title: Multilevel PWM methods at low modulation indices

abstract: When utilized at low amplitude modulation indices, existing multilevel carrier-based PWM strategies have no special provisions for this operating region, and several levels of the inverter go unused. This paper proposes some novel multilevel PWM strategies to take advantage of the multiple levels in both a diode-clamped inverter and a cascaded H-bridge inverter by utilizing all of the levels in the inverter even at low modulation indices. Simulation results show what effects the different strategies have on the active device utilization. A prototype 6-level diode-clamped inverter and an 11-level cascaded 11-bridge inverter have been built and controlled with the novel PWM strategies proposed in this paper.


title: How We’re Predicting AI—or Failing To

abstract: This paper will look at the various predictions that have been made about AI and propose decomposition schemas for analysing them. It will propose a variety of theoretical tools for analysing, judging and improving these predictions. Focusing specifically on timeline predictions (dates given by which we should expect the creation of AI), it will show that there are strong theoretical grounds to expect predictions to be quite poor in this area. Using a database of 95 AI timeline predictions, it will show that these expectations are born out in practice: expert predictions contradict each other considerably, and are indistinguishable from non-expert predictions and past failed predictions. Predictions that AI lie 15 to 25 years in the future are the most common, from experts and non-experts alike.


title: Trusted Click: Overcoming Security issues of NFV in the Cloud

abstract: Network Function Virtualization has received a large amount of research and recent efforts have been made to further leverage the cloud to enhance NFV. However, since there are privacy and security issues with using cloud computing, work has been done to allow for operating on encrypted data, which introduces a large amount of overhead in both computation and data, while only providing a limited set of operations, since these encryption schemes are not fully homomorphic. We propose using trusted computing to circumvent these limitations by having hardware enforce data privacy and provide guaranteed computation. Prior work has shown that Intel's Software Guard Extensions can be used to protect the state of network functions, but there are still questions about the usability of SGX in arbitrary NFV applications and the performance of SGX in these applications. We extend prior work to show how SGX can be used in network deployments by extending the Click modular router to perform secure packet processing with SGX. We also present a performance evaluation of SGX on real hardware to show that processing inside of SGX has a negligible performance impact, compared to performing the same processing outside of SGX.


title: On Network Theory

abstract: Research on social networks has grown considerably in the last decade. However, there is a certain amount of confusion about network theory---for example, what it is, what is distinctive about it, and how to generate new theory. This paper attempts to remedy the situation by clarifying the fundamental concepts of the field (such as the network) and characterizing how network reasoning works. We start by considering the definition of network, noting some confusion caused by two different perspectives, which we refer to as realist and nominalist. We then analyze two well-known network theories, Granovetter's strength of weak ties theory [Granovetter, M. S. 1973. The strength of weak ties. Amer. J. Sociol.78(6) 1360--1380] and Burt's structural holes theory [Burt, R. S. 1992. Structural Holes: The Social Structure of Competition. Havard University Press, Cambridge, MA], to identify characteristic elements of network theorizing. We argue that both theories share an underlying theoretical model, which we label the network flow model, from which we derive additional implications. We also discuss network phenomena that do not appear to fit the flow model and discuss the possibility of a second fundamental model, which we call the bond model. We close with a discussion of the merits of model-based network theorizing for facilitating the generation of new theory, as well as a discussion of endogeneity in network theorizing.


title: Minimum jerk for human catching movements in 3D

abstract: To investigate fast human reaching movements in 3D, we asked 11 right-handed persons to catch a tennis ball while we tracked the movements of their arms. To ensure consistent trajectories of the ball, we used a catapult to throw the ball from three different positions. Tangential velocity profiles of the hand were in general bell-shaped and hand movements in 3D coincided with well known results for 2D point-to-point movements such as minimum jerk theory or the 2/3rd power law. Furthermore, two phases, consisting of fast reaching and slower fine movements at the end of hand placement could clearly be seen. The aim of this study was to find a way to generate human-like (catching) trajectories for a humanoid robot.


title: Semi- and Weakly- Supervised Semantic Segmentation with Deep Convolutional Neural Networks

abstract: Successful semantic segmentation methods typically rely on the training datasets containing a large number of pixel-wise labeled images. To alleviate the dependence on such a fully annotated training dataset, in this paper, we propose a semi- and weakly-supervised learning framework by exploring images most only with image-level labels and very few with pixel-level labels, in which two stages of Convolutional Neural Network (CNN) training are included. First, a pixel-level supervised CNN is trained on very few fully annotated images. Second, given a large number of images with only image-level labels available, a collaborative-supervised CNN is designed to jointly perform the pixel-level and image-level classification tasks, while the pixel-level labels are predicted by the fully-supervised network in the first stage. The collaborative-supervised network can remain the discriminative ability of the fully-supervised model learned with fully labeled images, and further enhance the performance by importing more weakly labeled data. Our experiments on two challenging datasets, i.e, PASCAL VOC 2007 and LabelMe LMO, demonstrate the satisfactory performance of our approach, nearly matching the results achieved when all training images have pixel-level labels.


title: Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning

abstract: Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.


GPS Spoofing Countermeasures


Fe b 20 15 Constrained sampling method for analytic continuation


title: SMART CITY PLANNING - DEVELOPING AN URBAN CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES

abstract: Electric mobility is considered to be an essential building block of a sustainable transportation paradigm. Particularly for the urban metropolis of the 21st century electric vehicles embody a promise of decreased air pollution and increased quality of living. However, city planners need to muster enormous investments into urban charging infrastructure in advance. We develop a decision support system to help city planners use these funds more efficiently and place charging infrastructure where it is truly needed. We construct a regression model to determine factors that influence the utilization of charge points in Amsterdam, one of the most "electrified" cities in the world. We include data on more than 50,000 points of interest, for instance shopping malls and museums, as well as charging data from 273 charging points with 427 individual outlets in the city of Amsterdam. From these influences we derive a decision support system that places charge points where they are expected to experience the highest utilization.


Bottom-Up and Top-Down Attention for Image Captioning and VQA.


title: Targeting of tumor endothelial cells combining 2 Gy/day of X-ray with Everolimus is the effective modality for overcoming clinically relevant radioresistant tumors

abstract: Radiotherapy is widely used to treat cancer because it has the advantage of physically and functionally conserving the affected organ. To improve radiotherapy and investigate the molecular mechanisms of cellular radioresistance, we established a clinically relevant radioresistant (CRR) cell line, SAS-R, from SAS cells. SAS-R cells continue to proliferate when exposed to fractionated radiation (FR) of 2 Gy/day for more than 30 days in vitro. A xenograft tumor model of SAS-R was also resistant to 2 Gy/day of X-rays for 30 days. The density of blood vessels in SAS-R tumors was higher than in SAS tumors. Everolimus, a mammalian target of rapamycin (mTOR) inhibitor, sensitized microvascular endothelial cells to radiation, but failed to radiosensitize SAS and SAS-R cells in vitro. Everolimus with FR markedly reduced SAS and SAS-R tumor volumes. Additionally, the apoptosis of endothelial cells (ECs) increased in SAS-R tumor tissues when both Everolimus and radiation were administered. Both CD34-positive and tomato lectin-positive blood vessel densities in SAS-R tumor tissues decreased remarkably after the Everolimus and radiation treatment. Everolimus-induced apoptosis of vascular ECs in response to radiation was also followed by thrombus formation that leads to tumor necrosis. We conclude that FR combined with Everolimus may be an effective modality to overcome radioresistant tumors via targeting tumor ECs.


title: VR-GIS: an integrated platform of VR navigation and GIS analysis for city/region simulation

abstract: This paper introduces a virtual city oriented VR-GIS platform which synthesizes several latest information technologies including virtual reality, 3D geographical information system, remote sensing and multi-dimensional visualization. The platform is a seamless integration of VR functions and GIS analysis methods, which can be used to organize and present massive spatial data. It also supplies 3D spatial analysis functions, 3D visualization for spatial process and natural simulation, and serves as an engine platform for digital city.


