title: Multiple-Instance Learning for Medical Image and Video Analysis

abstract: Multiple-instance learning (MIL) is a recent machine-learning paradigm that is particularly well suited to medical image and video analysis (MIVA) tasks. Based solely on class labels assigned globally to images or videos, MIL algorithms learn to detect relevant patterns locally in images or videos. These patterns are then used for classification at a global level. Because supervision relies on global labels, manual segmentations are not needed to train MIL algorithms, unlike traditional single-instance learning (SIL) algorithms. Consequently, these solutions are attracting increasing interest from the MIVA community: since the term was coined by Dietterich et al. in 1997, 73 research papers about MIL have been published in the MIVA literature. This paper reviews the existing strategies for modeling MIVA tasks as MIL problems, recommends general-purpose MIL algorithms for each type of MIVA tasks, and discusses MIVA-specific MIL algorithms. Various experiments performed in medical image and video datasets are compiled in order to back up these discussions. This meta-analysis shows that, besides being more convenient than SIL solutions, MIL algorithms are also more accurate in many cases. In other words, MIL is the ideal solution for many MIVA tasks. Recent trends are discussed, and future directions are proposed for this emerging paradigm.


title: Solution of inverse kinematic problem for serial robot using quaterninons

abstract: A new inverse kinematic solution for serial robot manipulators is represented in this paper. Major aims of this paper are to obtain singularity avoiding inverse kinematic solutions and formulize kinematic problems in a compact closed form. Our solution method is based on screw theory and it uses quaternions as a screw motion operator. Screw theory methods based on line transformation. All screw motions are represented as a rotation about a line together with a translation along the line with respect to base frame. Thus screw theory methods do not suffer from singularities. Two quaterninos are used to represent screw motion. First one is for orientation and second one is for translation. Thus we formulize kinematic problems in a compact closed form. 6R-DOF industrial robot manipulators forward and inverse kinematic equations are derived using this new formulation and also it compared with D-H convention that is the most common method in robot kinematic.


title: Enhancing the security of user data using the keyword encryption and hybrid cryptographic algorithm in cloud

abstract: Cloud computing is a popular model for accessing the computer resources. The data owner outsources their data on cloud server that can be accessed by an authorized user. In Cloud Computing public key encryption with equality test (PKEET) provides an alternative to public key encryption by simplify the public key and credential administration at Public Key Infrastructure (PKI). However it still faces the security risk in outsourced computation on encrypted data. Therefore this paper proposed a novel identity based hybrid encryption (RSA with ECC) to enhance the security of outsourced data. In this approach sender encrypts the sensitive data using hybrid algorithm. Then the proxy re encryption is used to encrypt the keyword and identity in standardize toward enrichment security of data.


title: Rapid detection and identification of pathogens in patients with continuous ambulatory peritoneal dialysis (CAPD) associated peritonitis by 16s rRNA gene sequencing.

abstract: Peritonitis still remains a serious complication with high rate of morbidity and mortality in patients on CAPD. Rapid and accurate identification of pathogens causing peritonitis in a CAPD patient is essential for early and optimal treatment. The aim of this study was to use 16S rRNA and ITS gene sequencing to identify common bacterial and fungal pathogens directly from the peritoneal fluid without culturing. Ninety one peritoneal fluids obtained from 91 different patients on CAPD suspected for peritonitis were investigated for etiological agents by 16S rRNA and ITS gene sequencing. Data obtained by molecular method was compared with the results obtained by culture method. Among the 45 patients confirmed for peritonitis based on international society of peritoneal dialysis (ISPD) guidelines, the etiological agents were identified in 37(82.2%) samples by culture method, while molecular method identified the etiological agents in 40(88.9%) samples. Despite the high potential application of the 16S rRNA and ITS gene sequencing in comparison to culture method to detect the vast majority of etiological agents directly from peritoneal fluids; it could not be used as a standalone test as it lacks sensitivity to identify some bacterial species due to high genetic similarity in some cases and inadequate database in Gene Bank. However, it could be used as a supplementary test to the culture method especially in the diagnosis of culture negative peritonitis.


title: Improvement of Power-Conversion Efficiency of AC–DC Boost Converter Using 1:1 Transformer

abstract: This paper proposes a circuit structure that can improve the power-conversion efficiency $\eta _{e}$ of an AC–DC boost converter. The circuit uses a 1:1 transformer and a voltage boost circuit composed of an inductor, a capacitor, and a diode. The transformer forces the converter to operate in continuous conduction mode for input current, and thereby reduces input ripple. The voltage boost circuit enables the switch to operate in critical conduction mode, so the power loss due to turn- on of switch and reverse recovery of diode is minimized. The proposed converter has high $\eta _{e}$ over a wide range of AC input voltage $V_{{\rm{ac}}}:\,\eta _{e}= 92.9\% $ at $V_{{\rm{ac}}}= {\text{85}}\,{\text{V}}$ and $\eta _{e}= 97.4\% $ at $V_{{\rm{ac}}}= {\text{265}}\,{\text{V}}$ , when the converter was operated at DC output voltage of 400 V and output power of 500 W. The temperature of the switch stabilized at ∼84 °C for $V_{{\rm{ac}}}= {\text{85}}\,{\text{V}}$ , at ∼70 °C for $V_{{\rm{ac}}}= {\text{110}}\,{\text{V}}$ , and at ∼42 °C for $V_{{\rm{ac}}}= {\text{220}}\,{\text{V}}$ , whereas that in other AC–DC converters either failed or stabilized at much higher levels. The proposed converter is well suited to systems that require an input voltage range of 85–265 V in root-mean-square value.


title: Sequence-based class tagging for robust transcription in ASR.

abstract: We present a method of modeling non-lexical vocabulary items such as numbers, times, dates, monetary amounts and address components that avoids the data sparsity and out-of-vocabulary problems of written-domain language models. Like previous approaches, we use a class-based language model and efficient finite-state class grammars during run-time decoding. We mitigate the problem of context-independent replacement of class items by employing a contextual sequence labeling model to identify which class instances should be replaced, leaving others to appear in their original form. Applied to the task of general voice-search audio transcription, our method achieves 10% relative error reduction (on the numeric error rate metric) compared to the previous system (based on a verbalizer transducer). On a numeric entity recognition task, our method achieves a 23% relative error reduction on the same metric. In both cases, word error rate remains the same or is reduced.


title: Design of heart rate variability processor for portable 3-lead ECG monitoring system-on-chip

abstract: The worldwide population of people over the age of 65 has been predicted to more than double from 1990 to 2025. Therefore, ubiquitous health-care systems have become an important topic of research in recent years. In this paper, an integrated system for portable electrocardiography (ECG) monitoring, with an on-board processor for time-frequency analysis of heart rate variability (HRV), is presented. The main function of proposed system comprises three parts, namely, an analog-to-digital converter (ADC) controller, an HRV processor, and a lossless compression engine. At the beginning, ECG data acquired from front-end circuits through the ADC controller is passed through the HRV processor for analysis. Next, the HRV processor performs real-time analysis of time-frequency HRV using the Lomb periodogram and a sliding window configuration. The Lomb periodogram is suited for spectral analysis of unevenly sampled data and has been applied to time-frequency analysis of HRV in the proposed system. Finally, the ECG data are compressed by 2.5 times using the lossless compression engine before output using universal asynchronous receiver/transmitter (UART). Bluetooth is employed to transmit analyzed HRV data and raw ECG data to a remote station for display or further analysis. The integrated ECG health-care system design proposed has been implemented using UMC 90nm CMOS technology.


