptic Assistive Technology for Individuals Who Are Blind or Visually Impaired

abstract: This paper considers issues relevant for the design and use of haptic technology for assistive devices for individuals who are blind or visually impaired in some of the major areas of importance: Braille reading, tactile graphics, orientation and mobility. We show that there is a wealth of behavioral research that is highly applicable to assistive technology design. In a few cases, conclusions from behavioral experiments have been directly applied to design with positive results. Differences in brain organization and performance capabilities between individuals who are “early blind” and “late blind” from using the same tactile/haptic accommodations, such as the use of Braille, suggest the importance of training and assessing these groups individually. Practical restrictions on device design, such as performance limitations of the technology and cost, raise questions as to which aspects of these restrictions are truly important to overcome to achieve high performance. In general, this raises the question of what it means to provide functional equivalence as opposed to sensory equivalence.


title: Trainable COSFIRE filters for vessel delineation with application to retinal images

abstract: Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se = 0.7655, Sp = 0.9704; STARE: Se = 0.7716, Sp = 0.9701; CHASE_DB1: Se = 0.7585, Sp = 0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.


title: Ocular Pain and Impending Blindness During Facial Cosmetic Injections: Is Your Office Prepared?

abstract: Soft tissue filler injections are the second most common non-surgical procedure performed by the plastic surgeon. Embolization of intravascular material after facial injection is a rare but terrifying outcome due to the high likelihood of long-term sequela such as blindness and cerebrovascular accident. The literature is replete with examples of permanent blindness caused by injection with autologous fat, soft tissue fillers such as hyaluronic acid, PLLA, calcium hydroxyl-apatite, and even corticosteroid suspensions. However, missing from the discussion is an effective treatment algorithm that can be quickly and safely followed by injecting physicians in the case of an intravascular injection with impending blindness. In this report, we present the case of a 64-year-old woman who suffered from blindness and hemiparesis after facial cosmetic injections performed by a family physician. We use this case to create awareness that this complication has become more common as the number of injectors and patients seeking these treatments have increased exponentially over the past few years. We share in this study our experience with the incorporation of a "blindness safety kit" in each of our offices to promptly initiate treatment in someone with embolization and impending blindness. The kit contains a step-by-step protocol to follow in the event of arterial embolization of filler material associated with ocular pain and impending loss of vision. ::: ::: ::: LEVEL OF EVIDENCE V ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: Critical factors in successful project implementation

abstract: This paper describes a process used to determine critical success factors that are felt to be predictive of successful project management. Full time managers who have had experience with projects were used to generate critical success factors that they felt to be crucial to successful project implementation. Ten factors were discovered that relate well to previous theoretical formulations in the literature. In addition, these ten factors have been linked together in an interdependent quasi-sequential framework. This research has provided the basis for developing a behavioral instrument to be used as a diagnostic for assessing the status of any project as determined by the ten factor model.


abstract: Congenital tumors of the central nervous system (CNS) are often arbitrarily divided into "definitely congenital" (present or producing symptoms at birth), "probably congenital" (present or producing symptoms within the first week of life), and "possibly congenital" (present or producing symptoms within the first 6 months of life). They represent less than 2% of all childhood brain tumors. The clinical features of newborns include an enlarged head circumference, associated hydrocephalus, and asymmetric skull growth. At birth, a large head or a tense fontanel is the presenting sign in up to 85% of patients. Neurological symptoms as initial symptoms are comparatively rare. The prenatal diagnosis of congenital CNS tumors, while based on ultrasonography, has significantly benefited from the introduction of prenatal magnetic resonance imaging studies. Teratomas constitute about one third to one half of these tumors and are the most common neonatal brain tumor. They are often immature because of primitive neural elements and, rarely, a component of mixed malignant germ cell tumors. Other tumors include astrocytomas, choroid plexus papilloma, primitive neuroectodermal tumors, atypical teratoid/rhabdoid tumors, and medulloblastomas. Less common histologies include craniopharyngiomas and ependymomas. There is a strong predilection for supratentorial locations, different from tumors of infants and children. Differential diagnoses include spontaneous intracranial hemorrhage that can occur in the presence of coagulation factor deficiency or underlying vascular malformations, and congenital brain malformations, especially giant heterotopia. The prognosis for patients with congenital tumors is generally poor, usually because of the massive size of the tumor. However, tumors can be resected successfully if they are small and favorably located. The most favorable outcomes are achieved with choroid plexus tumors, where aggressive surgical treatment leads to disease-free survival.


title: Human vs. Computer in Scene and Object Recognition

abstract: Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local/global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward.


title: VSA-HD: From the enumeration analysis to the prototypical implementation

abstract: This paper presents design, implementation and performance of a new Variable Stiffness Actuator (VSA) based on Harmonic Drives (VSA-HD), which is an improvement over past work reported in [1], [2]. While previous prototypes have been developed to demonstrate the effectiveness of the variable stiffness actuation principle and the possibility to develop a compact and reliable actuator, the VSA-HD has been obtained by exploring the performance of the enumeration of all VSA made out a basic components set (i.e. two prime movers, two harmonic-drive gears, and the output shaft) and all the feasible interconnections between them as presented in [3]. Along this enumeration the VSA-HD conceptual layout has been selected as being good trade-off between mechanical complexity and overall performance. This paper discusses in depth the actuator mechanical layout, highlighting the main characteristics of the new design. A model for the actuator is introduced and validated by experimental results.


title: Isolated Syndesmosis Ankle Injury

abstract: Full article available online at Healio.com/Orthopedics. Search: 20121120-13 Isolated syndesmosis injuries often go unrecognized and are diagnosed as lateral ankle sprains; however, they are more disabling than lateral ankle sprains. The reported incidence of isolated syndesmosis injuries in acute ankle sprains ranges between 1% and 16%. When ankle disability lasts for more than 2 months after an ankle sprain, the incidence increases to 23.6%. Diagnostic workup may include stress radiographs, magnetic resonance imaging, or diagnostic arthroscopy. A simple stress test radiograph may reveal an unstable grade III syndesmosis sprain that may go unrecognized on plain anteroposterior and mortise or lateral radiographs of the ankle. The duration of symptoms in isolated syndesmosis injury is longer and more severe, often leading to chronic symptoms or ankle instability requiring operative stabilization. This article describes the clinical presentation, injury classification, and operative sta bilization techniques of isolated syndesmosis injuries. The authors performed their preferred operative stabilization technique for isolated syndesmosis injury—arthroscopic debridement of the ankle with syndesmotic stabilization with a syndesmotic screw— in 4 patients. All patients were evaluated 1 year postoperatively with subjective and objective assessment scales. Three of 4 patients showed good improvement of general subjective ankle symptoms and subjective ankle instability rating and a high Sports Ankle Rating System score after 1 year.