title: Video Super-Resolution via Bidirectional Recurrent Convolutional Networks

abstract: Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.


title: A Sentiment Treebank and Morphologically Enriched Recursive Deep Models for Effective Sentiment Analysis in Arabic

abstract: Accurate sentiment analysis models encode the sentiment of words and their combinations to predict the overall sentiment of a sentence. This task becomes challenging when applied to morphologically rich languages (MRL). In this article, we evaluate the use of deep learning advances, namely the Recursive Neural Tensor Networks (RNTN), for sentiment analysis in Arabic as a case study of MRLs. While Arabic may not be considered the only representative of all MRLs, the challenges faced and proposed solutions in Arabic are common to many other MRLs. We identify, illustrate, and address MRL-related challenges and show how RNTN is affected by the morphological richness and orthographic ambiguity of the Arabic language. To address the challenges with sentiment extraction from text in MRL, we propose to explore different orthographic features as well as different morphological features at multiple levels of abstraction ranging from raw words to roots. A key requirement for RNTN is the availability of a sentiment treebank; a collection of syntactic parse trees annotated for sentiment at all levels of constituency and that currently only exists in English. Therefore, our contribution also includes the creation of the first Arabic Sentiment Treebank (A r S en TB) that is morphologically and orthographically enriched. Experimental results show that, compared to the basic RNTN proposed for English, our solution achieves significant improvements up to 8% absolute at the phrase level and 10.8% absolute at the sentence level, measured by average F1 score. It also outperforms well-known classifiers including Support Vector Machines, Recursive Auto Encoders, and Long Short-Term Memory by 7.6%, 3.2%, and 1.6% absolute respectively, all models being trained with similar morphological considerations.


Perception of motion, depth and form


title: Nuclear magnetic resonance imaging with hyperpolarised helium-3

abstract: BACKGROUND ::: Magnetic resonance imaging (MRI) relies on magnetisation of hydrogen nuclei (protons) of water molecules in tissue as source of the signal. This technique has been valuable for studying tissues that contain significant amounts of water, but biological settings with low proton content, notably the lungs, are difficult to image. We report use of spin-polarised helium-3 for lung MRI. ::: ::: ::: METHODS ::: A volunteer inhaled hyperpolarised 3He to fill the lungs, which were imaged with a conventional MRI detector assembly. The nuclear spin polarisation of helium, and other noble gases, can be greatly enhanced by laser optical pumping and is about 10(5) times larger than the polarisation of water protons. This enormous gain in polarisation easily overcomes the loss in signal due to the lower density of the gas. ::: ::: ::: FINDINGS ::: The in-vivo experiment was done in a whole-body MRI scanner. The 3He image showed clear demarcation of the lung against diaphragm, heart, chest wall, and blood vessels (which gave no signal). The signal intensity within the air spaces was greatest in lung regions that are preferentially ventilated in the supine position; less well ventilated areas, such as the apices, showed a weaker signal. ::: ::: ::: INTERPRETATION ::: MRI with hyperpolarised 3He gas could be an alternative to established nuclear medicine methods. The ability to image air spaces offers the possibility of investigating physiological and pathophysiological processes in pulmonary ventilation and differences in its regional distribution.


title: A Review and Characterization of Progressive Visual Analytics

abstract: Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.


title: Light field video capture using a learning-based hybrid imaging system

abstract: Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings after capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time.


title: Multi-Hypothesis Visual-Inertial Flow

abstract: Estimating the correspondences between pixels in sequences of images is a critical first step for a myriad of tasks including vision-aided navigation (e.g., visual odometry (VO), visual-inertial odometry (VIO), and visual simultaneous localization and mapping (VSLAM)) and anomaly detection. We introduce a new unsupervised deep neural network architecture called the Visual Inertial Flow (VIFlow) network and demonstrate image correspondence and optical flow estimation by an unsupervised multi-hypothesis deep neural network receiving grayscale imagery and extra-visual inertial measurements. VIFlow learns to combine heterogeneous sensor streams and sample from an unknown, un-parametrized noise distribution to generate several (4 or 8 in this work) probable hypotheses on the pixel-level correspondence mappings between a source image and a target image . We quantitatively benchmark VIFlow against several leading vision-only dense correspondence and flow methods and show a substantial decrease in runtime and increase in efficiency compared to all methods with similar performance to state-of-the-art (SOA) dense correspondence matching approaches. We also present qualitative results showing how VIFlow can be used for detecting anomalous independent motion.


title: Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection

abstract: Weld bead inspection is important for high-quality welding. This paper summarizes our work on weld bead profile measurement, monitoring, and defect detection using a structured light-based vision inspection system. The configuration of the sensor is described and analyzed. In this configuration, the system presented in this paper can easily be calibrated. The image processing and extraction algorithms for laser profiles and feature points are presented. The dimensional parameters of the weld bead are measured, and the weld defects are detected during multilayer welding processes. Experiments using the vision inspection system were conducted with satisfactory results for online inspection.


title: ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting

abstract: Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year's worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.


Educational data mining: A survey and a data mining-based analysis of recent works


title: Inductively-Coupled Miniaturized-Element Frequency Selective Surfaces With Narrowband, High-Order Bandpass Responses

abstract: We introduce a new technique for designing miniaturized-element frequency selective surfaces (MEFSSs) with narrowband, bandpass responses of order $N\ge2$ . The proposed structure is composed of two-dimensional periodic arrays of subwavelength inductive wire grids separated by dielectric substrates. A simple equivalent circuit model, composed of transmission line resonators coupled together with shunt inductors, is presented for this structure. Using this equivalent circuit model, an analytical synthesis procedure is developed that can be used to synthesize the MEFSS from its desired system-level performance indicators such as the center frequency of operation and bandwidth. Using this synthesis procedure, a prototype of the proposed MEFSS with a second-order bandpass response, center frequency of 21 GHz, and fractional bandwidth of 5% is designed, fabricated, and experimentally characterized. The measurement results confirm the theoretical predictions and the design procedure of the structure and demonstrate that the proposed MEFSS has a stable frequency response with respect to the angle of incidence of the EM wave in the $\pm40^\circ$ range for both TE and TM polarizations of incidence.


title: A Texture-Based Neural Network Classifier for Biometric Identification using Ocular Surface Vasculature

abstract: In an earlier work we had explored the possibility of utilizing the vascular pattern of the sclera, episclera, and conjunctiva as a biometric indicator. These blood vessels, which can be observed on the white part of the human eye, demonstrate rich and seemingly unique details in visible light, and can be easily imaged using commercially available digital cameras. In this work we discuss a new method to represent and match the textural intricacies of this vascular structure using wavelet-derived features in conjunction with neural network classifiers. Our experimental results, based on the evidence of 50 subjects, indicate the potential of the proposed scheme to characterize the individuality of the ocular surface vascular patterns and further confirm our assertion that these patterns are indeed unique across individuals.


title: A sensing array of radically coupled genetic ‘biopixels’

abstract: Although there has been considerable progress in the development of engineering principles for synthetic biology, a substantial challenge is the construction of robust circuits in a noisy cellular environment. Such an environment leads to considerable intercellular variability in circuit behaviour, which can hinder functionality at the colony level. Here we engineer the synchronization of thousands of oscillating colony ‘biopixels’ over centimetre-length scales through the use of synergistic intercellular coupling involving quorum sensing within a colony and gas-phase redox signalling between colonies. We use this platform to construct a liquid crystal display (LCD)-like macroscopic clock that can be used to sense arsenic via modulation of the oscillatory period. Given the repertoire of sensing capabilities of bacteria such as Escherichia coli, the ability to coordinate their behaviour over large length scales sets the stage for the construction of low cost genetic biosensors that are capable of detecting heavy metals and pathogens in the field.


