Self- and Super-organizing Maps in R: The kohonen Package

In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.

Self- and Super-organizing Maps in R: The kohonen Package

In this age of ever-increasing data set sizes, especially in the natural sciences, visualisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion

Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. In the presence of only very few labeled pixels, this task becomes challenging. In this paper we address the following two research questions: 1) Can a simple neural network with just a single hidden layer achieve state of the art performance in the presence of few labeled pixels? 2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets? We give a positive answer to the first question by using three tricks within a very basic shallow Convolutional Neural Network (CNN) architecture: a tailored loss function, and smooth- and label-based data augmentation. The tailored loss function enforces that neighborhood wavelengths have similar contributions to the features generated during training. A new label-based technique here proposed favors selection of pixels in smaller classes, which is beneficial in the presence of very few labeled pixels and skewed class distributions. To address the second question, we introduce a new sampling procedure to generate disjoint train and test set. Then the train set is used to obtain the CNN model, which is then applied to pixels in the test set to estimate their labels. We assess the efficacy of the simple neural network method on five publicly available hyperspectral images. On these images our method significantly outperforms considered baselines. Notably, with just 1% of labeled pixels per class, on these datasets our method achieves an accuracy that goes from 86.42% (challenging dataset) to 99.52% (easy dataset). Furthermore we show that the simple neural network method improves over other baselines in the new challenging supervised setting. Our analysis substantiates the highly beneficial effect of using the entire image (so train and test data) for constructing a model.Comment: Remote Sensing 201

Chemometrics for ion mobility spectrometry data:Recent advances and future prospects

Contains fulltext : 161386.pdf (publisher's version ) (Open Access)Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further

Noncooperative and Cooperative Transmission Schemes with Precoding and Beamforming

The next generation mobile networks are expected to provide multimedia applications with a high quality of service. On the other hand, interference among multiple base stations (BS) that co-exist in the same location limits the capacity of wireless networks. In conventional wireless networks, the base stations do not cooperate with each other. The BSs transmit individually to their respective mobile stations (MS) and treat the transmission from other BSs as interference. An alternative to this structure is a network cooperation structure. Here, BSs cooperate with other BSs to simultaneously transmit to their respective MSs using the same frequency band at a given time slot. By doing this, we significantly increase the capacity of the networks. This thesis presents novel research results on a noncooperative transmission scheme and a cooperative transmission scheme for multi-user multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). We first consider the performance limit of a noncooperative transmission scheme. Here, we propose a method to reduce the interference and increase the throughput of orthogonal frequency division multiplexing (OFDM) systems in co-working wireless local area networks (WLANs) by using joint adaptive multiple antennas(AMA) and adaptive modulation (AM) with acknowledgement (ACK) Eigen-steering. The calculation of AMA and AM are performed at the receiver. The AMA is used to suppress interference and to maximize the signal-to-interference-plus-noise ratio (SINR). The AM scheme is used to allocate OFDM sub-carriers, power, and modulation mode subject to the constraints of power, discrete modulation, and the bit error rate (BER). The transmit weights, the allocation of power, and the allocation of sub-carriers are obtained at the transmitter using ACK Eigen-steering. The derivations of AMA, AM, and ACK Eigen-steering are shown. The performance of joint AMA and AM for various AMA configurations is evaluated through the simulations of BER and spectral efficiency (SE) against SIR. To improve the performance of the system further, we propose a practical cooperative transmission scheme to mitigate against the interference in co-working WLANs. Here, we consider a network coordination among BSs. We employ Tomlinson Harashima precoding (THP), joint transmit-receive beamforming based on SINR (signal-to-interference-plus-noise-ratio) maximization, and an adaptive precoding order to eliminate co-working interference and achieve bit error rate (BER) fairness among different users. We also consider the design of the system when partial channel state information (CSI) (where each user only knows its own CSI) and full CSI (where each user knows CSI of all users) are available at the receiver respectively. We prove analytically and by simulation that the performance of our proposed scheme will not be degraded under partial CSI. The simulation results show that the proposed scheme considerably outperforms both the existing noncooperative and cooperative transmission schemes. A method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming is also proposed. The algorithm eliminates the interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights cancel the remaining one. A new novel iterative method is applied to generate the transmit-receive antenna weights. To achieve SER fairness among different users and further improve the performance of MIMO systems, we develop algorithms that provide equal SINR across all users and order the users so that the minimum SINR for each user is maximized. The simulation results show that the proposed scheme considerably outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and approaches an interference free performance under the same configuration. We could improve the performance of the proposed interference cancellation further. This is because the proposed interference cancellation does not consider receiver noise when calculating the transmit-receive weight antennas. In addition, the proposed scheme mentioned above is designed specifically for a single-stream multi-user transmission. Here, we employ THP precoding and an iterative method based on the uplink-downlink duality principle to generate the transmit-receive antenna weights. The algorithm provides an equal SINR across all users. A simpler method is then proposed by trading off the complexity with a slight performance degradation. The proposed methods are extended to also work when the receiver does not have complete Channel State Informations (CSIs). A new method of setting the user precoding order, which has a much lower complexity than the VBLAST type ordering scheme but with almost the same performance, is also proposed. The simulation results show that the proposed schemes considerably outperform existing cooperative transmission schemes in terms of SER performance and approach an interference free performance. In all the cooperative transmission schemes proposed above, we use THP to cancel part of the interference. In this thesis, we also consider an alternative approach that bypasses the use of THP. The task of cancelling the interference from other users now lies solely within the transmit-receive antenna weights. We consider multiuser Gaussian broadcast channels with multiple antennas at both transmitter and receivers. An iterative multiple beamforming (IMB) algorithm is proposed, which is flexible in the antenna configuration and performs well in low to moderate data rates. Its capacity and bit error rate performance are compared with the ones achieved by the traditional zero-forcing method

