Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase and timing recovery based on frame synchronization and fractional-spaced frequency domain equalization for CPM

In this paper, a new synchronization receiver architecture for continuous phase modulation (CPM) and relevant carrier phase and symbol timing recovery methods based on frame synchronization and fractional-spaced frequency domain equalization (FDE) are proposed. With frame synchronization and fractional-spaced FDE, frequency selective fading can be equalized for the up-sampled CPM signal. At the same time, carrier phase synchronization and symbol timing recovery can be obtained by canceling the effects of carrier phase and timing error of frame synchronization. Equalized signal in time domain can be demodulated and detected with CPM receivers designed for additive white Gaussian noise (AWGN) channel. Probability distributions of the timing error of frame synchronization algorithm proposed by Minn and bit error rate (BER) performances of ARTM CPM, FQPSK and SOQPSK-MIL modulations are simulated by computer simulations, and simulation results show that the proposed carrier phase and symbol timing recovery methods have better synchronization and equalization performance.
The effects of ethanol on memory and neuroplasticity in a vertebrate and an invertebrate model of learning

Binge drinking is characterised by cycles of ethanol intoxication and withdrawal and is thought to be highly deleterious for the normal functioning of the nervous system. The behavioural and neurophysiological consequences of rapid escalation of blood alcohol concentration and subsequent withdrawal, and their effects on learning and memory and underlying neural circuitry can be studied in suitable animal models. Here, spatial and instrumental learning as well as hippocampal LTP were assessed in C57BL/6J mice for the effects of adolescent intermittent ethanol (AIE) and other ethanol treatments. AIE treatment did not impair spatial or non-spatial memory when tested in adulthood. However, if mice were trained whilst intoxicated during AIE treatment, spatial memory was impaired. Post-training injections of ethanol impaired performance in operant conditioning. A rapid rise and fall in ethanol concentration, prior to stimulation, blocked LTP induction in drug naive hippocampal slices; an effect that was not seen if the ethanol concentration was gradually increased and decreased. Moreover, AIE treatment caused an NMDA receptor-dependent transient increase in hippocampal LTP. The second part of this study used a novel molluscan model Lymnaea stagnalis and demonstrated that high concentrations of ethanol blocked acquisition and retrieval of an associative memory. However, if acquisition occurred in the presence of ethanol then memory could also be retrieved under ethanol, demonstrating ethanol state dependency. By utilising the cerebral giant cells, a modulatory neuron type with known involvement in memory formation, it was found that ethanol reduced the tonic firing frequency as well as the peak-to-trough and half-width parameters of individual action potentials. The development of in vivo and in vitro ethanol treatment and test protocols, and the findings based on their use, open up new avenues for future systematic investigations on ethanol’s effects on behaviour and underlying neural circuitry in both vertebrate and invertebrate model systems.
Automatic scoring of the intelligibility in patients with cancer of the oral cavity

After surgical treatment of cancer of the oral cavity patients often suffer from functional restrictions such as speech disorders. In this paper we present a novel approach to assess the outcome of the treatment w.r. t. the intelligibility of the patient using the result of an automatic speech recognition system. The word recognition rate was taken as intelligibility score. Compared to four speech experts this method yields results that are as good as the best speech expert compared to the other experts. The correlation between our system and the mean opinion of the experts is . 92. Furthermore we show that our system has better performance than the average expert and is more reliable. Index Terms: Speech intelligibility, Speech processing, Biomedical acoustics, Acoustic applications
Hidden Markov models for time series of counts with excess zeros

Integer-valued time series are often modeled with Markov models or hidden Markov models (HMM). However, when the series represents count data it is often subject to excess zeros. In this case, usual distributions such as binomial or Poisson are unable to estimate the zero mass correctly. In order to overcome this issue, we introduce zero-inflated distributions in the hidden Markov model. The empirical results on simulated and real data show good convergence properties, while excess zeros are better estimated than with classical HMM.
The Graph Analysis Toolbox: Image Processing on Arbitrary Graphs

We present a theoretical and computational framework for the analysis of data associated with the node set of an arbitrary graph. The algorithms described here are collected in a MATLAB software package named the Graph Analysis Toolbox. Our purpose is to describe the functionality of the Graph Analysis Toolbox and the associated theoretical framework in the context of biologically inspired, space-variant computer vision.
Skill parametrization approaches and skill architecture for human-robot interaction

