title: Association of hereditary elliptocytosis and Gilbert’s syndrome as the cause of biliary calculosis: Case report

abstract: SUMMARY Introduction Biliary calculosis is rare in children. It occurs associated with different haemolytic and non-haemolytic disorders, which are sometimes also combined. Case Outline A 15-year-old male was hospitalized due to biliary calculosis and non-conjugated hyper- bilirubinemia. A mild non-conjugated hyperbilirubinemia, without anaemia and other symptoms of liver dysfunction, was registered at age 8 years, and 7 years later cholelithiasis with transitory choledocho- lithiasis. The finding of ellyptocytes in blood smear, which was also verified in mother, normal haemo- globin count and the absence of diseases followed by secondary dysmorphic erythrocytes of this type, indicated a clinically milder (compensated) hereditary ellyptocytosis, while more than a double increase of non-conjugated serum bilirubin fracture after a three-day hypocaloric diet (400 kcal per day) showed the concurrent presence of Gilbert's syndrome. In the laparascopically removed gallbladder a larger number of small pigmented calculi were disclosed. Conclusion Gilbert's syndrome is an essential precipitating factor of biliary calculosis in patients with chronic haemolytic condition. Thus, in all cases of biliary calculosis and non-conjugated hyperbilirubi- nemia with absent clinical and laboratory parameters of liver disorders and anaemia, except in compen- sated haemolytic disease and Gilbert's syndrome as isolated disorders, a possibility of their association should be taken into consideration.


title: Optimizing Quantiles in Preference-based Markov Decision Processes

abstract: In the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion. Both finite and infinite horizons are considered. Finally we experimentally evaluate our approach on random MDPs and on a data center control problem.


title: Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

abstract: In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate this for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^{-4}) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F1-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that perform superior as well as produce results with higher stability on unseen data.


title: Towards a context sensitive approach to searching information based on domain specific knowledge sources

abstract: In the context of document retrieval in the biomedical domain, this paper introduces a novel approach to searching for biomedical information using contextual semantic information. More specifically, we propose to combine the contextual semantic information in documents and user queries in an attempt to improve the performance of biomedical information retrieval (IR) systems. Contextual information provides knowledge about a domain in a global context or statistical properties of a sub collection of documents related to a given query in a local context. In our context sensitive IR approach, terms denoting concepts are extracted from each document using several biomedical terminologies. Preferred terms denoting concepts are used to enrich the semantics of the document content via document expansion. The user query is expanded using terms extracted from the top-ranked expanded documents via a blind feedback query expansion approach. In addition, we aim to evaluate the utility of incorporating several terminologies within the proposed context sensitive approach. The experiments carried out on the TREC Genomics 2004 and 2005 test sets show that our context-sensitive IR approach significantly outperforms state-of-the-art baseline approaches.


title: A Chip-Multiprocessor Architecture with Speculative Multithreading

abstract: Much emphasis is now being placed on chip-multiprocessor (CMP) architectures for exploiting thread-level parallelism in applications. In such architectures, speculation may be employed to execute applications that cannot be parallelized statically. In this paper, we present an efficient CMP architecture for the speculative execution of sequential binaries without source recompilation. We present software support that enables the identification of threads from a sequential binary. The hardware includes a memory disambiguation mechanism that enables the detection of interthread memory dependence violations during speculative execution. This hardware is different from past proposals in that it does not rely on a snoopy-based cache-coherence protocol. Instead, it uses an approach similar to a directory-based scheme. Furthermore, the architecture includes a simple and efficient hardware mechanism to enable register-level communication between on-chip processors. Evaluation of this software-hardware approach shows that it is quite effective in achieving high performance when running sequential binaries.


Automatic Door Access System Using Face Recognition


title: Real-time control of soft-robots using asynchronous finite element modeling

abstract: Finite Element analysis can provide accurate deformable models for soft-robots. However, using such models is very difficult in a real-time system of control. In this paper, we introduce a generic solution that enables a high-rate control and that is compatible with strong real-time constraints. From a Finite Element analysis, computed at low rate, an inverse model of the robot outputs the setpoint values for the actuator in order to obtain a desired trajectory. This inverse problem uses a QP (quadratic-programming) algorithm based on the equations set by the Finite Element Method. To improve the update rate performances, we propose an asynchronous simulation framework that provides a better trade-off between the deformation accuracy and the computational burden. Complex computations such as accurate FEM deformations are done at low frequency while the control is performed at high frequency with strong real-time constraints. The two simulation loops (high frequency and low frequency loops) are mechanically coupled in order to guarantee mechanical accuracy of the system over time. Finally, the validity of the multi-rate simulation is discussed based on measurements of the evolution in the QP matrix and an experimental validation is conducted to validate the correctness of the high-rate inverse model on a real robot.


title: Progress and Recent Realizations of Miniaturized Inductive Proximity Sensors for Automation

abstract: Noncontacting inductive proximity sensors (IPSs) are widely preferred for position detection in industrial applications. Advances in computer-aided design simulation tools, integration, and assembly techniques for the last ten years have made possible continued sensor-feature improvements in parallel to size reduction toward very small miniaturized versions. This paper refers to the progress in the past years, presenting recently gained knowledge in sensing principle, evaluation electronics, and manufacturing. The most significant improvements are the usage of a drum-core topology, application-specified integrated circuit implementation, and face-down flip-chip technology. The fundamentals shown in this paper were essential for the design and development of a worldwide innovation: the smallest single-component IPS family that has the largest switching distance. Mounted in a stainless steel tube with a diameter of only 3 mm and a length of 33 mm, the smallest sensor in the family is able to detect metal targets at distances of up to 3 mm and provide an output switching signal with high-current capacity


title: Large Panel Level Fan Out Package Built up Study with Film Type Encapsulation Material

abstract: Die first and face up type FO-PLP preparation was studied referring the organic substrate fabrication process. 660 mm x 515 mm size carrier was used and 640 mm x 495 mm area was encapsulated with the film molding material comparing with granule material. 3 mm x 3mm to 10 mm x 10 mm dies were mounted on the panel using temporary adhesive. Die shift at compression molding was evaluated in each die size. Larger die showed smaller die shift distance. The affection of the carrier CTE to the die shift was also investigated. The board which had CTEs of 6, 8, and 14 ppm/K were used. CTE of 14 ppm/K material showed the minimum di shift. Tracking of the warpage during the preparation process by each process step indicated that each material selection can control the warpage. Wiring layer was fabricated on the die molded panel which had the size of 510 mm x 407 mm. Laminate type insulation material was used. Filled via of 35 micron diameter and L/S = 15 / 15 micron was formed by 355 nm laser drilling and SAP.


title: NVMain: An Architectural-Level Main Memory Simulator for Emerging Non-volatile Memories

abstract: Emerging non-volatile memory (NVM) technologies, such as PCRAM and STT-RAM, have demonstrated great potentials to be the candidates as replacement for DRAM-based main memory design for computer systems. It is important for computer architects to model such emerging memory technologies at the architecture level, to understand the benefits and limitations for better utilizing them to improve the performance/energy/reliability of future computing systems. In this paper, we introduce an architectural-level simulator called NV Main, which can model main memory design with both DRAM and emerging non-volatile memory technologies, and can facilitate designers to perform design space explorations utilizing these emerging memory technologies. We discuss design points of the simulator and provide validation of the model, along with case studies on using the tool for design space explorations.