title: Trehalose Is a Potent PCR Enhancer: Lowering of DNA Melting Temperature and Thermal Stabilization of Taq Polymerase by the Disaccharide Trehalose,

abstract: Compatible solutes are a class of compounds that stabilize cells and cellular components exposed to extreme conditions. In bacterial systems, the uptake or synthesis of compatible solutes renders the cells and their enzymatic machinery more resistant to stress-inducing environmental conditions such as high osmolarity or high temperatures (1)(2). Compatible solutes comprise a heterogeneous group of compounds, covering amino acids and their derivatives (3), sugars (4), and more obscure compounds such as the pyrimidine derivative ectoine (5). ::: ::: The compatible solute trehalose is a nonreducing disaccharide in which two d-glucose units are linked by an α,α-1,1-glycosidic bond. It is synthesized by a variety of eukaryotic organisms, conferring tolerance against desiccation, dehydration, heat, cold, and oxidation (6). The addition of trehalose increases the enzymatic activity of several euthermal enzymes used for cDNA synthesis or restriction digestion of DNA (7)(8). Trehalose also enhances the priming specificity in differential-display reverse transcription-PCR (9) through high-temperature priming and a thermoactivated reverse transcriptase. ::: ::: PCR amplifications are frequently impaired by high GC content of the target sequence, leading to low yield and specificity of products, with no product at all in the worst cases. Locally high-temperature melting regions within the template can act as permanent termination sites (10). Several low-molecular-weight products have been identified that enhance the PCR of difficult templates, e.g., dimethyl sulfoxide (11) and other sulfoxides (12), formamide (13), nonionic detergents (14), and compounds belonging to the family of compatible solutes, such as betaine (15)(16)(17). The latter is present in most of the commercially available PCR-enhancing solutions (18). ::: ::: Here we report the application of trehalose as a potent PCR enhancer for GC-rich templates. This compound avoids false negatives in PCR typing (16). In this study, we used trehalose in real-time, reverse transcription-PCR amplification of the mouse …


title: A Wideband Cavity-Backed Elliptical Printed Dipole Antenna With Enhanced Radiation Patterns

abstract: A new wideband unidirectional cavity-backed elliptical printed dipole antenna with enhanced radiation patterns is proposed. The cavity utilized in the proposed antenna is a simple cylindrical cavity, which can generate broadside radiation patterns. An elliptical printed dipole director is placed in front of the elliptical printed dipole antenna for improving the radiation patterns in the upper frequency band. The antenna is fed by an SMA connector through a step-tapered balun accomplishing a transition from a microstrip line to a parallel stripline. The antenna is fabricated and measured. Results show that the antenna achieves an impedance bandwidth of 129.7% for ${\rm VSWR}\leq 2.0$ from 1.55 to 7.27 GHz. Stable unidirectional radiation patterns with an antenna gain of 8.04–13.27 dBi, low back radiation, and low cross polarization are found over the operating frequencies.


title: A convolutional neural network approach for acoustic scene classification

abstract: This paper presents a novel application of convolutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the-art systems which competed in the “Detection and Classification of Acoustic Scenes and Events” (DCASE) challenges held in 20161 and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner's score.


Decision tree methods: applications for classification and prediction


title: Gated-Attention Readers for Text Comprehension

abstract: In this paper we study the problem of answering cloze-style questions over documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader. This enables the reader to build query-specific representations of tokens in the document for accurate answer selection. The GA Reader obtains state-of-the-art results on three benchmarks for this task--the CNN \& Daily Mail news stories and the Who Did What dataset. The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention. The code is available at this https URL


title: Techniques and tools for estimating ionospheric effects in interferometric and polarimetric SAR data

abstract: The InSAR Scientific Computing Environment (ISCE) is a flexible, extensible software tool designed for the end-to-end processing and analysis of synthetic aperture radar data. ISCE inherits the core of the ROI_PAC interferometric tool, but contains improvements at all levels of the radar processing chain, including a modular and extensible architecture, new focusing approach, better geocoding of the data, handling of multi-polarization data, radiometric calibration, and estimation and correction of ionospheric effects. In this paper we describe the characteristics of ISCE with emphasis on the ionospheric modules. To detect ionospheric anomalies, ISCE implements the Faraday rotation method using quad-polarimetric images, and the split-spectrum technique using interferometric single-, dual- and quad-polarimetric images. The ability to generate co-registered time series of quad-polarimetric images makes ISCE also an ideal tool to be used for polarimetric-interferometric radar applications.


title: Classification with Joint Time-Frequency Scattering

abstract: In time series classification and regression, signals are typically mapped into some intermediate representation used for constructing models. Since the underlying task is often insensitive to time shifts, these representations are required to be time-shift invariant. We introduce the joint time-frequency scattering transform, a time-shift invariant representation which characterizes the multiscale energy distribution of a signal in time and frequency. It is computed through wavelet convolutions and modulus non-linearities and may therefore be implemented as a deep convolutional neural network whose filters are not learned but calculated from wavelets. We consider the progression from mel-spectrograms to time scattering and joint time-frequency scattering transforms, illustrating the relationship between increased discriminability and refinements of convolutional network architectures. The suitability of the joint time-frequency scattering transform for time-shift invariant characterization of time series is demonstrated through applications to chirp signals and audio synthesis experiments. The proposed transform also obtains state-of-the-art results on several audio classification tasks, outperforming time scattering transforms and achieving accuracies comparable to those of fully learned networks.


title: An Improved Two-factor Authentication Protocol

abstract: Most recently, Yang et al proposed a new set of security requirements for two-factor smart-card-based password mutual authentication and then suggested a new scheme satisfying all their security requirements. In this paper, however, we first show one critical security weakness being overlooked, i.e., allowing key-compromise impersonation. We provide an attack to illustrate the adversary is able to masquerade any user to access the server's service in their protocol once if the long-term key of the server is compromised. Thereafter, we suggests key-compromise impersonation resilience should be added as one more important security requirement for two-factor smart-card based password mutual authentication and then propose an improved protocol to eliminate the security weakness existing in Yang et al's protocol.


title: Bayesian Canonical correlation analysis

abstract: Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and inference methods for CCA which are attractive for their potential in hierarchical extensions and for coping with the combination of large dimensionalities and small sample sizes. The existing methods have not been particularly successful in fulfilling the promise yet; we introduce a novel efficient solution that imposes group-wise sparsity to estimate the posterior of an extended model which not only extracts the statistical dependencies (correlations) between data sets but also decomposes the data into shared and data set-specific components. In statistics literature the model is known as inter-battery factor analysis (IBFA), for which we now provide a Bayesian treatment.


title: Ray-optics cloaking devices for large objects in incoherent natural light

abstract: A cloak that can hide living creatures from sight is a common feature of mythology but still remains unrealized as a practical device. To preserve the wave phase, the previous cloaking solution proposed by Pendry and colleagues required transformation of the electromagnetic space around the hidden object in such a way that the rays bending around the object inside the cloak region have to travel faster than those passing it by. This difficult phase preservation requirement is the main obstacle for building a broadband polarization-insensitive cloak for large objects. Here we propose a simplified version of Pendry’s cloak by abolishing the requirement for phase preservation, as it is irrelevant for observation using incoherent natural light with human eyes, which are phase and polarization insensitive. This allows for a cloak design on large scales using commonly available materials. We successfully demonstrate the cloaking of living creatures, a cat and a fish, from the eye.


title: IAAI Articles AI-Based Software Defect Predictors: Applications and Benefits in a Case Study

abstract: Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.


title: Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller

abstract: Recently, Multisensor Fire Detection System (MSFDS) is one of the important research issues. Here, a fire fighter robot is fabricated providing extinguishment platform. The base of the robot is made of the wood of ‘Rashed tree’, locally known as ‘Kerosene wood’. There is about 1 liter water reserving capacity. An arduino based simple algorithm is used for detection of fire and measurement of distance from fire source while the robot is on its way to extinguish fire. When the fire is detected and the robot is at a distance near to fire, a centrifugal pump is used to throw water for extinguishment purpose. A water spreader is used for effective extinguishing. It is seen that velocity of water is greatly reduced due to the use of water spreader. Two sensors: LM35 and Arduino Flame Sensors are used to detect the fire and distances on its way towards fire. Sensitivity of these sensors at different day times and distances is tested through analog reading of the serial monitor.