Comparison of penile length at 6–24 months between children with unilateral cryptorchidism and a healthy normal cohort


title: Quantifying Differential Privacy under Temporal Correlations

abstract: Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuous generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release. First, we model the temporal correlations using Markov model and analyze the privacy leakage of a DP mechanism when adversaries have knowledge of such temporal correlations. Our analysis reveals that the privacy loss of a DP mechanism may accumulate and increase over time. We call it temporal privacy leakage. Second, to measure such privacy loss, we design an efficient algorithm for calculating it in polynomial time. Although the temporal privacy leakage may increase over time, we also show that its supremum may exist in some cases. Third, to bound the privacy loss, we propose mechanisms that convert any existing DP mechanism into one against temporal privacy leakage. Experiments with synthetic data confirm that our approach is efficient and effective.


title: Computational Physiology — Papers from the AAAI 2011 Spring Symposium (SS-11-04) HBase, MapReduce, and Integrated Data Visualization for Processing Clinical Signal Data

abstract: Processing high-density clinical signal data (data from biomedical sensors deployed in the clinical environment) is resource intensive and time consuming. We propose a novel approach to storing and processing clinical signal data based on the Apache HBase distributed column-store and the MapReduce programming paradigm with an integrated web-based data visualization layer. An integrated solution negates the need to marshal data into and out of the storage system while also easily parallelizing the computation, a problem that is becoming more and more important due to increasing numbers of sensors and resulting data. We estimate upwards of 50TB of clinical signal data for a 200-bed medical center within the next 5 years. Consequently, efficient processing of clinical signal data is a vital step towards multivariate analysis of the signal data in order to develop better ways of describing a patient’s clinical status.


title: From Very Weak to Very Strong: Analyzing Password-Strength Meters

abstract: Millions of users are exposed to password-strength meters/checkers at highly popular web services that use user- chosen passwords for authentication. Recent studies have found evidence that some meters actually guide users to choose better passwords—which is a fairly rare-bit of good news in password research. However, these meters are mostly based on ad-hoc design. At least, as we found, most vendors do not provide any explanation of their design choices, sometimes making them appear to be a black box. We analyze password meters deployed in selected popular websites, by measuring the strength labels assigned to common passwords from several password dictionaries. From this empirical analysis with millions of passwords, we report prominent characteristics of meters as deployed at popular websites. We shed light on how the server-end of some meters functions, provide examples of highly inconsistent strength outcomes for the same password in different meters, along with examples of many weak passwords being labeled as strong or even very strong. These weaknesses and inconsistencies may confuse users in choosing a stronger password, and thus may weaken the purpose of these meters. On the other hand, we believe these findings may help improve existing meters, and possibly make them an effective tool in the long run.


title: Engaging Students in Learning Activities: It Is Not Autonomy Support or Structure but Autonomy Support and Structure

abstract: We investigated 2 engagement-fostering aspects of teachers’ instructional styles—autonomy support and structure—and hypothesized that students’ engagement would be highest when teachers provided high levels of both. Trained observers rated teachers’ instructional styles and students’ behavioral engagement in 133 public high school classrooms in the Midwest, and 1,584 students in Grades 9–11 reported their subjective engagement. Correlational and hierarchical linear modeling analyses showed 3 results: (a) Autonomy support and structure were positively correlated, (b) autonomy support and structure both predicted students’ behavioral engagement, and (c) only autonomy support was a unique predictor of students’ self-reported engagement. We discuss, first, how these findings help illuminate the relations between autonomy support and structure as 2 complementary, rather than antagonistic or curvilinear, engagement-fostering aspects of teachers’ instructional styles and, second, the somewhat different results obtained for the behavioral versus self-report measures of students’ classroom engagement.


title: Situated Tangible Robot Programming

abstract: This paper introduces situated tangible robot programming, whereby a robot is programmed by placing specially designed tangible "blocks" in its workspace. These blocks are used for annotating objects, locations, or regions, and specifying actions and their ordering. The robot compiles a program by detecting blocks and objects in its workspace and grouping them into instructions by solving constraints. We present a proof-of-concept implementation using blocks with unique visual markers in a pick-and-place task domain. Three user studies evaluate the intuitiveness and learnability of situated tangible programming and iterate the block design. We characterize common challenges and gather feedback on how to further improve the design of blocks. Our studies demonstrate that people can interpret, generalize, and create many different situated tangible programs with minimal instruction or with no instruction at all.


title: Adaptive Power Management through Thermal Aware Workload Balancing in Internet Data Centers

abstract: The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today’s IDCs are achieving significant advances in communication and computation capabilities. However, along with the increasing demand from IDC clients, power consumption for powering up and cooling these IDCs has been skyrocketing. Most existing works optimize the power consumption of either servers or Computer Room Air Conditioners (CRACs), and overlook the correlation between the power consumption of these two types of equipment. In this paper, we propose an adaptive power control method which leverages the correlation between the power consumption of servers and CRACs. To capture the workload uncertainties and thermal dynamics, we exploit Recursive-Least Square based Model Predictive Control (MPC) to solve the power control problem. Performance evaluations shows the effective power peak reduction using our approach.


title: Detect overlapping and hierarchical community structure in networks

abstract: Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper proposes an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results.


title: Applying deep learning approaches for network traffic prediction

abstract: Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed size. RNN contains different network architectures like simple RNN, long short term memory (LSTM), gated recurrent unit (GRU), identity recurrent unit (IRNN) which is capable to learn the temporal patterns and long range dependencies in large sequences of arbitrary length. To leverage the efficacy of RNN approaches towards traffic matrix estimation in large networks, we use various RNN networks. The performance of various RNN networks is evaluated on the real data from GEANT backbone networks. To identify the optimal network parameters and network structure of RNN, various experiments are done. All experiments are run up to 200 epochs with learning rate in the range [0.01-0.5]. LSTM has performed well in comparison to the other RNN and classical methods. Moreover, the performance of various RNN methods is comparable to LSTM.


title: Methotrexate regulates the expression of glucocorticoid receptor alpha and beta isoforms in normal human peripheral mononuclear cells and human lymphocyte cell lines in vitro

abstract: MTX is an effective therapy for autoimmune-inflammatory diseases. The mechanisms that mediate these actions are not completely clear. It is accepted that many of these effects are mediated through the release of adenosine with the activation of the adenosine receptor A2. MTX is used as a steroid sparing agent. An improved in vitro GC cell sensitivity in GC insensitive asthma patients has been demonstrated after MTX treatment. Most GC actions are mediated by the GCR. The effect of MTX on GCRs expression has not been previously evaluated. Therefore, we evaluate if MTX regulates the expression of glucocorticoid receptors, increasing the expression of the active receptor (GCR α) and/or decreasing the expression of the dominant negative receptor (GCR β). We show that MTX increases the mRNA and protein levels of GCR α and decreases or leaves unchanged the protein expression of the GCR β in CEM cells in culture. This effect was also observed in other lymphocytes (Jurkat and Raji) and in PBMNC from healthy volunteers. We also show that upon MTX treatment PBMC from normal volunteers exhibit a higher sensitivity to DEX inhibition on LPS-induced TNF α release. To explore if these actions are mediated by adenosine through the adenosine receptor A2 we evaluate the effect of adenosine on the GCRs expression and the effect of an A2 receptor blocker (DMPX) on MTX effects on GCRs expression. Our results show that adenosine does not mimic and DMPX can enhance MTX effects on these receptors. We conclude that MTX increases the GCR α/GCR β ratio of expression in lymphocytes which could mediate its previously reported effects in improving cell glucocorticoid sensitivity. These actions are not mediated by the adenosine receptor A2.


title: General Factor of Personality Questionnaire (GFPQ): Only one Factor to Understand Personality?

abstract: This study proposes a psychometric approach to assess the General Factor of Personality (GFP) to explain the whole personality. This approach defends the existence of one basic factor that represents the overall personality. The General Factor of Personality Questionnaire (GFPQ) is presented to measure the basic, combined trait of the complete personality. The questionnaire includes 20 items and is constituted by two scales with 10 items each one: the Extraversion Scale (ES) and the Introversion Scale (IS). The GFPQ shows adequate internal consistency and construct validity, while the relationships with the personality factors of other models and with psychopathology are as expected. It correlates positively and significantly with Extraversion (E) and Psychoticism (P), and negatively with Neuroticism (N) of Eysenck's EPQ (Eysenck Personality Questionnaire); it correlates positively and significantly with the Sensation Seeking Scaled (SSS) of Zuckerman, and is inside the expected direction with Sensitivity to Reward (SR) and Sensitivity to Punishment (SP) of the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), which represent the approach and avoidance trends of behavior, respectively. It not only relates negatively with the personality disorders of the anxiety spectrum, but also with the emotional disorders in relation to anxiety and depression, and it relates positively with the antisocial personality disorder.


title: Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery

abstract: We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation and classification process. Within the context of X-ray security screening, limited availability of training for particular items of interest can thus pose a problem. To overcome this issue, we employ a transfer learning paradigm such that a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be specifically optimized as a later secondary process that targets specific this application domain. For the classical handgun detection problem we achieve 98.92% detection accuracy outperforming prior work in the field and furthermore extend our evaluation to a multiple object classification task within this context.


title: Selective stiffening of soft actuators based on jamming

abstract: The ability to selectively stiffen otherwise compliant soft actuators increases their versatility and dexterity. We investigate granular jamming and layer jamming as two possible methods to achieve stiffening with PneuFlex actuators, a type of soft continuum actuator. The paper details five designs of jamming compartments that can be attached to an actuator. We evaluate the stiffening of the five different prototypes, achieving an up to 8-fold increase in stiffness. The strength of the most effective prototype based on layer jamming is also validated in the context of pushing buttons, resulting in an 2.23-fold increase in pushing force.