title: Application of Improved KNN Algorithm in Air Quality Assessment

abstract: In order to solve the problem of low accuracy and low efficiency in air quality detection, this paper proposes an improved KNN algorithm. Firstly, Cluster processing is performed in different categories of samples in the data set to reduce the amount of data in the experimental sample. Then feature selection and preprocessing of the data is performed, and determine the optimal parameters of the model based on the grid search, improving the k-value selection strategy in the original KNN algorithm. Finally an accurate and rapid assessment of air quality is achieved. The experimental results show that the improved KNN algorithm has an air quality evaluation accuracy of 94.53%, which is superior to the traditional algorithm, it improves the accuracy and efficiency of air quality evaluation, and plays a certain role in analyzing air quality.


title: Overview of radar waveform diversity

abstract: Radar waveform diversity has received considerable attention in recent years due to increasing spectral congestion and the burgeoning capabilities of digital waveform generation. The promise of waveform diversity is far greater utilization of available degrees of freedom to enhance sensing performance and to even facilitate new operating modes. This tutorial provides an overview of this very broad topic, from the basic principles upon which it is founded to the myriad different areas being explored in research for practical sensing applications.


title: FAST POWER LINE DETECTION AND LOCALIZATION USING STEERABLE FILTER FOR ACTIVE UAV GUIDANCE

abstract: In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.


title: A novel prototype and simulation model for real time solid wastebin monitoring system

abstract: This research deals with an exclusive solution to monitor the solid waste bin condition on real time. The system architecture ::: is designed using wireless sensor networks. A set of carefully chosen sensors are used to measure the status of the bins and ::: ZigBee and GPRS are used as communication technologies. The physical architecture of the system contains three levels ::: such as smart bins for the measurement and transmission of bin status, gateways for storing and forwarding bin data ::: to server and control station for storing and analyzing the data. After the framework design, a simulation is performed ::: using Castalia to ensure the feasibility and accuracy of the system. The simulation is performed for ten bins and taking ::: ten samples from each bin where a fi ll level threshold of 15 is considered. The simulation result shows that, the proposed ::: system would be able to automate the solid waste monitoring process that helps to optimize waste collection route.


title: NDA Waveform Estimation in the Low-SNR Regime

abstract: This correspondence addresses the problem of nondata-aided waveform estimation for digital communications. Based on the unconditional maximum likelihood criterion, the main contribution of this correspondence is the derivation of a closed-form solution to the waveform estimation problem in the low signal-to-noise ratio regime. The proposed estimation method is based on the second-order statistics of the received signal and a clear link is established between maximum likelihood estimation and correlation matching techniques. Compression with the signal-subspace is also proposed to improve the robustness against the noise and to mitigate the impact of abnormals or outliers.


title: TeraHertz Photonics for Wireless Communications

abstract: Optical fibre transmission has enabled greatly increased transmission rates with 10 Gb/s common in local area networks. End users find wireless access highly convenient for mobile communication. However, limited spectrum availability at microwave frequencies results in per-user transmission rates limited to much lower values, e.g., 500 Mb/s for 5-GHz band IEEE 802.11ac. Extending the high data-rate capacity of optical fiber transmission to wireless devices requires greatly increased carrier frequencies. This paper will describe how photonic techniques can enable ultrahigh capacity wireless data distribution and transmission using signals at millimeter-wave and TeraHertz (THz) frequencies.


title: Proteomics of Human Neurodegenerative Diseases

abstract: The technology, experimental approaches, and bioinformatics that support proteomic research are evolving rapidly. The application of these new capabilities to the study of neurodegenerative diseases is providing insight into the biochemical pathogenesis of neurodegeneration as well as fueling major efforts in biomarker discovery. Here, we review the fundamentals of commonly used proteomic approaches and the outcomes of these investigations with autopsy and cerebrospinal fluid samples from patients with neurodegenerative diseases.


title: Automated Bug Finding in Video Games: A Case Study for Runtime Monitoring

abstract: Runtime verification is the process of observing a sequence of events generated by a running system and comparing it to some formal specification for potential violations. We show how the use of a runtime monitor can greatly speed up the testing phase of a video game under development by automating the detection of bugs when the game is being played. We take advantage of the fact that a video game, contrarily to generic software, follows a special structure that contains a “game loop.” This game loop can be used to centralize the instrumentation and generate events based on the game's internal state. We report on experiments made on a sample of six real-world video games of various genres and sizes by successfully instrumenting and efficiently monitoring various temporal properties over their execution, including actual bugs reported in the games' bug tracking database in the course of their development.


title: Transparent Triboelectric Nanogenerators and Self-Powered Pressure Sensors Based on Micropatterned Plastic Films

abstract: Transparent, flexible and high efficient power sources are important components of organic electronic and optoelectronic devices. In this work, based on the principle of the previously demonstrated triboelectric generator, we demonstrate a new high-output, flexible and transparent nanogenerator by using transparent polymer materials. We have fabricated three types of regular and uniform polymer patterned arrays (line, cube, and pyramid) to improve the efficiency of the nanogenerator. The power generation of the pyramid-featured device far surpassed that exhibited by the unstructured films and gave an output voltage of up to 18 V at a current density of ∼0.13 μA/cm2. Furthermore, the as-prepared nanogenerator can be applied as a self-powered pressure sensor for sensing a water droplet (8 mg, ∼3.6 Pa in contact pressure) and a falling feather (20 mg, ∼0.4 Pa in contact pressure) with a low-end detection limit of ∼13 mPa.


title: An ensemble of neural networks for weather forecasting

abstract: This study presents the applicability of an ensemble of artificial neural networks (ANNs) and learning paradigms for weather forecasting in southern Saskatchewan, Canada. The proposed ensemble method for weather forecasting has advantages over other techniques like linear combination. Generally, the output of an ensemble is a weighted sum, which are weight-fixed, with the weights being determined from the training or validation data. In the proposed approach, weights are determined dynamically from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight. The proposed ensemble model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN), radial basis function network (RBFN), Hopfield model (HFM) predictive models and regression techniques. The data of temperature, wind speed and relative humidity are used to train and test the different models. With each model, 24-h-ahead forecasts are made for the winter, spring, summer and fall seasons. Moreover, the performance and reliability of the seven models are then evaluated by a number of statistical measures. Among the direct approaches employed, empirical results indicate that HFM is relatively less accurate and RBFN is relatively more reliable for the weather forecasting problem. In comparison, the ensemble of neural networks produced the most accurate forecasts.