title: A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology

abstract: An animal's ability to navigate through space rests on its ability to create a mental map of its environment. The hippocampus is the brain region centrally responsible for such maps, and it has been assumed to encode geometric information (distances, angles). Given, however, that hippocampal output consists of patterns of spiking across many neurons, and downstream regions must be able to translate those patterns into accurate information about an animal's spatial environment, we hypothesized that 1) the temporal pattern of neuronal firing, particularly co-firing, is key to decoding spatial information, and 2) since co-firing implies spatial overlap of place fields, a map encoded by co-firing will be based on connectivity and adjacency, i.e., it will be a topological map. Here we test this topological hypothesis with a simple model of hippocampal activity, varying three parameters (firing rate, place field size, and number of neurons) in computer simulations of rat trajectories in three topologically and geometrically distinct test environments. Using a computational algorithm based on recently developed tools from Persistent Homology theory in the field of algebraic topology, we find that the patterns of neuronal co-firing can, in fact, convey topological information about the environment in a biologically realistic length of time. Furthermore, our simulations reveal a “learning region” that highlights the interplay between the parameters in combining to produce hippocampal states that are more or less adept at map formation. For example, within the learning region a lower number of neurons firing can be compensated by adjustments in firing rate or place field size, but beyond a certain point map formation begins to fail. We propose that this learning region provides a coherent theoretical lens through which to view conditions that impair spatial learning by altering place cell firing rates or spatial specificity.


title: Character-Level Convolutional Neural Network for Paraphrase Detection and Other Experiments

abstract: The central goal of this paper is to report on the results of an experimental study on the application of character-level embeddings and basic convolutional neural network to the shared task of sentence paraphrase detection in Russian. This approach was tested in the standard run of Task 2 of that shared task and revealed competitive results, namely 73.9% accuracy against the test set. It is compared against a word-level convolutional neural network for the same task, and varied other approaches, such as rule-based and classical machine learning.


title: Laplacian Regularized Low-Rank Representation and Its Applications

abstract: Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition, computer vision and signal processing. In the real world, data often reside on low-dimensional manifolds embedded in a high-dimensional ambient space. However, the LRR method does not take into account the non-linear geometric structures within data, thus the locality and similarity information among data may be missing in the learning process. To improve LRR in this regard, we propose a general Laplacian regularized low-rank representation framework for data representation where a hypergraph Laplacian regularizer can be readily introduced into, i.e., a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR). By taking advantage of the graph regularizer, our proposed method not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information in data. The extensive experimental results on image clustering, semi-supervised image classification and dimensionality reduction tasks demonstrate the effectiveness of the proposed method.


Xenotransplantation's benefits outweigh risks


title: Production Planning and Inventory Control in Automotive Supply Chain Networks

abstract: This paper addresses a non-linear optimization model by integrating production planning and inventory control in the automotive industry at the strategic and operational level. In order to provide an effective modeling, we developed a framework to integrate manufacturing system and suppliers within an automotive supply chain network. The numerical experiments demonstrate the efficiency of the proposed model on minimization of total delivery cost and due date delivery.


title: A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks

abstract: In this paper we propose a benchmark dataset for crop/weed discrimination, single plant phenotyping and other open computer vision tasks in precision agriculture. The dataset comprises 60 images with annotations and is available online (http://github.com/cwfid). All images were acquired with the autonomous field robot Bonirob in an organic carrot farm while the carrot plants were in early true leaf growth stage. Intra- and inter-row weeds were present, weed and crop were approximately of the same size and grew close together. For every dataset image we supply a ground truth vegetation segmentation mask and manual annotation of the plant type (crop vs. weed). We provide initial results for the phenotyping problem of crop/weed classification and propose evaluation methods to allow comparison of different approaches. By opening this dataset to the community we want to stimulate research in this area where the current lack of public datasets is one of the barriers for progress.


title: Harmonious Genetic Clustering

abstract: To automatically determine the number of clusters and generate more quality clusters while clustering data samples, we propose a harmonious genetic clustering algorithm, named HGCA, which is based on harmonious mating in eugenic theory. Different from extant genetic clustering methods that only use fitness, HGCA aims to select the most suitable mate for each chromosome and takes into account chromosomes gender, age, and fitness when computing mating attractiveness. To avoid illegal mating, we design three mating prohibition schemes, i.e., no mating prohibition, mating prohibition based on lineal relativeness, and mating prohibition based on collateral relativeness, and three mating strategies, i.e., greedy eugenics-based mating strategy, eugenics-based mating strategy based on weighted bipartite matching, and eugenics-based mating strategy based on unweighted bipartite matching, for harmonious mating. In particular, a novel single-point crossover operator called variable-length-and-gender-balance crossover is devised to probabilistically guarantee the balance between population gender ratio and dynamics of chromosome lengths. We evaluate the proposed approach on real-life and artificial datasets, and the results show that our algorithm outperforms existing genetic clustering methods in terms of robustness, efficiency, and effectiveness.


title: ViewDroid: towards obfuscation-resilient mobile application repackaging detection

abstract: In recent years, as mobile smart device sales grow quickly, the development of mobile applications (apps) keeps accelerating, so does mobile app repackaging. Attackers can easily repackage an app under their own names or embed advertisements to earn pecuniary profits. They can also modify a popular app by inserting malicious payloads into the original app and leverage its popularity to accelerate malware propagation. In this paper, we propose ViewDroid, a user interface based approach to mobile app repackaging detection. Android apps are user interaction intensive and event dominated, and the interactions between users and apps are performed through user interface, or views. This observation inspires the design of our new birthmark for Android apps, namely, feature view graph, which captures users' navigation behavior across app views. Our experimental results demonstrate that this birthmark can characterize Android apps from a higher level abstraction, making it resilient to code obfuscation. ViewDroid can detect repackaged apps at a large scale, both effectively and efficiently. Our experiments also show that the false positive and false negative rates of ViewDroid are both very low.


title: Optimization-based Full Body Control for the DARPA Robotics Challenge

abstract: We describe our full body humanoid control approach developed for the simulation phase of the DARPA Robotics Challenge DRC, as well as the modifications made for the DARPA Robotics Challenge Trials. We worked with the Boston Dynamics Atlas robot. Our approach was initially targeted at walking, and it consisted of two levels of optimization: a high-level trajectory optimizer that reasons about center of mass and swing foot trajectories, and a low-level controller that tracks those trajectories by solving floating base full body inverse dynamics using quadratic programming. This controller is capable of walking on rough terrain, and it also achieves long footsteps, fast walking speeds, and heel-strike and toe-off in simulation. During development of these and other whole body tasks on the physical robot, we introduced an additional optimization component in the low-level controller, namely an inverse kinematics controller. Modeling and torque measurement errors and hardware features of the Atlas robot led us to this three-part approach, which was applied to three tasks in the DRC Trials in December 2013.


title: Milroy's disease associated with scrotal lymphangioma circumscriptum.

abstract: INTRODUCTION Milroy’s Disease (MD) is characterized by peripheral edema of the lower extremities at birth or in early childhood which is due to complete aplasia of dermal lymphatics. Diagnosis of MD can be made easily by imaging the lymphatic channels with radionuclide lymphoscintigraphy or dynamic magnetic resonance lymphangiography. Lymphangioma circumscriptum (LC) is also a benign disorder of lymphatic vascular channels and considered to be a circumscribed developmental disease of lymphatic tissues in the dermis which is characterized by lymphatic cisterns in the subcutaneous tissue and communicate with dilated dermal lymphatics. LC is generally localized at any anatomic site of human body but more frequently affect the chest, axillary folds, shoulders, neck, buccal mucosa, proximal limbs and rarely hips, groins, and genital area. The pathognomonic appearance of LC is multiple translucent or hemorrhagic vesicular lesions with clear leakage. Association of MD with LC is very rare.(1)


title: Qualitative Comparison of Community Detection Algorithms

abstract: Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.