There is an ongoing shift in industries from mass production to low-batch-production with highly individualized goods. This increases the programming effort required for the producing machines and robots, which is currently carried out by robot experts. For keeping the production economical, new programming approaches are required, allowing shop-floor workers to instruct robots. One approach is to develop robotic skills, which are pre-programmed software modules that only need to be parametrized by the shop-floor user. In this paper, a new software architecture for robot skills is presented, which aims at robustness and human-robot interaction. In addition, four basic demands on the skill parametrization are described that fasten up the process and increase intuitiveness for the user. We give several examples and implement a screwing skill and a pick & place skill, which are demonstrated in two case studies.
Effect of Extracellular Matrix (ECM) Protein Micropatterns on the Behavior of Human Neuroblastoma Cells

Follow this and additional works at: http://digitalcommons.unl. edu/engmechdiss Part of the Bioelectrical and Neuroengineering Commons, Biological Engineering Commons, Biomaterials Commons, Biomechanical Engineering Commons, Biomedical Devices and Instrumentation Commons, Molecular, Cellular, and Tissue Engineering Commons, Nanoscience and Nanotechnology Commons, and the Polymer and Organic Materials Commons
Feature Selection and Improving Classification Performance for Malware Detection

After analyzing the advance of technology, it is clear that use of the Internet, computers, smart phones and tablets has become ubiquitous and therefore, the creation and proliferation of cyber threats and attacks has grown exponentially. Consequently, Anti-Virus companies and researchers have developed new approaches for dealing with discovering and classifying malware. Among these, machine learning and Big Data technologies have been used for feature extraction, detection, and clustering of cyber threats. In this paper, we created and analyzed a dataset of malware and clean files (goodware) from the static and dynamic features provided by the online framework VirusTotal. The purpose is to select the smallest number of features that keep the classification accuracy as high as possible given that the training execution time increase in polynomial time with respect to the number of features. In this research, we found that "9" features are enough to distinguish malware from "goodware" files within an accuracy of 99.60%. Selecting the most representative features for malware detection relies on the possibility of creating an embedded program that monitors the processes executed by the OS looking for the characteristics that match malware behavior. Thus, feature selection was made taking the most important features that keep the accuracy high and allows the creation of monitoring malware detection programs with a low overhead cost. In addition, classification algorithms such as Random Forest (RF), Support Vector Machine (SVM) and Neural Networks (NN) were used in a novel combination that not only showed an increase in accuracy, but also in the training speed from hours to just minutes.
Generation of Cre transgenic mice with postnatal RPE-specific ocular expression.

PURPOSE To generate and characterize a constitutively active, RPE-specific, cre-expressing transgenic mouse line. This line can be used to create RPE-specific knockouts by crossing with mice harboring loxP-flanked (floxed) genes. METHODS A transgene construct was assembled with the BEST1 promoter driving cre expression. Transgenic mice were generated on a C57BL/6 background. Cre expression was assessed by immunofluorescence and Western blot analysis. Cre enzymatic activity was tested by crossing to three lines with floxed DNA regions and detecting deletion of the intervening sequences or through histochemical detection of lacZ activity. Potential cre-mediated toxicity was assessed by retinal histology up to 24 months of age and by electroretinography. RESULTS The BEST1-cre line with expression in the highest percentage of RPE cells displayed a patchy mosaic expression pattern, with 50% to 90% of RPE cells expressing cre. In mice outcrossed to a mixed B6/129 background, expression was consistently found in 90% of RPE cells. Within the eye, only the RPE cells were immunoreactive with an anti-cre antibody. Maximum cre expression quantified by Western blot analysis occurred at P28. Crosses with three lines containing floxed sequences revealed RPE-specific cre activity in the eye and extraocular expression limited to the testes. Histology and electroretinography showed no cre-mediated RPE toxicity. CONCLUSIONS This BEST1-cre transgenic line enables generation of RPE-specific knockout mice. The mosaic expression pattern provides an internal control; the non-cre-expressing RPE cells continue to express the floxed genes. These mice should facilitate study of the multifunctional RPE and the generation of mouse models of human retinal disease.
DAMIA - A Data Mashup Fabric for Intranet Applications

Damia is a lightweight enterprise data integration service where line of business users can create and catalog high value data feeds for consumption by situational applications. Damia is inspired by the Web 2.0 mashup phenomenon. It consists of (1) a browser-based user-interface that allows for the specification of data mashups as data flow graphs using a set of operators, (2) a server with an execution engine, as well as (3) APIs for searching, debugging, executing and managing mashups. Damia offers a framework and functionality for dynamic entity resolution, streaming and other higher value features particularly important in the enterprise domain. Damia is currently in perpetual beta in the IBM Intranet. In this demonstration, we showcase the creation and execution of several enterprise data mashups, thereby illustrating the architecture and features of the overall Damia system.
Research of three-dimensional indoor positioning based on RSSI