title: Less Data Delivers Higher Search Effectiveness for Keyword Queries

abstract: As many users, such as scientists, do not know the schema and/or content of their databases, they cannot precisely formulate their information needs using formal query languages, such as SQL. To help these users, researchers have proposed keyword query interfaces over which users can submit their information need using a set of keywords without the precise knowledge about the schema or content of the database. Despite their usability, keyword query interfaces suffer from low effectiveness in answering queries. Therefore, they may return many non-relevant answers or do not return many answers related to the input queries. It is well established that the effectiveness of answering queries decreases as the size of the dataset grows, given all other conditions are the same. In this paper, we propose an approach that uses only a relatively small subset of the database to answer most queries effectively. Since this subset may not contain the relevant answers to many queries, we also propose a method that predicts whether a query can be answered more effectively using this subset or the entire database. Our comprehensive empirical studies using multiple real-world databases and query workloads indicate that our approach significantly improves both the effectiveness and efficiency of answering queries.


title: The first finding of the Black Sea snail Lithoglyphus naticoides (Gastropoda) and its associated species-specific trematoda in the Upper Volga basin

abstract: After the first finding in the Volga River delta in early 1990s, the Ponto-Azov snail Lithoglyphus naticoides expanded its range toward the northern water bodies of the Upper Volga basin. During 2004–2007, new populations of L. naticoides with a high level of infection by specific trematode species were found in the Cheboksary and Rybinsk reservoirs. In addition, metacercariae of the genus Apophallus associated with L. naticoides were noted in the fishes of the Gorky and Ivankov reservoirs.


title: A Dual-Band Circularly-Polarized Patch Antenna with a Novel Asymmetric Slot for WiMAX Application

abstract: A compact design of a circularly-polarized (CP) microstrip antenna to achieve dual-band behavior for WiMAX applications is presented. A single-layered feed is used to excite a single square patch integrated with a novel asymmetrical slot and two different truncated corners to achieve CP polarization in both bands. Besides its struc- tural simplicity, ease of fabrication and low cost, the pro- posed antenna features a satisfactory impedance band- width of 7.2 % in the lower band (2.53 GHz) and 3.6 % in the upper band (5.73 GHz). The measured radiation pat- tern of the proposed antenna demonstrates directional patterns in both E- and H-planes with a 3 dB axial ratio of 2 % and 3.2 % in the lower and upper band, respectively.


title: Commercial 4G LTE cellular networks for supporting emerging IoT applications

abstract: This paper presents a detailed realistic performance analysis and feasibility assessment of commercial LTE cellular networks when used to support a diverse set of emerging IoT applications ranging from mission-critical applications that require ultra-reliable and stringent E2E latency to those that require support of massive number of connected devices with relaxed latency and reliability requirements (e. g., smart meters). Since total uplink (UL) traffic of most of these IoT applications is higher than total downlink (DL) traffic, we focus our analysis only on UL performance. It is shown that current commercial 4G LTE systems can't support emerging mission-critical IoT applications with latency requirements of less than 100 ms. 4G LTE systems can satisfy the requirements of IoT applications when latency requirements are > 150 ms.


title: Firewall Rules Analysis

abstract: In this paper, we propose a method to analyze the firewall policy or rule-set using Relational Algebra and Raining 2D-Box Model. It can discover all the anomalies in the firewall rule-set in the format that is usually used by many firewall products such as Cisco Access Control List, IPTABLES, IPCHAINS and Check Point Firewall-1. While the existing analyzing methods consider the anomalies between any two rules in the firewall rule-set, we consider more than two rules together at the same time to discover the anomaly. Therefore we can find all the hidden anomalies in the firewall rule-set. Results from analyzing can be used with the proposed rules-combination method presented in this paper, to minimize the firewall rule without changing the policy. Finally, we have developed an application based on the proposed analyzing method. This application could help administrator to analyze and modify a complex firewall policy with less error.


title: Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

abstract: With quantum computing technologies nearing the era of commercialization and quantum supremacy, machine learning (ML) appears as one of the promising"killer"applications. Despite significant effort, there has been a disconnect between most quantum machine learning proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices to demonstrate quantum enhancement in the near future. In this contribution to the focus collection on"What would you do with 1000 qubits?", we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are still struggling, such as generative models in unsupervised or semisupervised learning, instead of the popular and much more tractable ML techniques. We also highlight the case of classical datasets with potential quantum-like statistical correlations where quantum models could be more suitable. We focus on hybrid quantum-classical approaches and illustrate some of the key challenges we foresee for near-term implementations. Finally, we introduce the quantum-assisted Helmholtz machine (QAHM); an attempt to use near-term quantum devices to tackle high-resolution datasets on continuous variables. Instead of using quantum computers to assist deep learning, as previous approaches do, the QAHM uses deep learning to extract a low-dimensional binary representation of data, suitable for relatively small quantum processors which can assist the training of an unsupervised generative model. Although we illustrate this concept on a quantum annealer, other quantum platforms could benefit as well from this hybrid quantum-classical framework.


title: ParaBox: Exploiting Parallelism for Virtual Network Functions in Service Chaining

abstract: Service Function Chains (SFCs) comprise a sequence of Network Functions (NFs) that are typically traversed in-order by data flows. Consequently, SFC delay grows linearly with the length of the SFC. Yet, for highly latency sensitive applications, this delay may be unacceptable---particularly when the constituent NFs are virtualized, running on commodity servers. In this paper, we investigate how SFC latency may be reduced by exploiting opportunities for parallel packet processing across NFs. We propose ParaBox, a novel hybrid packet processing architecture that, when possible, dynamically distributes packets to VNFs in parallel and merges their outputs intelligently to ensure the preservation of correct sequential processing semantics. To demonstrate the feasibility of our approach, we implement a ParaBox prototype on top of the DPDK-enabled Berkeley Extensible Software Switch. Our preliminary experiment results show that ParaBox can not only significantly reduce the service chaining latency, but also improve throughput.


title: Community detection: Topological vs. topical

abstract: The evolution of the Web has promoted a growing interest in social network analysis, such as community detection. Among many different community detection approaches, there are two kinds that we want to address: one considers the graph structure of the network (topology-based community detection approach); the other one takes the textual information of the network nodes into consideration (topic-based community detection approach). This paper conducted systematic analysis of applying a topology-based community detection approach and a topic-based community detection approach to the coauthorship networks of the information retrieval area and found that: (1) communities detected by the topology-based community detection approach tend to contain different topics within each community; and (2) communities detected by the topic-based community detection approach tend to contain topologically-diverse sub-communities within each community. The future community detection approaches should not only emphasize the relationship between communities and topics, but also consider the dynamic changes of communities and topics.


title: Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies

abstract: In this work, we provide theoretical guarantees for reward decomposition in deterministic MDPs. Reward decomposition is a special case of Hierarchical Reinforcement Learning, that allows one to learn many policies in parallel and combine them into a composite solution. Our approach builds on mapping this problem into a Reward Discounted Traveling Salesman Problem, and then deriving approximate solutions for it. In particular, we focus on approximate solutions that are local, i.e., solutions that only observe information about the current state. Local policies are easy to implement and do not require substantial computational resources as they do not perform planning. While local deterministic policies, like Nearest Neighbor, are being used in practice for hierarchical reinforcement learning, we propose three stochastic policies that guarantee better performance than any deterministic policy.


title: Learning to track 3D human motion from silhouettes

abstract: We describe a sparse Bayesian regression method for recovering 3D human body motion directly from silhouettes extracted from monocular video sequences. No detailed body shape model is needed, and realism is ensured by training on real human motion capture data. The tracker estimates 3D body pose by using Relevance Vector Machine regression to combine a learned autoregressive dynamical model with robust shape descriptors extracted automatically from image silhouettes. We studied several different combination methods, the most effective being to learn a nonlinear observation-update correction based on joint regression with respect to the predicted state and the observations. We demonstrate the method on a 54-parameter full body pose model, both quantitatively using motion capture based test sequences, and qualitatively on a test video sequence.