STUDYING EXPECTATION VIOLATIONS IN SOCIO-TECHNICAL SYSTEMS: A CASE STUDY OF THE MOBILE APP COMMUNITY


title: openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit

abstract: We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e.g., acoustic or visual low-level descriptors, introducing a prior step of vector quantisation. The openXBOW toolkit supports arbitrary numeric input features and text input and concatenates computed subbags to a final bag. It provides a variety of extensions and options. To our knowledge, openXBOW is the first publicly available toolkit for the generation of crossmodal bags-of-words. The capabilities of the tool are exemplified in two sample scenarios: time-continuous speech-based emotion recognition and sentiment analysis in tweets where improved results over other feature representation forms were observed.


title: A new tubular reactor for mass production of microalgae outdoors

abstract: A novel reactor for outdoor production of microalgae is described. Air-lift is used for circulation of the culture in transparent tubes lying on the ground and interconnected by a manifold. Dissolved O2 is removed through a gas-separator placed 2.0 m above the tubes and water-spray is used for cooling. The manifold permits short-run durations between leaving the gas separator and re-entering it, preventing thereby damaging accumulation of dissolved oxygen. Day temperature control in summer is attained using water-spray. In winter, temperature in the tubes rises rapidly in the morning, as compared to an open raceway even if placed in a greenhouse. The number of hours along which optimal temperature prevails in the culture throughout the year increased significantly. Very high daily productivity computed on a volumetric basis (e.g. 550 mg dry wt l−1 culture) was obtained and preliminary observations indicate that a significantly higher output, e.g. 1500 mg dry wt l−1 d−1 is attainable. Much more research is required to assess the year-round, sustained productivity attainable in this reactor.


title: Hand gesture recognition using machine learning and the Myo armband

abstract: Gesture recognition has multiple applications in medical and engineering fields. The problem of hand gesture recognition consists of identifying, at any moment, a given gesture performed by the hand. In this work, we propose a new model for hand gesture recognition in real time. The input of this model is the surface electromyography measured by the commercial sensor the Myo armband placed on the forearm. The output is the label of the gesture executed by the user at any time. The proposed model is based on the Λ-nearest neighbor and dynamic time warping algorithms. This model can learn to recognize any gesture of the hand. To evaluate the performance of our model, we measured and compared its accuracy at recognizing 5 classes of gestures to the accuracy of the proprietary system of the Myo armband. As a result of this evaluation, we determined that our model performs better (86% accurate) than the Myo system (83%).


title: Explaining Recurrent Neural Network Predictions in Sentiment Analysis

abstract: Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.


Efficient Inference and Structured Learning for Semantic Role Labeling


title: Matrix factorization+ for movie recommendation

abstract: We present a novel model for movie recommendations using additional visual features extracted from pictural data like posters and still frames, to better understand movies. In particular, several context-based methods for recommendation are shown to be special cases of our proposed framework. Unlike existing context-based approaches, our method can be used to incorporate visual features - features that are lacking in existing context-based approaches for movie recommendations. In reality, movie posters and still frames provide us with rich knowledge for understanding movies as well as users' preferences. For instance, user may want to watch a movie at the minute when she/he finds some released posters or still frames attractive. Unfortunately, such unique features cannot be revealed from rating data or other forms of context being used in most of existing methods. In this paper, we take a step forward in this direction and investigate both low-level and high-level visual features from the movie posters and still frames for further improvement of recommendation methods. Extensive experiments on real world datasets show that our approach leads to significant improvement over several state-of-the-art methods.


title: Cooperative spectrum sensing in cognitive radio networks

abstract: In cognitive networks, cognitive (unlicensed) users need to continuously monitor spectrum to detect the presence of primary (licensed) users. In this paper, we illustrate the benefits of cooperation in cognitive radio. We show that by allowing the cognitive radios operating in the same band to cooperate we can reduce the detection time and thus increasing their agility. We first consider the case of two cognitive users and show how the inherent asymmetry in the network can be exploited to increase the probability of detection. We then extend our work to multiple cognitive user networks. We also propose a practical algorithm which allows cooperation in random networks


title: 3.3kW CLLC converter with synchronous rectification for plug-in electric vehicles

abstract: This paper presents an innovative technique for synchronous rectification in the bidirectional CLLC converter with the integrated transformer for plug-in electric vehicle (EV) applications. To improve the efficiency and power density, the integrated transformer is introduced and simulated using Finite Element Analysis (FEA) method. In addition, synchronous rectification method is implemented by controlling the turn-on and turn-off timings based on the phase difference between resonant currents of primary and secondary sides. Furthermore, a simplified closed loop design is discussed and implemented on a digital signal processor (TMS320F28335). The effectiveness of the control loop is verified through the experimental results. A 3.3kW proof-of-concept prototype is built with power density of 1kW/L, a gravimetric power density of 0.9kW/kg and peak efficiency of 97.5%.


title: FMRI Connectivity Analysis of Acupuncture Effects on an Amygdala-Associated Brain Network

abstract: Background: Recently, increasing evidence has indicated that the primary acupuncture effects are mediated by the central nervous system. However, specific brain networks underpinning these effects remain unclear. Results: In the present study using fMRI, we employed a within-condition interregional covariance analysis method to investigate functional connectivity of brain networks involved in acupuncture. The fMRI experiment was performed before, during and after acupuncture manipulations on healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway for pain modulation. We first identified significant fMRI signal changes during acupuncture stimulation in the left amygdala, which was subsequently selected as a functional reference for connectivity analyses. Our results have demonstrated that there is a brain network associated with the amygdala during a resting condition. This network encompasses the brain structures that are implicated in both pain sensation and pain modulation. We also found that such a pain-related network could be modulated by both verum acupuncture and sham acupuncture. Furthermore, compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations among the amygdala-associated network. Conclusion: Our findings indicate that acupuncture may change this amygdala-specific brain network into a functional state that underlies pain perception and pain modulation.


title: A flat pipeline inspection robot with two wheel chains

abstract: This paper presents a new pipeline inspection robot that has multiple sensors for inspection of 80–100mm pipelines. The special feature of this robot is realization of driving and steering capability by using only two wheel chains. Compared to popularly employed pipeline robots using three wheel chains, the new design allows simple robot control and easy user interface, specially at T-branch. As another advantage, the flat shape of this robot allows mounting additional sensors on the both sides of the robot. The kinematics and three control modes are described. Finally, the performance of this robot system is verified by experimentation.


title: Segmentation of overlapping cervical cells: A variational method with star-shape prior

abstract: Accurate and automatic detection and delineation of cervical cells are two critical precursor steps to automatic Pap smear image analysis and detecting pre-cancerous changes in the uterine cervix. To overcome noise and cell occlusion, many segmentation methods resort to incorporating shape priors, mostly enforcing elliptical shapes (e.g. [1]). However, elliptical shapes do not accurately model cervical cells. In this paper, we propose a new continuous variational segmentation framework with star-shape prior using directional derivatives to segment overlapping cervical cells in Pap smear images. We show that our star-shape constraint better models the underlying problem and outperforms state-of-the-art methods in terms of accuracy and speed.


title: Trio: A System for Integrated Management of Data, Accuracy, and Lineage

abstract: Trio is a new database system that manages not only data, but also the accuracy and lineage of the data. Approximate (uncertain, probabilistic, incomplete, fuzzy, and imprecise!) databases have been proposed in the past, and the lineage problem also has been studied. The goals of the Trio project are to distill previous work into a simple and usable model, design a query language as an understandable extension to SQL, and most importantly build a working system---a system that augments conventional data management with both accuracy and lineage as an integral part of the data. This paper provides numerous motivating applications for Trio and lays out preliminary plans for the data model, query language, and prototype system.


title: Abnormal gesture recognition based on multi-model fusion strategy

abstract: In this paper, we present a novel refined fused model combining masked Res-C3D network and skeleton LSTM for abnormal gesture recognition in RGB-D videos. The key to our design is to learn discriminative representations of gesture sequences in particular abnormal gesture samples by fusing multiple features from different models. First, deep spatiotemporal features are well extracted by 3D convolutional neural networks with residual architecture (Res-C3D). As gestures are mainly derived from the arm or hand movements, a masked Res-C3D network is built to decrease the effect of background and other variations via exploiting the skeleton of the body to reserve arm regions with discarding other regions. And then, relative positions and angles of different key points are extracted and used to build a time-series model by long short-term memory network (LSTM). Based the above representations, a fusion scheme for blending classification results and remedy model disadvantage by abnormal gesture via a weight fusion layer is developed, in which the weights of each voting sub-classifier being advantage to a certain class in our ensemble model are adaptively obtained by training in place of fixed weights. Our experimental results show that the proposed method can distinguish the abnormal gesture samples effectively and achieve the state-of-the-art performance in the IsoGD dataset.