Performance-based Pricing Models in Online Advertising : Cost per Click versus Cost per Action


title: A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks

abstract: Precipitation prediction, such as short-term rainfall prediction, is a very important problem in the field of meteorological service. In practice, most of recent studies focus on leveraging radar data or satellite images to make predictions. However, there is another scenario where a set of weather features are collected by various sensors at multiple observation sites. The observations of a site are sometimes incomplete but provide important clues for weather prediction at nearby sites, which are not fully exploited in existing work yet. To solve this problem, we propose a multi-task convolutional neural network model to automatically extract features from the time series measured at observation sites and leverage the correlation between the multiple sites for weather prediction via multi-tasking. To the best of our knowledge, this is the first attempt to use multi-task learning and deep learning techniques to predict short-term rainfall amount based on multi-site features. Specifically, we formulate the learning task as an end-to-end multi-site neural network model which allows to leverage the learned knowledge from one site to other correlated sites, and model the correlations between different sites. Extensive experiments show that the learned site correlations are insightful and the proposed model significantly outperforms a broad set of baseline models including the European Centre for Medium-range Weather Forecasts system (ECMWF).


title: Failure Behavior Analysis for Reliable Distributed Embedded Systems

abstract: Failure behavior analysis is a very important phase in developing large distributed embedded systems with weak safety requirements which do graceful degradation in case of failures. Today, the analysis will usually be done by standard methods like FTA and FMEA considering the existence of faults, only. Gradations of errors are not regarded, although this is a very coarse system behavior approximation. In contrast to that, our advanced failure behavior analysis yields more sophisticated and graded results.We obtain comprehensive results by assigning a quality description to all the information in a system and extending the pure information flow to an information quality flow, that models system failure behavior, too. We model this information quality flow by object-oriented hierarchical petri nets. Large parts of these nets can automatically be generated from the existing behavioral system structure. A net simulator enables us to perform all the sophisticated analyses we need to examine the failure behavior.


A Review on Performance Analysis of Cloud Computing Services for Scientific Computing


E ffi cient Crowdsourcing of Unknown Experts using Multi-Armed Bandits Long


title: LittleTable: A Time-Series Database and Its Uses

abstract: We present LittleTable, a relational database that Cisco Meraki has used since 2008 to store usage statistics, event logs, and other time-series data from our customers' devices. LittleTable optimizes for time-series data by clustering tables in two dimensions. By partitioning rows by timestamp, it allows quick retrieval of recent measurements without imposing any penalty for retaining older history. By further sorting within each partition by a hierarchically-delineated key, LittleTable allows developers to optimize each table for the specific patterns with which they intend to access it. LittleTable further optimizes for time-series data by capitalizing on the reduced consistency and durability needs of our applications, three of which we present here. In particular, our applications are single-writer and append-only. At most one process inserts a given type of data collected from a given device, and applications never update rows written in the past, simplifying both lock management and crash recovery. Our most recently written data is also recoverable, as it can generally be re-read from the devices themselves, allowing LittleTable to safely lose some amount of recently-written data in the event of a crash. As a result of these optimizations, LittleTable is fast and efficient, even on a single processor and spinning disk. Querying an uncached table of 128-byte rows, it returns the first matching row in 31 ms, and it returns 500,000 rows/second thereafter, approximately 50% of the throughput of the disk itself. Today Meraki stores 320 TB of data across several hundred LittleTable servers system-wide.


title: Space vector modulation applied to three-phase three-switch two-level unidirectional PWM rectifier

abstract: This work presents a methodology to apply space vector modulation to a three-phase three-switch two-level Y-connected unidirectional pulsewidth modulation (PWM) rectifier. Converter switching stages are analyzed to determine switch control signals for space vector modulation. A switching sequence is proposed in order to minimize the number of switch commutations and to reduce the switching losses. Duty cycle functions are determined and the desired switching sequences are performed by a simple PWM modulator with no need of to determine the present sector of vector. For this propose is just necessary to impose the desired current sectors from input voltage references. The vector control structure used with the proposed modulation technique is also described. In order to validate the proposed modulation technique, experimental results are presented for a 20 kW prototype.


Retention And Recruiting Activities In A Mechanical Engineering Program


title: Concurrency control algorithm for shared disk cloud DBMS

abstract: Using shared disk architecture for relational cloud DBMSs enhances their performance and throughput and increases the scalability. In such architectures, transactions are not distributed between database instances and data are not migrated, whereas any database instance can read and access any database object. Lock technology control for concurrent transactions ensures their consistency especially in shared disk architecture, using traditional granularity database locks for cloud database, can cause numerous problems. This paper proposes an optimistic concurrency control algorithm that uses soft locks and minimizes the number of accessed database instances for validating a transaction. It creates a lock manager for all database objects and distributes it over database instances until it does not have to validate the transaction, neither with single database instance if it is owned by only a database instance nor with all database instances if it is replicated on all database instances. The proposed algorithm is evaluated against other cloud concurrency control algorithms and the results confirms its effectiveness.


LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl.


title: Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula

abstract: This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the information gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been characterized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions.


title: Socially Optimal Mining Pools

abstract: Mining for Bitcoins is a high-risk high-reward activity. Miners, seeking to reduce their variance and earn steadier rewards, collaborate in pooling strategies where they jointly mine for Bitcoins. Whenever some pool participant is successful, the earned rewards are appropriately split among all pool participants. Currently a dozen of different pooling strategies (i.e., methods for distributing the rewards) are in use for Bitcoin mining. We here propose a formal model of utility and social welfare for Bitcoin mining (and analogous mining systems) based on the theory of discounted expected utility, and next study pooling strategies that maximize the social welfare of miners. Our main result shows that one of the pooling strategies actually employed in practice--the so-called geometric pay pool--achieves the optimal steady-state utility for miners when its parameters are set appropriately. Our results apply not only to Bitcoin mining pools, but any other form of pooled mining or crowdsourcing computations where the participants engage in repeated random trials towards a common goal, and where"partial"solutions can be efficiently verified.


Classification of Book Genres By Cover and Title


title: Six Weeks of Core Stability Training Improves Landing Kinetics Among Female Capoeira Athletes: A Pilot Study

abstract: Abstract Core stability training (CST) has increased in popularity among athletes and the general fitness population despite limited evidence CST programmes alone lead to improved athletic performance. In female athletes, neuromuscular training combining balance training and trunk and hip/pelvis dominant CST is suggested to reduce injury risk, and specifically peak vertical ground reaction forces (vGRF) in a drop jump landing task. However, the isolated effect of trunk dominant core stability training on vGRF during landing in female athletes had not been evaluated. Therefore, the objective of this study was to evaluate landing kinetics during a drop jump test following a CST intervention in female capoeira athletes. After giving their informed written consent, sixteen female capoeira athletes (mean ± SD age, stature, and body mass of 27.3 ± 3.7 years, 165.0 ± 4.0 cm, and 59.7 ± 6.3 kg, respectively) volunteered to participate in the training program which consisted of static and dynamic CST sessions, three times per week for six weeks. The repeated measures T-test revealed participants significantly reduced relative vGRF from pre- to post-intervention for the first (3.40 ± 0.78 vs. 2.85 ± 0.52 N·NBW-1, respectively [p<0.05, effect size = 0.60]), and second landing phase (5.09 ± 1.17 vs. 3.02 ± 0.41 N·NBW-1, respectively [p<0.001, effect size = 0.87]). The average loading rate was reduced from pre- to post-intervention during the second landing phase (30.96 ± 18.84 vs. 12.06 ± 9.83 N·NBW·s-1, respectively [p<0.01, effect size = 0.68]). The peak loading rate was reduced from pre- to postintervention during the first (220.26 ± 111.51 vs. 120.27 ± 64.57 N· NBW·s-1 respectively [p<0.01, effect size = 0.64]), and second (99.52 ± 54.98 vs. 44.71 ± 30.34 N· NBW·s-1 respectively [p<0.01, effect size = 0.70]) landing phase. Body weight, average loading rate during the first landing phase, and jump height were not significantly different between week 0 and week 6 (p=0.528, p=0.261, and p=0.877, respectively). This study provides evidence that trunk dominant core stability training improves landing kinetics without improving jump height, and may reduce lower extremity injury risk in female athletes.