A Concise Introduction to Psychodrama, Sociodrama and Sociometry


title: Perceptual category mapping between English and Korean obstruents in non-CV positions: Prosodic location effects in second language identification skills

abstract: Abstract This study examines the degree to which mapping patterns between native language (L1) and second language (L2) categories for one prosodic context will generalize to other prosodic contexts, and how position-specific neutralization in the L1 influences the category mappings. Forty L1-Korean learners of English listened to English nonsense words consisting of /p b t d f v θ ð/ and /ɑ/, with the consonants appearing in pre-stressed intervocalic, post-stressed intervocalic, or coda context, and were asked to identify the consonant with both Korean and English labeling and to give gradient evaluations of the goodness of each label to the stimuli. Results show that the mapping patterns differ extensively from those found previously with the same subjects for consonants in initial, onset context. The mapping patterns for the intervocalic context also differed by position with respect to stress location. Coda consonants elicited poor goodness-of-fit and noisier mapping patterns for all segments, suggesting that an L1 coda neutralization process put all L2-English sounds in codas as “new” sounds under the Speech Learning Model (SLM) framework ( Flege, 1995 ). Taken together, the results indicate that consonant learning needs to be evaluated in terms of position-by-position variants, rather than just being a general property of the overall consonant systems.


title: 4-Aminopyridine and dendrotoxin induce repetitive firing in rat visceral sensory neurones by blocking a slowly inactivating outward current

abstract: In a subpopulation of rat visceral afferent neurones we have identified a potassium (K) current which is novel to mammalian neurones. It activates rapidly at potentials positive to - 70 mV but shows only slow and incomplete inactivation and is inhibited by 1-30 microM 4-aminopyridine (4-AP) or 3-10 nM dendrotoxin (DTX). Inhibition of this slowly inactivating current suppresses spike adaptation and leads to pronounced repetitive firing. In contrast, other visceral afferent neurones possessing the normal transient A-current were insensitive to 4-AP at concentrations below 100 microM. We suggest that inhibition of the slowly inactivating current may contribute to the convulsant actions of 4-AP and DTX.


title: Illustrious personalities of ancient and Byzantine Greece with renal problems.

abstract: It is a traditional practice to divide history in two main categories. First, Great History, that is the important events, such as wars, revolutions, conquests, that shaped our world during the past centuries. Secondly, Minor History, that is events in the lives of important persons, gossips at the Courts, trivial coincidences, that made people wonder and discuss them. The division, although generally accepted, is not clear cut, and there is a huge overlap between the two categories. In this article, we present a particular group of data from the second category, renal problems of illustrious personalities from Ancient Greek and Byzantine eras that, in many cases, influenced the outcome of Great History.


Ceramides in Alzheimer's Disease: Key Mediators of Neuronal Apoptosis Induced by Oxidative Stress and Aβ Accumulation


title: Are Consumers Strategic? Structural Estimation from the Air-Travel Industry

abstract: Consumers often consider delaying a purchase strategically, anticipating that prices might decrease. Combining two unique data sources from the air-travel industry posted fare data and booking data, we use a structural model to estimate the fraction of strategic consumers in the population, assuming different levels of sophistication in consumers' perception of future prices: perfect foresight and weak-and strong-form rational expectations. We find that 5.2% to 19.2% of the population is strategic across markets, measured by the first and third quartiles. Our intermarket analysis indicates that shorter trips with more attractive outside options are populated with more strategic consumers. Using a nonparametric approach, we further find that most strategic consumers arrive either at the beginning of the booking horizon or close to departure. Finally, our counterfactual analysis shows that, contrary to conventional wisdom, the presence of strategic consumers does not necessarily hurt revenues. Rather, the impact varies by market. Commitment to a nondecreasing pricing strategy is more likely to benefit business markets than leisure markets, or it could even hurt leisure markets. Intermarket analysis shows that city pairs with lower Internet penetration, higher average price, and shorter distances tend to benefit more from such commitment as well. ::: ::: This paper was accepted by Yossi Aviv, operations management.


title: Dynamic arm swinging in human walking

abstract: Humans tend to swing their arms when they walk, a curious behaviour since the arms play no obvious role in bipedal gait. It might be costly to use muscles to swing the arms, and it is unclear wheth...


title: Gait Analysis Using a Shoe-Integrated Wireless Sensor System

abstract: We describe a wireless wearable system that was developed to provide quantitative gait analysis outside the confines of the traditional motion laboratory. The sensor suite includes three orthogonal accelerometers, three orthogonal gyroscopes, four force sensors, two bidirectional bend sensors, two dynamic pressure sensors, as well as electric field height sensors. The "GaitShoe" was built to be worn in any shoe, without interfering with gait and was designed to collect data unobtrusively, in any environment, and over long periods. The calibrated sensor outputs were analyzed and validated with results obtained simultaneously from the Massachusetts General Hospital, Biomotion Laboratory. The GaitShoe proved highly capable of detecting heel-strike and toe-off, as well as estimating foot orientation and position, inter alia.


title: What is the Essence of a Claim? Cross-Domain Claim Identification

abstract: Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.


title: Development and design of a robotic manta ray featuring flexible pectoral fins

abstract: This paper showed our preliminary work in probing into fish robot with pectoral fin propulsion. The aim of this project is to develop a fish robot mimicking manta rays that swim with flexible pectoral fins in oscillation motion. Based on the analysis of motion feature and skeletal structure of pectoral fin of manta rays, a kind of flexible pectoral fin made of silicone rubber was designed. Experiments on the propulsion of fin prototype were carried out in tank. Experiment results showed that flexible pectoral fin in oscillation motion could generate great thrust and the thrust would increase with fin-beat frequency and amplitude increasing. In the end, a robot fish with flexible pectoral fins was built. The speed of robot fish could reach the maximum of 1.4 times BL (body length) per second. Experiments proved that it was feasible to develop a robot fish featuring flexible pectoral fins in oscillation motion and the method to mimic manta rays was a great idea to improve performance of underwater vehicles.