Simultaneous analysis of plasma and CSF by NMR and hierarchical models fusion

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl—experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood–brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease

Posttraumatic stress disorder among female street-based sex workers in the greater Sydney area, Australia

BACKGROUND: This paper examines rates of exposure to work-related violence and other trauma, and the prevalence of lifetime and current posttraumatic stress disorder (PTSD) among female street-based sex workers. It also investigates associations between current PTSD symptoms and: demographic characteristics, psychiatric comorbidity, injecting and sex risk behaviours, and trauma history. METHODS: Cross sectional data collected from 72 women via face to face structured interviews. The interview included structured diagnostic assessment of DSM-IV PTSD; drug dependence; depression; experience of childhood trauma; and an assessment of sex working history. RESULTS: All but one of the women interviewed reported experiencing trauma, with the majority reporting multiple traumas that typically began in early childhood. Child sexual abuse, adult sexual assault and work related violence were commonly reported. Just under half of the women met DSM-IV criteria for PTSD and approximately one-third reported current PTSD symptoms. Adult sexual assault was associated with current PTSD symptoms. Depression and drug dependence were also highly prevalent; cocaine dependence in particular was associated with elevated rates of injecting risk and sexual risk behaviours. CONCLUSION: These women reported complex trauma histories and despite ongoing opportunities for clinical intervention, they continued to experience problems, suggesting that current models of treatment may not be appropriate. More targeted interventions, and integrated mental health and drug treatment services are needed to address the problems these women are experiencing. Outreach services to these women remain a priority. Education strategies to reduce risky injecting and sexual behaviours among sex workers should also remain a priority

Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis

Contains fulltext : 93904.pdf (publisher's version ) (Open Access

Retreatment of hepatitis C non-responsive to Interferon. A placebo controlled randomized trial of Ribavirin monotherapy versus combination therapy with Ribavirin and Interferon in 121 patients in the Benelux [ISRCTN53821378]

BACKGROUND: Evidence based medicine depends on unbiased selection of completed randomized controlled trials. For completeness it is important to publish all trials. This report describes the first large randomised controlled trial where combination therapy was compared to placebo therapy and to ribavirin monotherapy, which has not been published untill now. METHODS: One hundred and twenty one patients with chronic hepatitis C and elevated transaminases who did not respond to previous treatment with standard interferon monotherapy, were included from 16 centers in Belgium, the Netherlands and Luxembourg between 1992 and 1996. Patient poor-response characteristics were: genotype 1 (69%), HCV RNA above 2 × 10(6 )copies/ml (55%) and cirrhosis (38%). Patients were randomized to 6 months combination therapy with interferon alpha-2b (3 MU tiw) and ribavirin (1000–1200 mg / day), 6 months ribavirin monotherapy (1000–1200 mg / day) or 6 months ribavirin placebo. The study was double blinded for the ribavirin / placebo component. One patient did not fit the entry criteria, and 3 did not start. All 117 patients who received at least one dose of treatment were included in the intention to treat analysis. RESULTS: At the end of treatment, HCV RNA was undetectable in 35% of patients on combination therapy and in none of the patients treated with ribavirin monotherapy or placebo. The sustained virological response rate at 6 months after therapy was 15% for patients treated with interferon and ribavirin. During the 6 months treatment period 13% of patients on interferon ribavirin combination therapy, 13% of patients on ribavirin monotherapy and 11% of patients on placebo withdrew due to side effects or noncompliance. At 24 weeks of treatment the mean Hb level was 85% of the baseline value, which means a mean decrease from 9.1 mmol/l to 7.8 mmol/l. The Hb levels at the end of treatment were not significantly different from patients treated with ribavirin monotherapy (p = 0.76). End of treatment WBC was significantly lower in patients treated with combination therapy, compared to ribavirin (p < 0.01) as well as for patients treated with ribavirin monotherapy compared to placebo (p < 0.01). DISCUSSION: This belated report on the only placebo controlled study of interferon ribavirin combination therapy in non responders to standard doses of interferon monotherapy documents the effectiveness, be it limited, of this approach as well as the dynamics of the effects on blood counts

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Contains fulltext : 91843.pdf (publisher's version ) (Open Access)12 p