Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease.

Genome-wide association studies (GWAS) have identified several risk variants for late-onset Alzheimer's disease (LOAD)1, 2. These common variants have replicable but small effects on LOAD risk and generally do not have obvious functional effects. Low-frequency coding variants, not detected by GWAS, are predicted to include functional variants with larger effects on risk. To identify low-frequency coding variants with large effects on LOAD risk, we carried out whole-exome sequencing (WES) in 14 large LOAD families and follow-up analyses of the candidate variants in several large LOAD case–control data sets. A rare variant in PLD3 (phospholipase D3; Val232Met) segregated with disease status in two independent families and doubled risk for Alzheimer’s disease in seven independent case–control series with a total of more than 11,000 cases and controls of European descent. Gene-based burden analyses in 4,387 cases and controls of European descent and 302 African American cases and controls, with complete sequence data for PLD3, reveal that several variants in this gene increase risk for Alzheimer’s disease in both populations. PLD3 is highly expressed in brain regions that are vulnerable to Alzheimer’s disease pathology, including hippocampus and cortex, and is expressed at significantly lower levels in neurons from Alzheimer’s disease brains compared to control brains. Overexpression of PLD3 leads to a significant decrease in intracellular amyloid-β precursor protein (APP) and extracellular Aβ42 and Aβ40 (the 42- and 40-residue isoforms of the amyloid-β peptide), and knockdown of PLD3 leads to a significant increase in extracellular Aβ42 and Aβ40. Together, our genetic and functional data indicate that carriers of PLD3 coding variants have a twofold increased risk for LOAD and that PLD3 influences APP processing. This study provides an example of how densely affected families may help to identify rare variants with large effects on risk for disease or other complex traits.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

The governance of adaptation: choices, reasons, and effects. Introduction to the Special Feature

The governance of climate adaptation involves the collective efforts of multiple societal actors to address problems, or to reap the benefits, associated with impacts of climate change. Governing involves the creation of institutions, rules and organizations, and the selection of normative principles to guide problem solution and institution building. We argue that actors involved in governing climate change adaptation, as climate change governance regimes evolve, inevitably must engage in making choices, for instance on problem definitions, jurisdictional levels, on modes of governance and policy instruments, and on the timing of interventions. Yet little is known about how and why these choices are made in practice, and how such choices affect the outcomes of our efforts to govern adaptation. In this introduction we review the current state of evidence and the specific contribution of the articles published in this Special Feature, which are aimed at bringing greater clarity in these matters, and thereby informing both governance theory and practice. Collectively, the contributing papers suggest that the way issues are defined has important consequences for the support for governance interventions, and their effectiveness. The articles suggest that currently the emphasis in adaptation governance is on the local and regional levels, while underscoring the benefits of interventions and governance at higher jurisdictional levels in terms of visioning and scaling-up effective approaches. The articles suggest that there is a central role of government agencies in leading governance interventions to address spillover effects, to provide public goods, and to promote the long-term perspectives for planning. They highlight the issue of justice in the governance of adaptation showing how governance measures have wide distributional consequences, including the potential to amplify existing inequalities, access to resources, or generating new injustices through distribution of risks. For several of these findings, future research directions are suggested.

Feature-based camera pose estimation

The estimating of the relative orientation and position of a camera is one of the integral topics in the field of computer vision. The accuracy of a certain Finnish technology company’s traffic sign inventory and localization process can be improved by utilizing the aforementioned concept. The company’s localization process uses video data produced by a vehicle installed camera. The accuracy of estimated traffic sign locations depends on the relative orientation between the camera and the vehicle. This thesis proposes a computer vision based software solution which can estimate a camera’s orientation relative to the movement direction of the vehicle by utilizing video data. The task was solved by using feature-based methods and open source software. When using simulated data sets, the camera orientation estimates had an absolute error of 0.31 degrees on average. The software solution can be integrated to be a part of the traffic sign localization pipeline of the company in question.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Investigation of the role of long non-coding RNAs in oncogene induced cellular senescence

Cellular senescence is a stable arrest of cell proliferation induced by several factors such as activated oncogenes, oxidative stress and shortening of telomeres. Senescence acts as a tumour suppression mechanism to halt the progression of cancer. However, senescence may also impact negatively upon tissue regeneration, thus contributing to the effects of ageing. The eukaryotic genome is controlled by various modes of transcriptional and translational regulation. Focus has therefore centred on the role of long non- coding RNAs (lncRNAs) in regulating the genome. Accordingly, understanding how lncRNAs function to regulate the senescent genome is integral to improving our knowledge and understanding of tumour suppression and ageing. Within this study, I set out to investigate the expression of lncRNAs’ expression within models of senescence. Through a custom expression array, I have shown that expression of multiple different lncRNAs is up-regulated and down regulated in IMR90 replicative senescent fibroblasts and oncogene-induced senescent melanocytes. LncRNA expression was determined to be specific to stable senescence-associated cell arrest and predominantly within the nucleus of senescent cells. In order to examine the function of lncRNA expression in senescence, I selected lncRNA transcript ENST0000430998 (lncRNA_98) to focus my investigations upon. LncRNA_98 was robustly upregulated within multiple models of senescence and efficiently depleted using anti-sense oligonucleotide technology. Characterisation and unbiased RNA-sequencing of lncRNA_98 deficient senescent cells highlighted a list of genes that are regulated by lncRNA_98 expression in senescent cells and may regulate aspects of the senescence program. Specifically, the formation of SAHF was impeded upon depletion of lncRNA_98 expression and levels of total pRB protein expression severely decreased. Validation and recapitulation of consequences of pRB depletion was confirmed through lncRNA_98 knock-out cells generated using CRISPR technology. Surprisingly, inhibition of ATM kinase functions permitted the restoration of pRB protein levels within lncRNA_98 deficient cells. I propose that lncRNA_98 antagonizes the ability of ATM kinase to downregulate pRB expression at a post-transcriptional level, thereby potentiating senescence. Furthermore, lncRNA expression was detected within fibroblasts of old individuals and visualised within senescent melanocytes in human benign nevi, a barrier to melanoma progression. Conversely, mining of 337 TCGA primary melanoma data sets highlighted that the lncRNA_98 gene and its expression was lost from a significant proportion of melanoma samples, consistent with lncRNA_98 having a tumour suppressor functions. The data presented in this study illustrates that lncRNA_98 expression has a regulatory role over pRB expression in senescence and may regulate aspects of tumourigenesis and ageing.