Do alternative reproductive strategies in the Wellington tree weta represent different behavioural types?


title: A practical analytic model for daylight

abstract: Sunlight and skylight are rarely rendered correctly in computer graphics. A major reason for this is high computational expense. Another is that precise atmospheric data is rarely available. We present an inexpensive analytic model that approximates full spectrum daylight for various atmospheric conditions. These conditions are parameterized using terms that users can either measure or estimate. We also present an inexpensive analytic model that approximates the effects of atmosphere (aerial perspective). These models are fielded in a number of conditions and intermediate results verified against standard literature from atmospheric science. Our goal is to achieve as much accuracy as possible without sacrificing usability.


title: Implementation of image processing for detection of brain tumors

abstract: Processing of magnetic resonance images (MRI) is one among the parts of the image processing in medical field, which is the most emerging field from past few days. The tumor detection is often a preliminary phase. This paper describes the detection of the brain tumor by thresholding method. The proposed technique can be efficiently applied to detect and extract the brain tumor from MRI images obtained from patient's data base. It proves to be handy tool for the physicians working in this field.


title: Complexity of Shift Bribery in Committee Elections

abstract: We study the (parameterized) complexity of SHIFT BRIBERY for multiwinner voting rules. We focus on the SNTV, Bloc, k-Borda, and Chamberlin–Courant rules, as well as on approximate variants of the Chamberlin–Courant rule, since the original rule is NP-hard to compute. We show that SHIFT BRIBERY tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where SHIFT BRIBERY is easy in the single-winner cases, but is hard (and hard to approximate) in the multi-winner ones. We show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin–Courant rule sometimes affects the complexity of SHIFT BRIBERY.


title: Doubly Convolutional Neural Networks

abstract: Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea. In stead of allocating a set of convolutional filters that are independently learned, a DCNN maintains groups of filters where filters within each group are translated versions of each other. Practically, a DCNN can be easily implemented by a two-step convolution procedure, which is supported by most modern deep learning libraries. We perform extensive experiments on three image classification benchmarks: CIFAR-10, CIFAR-100 and ImageNet, and show that DCNNs consistently outperform other competing architectures. We have also verified that replacing a convolutional layer with a doubly convolutional layer at any depth of a CNN can improve its performance. Moreover, various design choices of DCNNs are demonstrated, which shows that DCNN can serve the dual purpose of building more accurate models and/or reducing the memory footprint without sacrificing the accuracy.


title: “Scholarship Boys” in Twilight: The Memoirs of Six Humanists in Post-Industrial Britain

abstract: The memoirs of six “scholarship boys”—Richard Hoggart, Frank Kermode, Eric Hobsbawm, Harold Perkin, A. H. Halsey, and Brian Magee—reveal the deeply varied experience of academically gifted working- and lower-middle-class males in the twentieth century. The arc from social outsider to cultural prominence drew upon a commitment to humanism acquired in their youth. Scholarship boys navigated the crosscurrents of post-industrial culture in many ways but shared an unwillingness to accept uncritically the sophisticated reductions of postmodern theory and the “creative destruction” of neoliberal practice. The life writing of these figures reveals the often-concealed subjectivities behind academic success, including the desire in old age to honor the lived worlds of their youth. Humanism remained a continuing though not always dominant strain in post-industrial culture.


title: Unified Pragmatic Models for Generating and Following Instructions

abstract: We extend models for both following and generating natural language instructions by adding an explicit pragmatic layer. These pragmatics-enabled models explicitly reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Given learned base listener and speaker models, we build a pragmatic listener that uses the base speaker to reason counterfactually about alternative action descriptions, and a pragmatic speaker that uses the base listener to simulate the interpretation of candidate instruction sequences. Evaluation of language generation and interpretation in the SAIL navigation and SCONE instruction following datasets shows that the pragmatic inference procedure improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpretable by humans) in diverse settings.


title: Comparison of Multipath TCP and CMT-SCTP based on intercontinental measurements

abstract: The market penetration of access devices with multiple network interfaces has increased dramatically over the last few years. As a consequence, there is a strong interest to use all of the available interfaces concurrently to improve data throughput. Corresponding extensions of established Transport protocols are receiving considerable attention within research and standardization. Currently two approaches are in the focus of the IETF: The Multipath TCP (MPTCP) extension for TCP and the Concurrent Multipath Transfer extension for SCTP (CMT-SCTP). This paper evaluates and compares implementations of these two loadsharing protocols by using both lab measurements and intercontinental testbed realized via the Internet between Europe and China. The experiments show that some performance critical aspects have not been taken into account in previous studies. Furthermore, they show that the simple scenario with two disjoint paths, which is typically used for evaluation, does not sufficiently cover the real Internet environment. Based on these insights, we highlight that the different path management strategies of the two protocols have a significant impact on their performance in real Internet scenarios.1


title: Re-examining the components of transformational and transactional leadership using the Multifactor Leadership

abstract: A total of 3786 respondents in 14 independent samples, ranging in size from 45 to 549 in US and foreign firms and agencies, completed the latest version of the Multifactor Leadership Questionnaire (MLQ Form 5X), each describing their respective leader. Based on prior literature, nine models representing different factor structures were compared to determine the best fit for the MLQ survey. The models were tested in an original set of nine samples, and then in a second replication set comprised of five samples. Results indicated the factor structure for the MLQ survey was best represented by six lower order factors and three correlated higher-order factors.


title: CORROSION BEHAVIOUR OF DUPLEX SAF2507 IN AQUEOUS ORGANIC ACID SOLUTIONS

abstract: This paper reports on mass loss and electrochemical polarization behaviour of duplex SAF2507, in aqueous solutions of acetic acid, formic acid and their mixtures, in the presence of specific contaminants, such as sulphuric acid. The effect of temperature and redox couples was also investigated. SAF2507 withstands corrosion in acetic acid solutions in a wide temperature and concentration range. The corrosion rate is higher in the more dissociated formic acid at the same concentration. Uniform corrosion with selective dissolution of the austenitic phase was observed in all tested solutions, the selectivity being strictly connected to solution aggressiveness. The presence of sulphuric acid in organic acid aqueous solutions is severely detrimental to the corrosion resistance of the duplex: the corrosion rate increases almost linearly with H2SO4 concentration. Few tens of ppm of Fe3+ or Cu2+ improve the corrrosion performance markedly, causing often the corrosion rate to decrease to very low values. The addition of H2O2 has a strong effect favouring the increase of the corrosion potential up to the passivity of duplex due to the possible formation of peracids.


title: Filling Your Shelves: Synthesizing Diverse Style-Preserving Artifact Arrangements

abstract: Our homes and workspaces are filled with collections of dozens of artifacts laid out on surfaces such as shelves, counters, and mantles. The content and layout of these arrangements reflect both context, e.g., kitchen or living room, and style, e.g., neat or messy. Manually assembling such arrangements in virtual scenes is highly time consuming, especially when one needs to generate multiple diverse arrangements for numerous support surfaces and living spaces. We present a data-driven method especially designed for artifact arrangement which automatically populates empty surfaces with diverse believable arrangements of artifacts in a given style. The input to our method is an annotated photograph or a 3D model of an exemplar arrangement, that reflects the desired context and style. Our method leverages this exemplar to generate diverse arrangements reflecting the exemplar style for arbitrary furniture setups and layout dimensions. To simultaneously achieve scalability, diversity and style preservation, we define a valid solution space of arrangements that reflect the input style. We obtain solutions within this space using barrier functions and stochastic optimization.