title: Correction: Wild-type and mutated IDH1/2 enzymes and therapy responses

abstract: The originally published version of this article contained an error in Figure 2. On the IDH1/2 mutated arrow, it incorrectly read ‘NADP+ ->NADPH’ instead of the correct ‘NADPH ->NADP+‘. This has now been corrected in the PDF and HTML versions of the article. The authors wish to apologize for any inconvenience caused.


title: Crosscutting patterns and design stability: An exploratory analysis

abstract: It is often claimed that inaccurate modularisation of crosscutting concerns hinders program comprehension and, as a consequence, leads to harmful software instabilities. However, recent studies have pointed out that crosscutting concerns are not always harmful to design stability. Hence, software maintainers would benefit from well documented patterns of crosscutting concerns and a better understanding about their actual impact on design stability. This paper presents a catalogue of crosscutting concern patterns recurrently observed in software systems. These patterns are described and classified based on an intuitive vocabulary that facilitates their recognition by software engineers. We analysed instances of the crosscutting patterns in object-oriented and aspect-oriented versions of three evolving programs. The outcomes of our exploratory evaluation indicated that: (i) a certain category of crosscutting patterns seems to be good indicator of harmful instabilities, and (ii) aspect-oriented solutions were unable to modularise concerns matching some crosscutting patterns.


title: Modeling and predicting mental workload in en route air traffic control: Critical review and broader implications. Human Factors: The

abstract: Objective: We perform a critical review of research on mental workload in en route air traffic control (ATC). We present a model of operator strategic behavior and workload management through which workload can be predicted within ATC and other complex work systems. Background: Air traffic volume is increasing worldwide. If air traffic management organizations are to meet future demand safely, better models of controller workload are needed. Method: We present the theoretical model and then review investigations of how effectively traffic factors, airspace factors, and operational constraints predict controller workload. Results: Although task demand has a strong relationship with workload, evidence suggests that the relationship depends on the capacity of the controllers to select priorities, manage their cognitive resources, and regulate their own performance. We review research on strategies employed by controllers to minimize the control activity and information-processing requirements of control tasks. Conclusion: Controller workload will not be effectively modeled until controllers' strategies for regulating the cognitive impact of task demand have been modeled. Application: Actual and potential applications of our conclusions include a reorientation of workload modeling in complex work systems to capture the dynamic and adaptive nature of the operator's work. Models based around workload regulation may be more useful in helping management organizations adapt to future control regimens in complex work systems.


title: Quantum cryptography: A survey

abstract: We survey some results in quantum cryptography. After a brief introduction to classical cryptography, we provide the quantum-mechanical background needed to present some fundamental protocols from quantum cryptography. In particular, we review quantum key distribution via the BB84 protocol and its security proof, as well as the related quantum bit commitment protocol and its proof of insecurity.


title: Time-series data mining

abstract: In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series. The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series data mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future. We hope that this article can provide a broad and deep understanding of the time-series data mining research field.


title: A Survey of Word Embeddings Evaluation Methods

abstract: Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora. Models proposing these representations have gained popularity in the recent years, but the issue of the most adequate evaluation method still remains open. This paper presents an extensive overview of the field of word embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation, summarizing 16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and experimental methods, systematize information about evaluation datasets and discuss some key challenges.


title: Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

abstract: In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.


A Quantitative Measure of Generative Adversarial Network Distributions


title: Autoethnography: a tool for practice and education

abstract: Ethnographic techniques are useful tools for developing a fine-grained, context-based understanding of user behavior. Because conventional ethnographic studies are time-intensive, interest has grown in techniques that can be applied more rapidly, to fit within the software development cycle---a sort of 'ethnography lite'. One such promising tool is the autoethnography, in which the investigator creates an ethnographic description and analysis of his/her own behavior, attempting to develop an objective understanding of the behaviors and work context under consideration by casting the investigator as both the informant 'insider' and the analyst 'outsider'. We demonstrate the potential of the autoethnography in HCI education through a case study of an HCI assignment in which autoethnography informs requirements analysis and system design. This paper argues that the autoethnography has a role to play in software development and is a useful teaching tool for HCI courses.


A COMPARATIVE STUDY OF SENTIMENT ANALYSIS TECHNIQUES 1


title: Patterning and Electronic Tuning of Laser Scribed Graphene for Flexible All-Carbon Devices

abstract: Engineering a low-cost graphene-based electronic device has proven difficult to accomplish via a single-step fabrication process. Here we introduce a facile, inexpensive, solid-state method for generating, patterning, and electronic tuning of graphene-based materials. Laser scribed graphene (LSG) is shown to be successfully produced and selectively patterned from the direct laser irradiation of graphite oxide films under ambient conditions. Circuits and complex designs are directly patterned onto various flexible substrates without masks, templates, post-processing, transferring techniques, or metal catalysts. In addition, by varying the laser intensity and laser irradiation treatments, the electrical properties of LSG can be precisely tuned over 5 orders of magnitude of conductivity, a feature that has proven difficult with other methods. This inexpensive method for generating LSG on thin flexible substrates provides a mode for fabricating a low-cost graphene-based NO2 gas sensor and enables its use as a...


title: AND THE A-TRAIN A New Dimension of Space-Based Observations of Clouds and Precipitation

abstract: CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASA's Aqua and Aura satellites, a NASA–CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profi...


title: A 42mW 26–28 GHz phased-array receive channel with 12 dB gain, 4 dB NF and 0 dBm IIP3 in 45nm CMOS SOI

abstract: This paper presents a low-power 26–28 GHz phased-array receive channel in 45nm CMOS SOI. The design alternates cascode amplifiers with switched-LC phase-shifter cells to result in 5-bit phase control with gain and rms phase error < 0.6 dB and 4°, respectively, over 32 phase states. The measured gain, noise figure (NF) and IIP3 are 12.2 dB, 4 dB and 0 dBm, respectively, and are achieved at a DC power of 42 mW. A gain control of 6-dB is also available without affecting the system NF. To our knowledge, this represents state-of-the-art in mm-wave phased-arrays with the best published linearity at low NF. Application areas include 5G base-stations and hand-held units.


title: WiFi-SLAM Using Gaussian Process Latent Variable Models

abstract: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.


title: Rapid Prototyping for Wearables

abstract: This half-day hands-on studio will teach how to design and develop effective interfaces for head mounted and wrist worn wearable computers through the application of user-centered design principles. Attendees will learn gain the knowledge and tools needed to rapidly develop prototype applications, and also complete a hands-on design task. They will also learn good design guidelines for wearable systems and how to apply those guidelines. A variety of tools will be used that do not require any hardware or software experience, many of which are free and/or open source. Attendees will also be provided with material that they can use to continue their learning after the studio is over.


title: System-of-Systems Engineering Management: A Review of Modern History and a Path Forward

abstract: As our knowledge of system of systems (SoS) has grown and evolved, so has our understanding of how to engineer and manage them. In systems engineering, we develop architectures and frameworks to bring meaning to this kind of uncertainty, but for SoS engineering (SoSE) we are still in search of how we can structure this understanding. In this paper, we review the SoS literature to illustrate the need to create an SoSE management framework based on the demands of constant technological progress in a complex dynamic environment. We conclude from this review that the history and evolution of defining SoS has shown that: (1) SoS can be defined by distinguishing characteristics and (2) SoS can be viewed as a network where the ldquobest practicesrdquo of network management can be applied to SoSE. We use these two theories as a foundation for our objective to create an effective SoSE management framework. To accomplish this, we utilize modified fault, configuration, accounting, performance, and security (FCAPS) network principles (SoSE management conceptual areas). Furthermore, cited distinguishing characteristics of SoS are also used to present a SoSE management framework. We conclude with a case analysis of this framework using a known and well-documented SoS (i.e., Integrated Deepwater System) to illustrate how to better understand, engineer, and manage within the domain of SoSE.