title: Indoor localization without the pain

abstract: While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, we ask the question: can we perform indoor localization with no pre-deployment effort? Our setting is an indoor space, such as an office building or a mall, with WiFi coverage but where we do not assume knowledge of the physical layout, including the placement of the APs. Users carrying WiFi-enabled devices such as smartphones traverse this space in normal course. The mobile devices record Received Signal Strength (RSS) measurements corresponding to APs in their view at various (unknown) locations and report these to a localization server. Occasionally, a mobile device will also obtain and report a location fix, say by obtaining a GPS lock at the entrance or near a window. The centerpiece of our work is the EZ Localization algorithm, which runs on the localization server. The key intuition is that all of the observations reported to the server, even the many from unknown locations, are constrained by the physics of wireless propagation. EZ models these constraints and then uses a genetic algorithm to solve them. The results from our deployment in two different buildings are promising. Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m, respectively, in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibration-intensive Horus scheme [29] from prior work.


title: A Framework for Blockchain-Based Applications

abstract: Blockchains have recently generated explosive interest from both academia and industry, with many proposed applications. But descriptions of many these proposals are more visionary projections than realizable proposals, and even basic definitions are often missing. We define "blockchain" and "blockchain network", and then discuss two very different, well known classes of blockchain networks: cryptocurrencies and Git repositories. We identify common primitive elements of both and use them to construct a framework for explicitly articulating what characterizes blockchain networks. The framework consists of a set of questions that every blockchain initiative should address at the very outset. It is intended to help one decide whether or not blockchain is an appropriate approach to a particular application, and if it is, to assist in its initial design stage.


title: Lossless point cloud geometry compression via binary tree partition and intra prediction

abstract: Characterized by geometry and photometry attributes, point cloud has become widely applied in the real-time presentation of various 3D objects and scenes. The development of even more precise capture devices and the increasing requirements for vivid rendering inevitably induce huge point capacity, thus making the point cloud compression a demanding issue. Considering the non-uniform sampling and time-variant geometry, appropriate structural representation for point cloud is important. In this paper, we propose a lossless geometry compression algorithm for 3D point cloud which serves as the basis of future adaptive improvement. We utilize the binary tree structure for effectively partitioning unorganized points into block structure. This hierarchical representation obtains roughly the same quantity level for each leaf node. Further analysis is conducted on an intra-geometry prediction via extended Travelling Salesman Problem (TSP), achieving an impressive performance in eliminating point-wise redundancy while preserving one single reference position for each block. The residual encoding is accomplished via a shallow neural network-based lossless compression algorithm, PAQ. Simulation results confirm the lossless compression of geometry from high quality capture, achieving approximately 3.5 times efficiency gain over the state of art algorithm implemented as MPEG Point Cloud Compression (PCC) reference software.


title: Security of autonomous vehicle as a cyber-physical system

abstract: Security of (semi)-autonomous vehicles is a growing concern due to, first, the growing reliance of car functionalities on diverse (semi)-autonomous systems; second, the increased exposure of the such functionalities to potential attackers; third, the interaction of a single vehicle with myriads of other smart systems in an modern urban traffic infrastructure. In this paper, we review the security objectives of Autonomous Vehicle (AV) and argue that AV is a kind of Cyber-Physical System (CPS) for control and operations of the vehicle. We attempt to identify the core issues of securing an AV by modeling an AV as a special kind of CPS, which tend to be implemented by a complex interconnected embedded system hardware. Subsequently, the technical challenges of AV security are identified.


Description of the YaCy Distributed Web Search Engine


title: Abnormality Detection in Mammography using Deep Convolutional Neural Networks

abstract: Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.


title: The operative treatment of scapular fractures

abstract: Fractures of the scapula occur mainly from direct trauma involving considerable violence and associated injuries of the shoulder and thorax are common. In most cases early functional treatment gives good or excellent results. Operative treatment may, however, be indicated, especially with displaced intra-articular fractures, fractures of the glenoid rim associated with humeral head subluxation, or unstable fractures of the scapular neck. Between 1967 and 1981, we treated 37 such fractures by open reduction and stable osteosynthesis. We were able to follow up 33 cases (89%), of which 21 (64%) had complete functional recovery. The other 12 had varying degrees of pain, loss of mobility, and weakness. Overall, however, 79% of the patients had good to excellent results.


title: Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores

abstract: Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brain's function and efficiency. Judiciously balancing the dual objectives of functional capability and implementation/operational cost, we develop a simple, digital, reconfigurable, versatile spiking neuron model that supports one-to-one equivalence between hardware and simulation and is implementable using only 1272 ASIC gates. Starting with the classic leaky integrate-and-fire neuron, we add: (a) configurable and reproducible stochasticity to the input, the state, and the output; (b) four leak modes that bias the internal state dynamics; (c) deterministic and stochastic thresholds; and (d) six reset modes for rich finite-state behavior. The model supports a wide variety of computational functions and neural codes. We capture 50+ neuron behaviors in a library for hierarchical composition of complex computations and behaviors. Although designed with cognitive algorithms and applications in mind, serendipitously, the neuron model can qualitatively replicate the 20 biologically-relevant behaviors of a dynamical neuron model.


title: One-class support vector machines - an application in machine fault detection and classification

abstract: Fast incipient machine fault diagnosis is becoming one of the key requirements for economical and optimal process operation management. Artificial neural networks have been used to detect machine faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for machine fault detection and classification in electro-mechanical machinery from vibration measurements using one-class support vector machines (SVMs). In order to evaluate one-class SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.


Development of a comparative genomic fingerprinting assay for rapid and high resolution genotyping of Arcobacter butzleri


Security Best Practices and Risk Assessment of SCADA and Industrial Control Systems


Information Extraction Tools for Portable Document Format


title: Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation

abstract: With the advent of new-generation depth sensors, the use of three-dimensional (3-D) data is becoming increasingly popular. As these sensors are commodity hardware and sold at low cost, a rapidly growing group of people can acquire 3- D data cheaply and in real time.


Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction


title: A Dialogical Approach to the Creation of New Knowledge in Organizations

abstract: Despite several insightful empirical studies on how new knowledge is created in organizations, there is still no satisfactory answer to the question, how is new knowledge created in organizations? The purpose of this paper is to address this question by focusing on direct social interaction, adopting a dialogical approach. The following argument is advanced. From a dialogical perspective, new knowledge in organizations originates in the individual ability to draw new distinctions concerning a task at hand. New distinctions may be developed because practitioners experience their situations in terms of already constituted distinctions, which lend themselves to further articulation. Further articulation develops when organizational members engage in dialogical exchanges. When productive, dialogue leads to self-distanciation, namely, to individuals taking distance from their customary and unreflective ways of acting as practitioners. Dialogue is productive depending on the extent to which participants engage relationally with one another. When this happens, participants are more likely to actively take responsibility for both the joint tasks in which they are involved and for the relationships they have with others. Self-distanciation leads to new distinctions through three processes of conceptual change (conceptual combination, conceptual expansion, and conceptual reframing), which, when intersubjectively accepted, constitute new knowledge. Several organizational examples, as well as findings from organizational knowledge research, are reinterpreted to illustrate the above points.


title: DisasterMapper: A CyberGIS framework for disaster management using social media data

abstract: Traditional GIS tools and systems are powerful for analyzing geographic information for various applications but they are not designed for processing dynamic streams of data. This paper presents a CyberGIS framework that can automatically synthesize multi-sourced data, such as social media and socioeconomic data, to track disaster events, to produce maps, and to perform spatial and statistical analysis for disaster management. Within our framework, Apache Hive, Hadoop, and Mahout are used as scalable distributed storage, computing environment and machine learning library to store, process and mine massive social media data. The proposed framework is capable of supporting big data analytics of multiple sources. A prototype is implemented and tested using the 2011 Hurricane Sandy as a case study.


title: A Comparative Analysis of Different NoSQL Databases on Data Model, Query Model and Replication Model

abstract: Relational databases like SQL have a limitation when it comes to data aggregation, which is used for business intelligence and data mining. Data aggregation becomes impossible on very large volumes of data when it comes to memory and time consumption. NoSQL databases provide an efficient framework to aggregate large volumes of data. There are different NoSQL databases like Key-value stores, Column Family/BigTable clones, Document databases and Graph databases. This paper compares different NoSQL databases against persistence, Replication, Transactions and Implementation language. This paper also discusses on performance and scalability aspects of different NoSQL databases.


title: Load identification and compensation for a Cable-Driven parallel robot

abstract: In Cable-Driven parallel robots, elastic cables are used to control the movement of a platform in the workspace. The accuracy of a robot system is one of the most important comparison criteria for various applications. In this paper, we present a new approach to increase the relative accuracy of a cable robot under changing payload, by compensating the error induced by elasticity. In the first step, the load is identified based on cable force sensors and the acceleration of the platform. Second, for the actual pose the stiffness matrix is computed and the expected displacement of the platform in Cartesian space is determined. Lastly, the displacement is compensated by adapting the cable lengths. Furthermore, the influence of the calibration of cable force sensors and their parameters' inaccuracies are discussed. The algorithms are implemented and evaluated on a 6-DOF cable robot. We could experimentally prove an improvement of over 50% in the relative accuracy under changing load.