Documenting frameworks using patterns


title: Consequences of display changes during interrupted visual search: Rapid resumption is target-speci�c

abstract: Visual search can be resumed more rapidly following a brief interruption to an old display than it can be initiated on a new display, pointing to a critical role for memory in search (Lleras, Rensink, & Enns, 2005). Here, we examine how thisrapid resumption is affected by changes made to the display during the interruption of search. Rapid resumption was found to depend on the prior presentation of the target, not merely the distractor items (Experiment 1), and was unaffected by the relocation of all distractor items (Experiment 2). Further, whereas changes to response-irrelevant features of the target did not eliminate rapid resumption (Experiment 3), changes to response-relevant features did (Experiment 4). These results point to the target specificity of rapid resumption and are consistent with reentrant theories of visual awareness.


title: A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

abstract: We consider the problem of using sentence compression techniques to facilitate queryfocused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.


title: On Application of Learning to Rank for E-Commerce Search

abstract: E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of LETOR for web search has been well studied, its use for E-Com search has not yet been well explored. In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-to-cart ratios, order rates, and revenue. We study these new challenges using experiments on industry data sets and report several interesting findings that can provide guidance on how to optimally apply LETOR to E-Com search: First, popularity-based features defined solely on product items are very useful and LETOR methods were able to effectively optimize their combination with relevance-based features. Second, query attribute sparsity raises challenges for LETOR, and selecting features to reduce/avoid sparsity is beneficial. Third, while crowdsourcing is often useful for obtaining relevance judgments for Web search, it does not work as well for E-Com search due to difficulty in eliciting sufficiently fine grained relevance judgments. Finally, among the multiple feedback signals, the order rate is found to be the most robust training objective, followed by click rate, while add-to-cart ratio seems least robust, suggesting that an effective practical strategy may be to initially use click rates for training and gradually shift to using order rates as they become available.


title: Facebook user's like behavior can reveal personality

abstract: Personality is an important factor that affects human's behavior. In recent years attraction towards online social networks has increased tremendously.Due to its unique features social networks are very suitable platforms for psychological analysis. By investigating 65 facebook users which participated in this research, correlation between personality traits and user liking behavior was observed. Classification algorithms were applied to classify personality traits based on user liking activity. Among classifiers, the SVM classifier outperforms other classifiers in most traits.


Treading the Impossible : A Tour of Set-Up Assumptions for Obtaining Universally Composable Security ?


title: Large-scale Semantic Parsing without Question-Answer Pairs

abstract: In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the F REE 917 and W EB Q UESTIONS benchmark datasets show our semantic parser improves over the state of the art.


title: Torque-Controlled Lightweight Arms and Articulated Hands: Do We Reach Technological Limits Now?

abstract: A communication arrangement is disclosed which enables data calls between data terminals and a computer over a telephone network without significantly increasing the call blocking probability of the telephone network. When a data call, consisting of several data transactions is originated, the arrangement stores the called station information for later use in reestablishing a network connection on subsequent data calls between the same users. By disassociating the data terminal-computer data call signaling from the telephone network data call signaling, the telephone network is available for other data or voice traffic during the time when no data calls are active. A termination signal cancels the stored information and concludes the data call between the users.


title: Solutions to hidden terminal problems in wireless networks

abstract: The floor acquisition multiple access (FAMA) discipline is analyzed in networks with hidden terminals. According to FAMA, control of the channel (the floor) is assigned to at most one station in the network at any given time, and this station is guaranteed to be able to transmit one or more data packets to different destinations with no collisions. The FAMA protocols described consist of non-persistent carrier or packet sensing, plus a collision-avoidance dialogue between a source and the intended receiver of a packet. Sufficient conditions under which these protocols provide correct floor acquisition are presented and verified for networks with hidden terminals; it is shown that FAMA protocols must use carrier sensing to support correct floor acquisition. The throughput of FAMA protocols is analyzed for single-channel networks with hidden terminals; it is shown that carrier-sensing FAMA protocols perform better than ALOHA and CSMA protocols in the presence of hidden terminals.


title: Mobile ECG measurement and analysis system using mobile phone as the base station

abstract: In this paper, we introduce an ECG measurement, analysis and transmission system which uses a mobile phone as a base station. The system is based on a small-sized mobile ECG recording device which sends measurement data wirelessly to the mobile phone. In the mobile phone the received data is analyzed and if abnormalities are found part of the measurement data is sent to a server for the use of medical personnel. The prototype of the system was made with a portable ECG monitor and Nokia 6681 mobile phone. The results show that modern smart phones are indeed capable for this kind of tasks. Thus, with their good networking and data processing capabilities, they are a potential part of the future wireless health care systems.


title: Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks

abstract: Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging pattern recognition problem. We develop hierarchical Bayesian neural networks to capture subject-specific variations and share statistical strength across subjects. Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy. We also develop methods for adapting our model to new subjects when a small number of subject-specific personalization data is available. Finally, we investigate active learning algorithms for interactively labeling personalization data in resource-constrained scenarios. Focusing on the problem of gesture recognition where inter-subject variations are commonplace, we demonstrate the effectiveness of our proposed techniques. We test our framework on three widely used gesture recognition datasets, achieving personalization performance competitive with the state-of-the-art.


title: Weakly-supervised TV logo detection

abstract: In this paper, a TV logo detection system is proposed based on the deep learning architecture for the specific TV logo detection task. Training a robust object detector typically requires a large amount of manually annotated data, which is time-consuming. To reduce the cost, we construct a TV logo detection system in a weakly-supervised framework, which is accomplished by a TV logo localization network based on Region Proposal Network (RPN) and a classification network based on Fast RCNN. Based on observed priors of a typical TV logo in pictures and video frames, data preparation and processing are performed by carrying out keyframe extraction and data augmentation. Since we build the localization network based on RPN, only a few bounding box annotations are employed for training the localization network. Then the well-trained localization network can produce numerous positive and negative proposals. These proposals along with the logo class labels for classification network training are exploited to train the classification network. To generate reasonable anchor boxes, k-means clustering is utilized to infer the scales and aspect ratios. Besides, for efficient training and better generalization ability, hard example mining is also explored. Experimental results demonstrate that the proposed weakly-supervised TV logo detection system achieves superior performances compared to the baseline Faster RCNN approach, with a mAP as about 92% in our newly proposed dataset.