title: Focus group interviews in nursing research: part 1

abstract: Focus groups are used by researchers in the social and behavioural sciences to explore phenomena and are accepted as a legitimate qualitative methodology. The primary goal of focus groups is to use interaction data resulting from discussion among participants to increase the depth of the enquiry and reveal aspects of the phenomenon assumed to be otherwise less accessible. This article, the first of three articles on focus groups, examines the nature of focus groups, issues regarding planning focus groups, selecting participants and the size of the groups. This article is aimed at students who are undertaking research modules as part of their academic studies or writing a research proposal as well as at novice researchers who intend to use focus groups as a means of data collection.


title: Learning Grasping Points with Shape Context

abstract: This paper presents work on vision based robotic grasping. The proposed method adopts a learning framework where prototypical grasping points are learnt from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labelled synthetic images. We evaluate and compare the performance of linear and non-linear classifiers. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects.


title: A Streamlined Photometric Stereo Framework for Cultural Heritage

abstract: In this paper, we propose a streamlined framework of robust 3D acquisition for cultural heritage using both photometric stereo and photogrammetric information. An uncalibrated photometric stereo setup is augmented by a synchronized secondary witness camera co-located with a point light source. By recovering the witness camera’s position for each exposure with photogrammetry techniques, we estimate the precise 3D location of the light source relative to the photometric stereo camera. We have shown a significant improvement in both light source position estimation and normal map recovery compared to previous uncalibrated photometric stereo techniques. In addition, with the new configuration we propose, we benefit from improved surface shape recovery by jointly incorporating corrected photometric stereo surface normals and a sparse 3D point cloud from photogrammetry.


title: Generating summaries of multiple news articles

abstract: We present a natural language system which summarizes a series of news articles on the same event. It uses summarization operators, identified through empirical analysis of a corpus of news summaries, to group together templates from the output of the systems developed for ARPA’s Message Understanding Conferences. Depending on the available resources (e.g., space), summaries of different length can be produced. Our research also provides a methodological framework for future work on the summarization task and on the evaluation of news summarization systems.


Open linked data and mobile devices as e-tourism tools. A practical approach to collaborative e-learning


title: Mobile Cloud Forensics: An Analysis of Seven Popular Android Apps

abstract: Using the evidence collection and analysis methodology for Android devices proposed by Martini, Do and Choo, we examined and analyzed seven popular Android cloud-based apps. Firstly, we analyzed each app in order to see what information could be obtained from their private app storage and SD card directories. We collated the information and used it to aid our investigation of each app database files and AccountManager data. To complete our understanding of the forensic artefacts stored by apps we analyzed, we performed further analysis on the apps to determine if the user authentication credentials could be collected for each app based on the information gained in the initial analysis stages. The contributions of this research include a detailed description of artefacts, which are of general forensic interest, for each app analyzed.


title: A Wireless and Batteryless Microsystem with Implantable Grid Electrode/3-Dimensional Probe Array for ECoG and Extracellular Neural Recording in Rats

abstract: This paper presents the design and implementation of an integrated wireless microsystem platform that provides the possibility to support versatile implantable neural sensing devices in free laboratory rats. Inductive coupled coils with low dropout regulator design allows true long-term recording without limitation of battery capacity. A 16-channel analog front end chip located on the headstage is designed for high channel account neural signal conditioning with low current consumption and noise. Two types of implantable electrodes including grid electrode and 3D probe array are also presented for brain surface recording and 3D biopotential acquisition in the implanted target volume of tissue. The overall system consumes less than 20 mA with small form factor, 3.9 × 3.9 cm2 mainboard and 1.8 × 3.4 cm2 headstage, is packaged into a backpack for rats. Practical in vivo recordings including auditory response, brain resection tissue and PZT-induced seizures recording demonstrate the correct function of the proposed microsystem. Presented achievements addressed the aforementioned properties by combining MEMS neural sensors, low-power circuit designs and commercial chips into system-level integration.


title: A reduced size helical antenna

abstract: Conventional axial mode helix antennas offer wide bandwidth, high gain, and circular polarization. They are in widespread use at UHF frequencies. Their physical size, however, can become excessively large at lower frequencies. In this paper we present a new design of the helical antenna, termed the stub loaded helix (SLH), which offers a substantial size reduction over a conventional axial mode helix without sacrificing performance. For the same number of turns, a stub loaded helix offers a 4:1 reduction in overall size while maintaining comparable gain performance. The usable bandwidth ofthe stub loaded helix is less than the conventional helix design (25% versus 50%) due to reduced axial ratio performance of the stub loaded helix.


title: Performance Evaluation of a High-Power-Density PMASynRM With Ferrite Magnets

abstract: Although motors that use rare-earth permanent magnets (PMs) typically exhibit high performance, high costs and concerns about the stability of raw material supplies are leading to their decreased production. However, the performance of such motors is not easily matched without the use of rare-earth PMs. This paper proposes and examines a PM-assisted synchronous reluctance motor with ferrite magnets that has the same power density as rare-earth PM synchronous motors. A suitable rotor structure for high torque and high power is discussed with respect to the demagnetization of ferrite magnets and the mechanical strength.


title: Can priors be trusted? Learning to anticipate roadworks

abstract: This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation during autonomous driving by assessing the probability that roadworks are being traversed. We compare two formulations of a roadwork prior: one based on Gaussian Process (GP) classification and the other on a more conventional Hidden Markov Model (HMM) in order to model correlations between nearby parts of a vehicle trajectory. Importantly, our formulation allows this prior to be updated efficiently and repeatedly to gain an ever more accurate model of the environment over time. In the absence of, or in addition to, any in-situ observations, information from dedicated web resources can readily be incorporated into the framework. We evaluate our model using real data from an autonomous car and show that although the GP and HMM are roughly commensurate in terms of mapping roadworks, the GP provides a more powerful representation and lower prediction error.


Classification with No Discrimination by Preferential Sampling


title: English class on the air: mobile language learning with cell phones

abstract: Busy, active adults often do not have the time or ability to study in traditional, face-to-face classroom settings. In addition, adult learners may need access to content or expertise as they go about their daily activities, which may take them far from their fixed computers. Yet, these adults often have compelling needs to learn new knowledge and skills. Mobile learning with cell phones offers a unique response to this need. Mobile phones can support many kinds of learning, including language learning. This article explores the emerging technologies available for mobile language learning, and provides a model for integrating research-based pedagogy with available and emerging technologies to develop learning objects for effective, engaging mobile language learning.


title: Recovering Surface Details under General Unknown Illumination Using Shading and Coarse Multi-view Stereo

abstract: Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision and high quality reconstruction is usually challenging especially when high detail is needed. This paper presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are our two important observations: (1) the illumination over the surface of an object tends to be piecewise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing geometry, which were previously overlooked. Thus we introduce TV to regularize the lighting and use visual hull to constrain partial vertices. The reconstruction is formulated as a constrained TVminimization problem that treats the shape and lighting as unknowns simultaneously. An augmented Lagrangian method is proposed to quickly solve the TV-minimization problem. As a result, our approach is robust, stable and is able to efficiently recover high quality of surface details even starting with a coarse MVS. These advantages are demonstrated by the experiments with synthetic and real world examples.


title: Design and characterization of novel paper-based inkjet-printed UHF antennas for RFID and sensing applications

abstract: In this paper, inkjet printed antennas on paper based substrates are investigated for the first time as a solution for the mass production of ultra-low cost radio frequency identification (RFID) and sensing applications. A cavity resonator method and a Transmission Line (TL) method were utilized for the RF characteristics extraction of paper substrate: dielectric constant (epsivr) and loss tangent (tandelta). A UHF RFID antenna was designed and developed on paper substrate using inkjet printing technology which could serve as a technology for a much simpler and faster method for fabricating RFID tags and ultra-low cost paper-based RF front-ends and antennas. Simulation and experimental results verify the characteristics of paper substrate and the performance of the RFID tag.