title: Deep learning and process understanding for data-driven Earth system science

abstract: Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.


title: Computation of the characteristics of a claw pole alternator through the finite element method

abstract: The paper presents the analysis of a 3-D numerical model developed for a claw pole alternator. The complex structure of the claw-pole magnetic circuit required a 3D FEM model and a double scalar potential magnetic φ-φred formulation, in order to reduce computing time and memory. The no-load and magnetization characteristics and the e.m.f. time dependence have been calculated. Working characteristics and the induced voltage in the static winding can be calculated by knowing the 3D distribution of the field in stationary magnetic regime for successive positions of the rotor considering the stator.


title: Global software engineering and agile practices: a systematic review

abstract: Agile practices have received attention from industry as an alternative to plan-driven software development approaches. Agile encourages, for example, small self-organized collocated teams, whereas global software engineering (GSE) implies distribution across cultural, temporal, and geographical boundaries. Hence, combining them is a challenge. A systematic review was conducted to capture the status of combining agility with GSE. The results were limited to peer-reviewed conference papers or journal articles, published between 1999 and 2009. The synthesis was made through classifying the papers into different categories (e.g. publication year, contribution type, research method). At the end, 81 papers were judged as primary for further analysis. The distribution of papers over the years indicated that GSE and Agile in combination has received more attention in the last 5 years. However, the majority of the existing research is industrial experience reports in which Agile practices were modified with respect to the context and situational requirements. The emergent need in this research area is suggested to be developing a framework that considers various factors from different perspectives when incorporating Agile in GSE. Practitioners may use it as a decision-making basis in early phases of software development.


title: AMGA: an archive-based micro genetic algorithm for multi-objective optimization

abstract: In this paper, we propose a new evolutionary algorithm for multi-objective optimization. The proposed algorithm benefits from the existing literature and borrows several concepts from existing multi-objective optimization algorithms. The proposed algorithm employs a new kind of selection procedure which benefits from the search history of the algorithm and attempts to minimize the number of function evaluations required to achieve the desired convergence. The proposed algorithm works with a very small population size and maintains an archive of best and diverse solutions obtained so as to report a large number of non-dominated solutions at the end of the simulation. Improved formulation for some of the existing diversity preservation techniques is also proposed. Certain implementation aspects that facilitate better performance of the algorithm are discussed. Comprehensive benchmarking and comparison of the proposed algorithm with some of the state-of-the-art multi-objective evolutionary algorithms demonstrate the improved search capability of the proposed algorithm.


title: Towards Architecting for Continuous Delivery

abstract: Continuous Delivery (CD) has emerged as an auspicious software development discipline, with the promise of providing organizations the capability to release valuable software continuously to customers. Our organization has been implementing CD for the last two years. Thus far, we have moved 22 software applications to CD. I observed that CD has created a new context for architecting these applications. In this paper, I will try to characterize such a context of CD, explain why we need to architect for CD, describe the implications of architecting for CD, and discuss the challenges this new context creates. This information can provide insights to other practitioners for architecting their software applications, and provide researchers with input for developing their research agendas to further study this increasingly important topic.


title: Do Alcohol Misuse, Service Utilisation, and Demographic Characteristics Differ between UK Veterans and Members of the General Public Attending an NHS General Hospital?

abstract: The aim of this paper was to provide insights into alcohol misuse within UK veterans to inform as to whether their presentations differ from the general public. This was done by exploring differences in the severity of alcohol misuse between UK veterans and the general public admitted to a general NHS hospital over an 18 month period using retrospective data. All patients admitted to the hospital were screened for alcohol misuse. Those deemed as experiencing problems were referred for specialist nurse-led support. A total of 2331 individuals were referred for this supported and administered with a standardised assessment that included measures of the severity of alcohol difficulties (AUDIT), dependency levels (LDQ), and assessed for the presence of withdrawal symptoms (CIWA-Ar). In addition, information was collected on service utilisation, referral category (medical or mental health), other substance misuse, and demographic characteristics. No differences were found between the severity of reported alcohol difficulties between veterans and non-veterans. Evidence was found to suggest that veterans were more likely to be referred for support with alcohol difficulties at an older age and to be admitted to hospital for longer periods of time. This could have considerable cost implications for the NHS. It was more common for veterans to present at hospital with physical health difficulties prior to being referred for support for alcohol.


title: Three-Dimensional Architecture of Substrate Integrated Waveguide Feeder for Fermi Tapered Slot Antenna Array Applications

abstract: A class of three-dimensional planar arrays in substrate integrated waveguide (SIW) technology is proposed, designed and demonstrated with 8 × 16 elements at 35 GHz for millimeter-wave imaging radar system applications. Endfire element is generally chosen to ensure initial high gain and broadband characteristics for the array. Fermi-TSA (tapered slot antenna) structure is used as element to reduce the beamwidth. Corrugation is introduced to reduce the resulting antenna physical width without degradation of performance. The achieved measured gain in our demonstration is about 18.4 dBi. A taper shaped air gap in the center is created to reduce the coupling between two adjacent elements. An SIW H-to-E-plane vertical interconnect is proposed in this three-dimensional architecture and optimized to connect eight 1 × 16 planar array sheets to the 1 × 8 final network. The overall architecture is exclusively fabricated by the conventional PCB process. Thus, the developed SIW feeder leads to a significant reduction in both weight and cost, compared to the metallic waveguide-based counterpart. A complete antenna structure is designed and fabricated. The planar array ensures a gain of 27 dBi with low SLL of 26 dB and beamwidth as narrow as 5.15 degrees in the E-plane and 6.20 degrees in the 45°-plane.


title: Evaluating the diagnostic criteria for panic disorder: measures of social morbidity as criteria

abstract: SummaryOver 9% of the subjects in the NIMH Epidemiologic Catchment Area sample reported a history of at least one panic attack, yet only 1.5% of the sample met DSM-III criteria for panic disorder. In light of the revisions of these criteria for DSM-IV, two of those diagnostic criteria, frequency of panic attacks and number of symptoms during a panicattack, are examined in this paper. We evaluated the relationship between those criteria and each of three measures of social morbidity: psychiatric hospitalization, emergency room (ER) use for emotional problems, and suicide attempts. Although only symptom count was significantly associated with either ER use or suicide attempts, both diagnostic criteria were associated with psychiatric hospitalization. The threshold for symptom count was also considered and while three appeard to have slightly better sensitivity and specificity, the continued use of four symptoms seemed acceptable. The diagnostic and clinical implications of the findings are considered.


“Searching for Sustainability: Strategies & Choice in the eBook Supply Chain"


title: Categorization of Comparative Sentences for Argument Mining

abstract: We present the first work on domain-independent comparative argument mining (CAM), which is the automatic extraction of direct comparisons from text. After motivating the need and identifying the widespread use of this so far under-researched topic, we present the first publicly available open-domain dataset for CAM. The dataset was collected using crowdsourcing and contains 7199 unique sentences for 217 distinct comparison target pairs selected over several domains, of which 27% contain a directed (better vs. worse) comparison. In classification experiments, we examine the impact of representations, features, and classifiers, and reach an F1-score of 88% with a gradient boosting model based on pre-trained sentence embeddings, especially reliably identifying non-comparative sentences. This paves the way for domain-independent comparison extraction from web-scale corpora for the use in result ranking and presentation for comparative queries.