Effective Pattern Discovery in Text Mining


Intelligent Predictions : an empirical study of the Cortical Learning Algorithm


title: The fuzzy comprehensive evaluation research of electric group purchase and operating efficiency based on improve rough set

abstract: The popularity of intelligent big data era in the community, making online shopping behavior play a more and more important role in the e-commerce model. This paper evaluated the operation efficiency of the electric business group buy site scientifically by puts forward the improved rough set method and fuzzy comprehensive evaluation method. First, on the basis of the analysis of the influence factors of the operation efficiency of the electric business group buying website, and build the evaluation index system; secondly, put forward a concept of information quantity by analyzing the deficiency of the original theory in the method of determining the attribute weight and combine the radix ratio in the domain of attribute set and the probability of the equivalence class in the domain, and then puts forward the improved rough set weight determination method, fuzzy comprehensive evaluation model for electricity suppliers to buy operational efficiency is constructed. Finally, combined with the practical data verify improved rough set method is reasonable.


title: A Detailed Evaluation of Threshold Algorithms for Answering Top-k queries in Peer-to-Peer Networks

abstract: Ranking queries, also known as top-k queries, have drawn considerable attention due to their usability in various applications. Several algorithms have been proposed for the evaluation of top-k queries. A large percentage of them follow the Threshold Approach. In p2p networks, top-k query processing can provide a lot of advantages both in time and bandwidth consumption. We focus on the main adaptations of threshold algorithms fulfilling the requirements of modern p2p applications. We introduce two algorithms optimized for ranking queries in p2p networks and present their characteristics. In the setting of a simulation of large p2p networks, we evaluate the performance of Threshold Algorithms. Our experiments demonstrate that in some cases a threshold algorithm can improve top-k query processing, while in others it is far more costly. The results show that distributed query processing can be more effective than a simple threshold algorithm in a p2p network.


title: The Effects of Sleep Restriction and Extension on School-Age Children: What a Difference an Hour Makes

abstract: The role of sleep in learning, memory, and other neurobehavioral functions has been extensively studied. It has been demonstrated that experimental manipulations of sleep can influence learning and memory function and that intensified learning and training can influence sleep or have correlates in brain functioning during sleep (Maquet, 2001; Pilcher & Huffcutt, 1996). However, limited experimental research has been focused on the role of sleep restriction and extension in child development. In a recent study we identified a significant relationship between sleep fragmentation and neurobehavioral functioning (NBF) in school-age children (Sadeh, Gruber, & Raviv, 2002). We found that an increased number of night wakings and lower sleep efficiency were associated with compromised NBF. Sleep duration was not correlated with NBF in this correlative naturalistic study. The goal of the present study was to assess the effects of moderate experimental changes in sleep duration on NBF in school-age children.


title: Biophysical approach to minor anxiety and Depressive Disorders

abstract: Anxiety and Depressive Disorders are two emerging concerns in the worldwide population being ranked respectively at the sixth and second position of the global burden of disease impacting public health. It is desirable to look for new effective strategies to cope with them bridging evidence based and personalized medicine. Bridging physics and biology living organisms can nowadays be viewed as dynamic processes sustained by a ceaseless flows of matter, energy and information within specific space-time quanta defined as morphogenetic fields. Life is a far from equilibrium process described as dissipative structure. A more dynamic approach replaced the old static concept of homeostasis and the process aimed to keep stability through dynamic changes has been recently defined as allostasis. Allostatic load represents the global effort that the system has to cope to maintain allostasis. Allostasis leads to strengthen of health potential toward salutogenesis while allostatic load leads to a decrease of health potential toward pathogenesis. Allostasis and allostaic load are in a dynamic relationship as an expression of the flow of information due to adaptive dynamics. Consequently, health and disease could be better understood as an expression of the allostasis/allostatic load dynamics. Information flows in biological system can be studied either by a chemical and molecular description either by an electromagnetic signals emission and processing approach. Indeed, electromagnetic signals are endogenously generated at different level in many cell components and supposedly play an active role in synchronizing either inner cell function at microscopic level either systemic adaptive response of organs, apparatus and whole organism. Accordingly, all adaptive responses both at microscopic and macroscopic level, either local or holistic, could be identified by their specific electromagnetic signals emission. Each adaptive pattern, being unique for any person at any time, identifies his own specific and personal electromagnetic signature including emotional patterns. A biophysical approach has been employed to treat minor anxiety and depressive disorders following previous evidences. The self-rating anxiety scale and the self-rating depression scale were used to assess the clinical pictures at baseline and after 3 months of a single biophysical procedure (Med Select 729) recorded on an aqueous solution (Nomabit Base) and self-administrated. A significant reduction of the score was reported in both anxiety (p < 0.0001) and depression (p < 0.0001) group showing a potential benefit of biophysical therapies in their management in a very personalized perspective.


title: Robust and autonomous dc bus voltage control and stability analysis for a dc microgrid

abstract: In this paper, a novel approach for robust and autonomous dc bus voltage control in a dc microgrid has been proposed. A nonlinear disturbance observer (NDO) only with local information is designed firstly to track power disturbances caused by load changing or the volatility of the renewable sources in the dc microgrid. With the observer, voltage droop method has been adopted in achieving power sharing and autonomous operation of a dc microgrid. Furthermore, an improved current feedforward is developed to suppress wide fluctuations of the dc bus voltage during transients. In addition, a notch filter has been used to effectively eliminate the second harmonic ripple components in the inductor currents of the droop control units to improve the power quality. The effectiveness of the proposed control scheme is verified on a laboratory prototype including two bidirectional dc-dc converters supplied by two programmable dc power supply systems, a dc load with a resistor connected to the dc bus via a buck type dc-dc converter and a three-phase unbalanced ac load.


title: Electrocardiogram (ECG) Classification Based On Dynamic Beats Segmentation

abstract: The Electrocardiogram (ECG) has been introduced for decades as a powerful tool for diagnosing heart diseases. The majority of publications in the automatic ECG based diagnosis domain have utilized a fixed segmentation for heartbeats which doesn't consider the changes that can occur in heart rate over time. In this paper, an automatic reliable method for diagnosing heartbeats with a new heart rate invariant segmentation procedure is proposed. The segmented heartbeats are decomposed using discrete wavelet transform (DWT). The resulting wavelet coefficients are reduced using Principle Component Analysis (PCA) and then classified using a Support Vector Machine (SVM) classifier into five main categories that represent 15 classes. Moreover, in order to increase the reliability of the proposed method, not only the overall accuracy is considered but also the average accuracy for each class. The achieved results using the MIT-BIH database for the average accuracy and the overall accuracy are 96.35% and 99.5% respectively.