Apparent usability vs. inherent usability: experimental analysis on the determinants of the apparent usability


title: Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies

abstract: In this paper, we present generic cloud performance models for evaluating Iaas, PaaS, SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and propose some new performance metrics. Our benchmark experiments are conducted mainly on IaaS cloud platforms over scale-out and scale-up workloads. Cloud benchmarking results are analyzed with the efficiency, elasticity, QoS, productivity, and scalability of cloud performance. Five cloud benchmarks were tested on Amazon IaaS EC2 cloud: namely YCSB, CloudSuite, HiBench, BenchClouds, and TPC-W. To satisfy production services, the choice of scale-up or scale-out solutions should be made primarily by the workload patterns and resources utilization rates required. Scaling-out machine instances have much lower overhead than those experienced in scale-up experiments. However, scaling up is found more cost-effective in sustaining heavier workload. The cloud productivity is greatly attributed to system elasticity, efficiency, QoS and scalability. We find that auto-scaling is easy to implement but tends to over provision the resources. Lower resource utilization rate may result from auto-scaling, compared with using scale-out or scale-up strategies. We also demonstrate that the proposed cloud performance models are applicable to evaluate PaaS, SaaS and hybrid clouds as well.


title: Pre-bond testing of the silicon interposer in 2.5D ICs

abstract: In interposer-based 2.5D integrated circuits, the passive silicon interposer is the least expensive component in the chip. Thus, it is desirable to test the interposer before bonding to ensure that more expensive and defect-free dies are not stacked on a faulty interposer. We present an efficient method to locate defects in a passive interposer before stacking. The proposed test architecture uses e-fuses that can be programmed to connect or disconnect functional paths inside the interposer. The concept of die footprint is utilized for interconnect testing, and the overall assembly and test flow is described. In order to reduce test time, the concept of weighted critical area is defined and utilized. We present HSPICE simulation results to demonstrate the effectiveness of the pre-bond test solution. The benefit of using weighted critical area is demonstrated using a commercial interposer from GLOBALFOUNDRIES.


title: MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

abstract: Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intra-track and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at this https URL .


title: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

abstract: Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.


title: Gigantomastia and autoimmunity: a case report

abstract: Gigantomastia is a rare breast condition characterised by diffuse, rapid, and excessive breast hypertrophy that can result in symptoms ranging from pain to necrosis and sepsis. It typically occurs in the setting of marked hormonal changes such as puberty and pregnancy. Rarely, gigantomastia has been reported to develop in the setting of an autoimmune illness. We present a case of gigantomastia developing in a patient with systemic lupus erythematosus, who was treated with methotrexate and bilateral mammoplasty.


title: Automatic knowledge extraction from OCR documents using hierarchical document analysis

abstract: Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format [1, 11, 18, 26]. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries and automatically extracts their most relevant knowledge from OCR documents by using text mining techniques. The framework can provide relevance ranking of knowledge to a given query. We tested the proposed framework on corpus of documents at GE Power where document consists of more than hundred pages in PDF.


title: Cross-Layer Self-Similar Coflow Scheduling for Machine Learning Clusters

abstract: In recent years, many companies have developed various distributed computation frameworks for processing machine learning (ML) jobs in clusters. Networking is a well-known bottleneck for ML systems and the cluster demands efficient scheduling for huge traffic (up to 1GB per flow) generated by ML jobs. Coflow has been proven an effective abstraction to schedule flows of such data-parallel applications. However, the implementation of coflow scheduling policy is constrained when coflow characteristics are unknown a prior, and when TCP congestion control misinterprets the congestion signal leading to low throughput. Fortunately, traffic patterns experienced by some ML jobs support to speculate the complete coflow characteristic with limited information. Hence this paper summarizes coflow from these ML jobs as self-similar coflow and proposes a decentralized self-similar coflow scheduler Cicada. Cicada assigns each coflow a probe flow to speculate its characteristics during the transportation and employs the Shortest Job First (SJF) to separate coflow into strict priority queues based on the speculation result. To achieve full bandwidth for throughput- sensitive ML jobs, and to guarantee the scheduling policy implementation, Cicada promotes the elastic transport-layer rate control that outperforms prior works. Large-scale simulations show that Cicada completes coflow 2.08x faster than the state-of-the-art schemes in the information-agnostic scenario.


title: DIDACache: An Integration of Device and Application for Flash-based Key-value Caching

abstract: In recent years, flash-based key-value cache systems have raised high interest in industry, such as Facebook's McDipper and Twitter's Fatcache. These cache systems typically use commercial SSDs to store and manage key-value cache data in flash. Such a practice, though simple, is inefficient due to the huge semantic gap between the key-value cache manager and the underlying flash devices. In this paper, we advocate to reconsider the cache system design and directly open device-level details of the underlying flash storage for key-value caching. This co-design approach bridges the semantic gap and well connects the two layers together, which allows us to leverage both the domain knowledge of key-value caches and the unique device properties. In this way, we can maximize the efficiency of key-value caching on flash devices while minimizing its weakness. We implemented a prototype, called DIDACache, based on the Open-Channel SSD platform. Our experiments on real hardware show that we can significantly increase the throughput by 35.5%, reduce the latency by 23.6%, and remove unnecessary erase operations by 28%.


title: Word2net: Deep Representations of Language

abstract: Word embeddings extract semantic features of words from large datasets of text. Most embedding methods rely on a log-bilinear model to predict the occurrence of a word in a context of other words. Here we propose word2net, a method that replaces their linear parametrization with neural networks. For each term in the vocabulary, word2net posits a neural network that takes the context as input and outputs a probability of occurrence. Further, word2net can use the hierarchical organization of its word networks to incorporate additional meta-data, such as syntactic features, into the embedding model. For example, we show how to share parameters across word networks to develop an embedding model that includes part-of-speech information. We study word2net with two datasets, a collection of Wikipedia articles and a corpus of U.S. Senate speeches. Quantitatively, we found that word2net outperforms popular embedding methods on predicting held- out words and that sharing parameters based on part of speech further boosts performance. Qualitatively, word2net learns interpretable semantic representations and, compared to vector-based methods, better incorporates syntactic information.


title: Bullying Among Lesbian, Gay, Bisexual, and Transgender Youth

abstract: Bullying of lesbian, gay, bisexual, and transgender (LGBT) youth is prevalent in the United States, and represents LGBT stigma when tied to sexual orientation and/or gender identity or expression. LGBT youth commonly report verbal, relational, and physical bullying, and damage to property. Bullying undermines the well-being of LGBT youth, with implications for risky health behaviors, poor mental health, and poor physical health that may last into adulthood. Pediatricians can play a vital role in preventing and identifying bullying, providing counseling to youth and their parents, and advocating for programs and policies to address LGBT bullying.


title: Multimodal emotion recognition using EEG and eye tracking data

abstract: This paper presents a new emotion recognition method which combines electroencephalograph (EEG) signals and pupillary response collected from eye tracker. We select 15 emotional film clips of 3 categories (positive, neutral and negative). The EEG signals and eye tracking data of five participants are recorded, simultaneously, while watching these videos. We extract emotion-relevant features from EEG signals and eye tracing data of 12 experiments and build a fusion model to improve the performance of emotion recognition. The best average accuracies based on EEG signals and eye tracking data are 71.77% and 58.90%, respectively. We also achieve average accuracies of 73.59% and 72.98% for feature level fusion strategy and decision level fusion strategy, respectively. These results show that both feature level fusion and decision level fusion combining EEG signals and eye tracking data can improve the performance of emotion recognition model.


title: Skin color segmentation using multi-color space threshold

abstract: Skin color segmentation has proven to be useful in various application, such as face detection, hand gesture analysis, image content filtering, etc. There are some challenges on skin-color detection, such as wide variety of illumination condition and skin-like color objects appear in the background of image. Therefore, a method is required to cope with illumination condition while detecting skin color. In our proposed method, we combine the chrominance channels of three color spaces, namely HSV, YCbCr and Normalized RGB, to produce better rate result for skin color segmentation. The result shows that the proposed method is able to detect skin pixel and get up to 91.05% correct result while being tested in ECU and HGR Dataset. This result is significantly higher than the other single color spaces.