title: Application of Blockchain Technology in Smart City Infrastructure

abstract: With the rapid growth of urbanization, smart city needs efficient and sustainable intelligent solutions in the transportation, environment, energy, government affairs. The smart city infrastructure, which combines the IoT, Big Data and Energy Internet, is one of the most effective solutions. It’s facing many problems such as poor security of IoT, equipment maintenance and upgrade difficulty, high operating costs of large data center construction, poor resistance to damage, the difficulty of establishing the energy Internet users trust , user privacy is easy to leak, market model is not applicable and so on. The Blockchain is a kind of distributed P2P network common intelligence accounting technology based on cryptography. The features of open, transparent, interconnection and peer-to-peer, storage and sharing in the Blockchain are relevant to the openness, interconnection, peer-to-peer and Shared characteristics of the energy Internet. Its features of redundant storage and distribution also make up for the disadvantages of large data center’s high operation cost, poor recovery ability and the difficulties for the maintenance of IoT equipment. This paper, first introduces the role of the big data, IoT, and the energy Internet in the construction of smart city and Blockchain technology. By analyzing their respective characteristics and comparing their similarities, corresponding solutions are put forward to aim at the problems such as poor security of IoT, upgrading of equipment maintenance and upgrading, construction and operation cost of large data center, poor flexibility in anti-attack, difficulty in establishing trust in energy Internet users, user privacy leakage and inapplicability of trading market mode. A kind of architecture of P2P light-heavy backup is put forward to overcome the high cost of Blockchain data storage.


title: Proactive Recommendation Delivery

abstract: The main purpose of Recommender Systems is to minimize the effects of information/choice overload. Recommendations are usually prepared based on the estimation of what would be useful or interesting to users. Thus, it is important that they are relevant to users, whether to their information needs, current activity or emotional state. This requires deep understanding of users' context but also the knowledge of the history of previous users' interactions within the system (e.g. clicks, views, etc.). But even when the recommendations are highly relevant, their delivery to users can be very problematic. Many existing systems require active user participation (explicit interaction with the recommender system) and attention. Or, on other side of spectrum, there are RS that handle recommendation delivery without any consideration for users' preferences of when, where or how the recommendations are being delivered. Proactive Recommender Systems promise a more autonomous approach for recommendation delivery, by anticipating information needs in advance and acting on users' behalf with minimal efforts and without disturbance. This paper describes our work and interest in identifying and analyzing the factors that can influence acceptance and use of proactively delivered recommendations.


title: Approximate Note Transcription for the Improved Identification of Difficult Chords

abstract: The automatic detection and transcription of musical chords from audio is an established music computing task. The choice of chord profiles and higher-level time-series modelling have received a lot of attention, resulting in methods with an overall performance of more than 70% in the MIREX Chord Detection task 2009. Research on the front end of chord transcription algorithms has often concentrated on finding good chord templates to fit the chroma features. In this paper we reverse this approach and seek to find chroma features that are more suitable for usage in a musically-motivated model. We do so by performing a prior approximate transcription using an existing technique to solve non-negative least squares problems (NNLS). The resulting NNLS chroma features are tested by using them as an input to an existing state-of-the-art high-level model for chord transcription. We achieve very good results of 80% accuracy using the song collection and metric of the 2009 MIREX Chord Detection tasks. This is a significant increase over the top result (74%) in MIREX 2009. The nature of some chords makes their identification particularly susceptible to confusion between fundamental frequency and partials. We show that the recognition of these diffcult


title: A grid middleware for distributed Java computing with MPI binding and process migration supports

abstract: "Grid" computing has emerged as an important new research field. With years of efforts, grid researchers have successfully developed grid technologies including security solutions, resource management protocols, information query protocols, and data management services. However, as the ultimate goal of grid computing is to design an infrastructure which supports dynamic, cross-organizational resource sharing, there is a need of solutions for efficient and transparent task re-scheduling in the grid.In this research, a new grid middleware is proposed, called G-JavaMPI. This middleware adds the parallel computing capability of Java to the grid with the support of a Grid-enabled message passing interface (MPI) for inter-process communication between Java processes executed at different grid points. A special feature of the proposed G-JavaMPI is the support of Java process migration with post-migration message redirection. With these supports, it is possible, to migrate executing Java process from site to site for continuous computation, if some site is scheduled to be turned down for system reconfiguration. Moreover, the proposed G-JavaMPI middleware is very portable since it requires no modification of underlying OS, Java virtual machine, and MPI package. Preliminary performance tests have been conducted. The proposed mechanisms have shown good migration efficiency in a simulated grid environment.


title: FAVORITISM AND REFEREE BIAS IN EUROPEAN SOCCER: EVIDENCE FROM THE SPANISH LEAGUE AND THE UEFA CHAMPIONS LEAGUE

abstract: In this paper, we test for, and find evidence of, referee bias in favor of home teams in European football using minute-by-minute analysis to control for within-game events. The context for the analysis is Spain's Primera Liga and the Union of European Football Association (UEFA) Champions League. We find that the award of sanctions by Spanish referees in the Champions League are not significantly different to those of the referees from other countries and as such are subject to the same sources of bias. In Primera Liga matches where the crowd is separated from the pitch by running tracks, we find that the probability of the award of a yellow card to the home team is higher and that of the away team is lower compared to matches played at stadia without running tracks. Similar results are found in the Champions League, where efforts are made to hire “neutral” referees. Referee behavior is also influenced by the size of the crowd in attendance.


title: Power reduces the press of the situation: Implications for creativity, conformity, and dissonance

abstract: Although power is often conceptualized as the capacity to influence others, the current research explores whether power psychologically protects people from influence. In contrast to classic social psychological research demonstrating the strength of the situation in directing attitudes, expressions, and intentions, 5 experiments (using experiential primes, semantic primes, and role manipulations of power) demonstrate that the powerful (a) generate creative ideas that are less influenced by salient examples, (b) express attitudes that conform less to the expressed opinions of others, (c) are more influenced by their own social value orientation relative to the reputation of a negotiating opponent, and (d) perceive greater choice in making counterattitudinal statements. This last experiment illustrates that power is not always psychologically liberating; it can create internal conflict, arousing dissonance, and thereby lead to attitude change. Across the experiments, high-power participants were immune to the typical press of situations, with intrapsychic processes having greater sway than situational or interpersonal ones on their creative and attitudinal expressions.


title: The Effect of Potential to Colour and COD Removal from Waste Textile Industry by Electrochemical Method

abstract: The waste textile industry is one of the problems to the human life as well as environment. In this study, the treatment of the waste textile industry by the electrochemical method using stainless steel as anode and cathode was investigated. The effect of potential to color and COD removal were investigated. The research consists of several stages, the electrode composition analysis using Scanning Electron Microscopy-Energy Dispersive X-Ray (SEM-EDX). The electrolysis waste textile industry has been done using various potential. Degradation parameter is Chemical Oxygen Demand (COD) were analysis using spectrometry method. The research results show that stainless steel electrode has composition are iron (72.2 %), chromium (18.9 %), nickel (7.6 %) and silica (1.4 %). After electrolysis at potential constant (3 V) shows percentage degradation of the waste textile industry is 98.56 %. The percentage reduction in COD value of textile waste electrolyzed in optimum condition is 50.38 %. As conclusions are the potential at 3 V and stainless-steel electrode as a good parameter and material for electrochemical degradation of the waste textile industry.


title: Intrusion detection based on k-means clustering and OneR classification

abstract: Intrusion detection system (IDS) is used to detect various kinds of attacks in interconnected network. Many machine learning methods have also been introduced by researcher recently to obtain high accuracy and detection rate. Unfortunately, a potential drawback of all those methods is the rate of false alarm. However, our proposed approach shows better results, by combining clustering (to identify groups of similarly behaved samples, i.e. malicious and non-malicious activity) and classification techniques (to classify all data into correct class categories). The approach, KM+1R, combines the k-means clustering with the OneR classification technique. The KDD Cup '99 set is used as a simulation dataset. The result shows that our proposed approach achieve a better accuracy and detection rate, particularly in reducing the false alarm.


title: Phonological systems in bilinguals: age of learning effects on the stop consonant systems of Korean-English bilinguals.