title: Automated Encoding of Clinical Guidelines into Computer-interpretable Format

abstract: Computer-interpretable guidelines (CIGs) are critical knowledge source for clinical decision support systems (CDSS). However, most of current CIGs are encoded by medical experts and knowledge engineers based on the clinical practice guidelines (CPGs). It is complex, time-consuming and error-prone. This paper proposes a model and a system framework that automates large part of the encoding process. The model employs a directed graph representing the knowledge of a guideline, and the framework consists of a pipeline of three steps: semi-structural guideline generation, graph reduction and validation, and CIG construction. Furthermore, we chose two CPGs issued by National Comprehensive Cancer Network (NCCN) to illustrate the use of this proposed framework. Automated encoding them into semi-products saves a tremendous amount of time, reducing 25 workdays for manual encoding work to 15 minutes of automated encoding plus 5 hours manual validation and correction. This indicates that automated encoding tools based on rigorous models is of practical value in a proper work framework.


title: How do you know they are learning? The importance of alignment in higher education

abstract: The success of any learning environment is determined by the degree to which there is adequate alignment among eight critical factors: 1) goals, 2) content, 3) instructional design, 4) learner tasks, 5) instructor roles, 6) student roles, 7) technological affordances, and 8) assessment. Evaluations of traditional, online, and blended approaches to higher education teaching indicate that the most commonly misaligned factor is assessment. Simply put, instructors may have lofty goals, high-quality content, and even advanced instructional designs, but most instructors tend to focus their assessment strategies on what is easy to measure rather than on what is important. Adequate assessment should encompass all four learning domains: cognitive, affective, conative, and psychomotor. This paper describes procedures for the development and use of reliable and valid assessments in higher education.


title: Olympus: an open-source framework for conversational spoken language interface research

abstract: We introduce Olympus, a freely available framework for research in conversational interfaces. Olympus' open, transparent, flexible, modular and scalable nature facilitates the development of large-scale, real-world systems, and enables research leading to technological and scientific advances in conversational spoken language interfaces. In this paper, we describe the overall architecture, several systems spanning different domains, and a number of current research efforts supported by Olympus.


title: DE-tree ray tracing and box-query performance data.

abstract: A solenoid valve especially intended for wet-room fittings. It has a housing with an intake and an outlet sealed off from each other and prevented from communicating while the valve is closed by a body that rests on a seat. The housing is essentially flower-pot shaped and has a threaded section at the open end positioned concentric and coaxial to the intake and outlet, which are in turn rotationally symmetric and face the open end such that the incoming flow is opposite the outgoing flow.


Adaptable Radial Axes Plots for Improved Multivariate Data Visualization


title: Applicability of artificial bee colony algorithm for nurse scheduling problems

abstract: AbstractThis paper describes the first Artificial Bee Colony (ABC) Algorithm approach applied to nurse scheduling evaluated under different working environments. For this purpose, the model has been applied on a real hospital where data taken from different departments of the hospital were used and the schedules from the model were compared with the existing schedules. The results obtained indicated that the proposed model exhibits success in solving the nurse scheduling problems in hospitals.


title: Evaluation of supply chain structures through modularization and postponement

abstract: Abstract This paper introduces a conceptual framework for evaluating different supply chain structures in the context of modularization and postponement. In our analysis, modularization is linked to inbound logistics as the combination of different components (or modules) allow for the assembly of the final product. Postponement, however, corresponds with the outbound logistics since it is through the distribution function that customers' specific demand is satisfied. Given this perspective, we introduce a taxonomy and develop a corresponding framework for the characterization of four supply chain structures, defined according to the combined levels of modularization and postponement: Rigid , Postponed , Modularized and Flexible . We also illustrate the applicability of the resulting framework through quantifying the total cost differential for utilizing a particular supply chain structure. By quantifying the total cost for employing a particular supply chain structure we can numerically illustrate structural results that allow for an objective comparison among them. As such, our findings substantiate the empirical evidence that vertical integration along the supply chain is not always desirable. A point of emphasis in this paper is the notion that firms may be better off making combined modularization (outsourcing) and postponement decisions as opposed to separate ones (as they currently do). This also suggests that modularization and postponement decisions need not be independent and that their joint consideration extends operational advantages worthy of contemplation.


Smart Nest Thermostat A Smart Spy in Your Home


title: A Head-Wearable Short-Baseline Stereo System for the Simultaneous Estimation of Structure and Motion

abstract: This paper presents a short-baseline real-time stereo vision system that is capable of the simultaneous and robust estimation of the ego-motion and of the 3D structure and the independent motion of thousands of points of the environment. Kalman lters estimate the position and velocity of world points in 3D Euclidean space. The six degrees of freedom of the ego-motion are obtained by minimizing the projection error of the current and previous clouds of static points. Experimental results with real data in indoor and outdoor environments demonstrate the robustness, accuracy and eciency of our approach. Since the baseline is as short as 13cm, the device is head-mountable, and can be used by a visually impaired person. Our proposed system can be used to augment the perception of the user in complex dynamic environments.


Why Business Intelligence? Significance of Business Intelligence Tools and Integrating BI Governance with Corporate Governance


title: Methods for the directed evolution of proteins

abstract: Directed evolution has proved to be an effective strategy for improving or altering the activity of biomolecules for industrial, research and therapeutic applications. The evolution of proteins in the laboratory requires methods for generating genetic diversity and for identifying protein variants with desired properties. This Review describes some of the tools used to diversify genes, as well as informative examples of screening and selection methods that identify or isolate evolved proteins. We highlight recent cases in which directed evolution generated enzymatic activities and substrate specificities not known to exist in nature.


title: Gated Feedback Recurrent Neural Networks

abstract: In this work, we propose a novel recurrent neural network (RNN) architecture. The proposed RNN, gated-feedback RNN (GF-RNN), extends the existing approach of stacking multiple recurrent layers by allowing and controlling signals flowing from upper recurrent layers to lower layers using a global gating unit for each pair of layers. The recurrent signals exchanged between layers are gated adaptively based on the previous hidden states and the current input. We evaluated the proposed GF-RNN with different types of recurrent units, such as tanh, long short-term memory and gated recurrent units, on the tasks of character-level language modeling and Python program evaluation. Our empirical evaluation of different RNN units, revealed that in both tasks, the GF-RNN outperforms the conventional approaches to build deep stacked RNNs. We suggest that the improvement arises because the GFRNN can adaptively assign different layers to different timescales and layer-to-layer interactions (including the top-down ones which are not usually present in a stacked RNN) by learning to gate these interactions.