Suppression of patterning effects in digital communications by an adaptive constrained coding

We quantify the error statistics and patterning effects in a 5x 40 Gbit/s WDM RZ-DBPSK SMF/DCF fibre link using hybrid Raman/EDFA amplification. We propose an adaptive constrained coding for the suppression of errors due to patterning effects. It is established, that this coding technique can greatly reduce the bit error rate (BER) value even for large BER (BER > 101). The proposed approach can be used in the combination with the forward error correction schemes (FEC) to correct the errors even when real channel BER is outside the FEC workspace.

Nonholonomically Constrained Dynamics and Optimization of Rolling Isolation Systems

Rolling Isolation Systems provide a simple and effective means for protecting components from horizontal floor vibrations. In these systems a platform rolls on four steel balls which, in turn, rest within shallow bowls. The trajectories of the balls is uniquely determined by the horizontal and rotational velocity components of the rolling platform, and thus provides nonholonomic constraints. In general, the bowls are not parabolic, so the potential energy function of this system is not quadratic. This thesis presents the application of Gauss's Principle of Least Constraint to the modeling of rolling isolation platforms. The equations of motion are described in terms of a redundant set of constrained coordinates. Coordinate accelerations are uniquely determined at any point in time via Gauss's Principle by solving a linearly constrained quadratic minimization. In the absence of any modeled damping, the equations of motion conserve energy. This mathematical model is then used to find the bowl profile that minimizes response acceleration subject to displacement constraint.

Inheritance of the CENP-A chromatin domain is spatially and temporally constrained at human centromeres.

BACKGROUND: Chromatin containing the histone variant CENP-A (CEN chromatin) exists as an essential domain at every centromere and heritably marks the location of kinetochore assembly. The size of the CEN chromatin domain on alpha satellite DNA in humans has been shown to vary according to underlying array size. However, the average amount of CENP-A reported at human centromeres is largely consistent, implying the genomic extent of CENP-A chromatin domains more likely reflects variations in the number of CENP-A subdomains and/or the density of CENP-A nucleosomes within individual subdomains. Defining the organizational and spatial properties of CEN chromatin would provide insight into centromere inheritance via CENP-A loading in G1 and the dynamics of its distribution between mother and daughter strands during replication. RESULTS: Using a multi-color protein strategy to detect distinct pools of CENP-A over several cell cycles, we show that nascent CENP-A is equally distributed to sister centromeres. CENP-A distribution is independent of previous or subsequent cell cycles in that centromeres showing disproportionately distributed CENP-A in one cycle can equally divide CENP-A nucleosomes in the next cycle. Furthermore, we show using extended chromatin fibers that maintenance of the CENP-A chromatin domain is achieved by a cycle-specific oscillating pattern of new CENP-A nucleosomes next to existing CENP-A nucleosomes over multiple cell cycles. Finally, we demonstrate that the size of the CENP-A domain does not change throughout the cell cycle and is spatially fixed to a similar location within a given alpha satellite DNA array. CONCLUSIONS: We demonstrate that most human chromosomes share similar patterns of CENP-A loading and distribution and that centromere inheritance is achieved through specific placement of new CENP-A near existing CENP-A as assembly occurs each cell cycle. The loading pattern fixes the location and size of the CENP-A domain on individual chromosomes. These results suggest that spatial and temporal dynamics of CENP-A are important for maintaining centromere identity and genome stability.

Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.

Rise, fragmentation, infringement and fear: emerging urban issues in Chinese clustering in Italy

China is today facing rapid economic development and the long-term implications of China’s rise for European economy, society and culture, are constantly debated but still almost unknown. Moreover, only recently a new volume edited by Kunzmann has clearly pointed out a particular field of research like the EU spatial impact of China’s convergence in the global market. The aim of the present paper is to deal with the spatial issues related to the growing Chinese communities, especially in Italy, that are part of a more general and considerable transformation process of the traditional Chinese enclaves in EU cities: from recognizable “Chinatowns” to new hybrid urban formations where housing, retail, wholesale and even commodity production often tend to match. Key-Concepts like rise, fragmentation, infringement and fear are useful in analysing some of the more controversial socio-economic dynamics of Chinese clusters especially in a traditionally manufactured-based country like Italy, where it’s recognizable a unique paradox of a “double competition” from outside and from inside. This statement poses a serious threat to local economic systems in terms of sustainability and social cohesion, making it necessary to rethink the role and the nature of public action in facing new forms of marginality at urban and regional level.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Future fish distributions constrained by depth in warming seas

European continental shelf seas have experienced intense warming over the past 30 years1. In the North Sea, fish have been comprehensively monitored throughout this period and resulting data provide a unique record of changes in distribution and abundance in response to climate change2, 3. We use these data to demonstrate the remarkable power of generalized additive models (GAMs), trained on data earlier in the time series, to reliably predict trends in distribution and abundance in later years. Then, challenging process-based models that predict substantial and ongoing poleward shifts of cold-water species4, 5, we find that GAMs coupled with climate projections predict future distributions of demersal (bottom-dwelling) fish species over the next 50 years will be strongly constrained by availability of habitat of suitable depth. This will lead to pronounced changes in community structure, species interactions and commercial fisheries, unless individual acclimation or population-level evolutionary adaptations enable fish to tolerate warmer conditions or move to previously uninhabitable locations.