This paper proposed a newly designed model to satisfy the growing demand of three-dimensional indoor positioning services. The existing different positioning services cannot meet the demand in our daily life, and the increasingly need of three-dimensional positioning services has attracted more attention. In this paper, we implement our analysis and research via studying the evaluation results of different indoor positioning models based on RSSI. Experimental results demonstrated that our proposed method of reducing the influences of environmental factors can greatly improve the positioning precise and reduce the positioning error.
LiverCancerMarkerRIF: a liver cancer biomarker interactive curation system combining text mining and expert annotations

Biomarkers are biomolecules in the human body that can indicate disease states and abnormal biological processes. Biomarkers are often used during clinical trials to identify patients with cancers. Although biomedical research related to biomarkers has increased over the years and substantial effort has been expended to obtain results in these studies, the specific results obtained often contain ambiguities, and the results might contradict each other. Therefore, the information gathered from these studies must be appropriately integrated and organized to facilitate experimentation on biomarkers. In this study, we used liver cancer as the target and developed a text-mining–based curation system named LiverCancerMarkerRIF, which allows users to retrieve biomarker-related narrations and curators to curate supporting evidence on liver cancer biomarkers directly while browsing PubMed. In contrast to most of the other curation tools that require curators to navigate away from PubMed and accommodate distinct user interfaces or Web sites to complete the curation process, our system provides a user-friendly method for accessing text-mining–aided information and a concise interface to assist curators while they remain at the PubMed Web site. Biomedical text-mining techniques are applied to automatically recognize biomedical concepts such as genes, microRNA, diseases and investigative technologies, which can be used to evaluate the potential of a certain gene as a biomarker. Through the participation in the BioCreative IV user-interactive task, we examined the feasibility of using this novel type of augmented browsing-based curation method, and collaborated with curators to curate biomarker evidential sentences related to liver cancer. The positive feedback received from curators indicates that the proposed method can be effectively used for curation. A publicly available online database containing all the aforementioned information has been constructed at http://btm.tmu. edu.tw/livercancermarkerrif in an attempt to facilitate biomarker-related studies. Database URL: http://btm.tmu. edu.tw/LiverCancerMarkerRIF/
Multilinguality in Electronic Commerce - Research Issues

We outline how language technology could be used to support multilinguality in electronic commerce. The growth in international trade and the increasing use of EDI in trade transactions brings new technical challenges also to language technology. Nowadays, trade transactions fre- quently cross language borders. While ever larger number of users get con- nected to electronic trading processes, it is no longer possible to presume that everyone knows e.g. English language. The need for native language support is quickly growing in electronic commerce. In this paper we first present a model of electronic bargaining. After that we consider what multilinguality support means to software construc- tion. OsiCon 3 EDIFACT software is used as an example. In the end we examine how language technology could be used in the various phases of the reference model to enable multilinguality.
Noise in Naming Games, partial synchronization and community detection in social networks

The Naming Games (NG) are agent-based models for agreement dynamics, peer pressure and herding in social networks, and protocol selection in autonomous ad-hoc sensor networks. By introducing a small noise term to the NG, the resulting Markov Chain model called Noisy Naming Games (NNG) are ergodic, in which all partial consensus states are recurrent. By using Gibbs-Markov equivalence we show how to get the NNG's stationary distribution in terms of the local specification of a related Markov Random Field (MRF). By ordering the partially-synchronized states according to their Gibbs energy, taken here to be a good measure of social tension, this method offers an enhanced method for community-detection in social interaction data. We show how the lowest Gibbs energy multi-name states separate and display the hidden community structures within a social network.
Driver Classification Based on Driving Behaviors

In this paper we develop a model capable of classifying drivers from their driving behaviors sensed by only low level sensors. The sensing platform consists of data available from the diagnostic outlet (OBD) of the car and smartphone sensors. We develop a window based support vector machine model to classify drivers. We test our model with two datasets collected under both controlled and naturalistic conditions. Furthermore, we evaluate the model using each sensor source (car and phone) independently and combining both the sensors. The average classification accuracies attained with data collected from three different cars shared between couples in a naturalistic environment were 75.83%, 85.83% and 86.67% using only phone sensors, only cars sensors and combined car and phone sensors respectively.
3DCoB: A new design approach for Monolithic 3D Integrated circuits

3D Monolithic Integration (3DMI) technology provides very high dense vertical interconnects with low parasitics. Previous 3DMI design approaches provide either cell-on-cell or transistor-on-transistor integration. In this paper we present 3D Cell-on-Buffer (3DCoB) as a novel design approach for 3DMI. Our approach provides a fully compatible sign-off physical implementation flow with the conventional 2D tools. We implement our approach on a set of benchmark circuits using 28nm-FDSOI technology. The sign-off performance results show 35% improvement compared to the same 2D design.
Proton isotropy boundaries as measured on mid- and low-altitude satellites