title: Combining Word Embedding with Information Retrieval to Recommend Similar Bug Reports

abstract: Similar bugs are bugs that require handling of many common code files. Developers can often fix similar bugs with a shorter time and a higher quality since they can focus on fewer code files. Therefore, similar bug recommendation is a meaningful task which can improve development efficiency. Rocha et al. propose the first similar bug recommendation system named NextBug. Although NextBug performs better than a start-of-the-art duplicated bug detection technique REP, its performance is not optimal and thus more work is needed to improve its effectiveness. Technically, it is also rather simple as it relies only upon a standard information retrieval technique, i.e., cosine similarity. In the paper, we propose a novel approach to recommend similar bugs. The approach combines a traditional information retrieval technique and a word embedding technique, and takes bug titles and descriptions as well as bug product and component information into consideration. To evaluate the approach, we use datasets from two popular open-source projects, i.e., Eclipse and Mozilla, each of which contains bug reports whose bug ids range from [1,400000]. The results show that our approach improves the performance of NextBug statistically significantly and substantially for both projects.


title: GPU-based offset surface computation using point samples

abstract: We present an efficient algorithm to perform approximate offsetting operations on geometric models using GPUs. Our approach approximates the boundary of an object with point samples and computes the offset by merging the balls centered at these points. The underlying approach uses Layered Depth Images (LDI) to organize the samples into structured points and performs parallel computations using multiple cores. We use spatial hashing to accelerate intersection queries and balance the workload among various cores. Furthermore, the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. We derive bounds on the accuracy of offset computation as a function of the sampling rate of LDI and offset distance. In practice, our GPU-based algorithm can accurately compute offsets of models represented using hundreds of thousands of points in a few seconds on a GeForce GTX 580 GPU. We observe more than 100 times speedup over prior serial CPU-based approximate offset computation algorithms.


title: Support Vector Machine based automatic electric meter reading system

abstract: The traditional method of manual electric meter reading is very tedious and is prone to lot of errors and has a lot of disadvantages. Some of these disadvantages include low efficiency, man power consuming. The existing methods of Automatic Meter Reading are based on measuring the electric impulse of the sensor. This is prone to wrong counting of the impulses which leads to faulty meter reading. And so a better option is to fit a image acquisition device like camera in front of the meter that will take realtime pictures of the meter readings. This picture is then processed, segmented and the individual digits are recognized using unsupervised feature learning technique-Support Vector Machine. The advantages of this method is that it can generalize over the large degree of variation between styles and recognition rules can be constructed by example. This highly efficient classifier is used for both detection and recognition of these digits.


title: Stereophonic Acoustic Echo Suppression Based on Wiener Filter in the Short-Time Fourier Transform Domain

abstract: An open-loop stereophonic acoustic echo suppression (SAES) method without preprocessing is presented for teleconferencing systems, where the Wiener filter in the short-time Fourier transform (STFT) domain is employed. Instead of identifying the echo path impulse responses with adaptive filters, the proposed algorithm estimates the echo spectra from the stereo signals using two weighting functions. The spectral modification technique originally proposed for noise reduction is adopted to remove the echo from the microphone signal. Moreover, a priori signal-to-echo ratio (SER) based Wiener filter is used as the gain function to achieve a trade-off between musical noise reduction and computational load for real-time operations. Computer simulation shows the effectiveness and the robustness of the proposed method in several different scenarios.


title: Introduction to Text Visualization

abstract: This book provides a systematic review of many advanced techniques to support the analysis of large collections of documents, ranging from the elementary to the profound, covering all the aspects of the visualization of text documents. Particularly, we start by introducing the fundamental concept of information visualization and visual analysis, followed by a brief survey of the field of text visualization and commonly used data models for converting document into a structured form for visualization. Then we introduce the key visualization techniques including visualizing document similarity, content, sentiments, as well as text corpus exploration system in details with concrete examples in the rest of the book.


title: Browsing Hierarchical Data with Multi-Level Dynamic Queries and Pruning

abstract: Abstract Users often must browse hierarchies with thousands of nodes in search of those that best match their information needs. ThePDQ Tree-browser(Pruning with Dynamic Queries) visualization tool was specified, designed and developed for this purpose. This tool presents trees in two tightly-coupled views, one a detailed view and the other an overview. Users can use dynamic queries, a method for rapidly filtering data, to filter nodes at each level of the tree. The dynamic query panels are user-customizable. Sub-trees of unselected nodes are pruned out, leading to compact views of relevant nodes. Usability testing of the PDQ Tree-browser, done with eight subjects, helped assess strengths and identify possible improvements. The PDQ Tree-browser was used in Network Management (600 nodes) and UniversityFinder (1100 nodes) applications. A controlled experiment, with 24 subjects, showed that pruning significantly improved performance speed and subjective user satisfaction. Future research directions are suggested.


title: A Metamodel Independent Approach to Difference Representation

abstract: It is of critical relevance that designers are able to comprehend the various kinds of design-level modifications that a system undergoes throughout its entire lifecycle. In this respect, an interesting and useful operation between subsequent system versions is the model difference calculation and representation. In this paper, a metamodel independent approach to the representation of model differences which is agnostic of the calculation method is presented. Given two models which conform to a metamodel, their difference is conforming to another metamodel derived from the former by an automated transformation. Difference models are first-class entities which induce transformations able to apply the modifications they specify. Finally, difference models can be composed sequentially and in parallel giving place to more complex modifications.


title: Live trace visualization for comprehending large software landscapes: The ExplorViz approach

abstract: The increasing code complexity in modern enterprise software systems exceeds the capabilities of most software engineers to understand the system's behavior by just looking at its program code. Large software landscapes, e.g., applications in a cloud infrastructure, further increase this complexity. A solution to these problems is visualizing the applications of the software landscape to ease program comprehension and to understand the respective communication. An established visualization concept is the 3D city metaphor. It utilizes the familiarity with navigating a city to improve program comprehension. Dynamic analysis, e.g., monitoring, can provide the required program traces of the communication. In this paper, we present our live visualization approach of monitoring traces for large software landscapes. It combines a landscape and a system level perspective. The landscape level perspective provides an overview of the software landscape utilizing the viewer's familiarity with UML. The system level perspective provides a visualization utilizing the city metaphor for each software system.


title: Fully integrated CMOS power amplifier design using the distributed active-transformer architecture

abstract: A novel on-chip impedance matching and power-combining method, the distributed active transformer is presented. It combines several low-voltage push-pull amplifiers efficiently with their outputs in series to produce a larger output power while maintaining a 50-/spl Omega/ match. It also uses virtual ac grounds and magnetic couplings extensively to eliminate the need for any off-chip component, such as tuned bonding wires or external inductors. Furthermore, it desensitizes the operation of the amplifier to the inductance of bonding wires making the design more reproducible. To demonstrate the feasibility of this concept, a 2.4-GHz 2-W 2-V truly fully integrated power amplifier with 50-/spl Omega/ input and output matching has been fabricated using 0.35-/spl mu/m CMOS transistors. It achieves a power added efficiency (PAE) of 41 % at this power level. It can also produce 450 mW using a 1-V supply. Harmonic suppression is 64 dBc or better. This new topology makes possible a truly fully integrated watt-level gigahertz range low-voltage CMOS power amplifier for the first time.


title: Reconfigurable Inverted Index

abstract: Existing approximate nearest neighbor search systems suffer from two fundamental problems that are of practical importance but have not received sufficient attention from the research community. First, although existing systems perform well for the whole database, it is difficult to run a search over a subset of the database. Second, there has been no discussion concerning the performance decrement after many items have been newly added to a system. We develop a reconfigurable inverted index (Rii) to resolve these two issues. Based on the standard IVFADC system, we design a data layout such that items are stored linearly. This enables us to efficiently run a subset search by switching the search method to a linear PQ scan if the size of a subset is small. Owing to the linear layout, the data structure can be dynamically adjusted after new items are added, maintaining the fast speed of the system. Extensive comparisons show that Rii achieves a comparable performance with state-of-the art systems such as Faiss.


title: Comparison of the tick repellent efficacy of chemical and biological products originating from Europe and the USA

abstract: The present paper investigates the efficacy of common anti-tick repellents in Europe and in the USA. There were tested Ixodes ricinus, Dermacentor reticulatus, and Rhipcephalus sanguineus when they are placed onto hands and clothes of male and female humans being treated with common tick repellents. It was seen that DEET needed high concentrations to repel ticks, while essential oils are mostly inefficient. On the other hand saltidin=icaridin, p-menthan-diol and IR 3535 showed long-lasting effects, which in the case of combinations of saltidin and Vitex extracts were even increased.