title: Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL

abstract: Internet evolution has affected all industrial and commercial activity and accelerated e-learning growth. Due to cost, time, or flexibility for designer courses and learners, e-learning has been adopted by corporations as an alternative training method. E-learning effectiveness evaluation is vital, and evaluation criteria are diverse. A large effort has been made regarding e-learning effectiveness evaluation; however, a generalized quantitative evaluation model, which considers both the interaffected relation between criteria and the fuzziness of subjective perception concurrently, is lacking. In this paper, the proposed new novel hybrid MCDM model addresses the independent relations of evaluation criteria with the aid of factor analysis and the dependent relations of evaluation criteria with the aid of DEMATEL. The AHP and the fuzzy integral methods are used for synthetic utility in accordance with subjective perception environment. Empirical experimental results show the proposed model is capable of producing effective evaluation of e-learning programs with adequate criteria that fit with respondent's perception patterns, especially when the evaluation criteria are numerous and intertwined.


title: A software-radio test-bed for measuring the performance of DVB-S2 receiver circuits

abstract: This paper presents a flexible and versatile DVB-S2 test-bed that combines a set of automated signal generation and analysis instruments along with a powerful software radio processing unit for executing performance analysis experiments of DVB-S2 receiver circuits using realistic scenarios. The test-bed provides a rich set of forward satellite channel impairments and also supports real DVB-S2 signals. All test-bed subunits are interconnected over a LAN and are controlled through a central processing unit, allowing fully automated data acquisition and processing of measurements collected from the various receiver circuits.


title: The "ecoGator" app: gamification for enhanced energy efficiency in Europe

abstract: In the course of the IEE (Intelligent Energy Europe) funded project "Efficiency 2.1 - New Media for top informed consumers regarding sustainable and energy efficient products" a consortium of 12 partners (led by the Austrian Energy Agency) from 10 European countries is charged with designing, developing and rolling out an "Efficiency adviser" smartphone application named "ecoGator". The main objective of the "ecoGator" app is to support consumer awareness of energy efficient purchase and use of products, enhancing energy-aware and ecologically sound behavior in everyday life. To reach that goal it is crucial to encourage consumers to use this application regularly. This paper describes the evaluation of the gamification strategy of the app "ecoGator". With a qualitative assessment (usability testing method) the gamification elements were tested in terms of motivation, understanding and ease of use. The results of the tests will be considered in the ongoing developing phase of the application and should support the ambitious project targets.


title: Supporting Voice over LTE: Solutions, Architectures, and Protocols

abstract: Modern cellular networks are expected to support both voice and a growing volume of data traffic. The rapid growth in data traffic has promoted network operators to move to Long Term Evolution (LTE), a 4th generation of wireless network infrastructure. However, LTE architecture does not support native circuit switching services and relies on the IP Multimedia Subsystem (IMS) for supporting voice and Short Messaging Service (SMS). Unfortunately, the uptake of IMS has not been as rapid as expected and deployments of IMS cores have been limited. This poses a major issue for operators who wish to deploy LTE in the near future. In particular, voice and SMS drive a majority of service provider revenue, who are concerned with voice quality, call continuity, and reliable SMS delivery in deployed LTE networks. In this paper we analyze several contending approaches to delivering voice services over LTE networks. Each approach will be illustrated with sequence diagrams to explain how voice and SMS services are rendered. We compare the proposed solutions in terms of complexity, cost, features, and interoperability.


title: Generator to grid adaptive synchronization technique based on reference model

abstract: The paralleling of synchronous generators is one of the main and frequently implemented power system control tasks. The significant importance of automatic implementation of this task is determined by the need of fast and reliable restoration of an electrical power system (EPS) after emergencies. The value of this task has been dramatically increasing during the last years with the growth of the penetration of distributed generation and emerging of the task of automatic intentional islanding and further resynchronization of distributed generators to the grid. However the matching algorithms of the currently existing synchronizers are heavily affected by rapidly changing operating conditions of EPS which increases the matching time, especially during restoration process. This paper proposes the synchronization technique which is based on the model reference adaptive control. The objective of the proposed technique is to increase the robustness of the synchronization process when the operating conditions of EPS are rapidly changed. The application of this technique allows reducing the time of synchronization process and improving its accuracy. The time domain simulation results obtained with software and hybrid software-hardware simulators prove the advantages of the proposed technique.


title: Low-k interconnect stack with a novel self-aligned via patterning process for 32nm high volume manufacturing

abstract: Interconnect process features are described for a 32nm high performance logic technology. Lower-k, yet highly manufacturable, Carbon-Doped Oxide (CDO) dielectric layers are introduced on this technology at three layers to address the demand for ever lower metal line capacitance. The pitches have been aggressively scaled to meet the expectation for density, and the metal resistance and electromigration performance have been carefully balanced to meet the high reliability requirements while maintaining the lowest possible resistance. A new patterning scheme has been used to limit any patterning damage to the lower-k ILD and address the increasingly difficult problem of via-to-metal shorting at these very tight pitches. The interconnect stack has a thick Metal-9 layer to provide a low resistance path for the power and I/O routing that has been carefully scaled to maintain a low resistance. The combined interconnect stack provides high density, performance, and reliability, and supports a Pb-free 32nm process.


title: End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design

abstract: We develop an end-to-end training algorithm for whole-image breast cancer diagnosis based on mammograms. It requires lesion annotations only at the first stage of training. After that, a whole image classifier can be trained using only image level labels. This greatly reduced the reliance on lesion annotations. Our approach is implemented using an all convolutional design that is simple yet provides superior performance in comparison with the previous methods. On DDSM, our best single-model achieves a per-image AUC score of 0.88 and three-model averaging increases the score to 0.91. On INbreast, our best single-model achieves a per-image AUC score of 0.96. Using DDSM as benchmark, our models compare favorably with the current state-of-the-art. We also demonstrate that a whole image model trained on DDSM can be easily transferred to INbreast without using its lesion annotations and using only a small amount of training data. Code availability: this https URL


title: Information Complexity in Bandit Subset Selection

abstract: We consider the problem of eciently exploring the arms of a stochastic bandit to identify the best subset of a specied size. Under the PAC and the xed-budget formulations, we derive improved bounds by using KL-divergence-based condence intervals. Whereas the application of a similar idea in the regret setting has yielded bounds in terms of the KL-divergence between the arms, our bounds in the pure-exploration setting involve the \Cherno information" between the arms. In addition to introducing this novel quantity to the bandits literature, we contribute a comparison between strategies based on uniform and adaptive sampling for pure-exploration problems, nding evidence in favor of the latter.


A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification


title: Automatic tracking and counting of moving objects

abstract: This work presents the conception and development of a software system to detect, track and count automatically moving objects in videos. The aim of the system is the automatically counting of moving turtles on a beach. To achieve a high recognition rate of the moving objects we consider three segmentation strategies (SubstractionGrayscale, SubstractionBinarization, Substraction Canny), two object identification methods (Grayscale Connected, FelzenwalbHuttenlocher) and two object recognition methods (Nearest Object Distance, Certain Recognition Matching. In the preprocessing step of our system we select parameters like the interesting area and the object size. We use the segmentation process for the recognition of potential objects. After the identification of objects as turtles we track their movements. In the last step we count the turtles. In order to have a representative test number of moving objects we use videos with 1661 moving cars. The best results of 98.98 percent we reached with the combination Canny Edge Detection, Grayscale Connected, and Certain Region Matching Strategy.


title: Generic Object Detection with Dense Neural Patterns and Regionlets

abstract: This paper addresses the challenge of establishing a bridge between deep convolutional neural networks and conventional object detection frameworks for accurate and efficient generic object detection. We introduce Dense Neural Patterns , short for DNPs, which are dense local features derived from discriminatively trained deep convolutional neural networks. DNPs can be easily plugged into conventional detection frameworks in the same way as other dense local features(like HOG or LBP). The effectiveness of the proposed approach is demonstrated with the Regionlets object detection framework. It achieved 46.1% mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL VOC 2010 dataset, which dramatically improves the original Regionlets approach without DNPs. It is the first approach efficiently applying dee p convolutional features for conventional object detection models.