Future fish distributions constrained by depth in warming seas

European continental shelf seas have experienced intense warming over the past 30 years1. In the North Sea, fish have been comprehensively monitored throughout this period and resulting data provide a unique record of changes in distribution and abundance in response to climate change2, 3. We use these data to demonstrate the remarkable power of generalized additive models (GAMs), trained on data earlier in the time series, to reliably predict trends in distribution and abundance in later years. Then, challenging process-based models that predict substantial and ongoing poleward shifts of cold-water species4, 5, we find that GAMs coupled with climate projections predict future distributions of demersal (bottom-dwelling) fish species over the next 50 years will be strongly constrained by availability of habitat of suitable depth. This will lead to pronounced changes in community structure, species interactions and commercial fisheries, unless individual acclimation or population-level evolutionary adaptations enable fish to tolerate warmer conditions or move to previously uninhabitable locations.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

MixKMeans: Clustering Question-Answer Archives

Community-driven Question Answering (CQA) systems that crowdsource experiential information in the form of questions and answers and have accumulated valuable reusable knowledge. Clustering of QA datasets from CQA systems provides a means of organizing the content to ease tasks such as manual curation and tagging. In this paper, we present a clustering method that exploits the two-part question-answer structure in QA datasets to improve clustering quality. Our method, {\it MixKMeans}, composes question and answer space similarities in a way that the space on which the match is higher is allowed to dominate. This construction is motivated by our observation that semantic similarity between question-answer data (QAs) could get localized in either space. We empirically evaluate our method on a variety of real-world labeled datasets. Our results indicate that our method significantly outperforms state-of-the-art clustering methods for the task of clustering question-answer archives.

Energy efficiency optimization in hardware-constrained large-scale MIMO systems

Large-scale multiple-input multiple-output (MIMO) communication systems can bring substantial improvement in spectral efficiency and/or energy efficiency, due to the excessive degrees-of-freedom and huge array gain. However, large-scale MIMO is expected to deploy lower-cost radio frequency (RF) components, which are particularly prone to hardware impairments. Unfortunately, compensation schemes are not able to remove the impact of hardware impairments completely, such that a certain amount of residual impairments always exists. In this paper, we investigate the impact of residual transmit RF impairments (RTRI) on the spectral and energy efficiency of training-based point-to-point large-scale MIMO systems, and seek to determine the optimal training length and number of antennas which maximize the energy efficiency. We derive deterministic equivalents of the signal-to-noise-and-interference ratio (SINR) with zero-forcing (ZF) receivers, as well as the corresponding spectral and energy efficiency, which are shown to be accurate even for small number of antennas. Through an iterative sequential optimization, we find that the optimal training length of systems with RTRI can be smaller compared to ideal hardware systems in the moderate SNR regime, while larger in the high SNR regime. Moreover, it is observed that RTRI can significantly decrease the optimal number of transmit and receive antennas.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.

Respiratory virus-associated severe acute respiratory illness (SARI) and viral clustering in Malawian children in a setting with a high prevalence of HIV, malaria and malnutrition

BACKGROUND:  We used four years of paediatric severe acute respiratory illness (SARI) sentinel surveillance in Blantyre, Malawi to identify factors associated with clinical severity and co-viral clustering.

METHODS:  From January 2011 to December 2014, 2363 children aged 3 months to 14 years presenting to hospital with SARI were enrolled. Nasopharyngeal aspirates were tested for influenza and other respiratory viruses. We assessed risk factors for clinical severity and conducted clustering analysis to identify viral clusters in children with co-viral detection.

RESULTS:  Hospital-attended influenza-positive SARI incidence was 2.0 cases per 10,000 children annually; it was highest children aged under 1 year (6.3 cases per 10,000), and HIV-infected children aged 5 to 9 years (6.0 cases per 10,000). 605 (26.8%) SARI cases had warning signs, which were positively associated with HIV infection (adjusted risk ratio [aRR]: 2.4, 95% CI: 1.4, 3.9), RSV infection (aRR: 1.9, 95% CI: 1.3, 3.0) and rainy season (aRR: 2.4, 95% CI: 1.6, 3.8). We identified six co-viral clusters; one cluster was associated with SARI with warning signs.

CONCLUSIONS:  Influenza vaccination may benefit young children and HIV infected children in this setting. Viral clustering may be associated with SARI severity; its assessment should be included in routine SARI surveillance.