title: Security Enhanced Java: Mandatory Access Control for the Java Virtual Machine

abstract: Since 70's, and despite its operational complex- ity, Mandatory Access Control (MAC) has demon- strated its reliability to enforce integrity and confi- dentiality. Surprisingly, the Java technology, despite its popularity, has not yet adopted this protection principle. Current security features within the JVM (JAAS and bytecode verifier) can be bypassed, as demonstrated by summer 2012 attacks. Thus, a MAC model for Java and a cross platform reference monitor are required for the Java Virtual Machine. Security Enhanced Java (SEJava) enables to control dynamical ly the information flows between al l the Java objects requiring neither bytecode nor source code instrumentations. The main idea is to consider Java types as security contexts, and method calls / field accesses as permissions. SEJava allows fine- grain MAC rules between the Java objects. Thus, SEJava controls all the information flows within the JVM. Our implementation is faster than concur- rent approaches while allowing both finer and more advanced controls. A use case shows the efficiency to protect against Common Vulnerability and Expo- sures in an efficient manner.


title: Homicidal hanging.

abstract: We report a case of homicide by hanging of a 29-year-old woman that was committed by her husband, who nearly escaped from justice. We offer some reflections on the rarity of this method of mechanical asphyxia in cases of homicide, and on the difficulties in the diagnosis of the medico-legal etiology of these situations. We also offer a short review of similar cases in the medico-legal literature.


title: Centrally Controlled Distributed VNF State Management

abstract: The realization of increased service flexibility and scalability through the combination of Virtual Network Functions (VNF) and Software Defined Networks (SDN) requires careful management of both VNF and forwarding state. Without coordination, service scalability comes at a high cost due to unacceptable levels of packet loss, reordering and increased latencies. Previously developed techniques has shown that these issues can be managed, at least in scenarios with low traffic rates and optimistic control plane latencies. In this paper we extend previous work on coordinated state management in order to remove performance bottlenecks, this is done through distributed state management and minimizing control plane interactions. Evaluation of our changes show substantial performance gains using a distributed approach while maintaining centralized control.


title: A combined protection and control strategy to enhance the LVRT capability of a wind turbine driven by DFIG

abstract: In order to enhance the low voltage ride-through (LVRT) capability of a wind turbine driven doubly fed induction generator (DFIG) this paper proposes a combined protection and control strategy including the Active Crowbar and Battery Energy Storage System. The conventional crowbar is activated to protect the rotor side converter during the fault interval, premature removal the crowbar will not achieve good purpose of protection for the converter, too late removal will make the motor absorption the reactive power too much from the grid. This problem can be improved by using a control scheme which is auto-switching the crowbar according to the size of rotor current. Furthermore, under fault condition the voltage will ripple in the DC link side, a battery energy storage system (BESS) connected to the DC bus is proposed which is controlled to attenuate the DC voltage ripple via absorbing the redundant power stored in the DC link capacitor. Simulation results show the effectiveness of this combined protection and control strategy.


title: Beyond human recognition: A CNN-based framework for handwritten character recognition

abstract: Because of the various appearance (different writers, writing styles, noise, etc.), the handwritten character recognition is one of the most challenging task in pattern recognition. Through decades of research, the traditional method has reached its limit while the emergence of deep learning provides a new way to break this limit. In this paper, a CNN-based handwritten character recognition framework is proposed. In this framework, proper sample generation, training scheme and CNN network structure are employed according to the properties of handwritten characters. In the experiments, the proposed framework performed even better than human on handwritten digit (MNIST) and Chinese character (CASIA) recognition. The advantage of this framework is proved by these experimental results.


title: Modern high-voltage IGBTs and their turn-off performance

abstract: The standard circuit for inverters for electrical drive and energy applications is the voltage source inverter. The preferred semiconductor from the kW up to the MW range is the IGBT. For high power inverters, high voltage IGBT modules are used. High voltage IGBTs have a limited turn-off capability. The use of large size or paralleled IGBTs leads to large inductive voltage drops in the commutation circuits. In this paper, a simple model for the turn-off of high voltage IGBTs with long carrier lifetime is derived from the device physics. The turn-off stress for the IGBT and the influence of the gate drive on it are analyzed and the characteristic behavior of IGBTs with field stop layers is explained.


title: Nested Kernel: An Operating System Architecture for Intra-Kernel Privilege Separation

abstract: Monolithic operating system designs undermine the security of computing systems by allowing single exploits anywhere in the kernel to enjoy full supervisor privilege. The nested kernel operating system architecture addresses this problem by "nesting" a small isolated kernel within a traditional monolithic kernel. The "nested kernel" interposes on all updates to virtual memory translations to assert protections on physical memory, thus significantly reducing the trusted computing base for memory access control enforcement. We incorporated the nested kernel architecture into FreeBSD on x86-64 hardware while allowing the entire operating system, including untrusted components, to operate at the highest hardware privilege level by write-protecting MMU translations and de-privileging the untrusted part of the kernel. Our implementation inherently enforces kernel code integrity while still allowing dynamically loaded kernel modules, thus defending against code injection attacks. We also demonstrate that the nested kernel architecture allows kernel developers to isolate memory in ways not possible in monolithic kernels by introducing write-mediation and write-logging services to protect critical system data structures. Performance of the nested kernel prototype shows modest overheads:


title: Tcf7 Is an Important Regulator of the Switch of Self-Renewal and Differentiation in a Multipotential Hematopoietic Cell Line

abstract: A critical problem in biology is understanding how cells choose between self-renewal and differentiation. To generate a comprehensive view of the mechanisms controlling early hematopoietic precursor self-renewal and differentiation, we used systems-based approaches and murine EML multipotential hematopoietic precursor cells as a primary model. EML cells give rise to a mixture of self-renewing Lin-SCA+CD34+ cells and partially differentiated non-renewing Lin-SCA-CD34- cells in a cell autonomous fashion. We identified and validated the HMG box protein TCF7 as a regulator in this self-renewal/differentiation switch that operates in the absence of autocrine Wnt signaling. We found that Tcf7 is the most down-regulated transcription factor when CD34+ cells switch into CD34- cells, using RNA-Seq. We subsequently identified the target genes bound by TCF7, using ChIP-Seq. We show that TCF7 and RUNX1 (AML1) bind to each other's promoter regions and that TCF7 is necessary for the production of the short isoforms, but not the long isoforms of RUNX1, suggesting that TCF7 and the short isoforms of RUNX1 function coordinately in regulation. Tcf7 knock-down experiments and Gene Set Enrichment Analyses suggest that TCF7 plays a dual role in promoting the expression of genes characteristic of self-renewing CD34+ cells while repressing genes activated in partially differentiated CD34- state. Finally a network of up-regulated transcription factors of CD34+ cells was constructed. Factors that control hematopoietic stem cell (HSC) establishment and development, cell growth, and multipotency were identified. These studies in EML cells demonstrate fundamental cell-intrinsic properties of the switch between self-renewal and differentiation, and yield valuable insights for manipulating HSCs and other differentiating systems.


title: Laser-assisted microshaping

abstract: Today the laser technologies are widely used in the processing of materials, including also the precise microshaping. Deep UV lasers have very good prospects in the lithograph, used for fabrication of chips, while the femtosecond pulsed lasers open the new horizons for the micro- and submicroprofiling. But these are the field of the future prospects. As for today's state-of-the-art, contribution to the application of lasers for these purposes are the numerous advantages of the laser techniques over the traditional methods, such as: wide variety of material to be treated, possibility of achieving narrow cuts and practically waste-free separation, small heat-affected zone, minimal mechanical effect and minimum thermal deformations, possibility of microshaping along a complex profile in two, or even three dimensions, possibility of fast and precise process switch-on and -off and to include in the processing feedback on the treated parameters.


title: No-wait Packet Scheduling for IEEE Time-sensitive Networks (TSN)

abstract: The IEEE Time-sensitive Networking (TSN) Task Group has recently standardized enhancements for IEEE 802.3 networks for enabling it to transport time-triggered traffic (aka scheduled traffic) providing them with stringent bounds on network delay and jitter while also transporting best-effort traffic. These enhancements primarily include dedicating one queue per port of the switch for scheduled traffic along with a programmable gating mechanism that dictates which of the queues are to be considered for transmission. While the IEEE 802.1Qbv standards define these mechanisms to handle scheduled traffic, it stops short of specifying algorithms to compute fine-grained link schedules for the streams of scheduled traffic. Further, the mechanisms in TSN require creation of so-called guard bands to isolate scheduled traffic from the best-effort traffic. These guard bands may potentially result in bandwidth wastage, and hence schedules with lower number of guard bands are preferred. In this paper, we introduce the No-wait Packet Scheduling Problem (NW-PSP) for modelling the scheduling in IEEE Time-sensitive Networks and map it to the No-wait Job-shop Scheduling Problem (NW-JSP), a well-known problem from the field of operational research. In particular, we present a Tabu search algorithm for efficient computing of schedules and a schedule compression technique to reduce number of guard bands in schedule. Our evaluations show that our Tabu search algorithm can compute near-optimal schedules for over 1500 flows and the subsequent schedule compression reduces the number of guard bands on an average by 24%.