Fitspiration and thinspiration: a comparison across three social networking sites


title: Graph-Variate Signal Analysis: Framework and Applications

abstract: Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis. Essentially, graph-variate signal analysis leverages graphs of reliable connectivity information to filter instantaneous bivariate functions of the multivariate signal. This opens up a new and robust approach to analyze joint signal and network dynamics at sample resolution. When graph connectivity is estimated from the multivariate signal itself, the appropriate consideration of instantaneous graph signal functions allows for a novel dynamic connectivity measure— graph-variate dynamic (GVD) connectivity —which is robust to spurious short-term dependencies. For this, we present appropriate functions for correlation, coherence and the phase-lag index. We show that our approach can determine signals with a single correlated couple against wholly uncorrelated signals up to 128 nodes in size (1 out of 8128 weighted edges). GVD connectivity is also shown to be more robust than other GSP approaches at detecting a randomly traveling spheroid on a three-dimensional grid and standard dynamic connectivity in determining differences in EEG resting-state and task-related activity. We also demonstrate its use in revealing hidden depth correlations from geophysical gamma ray data. We expect that the methods and framework presented will provide new approaches to data analysis in a variety of applied settings.


title: On the assessment of situational intrinsic and extrinsic motivation: The Situational Motivation Scale (SIMS).

abstract: The purpose of the present research was to develop and validate a situational (or state) measure of motivation, the Situational Motivation Scale (SIMS). The SIMS is designed to assess the constructs of intrinsic motivation, identified regulation, external regulation, and amotivation (E. L. Deci & R. M. Ryan, 1985, 1991) in field and laboratory settings. Five studies were conducted to develop and validate the SIMS. Overall, results show that the SIMS is composed of 4 internally consistent factors. The construct validity of the scale is also supported by correlations with other constructs as postulated by current theories. Moreover, the SIMS is responsive to experimental induction as evidenced by data gathered through a laboratory study. In sum, the SIMS represents a brief and versatile self-report measure of situational intrinsic motivation, identified regulation, external regulation, and amotivation.


title: Fixed point optimization of deep convolutional neural networks for object recognition

abstract: Deep convolutional neural networks have shown promising results in image and speech recognition applications. The learning capability of the network improves with increasing depth and size of each layer. However this capability comes at the cost of increased computational complexity. Thus reduction in hardware complexity and faster classification are highly desired. This work proposes an optimization method for fixed point deep convolutional neural networks. The parameters of a pre-trained high precision network are first directly quantized using L2 error minimization. We quantize each layer one by one, while other layers keep computation with high precision, to know the layer-wise sensitivity on word-length reduction. Then the network is retrained with quantized weights. Two examples on object recognition, MNIST and CIFAR-10, are presented. Our results indicate that quantization induces sparsity in the network which reduces the effective number of network parameters and improves generalization. This work reduces the required memory storage by a factor of 1/10 and achieves better classification results than the high precision networks.


title: CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs

abstract: Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication. This paper presents CMSIS-NN, efficient kernels developed to maximize the performance and minimize the memory footprint of neural network (NN) applications on Arm Cortex-M processors targeted for intelligent IoT edge devices. Neural network inference based on CMSIS-NN kernels achieves 4.6X improvement in runtime/throughput and 4.9X improvement in energy efficiency.


title: pypet: A Python Toolkit for Data Management of Parameter Explorations

abstract: pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.


title: Towards New Human-Humanoid Communication: Listening During Speaking by Using Ultrasonic Directional Speaker

abstract: This paper presents a new human-humanoid communication system by using a directional speaker. The directional speaker produces directional sound beams by using intermodulation of ultrasonic sound beams and non linearity in air. This technology solves problems and brings new ways of human-humanoid communication as follows: 1) A humanoid can recognize human speeches during speaking because a microphone installed in the humanoid does not capture self voices played by the directional speaker. 2) A humanoid can speak to a specific person as if it whispers. The directional speaker is installed at the position of the mouth of the humanoid. Preliminary experiments show the efficiency of the directional speaker to realize the above two functions.


title: Learning a Linear Influence Model from Transient Opinion Dynamics

abstract: Many social networks are characterized by actors (nodes) holding quantitative opinions about movies, songs, sports, people, colleges, politicians, and so on. These opinions are influenced by network neighbors. Many models have been proposed for such opinion dynamics, but they have some limitations. Most consider the strength of edge influence as fixed. Some model a discrete decision or action on part of each actor, and an edge as causing an ``infection'' (that is often permanent or self-resolving). Others model edge influence as a stochastic matrix to reuse the mathematics of eigensystems. Actors' opinions are usually observed globally and synchronously. Analysis usually skirts transient effects and focuses on steady-state behavior. There is very little direct experimental validation of estimated influence models. Here we initiate an investigation into new models that seek to remove these limitations. Our main goal is to estimate, not assume, edge influence strengths from an observed series of opinion values at nodes. We adopt a linear (but not stochastic) influence model. We make no assumptions about system stability or convergence. Further, actors' opinions may be observed in an asynchronous and incomplete fashion, after missing several time steps when an actor changed its opinion based on neighbors' influence. We present novel algorithms to estimate edge influence strengths while tackling these aggressively realistic assumptions. Experiments with Reddit, Twitter, and three social games we conducted on volunteers establish the promise of our algorithms. Our opinion estimation errors are dramatically smaller than strong baselines like the DeGroot, flocking, voter, and biased voter models. Our experiments also lend qualitative insights into asynchronous opinion updates and aggregation.


title: Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes

abstract: This paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. Six similarity measures are presented and the performance is evaluated by Correct Clustering Rate (CCR) and Time Cost (TC). The experimental results demonstrate that in outdoor surveillance scenes, the simpler PCA+Euclidean distance is competent for the clustering task even in case of noise, as more complex similarity measures such as DTW, LCSS are not efficient due to their high computational cost .


title: Water pipe leak detection using electromagnetic wave sensor for the water industry

abstract: This project concerns the use of an electromagnetic sensor for the detection of leaks/cracks in water pipes. As old metal pipes corrode, they start to become brittle, resulting in the potential for cracks to appear in the pipes. In addition corrosion can build up resulting in a restricted flow of water in the pipe. Using an electromagnetic (EM) sensor to monitor the signal reflected from the pipes in real time, provides the necessary information to determine where a leak in the pipe has occurred. Analysis of the reflected signal can provide the operator with information about the condition of the leak within the pipe. This work describes how the system was designed, and also its construction at a scale suitable for insertion into a 100mm diameter water pipe.


title: "There's a Pain App for That": Review of Patient-targeted Smartphone Applications for Pain Management.

abstract: OBJECTIVES ::: There are a growing number of pain self-management applications (apps) available for users to download on personal smartphones. The purpose of this study was to critically appraise the content and self-management functionality of currently available pain apps. ::: ::: ::: METHODS ::: An electronic search was conducted between May and June 2014 of the official stores for the 4 major operating systems. Two authors independently identified patient-focused apps with a stated goal of pain management. Discrepancies regarding selection were resolved through discussion with a third party. Metadata from all included apps were abstracted into a standard form. The content and functionality of each app as it pertained to pain self-management was rated. ::: ::: ::: RESULTS ::: A total of 279 apps met the inclusion criteria. Pain self-care skill support was the most common self-management function (77.4%). Apps also purported providing patients with the ability to engage in pain education (45.9%), self-monitoring (19%), social support (3.6%), and goal-setting (0.72%). No apps were comprehensive in terms of pain self-management, with the majority of apps including only a single self-management function (58.5%). In addition, only 8.2% of apps included a health care professional in their development, not a single app provided a theoretical rationale, and only 1 app underwent scientific evaluation. ::: ::: ::: DISCUSSION ::: Currently available pain self-management apps for patients are simplistic, lack the involvement of health care professionals in their development, and have not been rigorously tested for effectiveness on pain-related health outcomes. There is a need to develop and test theoretically and evidence-based apps to better support patients with accessible pain care self-management.