Abstract. Polar CAMMICE MICS proton pitch angle distributions with energies of 31-80 keV were analyzed to determine the locations where anisotropic pitch angle distributions (perpendicular flux dominating) change to isotropic distributions. We compared the positions of these mid-altitude isotropic distribution boundaries (IDB) for different activity conditions with low-altitude isotropic boundaries (IB) observed by NOAA 12. Although the obtained statistical properties of IDBs were quite similar to those of IBs, a small difference in latitudes, most pronounced on the nightside and dayside, was found. We selected several events during which simultaneous observations in the same local time sector were available from Polar at mid-altitudes, and NOAA or DMSP at low-altitudes. Magnetic field mapping using the Tsyganenko T01 model with the observed solar wind input parameters showed that the low- and mid-altitude isotropization boundaries were closely located, which leads us to suggest that the Polar IDB and low-altitude IBs are related. Furthermore, we introduced a procedure to control the difference between the observed and model magnetic field to reduce the large scatter in the mapping. We showed that the isotropic distribution boundary (IDB) lies in the region where R c /ρ~6, that is at the boundary of the region where the non-adiabatic pitch angle scattering is strong enough. We therefore conclude that the scattering in the large field line curvature regions in the nightside current sheet is the main mechanism producing isotropization for the main portion of proton population in the tail current sheet. This mechanism controls the observed positions of both IB and IDB boundaries. Thus, this tail region can be probed, in its turn, with observations of these isotropy boundaries. Keywords. Magnetospheric physics (Energetic particles, Precipitating; Magnetospheric configuration and dynamics; Magnetotail)
Forecasting the Next Shot Location in Tennis Using Fine-Grained Spatiotemporal Tracking Data

In professional sport, an enormous amount of fine-grain performance data can be generated at near millisecond intervals in the form of vision-based tracking data. One of the first sports to embrace this technology has been tennis, where Hawk-Eye technology has been used to both aid umpiring decisions, and to visualize shot trajectories for broadcast purposes. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this data for player performance analysis and prediction has been lacking. In this research, we use ball and player tracking data from “Hawk-Eye” to discover unique player styles and predict within-point events. We move beyond current analysis that only incorporates coarse match statistics (i.e., serves, winners, number of shots, and volleys) and use spatial and temporal information which better characterizes the tactics and tendencies of each player. Using a probabilistic graphical model, we are able to model player behaviors which enables us to: 1) find the factors such as location and speed of the incoming shot which are most conducive to a player hitting a winner (i.e., “sweet-spot”) or cause an error, and 2) do “live in-point” prediction - based on the shots being played during a rally we estimate the probability of the outcome (e.g., winner, continuation, or error) and the location of the next shot. As player behavior depends on the opponent, we use model adaptation to enhance our prediction. We show the utility of our approach by analyzing the play of Djokovic, Nadal, and Federer at the 2012 Australian Tennis Open.
A partitioning technique for improving the performance of PageRank on Hadoop

There are a lot of research results in large scale graph analysis on Hadoop. The performance of the graph analysis based on Hadoop is impacted by data partitioning. The effectiveness of data partitioning depends on how the data partitioning maintains data locality in each node of cluster, and this would be different from the problems faced with. One way of data partitioning known to be effective is partitioning data by domains. For instance, this technique could be very useful in partitioning data by areas analyzing web graphs. But this kind of improvement from the data partitioning is limited to specific problems. In this paper, we propose a data partitioning technique based on semi-clustering for analyzing web graphs with PageRank algorithm on Hadoop. With experiment, PageRank computation with our partitioning technique improves the performance, as the number of iterations increases. This method can be very effective in the case of large scale graph processing.
Reducing the effort of bug report triage: Recommenders for development-oriented decisions

A key collaborative hub for many software development projects is the bug report repository. Although its use can improve the software development process in a number of ways, reports added to the repository need to be triaged. A triager determines if a report is meaningful. Meaningful reports are then organized for integration into the project's development process. To assist triagers with their work, this article presents a machine learning approach to create recommenders that assist with a variety of decisions aimed at streamlining the development process. The recommenders created with this approach are accurate; for instance, recommenders for which developer to assign a report that we have created using this approach have a precision between 70% and 98% over five open source projects. As the configuration of a recommender for a particular project can require substantial effort and be time consuming, we also present an approach to assist the configuration of such recommenders that significantly lowers the cost of putting a recommender in place for a project. We show that recommenders for which developer should fix a bug can be quickly configured with this approach and that the configured recommenders are within 15% precision of hand-tuned developer recommenders.