title: Query expansion using thesaurus in improving Malay Hadith retrieval system

abstract: Thesaurus has become another valuable structure in any Information Retrieval system. It is a list of terms and concepts that provide a controlled vocabulary of words to use in document indexing, clustering, searching and retrieval. This paper present the results of expanding user's query using Malay thesaurus in the process of searching Malay documents from Malay Hadith retrieval system. The results obtained shows that the retrieval effectiveness improves by four percent when thesaurus is employed in the process of retrieving Malay translated Hadith documents, compared to when single term queries are employed.


title: Reinforcement Learning, Conditioning, and the Brain: Successes and Challenges

abstract: The field of reinforcement learning has greatly influenced the neuroscientific study of conditioning. This article provides an introduction to reinforcement learning followed by an examination of the successes and challenges using reinforcement learning to understand the neural bases of conditioning. Successes reviewed include (1) the mapping of positive and negative prediction errors to the firing of dopamine neurons and neurons in the lateral habenula, respectively; (2) the mapping of model-based and model-free reinforcement learning to associative and sensorimotor cortico-basal ganglia-thalamo-cortical circuits, respectively; and (3) the mapping of actor and critic to the dorsal and ventral striatum, respectively. Challenges reviewed consist of several behavioral and neural findings that are at odds with standard reinforcement-learning models, including, among others, evidence for hyperbolic discounting and adaptive coding. The article suggests ways of reconciling reinforcement-learning models with many of the challenging findings, and highlights the need for further theoretical developments where necessary. Additional information related to this study may be downloaded from http://cabn.psychonomic-journals.org/content/supplemental.


title: Comparing pain reduction following kyphoplasty and vertebroplasty

abstract: PURPOSE ::: The aim of the study was to compare the extent of pain reduction with kyphoplasty (KP) and vertebroplasty (VP) in the treatment of osteoporotic vertebral compression fractures (OVCF) using a visual analog scale (VAS). ::: ::: ::: METHODS ::: A computerized database search was performed to compare pain relief from VP and KP in OVCF. The available literature was analysed to quantify the amount of pain reduction using VAS as the primary outcome. ::: ::: ::: RESULTS ::: A total of 10 studies encompassing 902 patients met the inclusion criteria. Subgroup meta-analyses were performed according to the study design. VAS for the KP group was not significant compared with the VP group at 1 week, 1 month, and 6 months. Pain relief in the KP group was greater than that in the VP group at 12 months and 2 years. ::: ::: ::: CONCLUSION ::: KP is superior to VP in patients requiring pain relief. More large-scale, double-blinded controlled trials are necessary in order to quantify the pain relief afforded by VP more precisely.


title: Machine Learning In Medical Applications

abstract: Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. ML is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g. prediction of disease progression, extraction of medical knowledge for outcome research, therapy planning and support, and for the overall patient management. ML is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and intelligent alarming resulting in effective and efficient monitoring. It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care. Below, we summarize some major ML applications in medicine.


title: The separation of uranium from other metals in sulphate solution by fractional hydrolysis. II. Precipitation in the presence of phosphate, arsenate and silicate

abstract: The effect of the presence of phosphate, arsenate and silicate on the hydrolysis of uranium and other metal salts in sulphate solution has been studied by pH measurement during titrations with sodium hydroxide solutions. The results have been used to develop methods for the separation of uranium from the other metals studied.


title: In-network caching of Internet-of-Things data

abstract: The recent forecast of billions of devices, all connected to the Internet and generating low-rate monitoring, measurement, or automation data that many end-users/applications frequently request, signifies the need for applying in-network caching techniques to Internet-of-Things (IoT) traffic. Although time delay is not critically important for small-sized IoT content, the expected total traffic load on the Internet from a large number of devices is significant. However, the main challenge as opposed to the typically cached content at content routers, e.g. multimedia files, is that IoT data are transient and therefore require different caching policies. This paper studies in-network caching of IoT data at content routers in the Internet. An IoT data item is uniquely defined not only by its time and location tags, but also a time-range value set by end-users/applications. We provide a model for the trade-off between multihop communication costs and the freshness of a transient data item. Results show that the model can successfully capture the effect of data transiency and can accurately represent the expected gains of a caching system: considerable savings in terms of reduction of network load, especially for highly requested data items.


title: Intervention thresholds and ceilings can determine the haemoglobin outcome distribution in a haemodialysis population

abstract: We have explored the consequences of setting Introduction different thresholds and ceilings for erythropoietin dose changes in two randomized controlled studies of renal More is known of individual responses to erythropoie- anaemia management based on an established tin ( Epo) therapy than of strategies that predict haemo-


ACTIVE REPAIR OF DATA QUALITY RULES (Research-in-Progress) (Methods, concepts, and tools for IQ)


title: Loss of Y in leukocytes, dysregulation of autosomal immune genes and disease risks

abstract: Mosaic loss of chromosome Y (LOY) in blood is linked to increased risk for morbidity and mortality in men. LOY is the most common acquired mutation and is associated with diseases such as cancer and Alzhemer’s disease. We studied DNA, RNA and proteins in bulk, sorted- and single-cells in vivo and in vitro. We show that Alzheimer’s disease and prostate cancer patients had more LOY in NK cells and CD4+ T-lymphocytes, respectively. Furthermore, gene expression was profoundly altered in cells with LOY in a pleiotropic fashion and autosomal genes important for normal immune cell functions showed LOY associated transcriptional effect. Proteomic analysis also indicated that LOY leaves a footprint in the plasma proteome. We provide the first mechanistic explanation for the associations between LOY in blood and risk for disease in other organs.


title: Vision for Intelligent Vehicles & Applications (VIVA): Face detection and head pose challenge

abstract: Intelligent vehicles of the future are that which, having a holistic (i.e. inside and outside the vehicle) perception and understanding of the driving environment, make it possible for passengers to go from point A to point B safely and in a timely manner. This may happen by way of providing active assistance for drivers, giving full control to automated cars or some combination of the two. No matter how, a holistic perception and understanding of inside and outside the vehicle is absolutely necessary, and vision based techniques are expected to play an increasing role in this holistic view. The question is, how well do these vision techniques work in order to be used in time and safety critical driving situations? We introduce one part of the Vision for Intelligent Vehicles and Applications (VIVA), the face challenge. VIVA is a platform designed to share naturalistic driving data with the community in order to: present issues and challenges in vision from real-world driving conditions, benchmark existing vision approaches using proper metrics and progress the development of future vision algorithms. With a special focus on challenges from looking inside at the driver's face, this articles provides information on how the data is acquired and annotated, and how methods are compared.


title: Visual Saliency Prediction Using a Mixture of Deep Neural Networks

abstract: Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency without explicit knowledge of global scene semantic information. We propose a model (MxSalNet) that incorporates global scene semantic information in addition to local information gathered by a convolutional neural network. Our model is formulated as a mixture of experts. Each expert network is trained to predict saliency for a set of closely related images. The final saliency map is computed as a weighted mixture of the expert networks' output, with weights determined by a separate gating network. This gating network is guided by global scene information to predict weights. The expert networks and the gating network are trained simultaneously in an end-to-end manner. We show that our mixture formulation leads to improvement in performance over an otherwise identical non-mixture model that does not incorporate global scene information. Additionally, we show that our model achieves better performance than several other visual saliency models.


title: Dual-Polarized Broadband Array Antenna With BOR-Elements, Mechanical Design and Measurements

abstract: A dual polarized broadband phased array antenna designed for the frequency range 6-18 GHz, a 45deg conical grating lobe free scan volume and equipped with BOR-elements developed by Saab is presented. The aim with this array element is to bring about a dual polarized broadband array antenna that is easy to assemble, disassemble and connect to active microwave modules. Disassembling may be important for maintenance and upgrade reasons. Mechanical design and electromagnetic performance in the form of active reflection coefficient, calculated from measured mutual coupling coefficients, and measured active gain element pattern for a central and an edge element is presented. Edge effects in the array, which may be severe in small broadband arrays, are considered in this paper


title: Effect of sieving methodolgy on determining particle size of ground corn, sorghum, and wheat by sieving

abstract: Goals:  Evaluate four sieving methods to determine which method best estimates the average particle size of a corn, wheat, and sorghum sample at three different grind sizes (coarse, medium, and fine) ground on a hammermill and roller mill creating a 4 x 3 x 2 factorial arrangement.  Determine the analytical variation of the particle size method.  Determine if results from method S391.2 are linear to results S319.4 across multiple particle sizes.  Conduct a growth assay in nursery pigs to determine if a 50 μm increment results in different average daily feed intake (ADFI), average daily gain (ADG), and feed to gain (F/G).