abstract: Interaction of Korean and English stop systems in Korean-English bilinguals as a function of age of acquisition (AOA) of English was investigated. It was hypothesized that early bilinguals (mean AOA=3.8 years) would more likely be native-like in production of English and Korean stops and maintain greater independence between Korean and English stop systems than late bilinguals (mean AOA=21.4 years). Production of Korean and English stops was analyzed in terms of three acoustic-phonetic properties: voice-onset time, amplitude difference between the first two harmonics, and fundamental frequency. Late bilinguals were different from English monolinguals for English voiceless and voiced stops in all three properties. As for Korean stops, late bilinguals were different from Korean monolinguals for fortis stops in voice-onset time. Early bilinguals were not different from the monolinguals of either language. Considering the independence of the two stop systems, late bilinguals seem to have merged English voiceless and Korean aspirated stops and produced English voiced stops with similarities to both Korean fortis and lenis stops, whereas early bilinguals produced five distinct stop types. Thus, the early bilinguals seem to have two independent stop systems, whereas the late bilinguals likely have a merged Korean-English system.


title: Segmentation of White Blood Cells Image Using Adaptive Location and Iteration

abstract: Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and sub-image segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multi-scale cues to extract a rectangular region is proposed. In this manner, the location window not only can cover the whole WBC completely, but also achieve adaptive adjustment. In the sub-image segmentation, the sub-images preprocessed from the location window with a replace procedure are taken as initialization, and the GrabCut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.


title: Quasi-Recurrent Neural Networks

abstract: Recurrent neural networks are a powerful tool for modeling sequential data, but the dependence of each timestep's computation on the previous timestep's output limits parallelism and makes RNNs unwieldy for very long sequences. We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that alternates convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function that applies in parallel across channels. Despite lacking trainable recurrent layers, stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size. Due to their increased parallelism, they are up to 16 times faster at train and test time. Experiments on language modeling, sentiment classification, and character-level neural machine translation demonstrate these advantages and underline the viability of QRNNs as a basic building block for a variety of sequence tasks.


title: Image features that draw fixations

abstract: The ability to automatically detect 'visually interesting' regions in an image has many practical applications especially in the design of active machine vision systems. This paper describes a data-driven approach that uses eye tracking in tandem with principal component analysis to extract low-level image features that attract human gaze. Data analysis on an ensemble of image patches extracted at the observer's point of gaze revealed features that resemble derivatives of the 2D Gaussian operator. Dissimilarities between human and random fixations are investigated by comparing the features extracted at the point of gaze to the general image structure obtained by random sampling in Monte-Carlo simulations. Finally, a simple application where these features are used to predict fixations is illustrated.


An Introduction to Machine Learning Communications Systems.


title: Detection and classification of edges in color images [a review of vector-valued techniques] - Signal Processing Magazine, IEEE

abstract: Up to now, most of the color edge detection methods are monochromatic-based techniques, which produce, in general, better than when traditional gray-value techniques are applied. In this overview, we focus mainly on vector-valued techniques because it is easy to understand how to apply common edge detection schemes to every color component. Opposed to this, vector-valued techniques are new and different. The second part of the article addresses the topic of edge classification. While edges are often classified into step edges and ramp edges, we address the topic of physical edge classification based on their origin into shadow edges, reflectance edges, orientation edges, occlusion edges, and specular edges. In the rest of this article we discuss various vector-valued techniques for detecting discontinuities in color images. Then operators are presented based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection. We then discuss different approaches to a physical classification of edges by their origin.


title: Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies

abstract: In some real-world areas, it is important to enrich the data with external background knowledge so as to provide context and to facilitate pattern recognition. These areas may be described as data rich but knowledge poor. There are two challenges to incorporate this biological knowledge into the data mining cycle: (1) generating the ontologies; and (2) adapting the data mining algorithms to make use of the ontologies. This chapter presents the state-of-the-art in bringing the background ontology knowledgeinto the pattern recognition task for biomedical data.


Exact Algorithms for Unit Demand Pricing Problems


Learning in Reality: A case study of Stanley, the robot that Won the DARPA Challenge


Variation Autoencoder Based Network Representation Learning for Classification


title: Subgraph Matching Kernels for Attributed Graphs

abstract: We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate mappings of subgraphs by a exible scoring scheme comparing vertex and edge attributes by kernels. We show that subgraph matching kernels generalize several known kernels. To compute the kernel we propose a graph-theoretical algorithm inspired by a classical relation between common subgraphs of two graphs and cliques in their product graph observed by Levi (1973). Encouraging experimental results on a classification task of real-world graphs are presented.


title: Sparse Representation-Based Multiple Frame Video Super-Resolution

abstract: In this paper, we propose two multiple-frame super-resolution (SR) algorithms based on dictionary learning (DL) and motion estimation. First, we adopt the use of video bilevel DL, which has been used for single-frame SR. It is extended to multiple frames by using motion estimation with sub-pixel accuracy. We propose a batch and a temporally recursive multi-frame SR algorithm, which improves over single-frame SR. Finally, we propose a novel DL algorithm utilizing consecutive video frames, rather than still images or individual video frames, which further improves the performance of the video SR algorithms. Extensive experimental comparisons with the state-of-the-art SR algorithms verify the effectiveness of our proposed multiple-frame video SR approach.


title: Author ' s personal copy Partial Least Squares ( PLS ) methods for neuroimaging : A tutorial and review

abstract: article i nfo Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre- defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.


title: Modeling, design and optimization of dual mode circular planar monopulse slot array antenna in X-band

abstract: Seeker is widely used in literature of radar. The goal of this paper is designing a dual mode monopulse antenna for seeker. The dual mode technology is widely used in passive and active seekers. In this technology, antenna radiation and infrared sensor are used for target detection. Thus, the angle of target in azimuth plane is precisely detected. In this paper, a dual mode slot array antenna in X-band for an active seeker is designed and optimized. The Main lobe of the antenna is only in azimuth plane and does not shift with frequency variation. The gain of the antenna is suggested to be 23dBi for the best detection. We select the type of the antenna due to the available results in the literature. Then, designing of the antenna is done step by step. Simulation and optimization of antenna are performed by CST studio suite software.


title: Downregulation of TFAM inhibits the tumorigenesis of non-small cell lung cancer by activating ROS-mediated JNK/p38MAPK signaling and reducing cellular bioenergetics

abstract: // Deyao Xie 1, 2, * , Xiaoyi Wu 2, * , Linhua Lan 2, * , Fugeng Shangguan 2 , Xiaoming Lin 1 , Fuhong Chen 2 , Shan Xu 2, 3 , Ya Zhang 2 , Zilei Chen 2 , Kate Huang 4 , Rongrong Wang 4 , Lu Wang 2 , Xiaoxiao Song 5 , Yongzhang Liu 2 , Bin Lu 2 1 Department of Cardiothoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China 2 Protein Quality Control and Diseases Laboratory, Attardi Institute of Mitochondrial Biomedicine, School of Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China 3 Huzhou Health School, Huzhou, Zhejiang 313100, P.R. China 4 Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China 5 Department of Otolaryngology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China * These authors contributed equally to this work Correspondence to: Bin Lu, e-mail: lubmito@wmu.edu.cn Yongzhang Liu, e-mail: lyz@wmu.edu.cn Keywords: mitochondria, TFAM, non-small cell lung cancer, chemosensitivity, cellular bioenergetics Received: June 04, 2015 Accepted: January 07, 2016 Published: January 25, 2016 ABSTRACT Mitochondrial transcription factor A (TFAM) is essential for the replication, transcription and maintenance of mitochondrial DNA (mtDNA). The role of TFAM in non-small cell lung cancer (NSCLC) remains largely unknown. Herein, we report that downregulation of TFAM in NSCLC cells resulted in cell cycle arrest at G1 phase and significantly blocked NSCLC cell growth and migration through the activation of reactive oxygen species (ROS)-induced c-Jun amino-terminal kinase(JNK)/p38 MAPK signaling and decreased cellular bioenergetics. We further found that TFAM downregulation in NSCLC cells led to increased apoptotic cell death and enhanced the sensitivity of NSCLC cells to cisplatin. Tissue microarray (TMA) data showed that elevated expression of TFAM was related to the histological grade and TNM stage of NSCLC patients. We also demonstrated that TFAM is an independent prognostic factor for overall survival of NSCLC patients. Taken together, our findings suggest that TFAM could serve as a potential diagnostic biomarker and molecular target for the treatment of NSCLC, as well as for prediction of the effectiveness of chemotherapy.