Grounded Learning of Color Semantics with Autoencoders


title: MAVE: Multilevel wrApper Verification systEm

abstract: Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.


abstract: A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains Zoubin Ghahramani University of Cambridge United Kingdom zoubin@eng.cam.ac.uk Finale Doshi-Velez Massachusetts Institute of Technology Cambridge, Massachusetts finale@alum.mit.edu Abstract It is commonly stated that reinforcement learning (RL) algo- rithms require more samples to learn than humans. In this work, we investigate this claim using two standard problems from the RL literature. We compare the performance of human subjects to RL techniques. We find that context—the meaning- fulness of the observations—plays a significant role in the rate of human RL. Moreover, without contextual information, hu- mans often fare much worse than classic algorithms. Compar- ing the detailed responses of humans and RL algorithms, we also find that humans appear to employ rather different strate- gies from standard algorithms, even in cases where they had indistinguishable performance to them. Keywords: sequential decision-making; reinforcement learn- ing; computational models; machine learning The ability of humans to make sequential decisions under uncertainty has been widely studied in psychology and neu- roscience. The field of reinforcement learning (RL) studies the theoretical formulation and algorithmic implementation of artificial agents that make sequential decisions to maxi- mize their expected reward (Sutton & Barto, 1998). While RL algorithms often provide theoretical guarantees on the qual- ity of the agent’s long-term behaviour, the common lore in the RL community (Singh, 2009; Peters, Bagnell, & Schaal, 2006; Morimoto & Doya, 2005) is these approaches are painfully slow, requiring thousands of trials to learn to act in, what seem to humans, relatively simple domains. While RL has been applied as a theoretical tool for un- derstanding human decision making behaviour (Samejima & Doya, 2007; Daw, O’Doherty, Dayan, Seymour, & Dolan, 2006; Kakade & Dayan, 2002; Daw, Courville, & Tourtezky, 2006; Yoshida & Ishii, 2003; Acu˜na & Schrater, 2008; Dayan & Daw, 2008), the supposed “slowness” of RL methods has not been experimentally tested against human learning per- formance. Are these RL algorithms actually slower to learn than humans? To what extent is this lore biased by the fact that humans bring structural knowledge from previous expe- riences to new problems? For example, when entering a new building, a human will probably assume that he cannot walk through its walls, whereas RL problems would typically have to relearn this fact for each new location. Humans also tend to assume near-deterministic worlds, whereas RL algorithms are often initialized as believing all possible outcomes are equally likely. In this paper, we focus on the approaches humans take on problems where aspects of the environment cannot be fully- observed (formally partially observable Markov decision pro- cesses (POMDPs)). POMDPs offer a more realistic scenario for decision making under uncertainty than the simpler (fully- observable) Markov decision processes, since they assume that the state of the world is known, but inferred from noisy observations. In this setting, we show that, surprisingly, when put in an identical setup on standard decision making prob- lems, RL methods often learn faster and achieve better solu- tions than humans. Even more surprisingly, while human per- formance does improve when subjects are given contextual information about the problem, their average performance of- ten still does not match RL methods. Our work has inter- esting implications for our understanding of both human and machine decision making. Without contextual information, humans may require more experience than RL algorithms to perform well even on simple problems. However, making use of context is one of the important open problems for machine learning. Experiment We tested two hypotheses: first, that human subjects would perform significantly better if given contextual observations, and second, that human subjects would outperform RL algo- rithms. Performance was evaluated as the sum of rewards ob- tained during the last tenth of a learning trial. We also exam- ined which RL algorithms’ behaviour most closely matched human behaviour. Task Descriptions The tasks consisted of two common problems in the RL lit- erature, both formulated as POMDPs. Playing the role of the agent, the human subject—who had no initial knowledge about the structure of the problem—selected actions to take. The problem returned an observation, displayed on a com- puter screen, which depended on the underlying state of the environment, and an immediate reward. The subject’s goal was to maximize their cumulative rewards. Each task could be presented to the subject in two differ- ent versions. In the with-context version C+, the domain’s observations had meaning in the context of the task. In the context-free version C−, observations had no meaning; the C− version of the problem was meant to simulate what a RL algorithm might “see,” as a computer system cannot attribute meaning or significance to particular observations. In the first problem, the tigerworld task (Kaelbling, Littman, & Cassandra, 1995), players were confronted with two doors (see figure ?? for an illustration). Behind one door was a tiger (reward = −100); behind the other was a prize (reward = +10). At every iteration, players had three options:


title: A Differential Game Approach to Multi-agent Collision Avoidance

abstract: A multi-agent system consisting of $N$ agents is considered. The problem of steering each agent from its initial position to a desired goal while avoiding collisions with obstacles and other agents is studied. This problem, referred to as the multi-agent collision avoidance problem , is formulated as a differential game. Dynamic feedback strategies that approximate the feedback Nash equilibrium solutions of the differential game are constructed and it is shown that, provided certain assumptions are satisfied, these guarantee that the agents reach their targets while avoiding collisions.


title: 5G Radio Access Network Architecture: Design Guidelines and Key Considerations

abstract: While there is clarity on the wide range of applications that are to be supported by 5G cellular communications, and standardization of 5G has now started in 3GPP, there is no conclusion yet on the detailed design of the overall 5G RAN. This article provides a comprehensive overview of the 5G RAN design guidelines, key design considerations, and functional innovations as identified and developed by key players in the field.1 It depicts the air interface landscape that is envisioned for 5G, and elaborates on how this will likely be harmonized and integrated into an overall 5G RAN, in the form of concrete control and user plane design considerations and architectural enablers for network slicing, supporting independent business-driven logical networks on a common infrastructure. The article also explains key functional design considerations for the 5G RAN, highlighting the difference to legacy systems such as LTE-A and the implications of the overall RAN design.


title: Graph-Based Classification of Omnidirectional Images

abstract: Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods are designed for planar images that have different geometrical properties than omnidirectional ones. In this paper we study image classification task by taking into account the specific geometry of omnidirectional cameras with graph-based representations. In particular, we extend deep learning architectures to data on graphs; we propose a principled way of graph construction such that convolutional filters respond similarly for the same pattern on different positions of the image regardless of lens distortions. Our experiments show that the proposed method outperforms current techniques for the omnidirectional image classification problem.


title: Machine Learning for Networking: Workflow, Advances and Opportunities

abstract: Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks.


title: Millimeter-wave CMOS digital controlled artificial dielectric differential mode transmission lines for reconfigurable ICs

abstract: Digital control of the effective dielectric constant of a differential mode transmission line is shown up to 60GHz in standard CMOS technology. The effective dielectric constant is shown to increase from 5 to over 50 for the fixed artificial dielectric case. The digital controlled artificial dielectric transmission line (DiCAD) uses MOS switches to dynamically control the phase. DiCAD achieves 50% of the physically available tuning range with effective dielectric constants varying between 7 and 28. Measured results favorably agree with full-wave electromagnetic simulations.


title: Do hormones influence women's sex? Sexual activity over the menstrual cycle.

abstract: INTRODUCTION ::: In mammals, the effects of ovarian steroids influence sexual behavior. In humans, there are a few studies that take into account if ovulation occurs. ::: ::: ::: AIM ::: The study aims to investigate if a woman's sexuality changes during the week and over the menstrual cycle, and if so, in what way. ::: ::: ::: METHODS ::: This is a prospective cohort study; 1,957 heterosexual women were involved over the period January 2004-December 2011. Two subgroups were taken into consideration, women having a partner and singles. ::: ::: ::: MAIN OUTCOME MEASURE ::: Sexual interview and the Female Sexual Function Index and Female Sexual Distress Scale questionnaires were used to exclude women with sexual dysfunction. Women with ovulation confirmed by sonography were enrolled. Women were given diary cards on which to report their daily sexual activity. Serum concentrations of estradiol, progesterone, total testosterone, sex hormone binding globulin, and free androgen index were measured during the follicular, periovular, and luteal phase of the menstrual cycle. ::: ::: ::: RESULTS ::: One thousand one hundred eighty women (age range 18-40 years) were included in the analyses. Of them, 925 had a heterosexual relationship and 255 were single. Women with a partner had more sexual activity during the weekend, while the singles had a constant sexual activity over the week. The sexual activity of singles was higher during the ovulatory phase and lower during menses than that of the women with a partner. A linear correlation between sexual activity and androgenic hormonal profile during the menstrual cycle in women with and without a partner was observed. ::: ::: ::: CONCLUSIONS ::: Ovarian steroids modulate a woman's sexual activity. This aspect was more evident in singles than in women having a partner, in which a variety of nonhormonal factors can have a role.