title: Predicting defects in SAP Java code: An experience report

abstract: Which components of a large software system are the most defect-prone? In a study on a large SAP Java system, we evaluated and compared a number of defect predictors, based on code features such as complexity metrics, static error detectors, change frequency, or component imports, thus replicating a number of earlier case studies in an industrial context. We found the overall predictive power to be lower than expected; still, the resulting regression models successfully predicted 50–60% of the 20% most defect-prone components.


title: Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split-Spectrum Method

abstract: The differential ionospheric path delay is a major error source in L-band interferograms. It is superimposed to topography and ground deformation signals, hindering the measurement of geophysical processes. In this paper, we proceed toward the realization of an operational processor to compensate the ionospheric effects in interferograms. The processor should be robust and accurate to meet the scientific requirements for the measurement of geophysical processes, and it should be applicable on a global scale. An implementation of the split-spectrum method, which will be one element of the processor, is presented in detail, and its performance is analyzed. The method is based on the dispersive nature of the ionosphere and separates the ionospheric component of the interferometric phase from the nondispersive component related to topography, ground motion, and tropospheric path delay. We tested the method using various Advanced Land Observing Satellite Phased-Array type L-band synthetic aperture radar interferometric pairs with different characteristics: high to low coherence, moving and nonmoving terrains, with and without topography, and different ionosphere states. Ionospheric errors of almost 1 m have been corrected to a centimeter or a millimeter level. The results show how the method is able to systematically compensate the ionospheric phase in interferograms, with the expected accuracy, and can therefore be a valid element of the operational processor.


title: Design of a Tunable Stiffness Composite Leg for Dynamic Locomotion

abstract: Passively compliant legs have been instrumental in the development of dynamically running legged robots. Having properly tuned leg springs is essential for stable, robust and energetically efficient running at high speeds. Recent simulation studies indicate that having variable stiffness legs, as animals do, can significantly improve the speed and stability of these robots in changing environmental conditions. However, to date, the mechanical complexities of designing usefully robust tunable passive compliance into legs has precluded their implementation on practical running robots. This paper describes a new design of a “structurally controlled variable stiffness” leg for a hexapedal running robot. This new leg improves on previous designs’ performance and enables runtime modification of leg stiffness in a small, lightweight, and rugged package. Modeling and leg test experiments are presented that characterize the improvement in stiffness range, energy storage, and dynamic coupling properties of these legs. We conclude that this variable stiffness leg design is now ready for implementation and testing on a dynamical running robot.Copyright © 2009 by ASME


title: A neural mass model for MEG/EEG: coupling and neuronal dynamics

abstract: Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their functional significance remains a matter of debate. Some of the mechanisms that generate these signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. The kinetics of the ensuing population dynamics determine the frequency of oscillations. In this work we extended the classical nonlinear lumped-parameter model of alpha rhythms, initially developed by Lopes da Silva and colleagues [Kybernetik 15 (1974) 27], to generate more complex dynamics. We show that the whole spectrum of MEG/EEG signals can be reproduced within the oscillatory regime of this model by simply changing the population kinetics. We used the model to examine the influence of coupling strength and propagation delay on the rhythms generated by coupled cortical areas. The main findings were that (1) coupling induces phase-locked activity, with a phase shift of 0 or when the coupling is bidirectional, and (2) both coupling and propagation delay are critical determinants of the MEG/EEG spectrum. In forthcoming articles, we will use this model to (1) estimate how neuronal interactions are expressed in MEG/EEG oscillations and establish the construct validity of various indices of nonlinear coupling, and (2) generate event-related transients to derive physiologically informed basis functions for statistical modelling of average evoked responses.


title: Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder

abstract: We describe a machine learning technique for reconstructing image sequences rendered using Monte Carlo methods. Our primary focus is on reconstruction of global illumination with extremely low sampling budgets at interactive rates. Motivated by recent advances in image restoration with deep convolutional networks, we propose a variant of these networks better suited to the class of noise present in Monte Carlo rendering. We allow for much larger pixel neighborhoods to be taken into account, while also improving execution speed by an order of magnitude. Our primary contribution is the addition of recurrent connections to the network in order to drastically improve temporal stability for sequences of sparsely sampled input images. Our method also has the desirable property of automatically modeling relationships based on auxiliary per-pixel input channels, such as depth and normals. We show significantly higher quality results compared to existing methods that run at comparable speeds, and furthermore argue a clear path for making our method run at realtime rates in the near future.


title: Satellite Imagery Multiscale Rapid Detection with Windowed Networks

abstract: Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km2 and over 250 million pixels. Another challenge is that objects of interest are often minuscule (~10pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of > 0.2 km2/s. Building upon the TensorFlow Object Detection API paper, this pipeline offers a unified approach to multiple object detection frameworks that can run inference on images of arbitrary size. The SIMRDWN pipeline includes a modified version of YOLO (known as YOLT), along with the models of the TensorFlow Object Detection API: SSD, Faster R-CNN, and R-FCN. The proposed approach allows comparison of the performance of these four frameworks, and can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. For objects of very different scales (e.g. airplanes versus airports) we find that using two different detectors at different scales is very effective with negligible runtime cost. We evaluate large test images at native resolution and find mAP scores of 0.2 to 0.8 for vehicle localization, with the YOLT architecture achieving both the highest mAP and fastest inference speed.


title: BioInfer: a corpus for information extraction in the biomedical domain

abstract: BackgroundLately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora.ResultsWe present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation.ConclusionWe introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer.


title: Partial Procedural Geometric Model Fitting for Point Clouds

abstract: Geometric model fitting is a fundamental task in computer graphics and computer vision. However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e.g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models. This paper hence proposes a novel rigid geometric similarity metric, which is able to measure both the full similarity and the partial similarity between arbitrary geometric models. The proposed metric enables us to perform partial procedural geometric model fitting (PPGMF). The task of PPGMF is to search a procedural geometric model space for the model rigidly similar to a query of non-complete point set. Models in the procedural model space are generated according to a set of parametric modeling rules. A typical query is a point cloud. PPGMF is very useful as it can be used to fit arbitrary geometric models to non-complete (incomplete, over-complete or hybrid-complete) point cloud data. For example, most laser scanning data is non-complete due to occlusion. Our PPGMF method uses Markov chain Monte Carlo technique to optimize the proposed similarity metric over the model space. To accelerate the optimization process, the method also employs a novel coarse-to-fine model dividing strategy to reject dissimilar models in advance. Our method has been demonstrated on a variety of geometric models and non-complete data. Experimental results show that the PPGMF method based on the proposed metric is able to fit non-complete data, while the method based on other metrics is unable. It is also shown that our method can be accelerated by several times via early rejection.


title: SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation

abstract: While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming process. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments, hence making an important step towards the goal of automatically annotating these photos for browsing and retrieval. In the proposed method, first, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, a vocabulary of concepts is defined in a semantic space by relying on linguistic information. Finally, by exploiting the temporal coherence of concepts in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from event recognition to semantic indexing and summarization. Experimental results over egocentric set of nearly 31,000 images, show the prominence of the proposed approach over state-of-the-art methods.


title: Structural and electronic properties of organo-halide hybrid perovskites from ab initio molecular dynamics

abstract: The last two years have seen the unprecedentedly rapid emergence of a new class of solar cells, based on hybrid organic–inorganic halide perovskites. The success of this class of materials is due to their outstanding photoelectrochemical properties coupled to their low cost, mainly solution-based, fabrication techniques. Solution processed materials are however often characterized by an inherent flexible structure, which is hardly mapped into a single local minimum energy structure. In this perspective, we report on the interplay between structural and electronic properties of hybrid lead iodide perovskites investigated using ab initio molecular dynamics (AIMD) simulations, which allow the dynamical simulation of disordered systems at finite temperature. We compare the prototypical MAPbI3 (MA = methylammonium) perovskite in its cubic and tetragonal structure with the trigonal phase of FAPbI3 (FA = formamidinium), investigating different starting arrangements of the organic cations. Despite the relatively short time scale amenable to AIMD, typically a few tens of ps, this analysis demonstrates the sizable structural flexibility of this class of materials, showing that the instantaneous structure could significantly differ from the time and thermal averaged structure. We also highlight the importance of the organic–inorganic interactions in determining the fluxional properties of this class of materials. A peculiar spatial localization of the valence and conduction band edges is also found, with a dynamics in the range of 0.1 ps, which is associated with the positional dynamics of the organic cations within the cubo-octahedral perovskite cage. This asymmetry in the spatial localization of the band edges is expected to ease exciton dissociation and assist the initial stages of charge separation, possibly constituting one of the key factors for the impressive photovoltaic performances of hybrid lead-iodide perovskites.


title: AN INTRODUCTION TO RELATIONAL FRAME THEORY: BASICS AND APPLICATIONS

abstract: Relational Frame Theory (RFT) has made a very respectable empirical and theoretical showing in the psychological literature during the past decade, but the theory still remains unknown or unappreciated by most cognitive and behavioral psychologists. This article highlights why this might be the case, and presents RFT in a simplified, systematic manner, in part by comparing it to a well-known cognitive model. Finally, the article outlines RFT's relatively unique contributions to psychological accounts of language and cognition, and addresses some of RFT's scientific and applied implications. ********** Relational Frame Theory (RFT) has had a notable presence in the psychology literature since its development over a decade ago. Well over 30 empirical RFT studies have been published in peer-reviewed psychology journals in the past 10 years, and an even larger number of theoretical and descriptive treatments of it have been published as well. Recently, a book length treatment of RFT has been made available (Hayes, Barnes-Holmes, & Roche, 2001), summarizing supporting data and extending RFT analyses to a variety of psychological phenomena. In addition, RFT principles form the theoretical background of Acceptance and Commitment Therapy (ACT; see, for example, Hayes, Strosahl, & Wilson, 1999). Given the relatively frequent appearances of Relational Frame Theory in psychological literature, it is perhaps surprising that the theory remains virtually unknown outside behavioral circles, and even unrecognized or misunderstood by many academic behavioral psychologists. RFT has largely escaped notice and comprehension for at least three reasons. First, RFT intentionally makes use of technical, non-colloquial language to allow a scientific treatment of cognition. As such, published descriptions of RFT are undeniably technical, and not readily accessible to those who have not spent a considerable amount of time trying to understand the theory. Second, its significance and relevance to human psychopathology and language in general do not immediately seem obvious due to its nontraditional account of these phenomena. Finally, non-behavioral psychologists have long assumed that behaviorism has little or nothing to offer to the understanding of human language and cognition. Theories of these fundamentally important human processes that arise from the behavioral tradition are thus easy to ignore. The first purpose of this article is to convey the principles of Relational Frame Theory in relatively easy to understand fashion. In doing so, it is hoped that what RFT has to do with language, cognition, and psychopathology will become apparent. Since a vast amount of cognitive literature regarding these topic areas currently exists, RFT's relatively unique and important contributions to this literature will be outlined as well. To accomplish these goals, a popular and widely-known cognitive model of a "fear network" (Lang, 1985) will first be presented and briefly described. Lang's model contains some cosmetic similarities to RFT that will hopefully orient the reader to the analysis that follows. Following the description of this model, an RFT account of the same information presented in the model will be advanced, allowing a systematic introduction to the reader of the defining features of RFT. Finally, several reasons why RFT offers a unique and important approach to language, cognition, and human suffering will be described. Empirical evidence and more extensive arguments of technical points about RFT made throughout the article can be found, for example, in Hayes et al. (2001). RELATIONAL FRAME THEORY: BASICS AND APPLICATIONS Lang's Fear Network Lang's (1985) exemplary model of a fear network is presented succinctly in Figure 1. Stimulus propositions (indicated in ovals in Figure 1) involve "information about prompting external stimuli and the context in which they occur," and response propositions refer to "information about responding in this context, including expressive verbal behavior, overt acts, and the visceral and somatic events that mediate arousal and actions" (p. …


title: 16.3 Flexible thin-film NFC tags powered by commercial USB reader device at 13.56MHz

abstract: Our goal is to create thin low-cost flexible NFC tags to allow everyday objects to communicate to smartphones and computers and thus participate in the Internet of Things. We employ amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) thin-film transistor circuits processed at low temperatures, less than 250C, directly on thin polyester substrates. Reaching NFC standards with a-IGZO circuits is challenging because the technology is n-type only and the electron mobility (∼15cm7Vs) is lower compared to silicon. As the main result, we show that the most important NFC regulatory standards are met, even with relaxed 5 micron design rules, using optimized design topologies.


title: Modeling and Analysis of Noise Sources for Thin-Film Magnetoelectric Sensors Based on the Delta-E Effect

abstract: We present a comprehensive noise model for an electromechanical resonator that is utilized as a magnetic field sensor. The cantilever-type sensor is coated with a magnetostrictive film that exhibits a change in elastic modulus E with a magnetic field and therefore detunes the resonator—the so-called delta-E effect. The noise model contains all relevant noise sources from the operational electronics, the wiring, and the sensor itself. Measurements show good agreement up to a certain excitation voltage, where an additional dominant noise source appears. It is identified as originating from the magnetic film. With the results of the model, the operational parameters of such sensors are discussed. The model predicts that the limit of detection at 10 Hz for the present sensors can be improved to 60 pT/(Hz) $^{1/2}$ if the magnetic noise is eliminated.


title: Insulinoma presenting as alcoholic stupor.

abstract: We report a case of prolonged loss of consciousness due to hypoglycaemia following ethanol abuse in a non-diabetic. The patient also reported symptoms compatible with hypoglycaemia following heavy manual work. Further investigations revealed a pancreatic insulinoma, which was successfully removed surgically. The patient remains asymptomatic 18 months later, despite occasional episodes of ethanol abuse. This case illustrates how heavy exercise and/or alcohol abuse can aggravate spontaneous hypoglycaemia.


title: Frequency-domain methods for demosaicking of Bayer-sampled color images

abstract: This letter presents a new and simplified derivation of the frequency-domain representation of color images sampled with the Bayer color filter array. Two new demosaicking algorithms based on the frequency-domain representation are described and shown to give excellent results.


title: The burden of cardiovascular disease in the Indian subcontinent

abstract: Ischaemic heart disease and stroke are among the most common causes of death and disability in the world. The Indian subcontinent (including India, Pakistan, Bangladesh, Sri Lanka, and Nepal) has among the highest rates of cardiovascular disease (CVD) globally. Previous reports have highlighted the high CVD rates among South Asian immigrants living in Western countries, but the enormous CVD burden within the Indian subcontinent itself has been underemphasized. In this review, we discuss the existing data on the prevalence of CVD and its risk factors in the Indian subcontinent. We also review recent evidence indicating that the burden of coronary heart disease in the Indian subcontinent is largely explained on the basis of traditional risk factors, which challenges the common thinking that South Asian ethnicity per se is a strong independent risk factor for coronary heart disease. Finally, we suggest measures to implement in policy, capacity building, and research to address the CVD epidemic in the Indian subcontinent.


Analysis of Sentiments in Corporate Twitter Communication – A Case Study on an Issue of Toyota


title: Virtual Rheoscopic Fluids for Flow Visualization

abstract: Physics-based flow visualization techniques seek to mimic laboratory flow visualization methods with virtual analogues. In this work we describe the rendering of a virtual rheoscopic fluid to produce images with results strikingly similar to laboratory experiments with real-world rheoscopic fluids using products such as Kalliroscope. These fluid additives consist of microscopic, anisotropic particles which, when suspended in the flow, align with both the flow velocity and the local shear to produce high-quality depictions of complex flow structures. Our virtual rheoscopic fluid is produced by defining a closed-form formula for the orientation of shear layers in the flow and using this orientation to volume render the flow as a material with anisotropic reflectance and transparency. Examples are presented for natural convection, thermocapillary convection, and Taylor-Couette flow simulations. The latter agree well with photographs of experimental results of Taylor-Couette flows from the literature.


title: Conductive Inkjet-Printed Antennas on Flexible Low-Cost Paper-Based Substrates for RFID and WSN Applications

abstract: In this paper, a review of the authors' work on inkjet-printed flexible antennas, fabricated on paper substrates, is given. This is presented as a system-level solution for ultra-low-cost mass production of UHF radio-frequency identification (RFID) tags and wireless sensor nodes (WSN), in an approach that could be easily extended to other microwave and wireless applications. First, we discuss the benefits of using paper as a substrate for high-frequency applications, reporting its very good electrical/dielectric performance up to at least 1 GHz. The RF characteristics of the paper-based substrate are studied by using a microstrip-ring resonator, in order to characterize the dielectric properties (dielectric constant and loss tangent). We then give details about the inkjet-printing technology, including the characterization of the conductive ink, which consists of nano-silver particles. We highlight the importance of this technology as a fast and simple fabrication technique, especially on flexible organic (e.g., LCP) or paper-based substrates. A compact inkjet-printed UHF ldquopassive RFIDrdquo antenna, using the classic T-match approach and designed to match the IC's complex impedance, is presented as a demonstration prototype for this technology. In addition, we briefly touch upon the state-of-the-art area of fully-integrated wireless sensor modules on paper. We show the first-ever two-dimensional sensor integration with an RFID tag module on paper, as well as the possibility of a three-dimensional multilayer paper-based RF/microwave structure.