Clustering and labeling a web scale document collection using Wikipedia clusters

Clustering is an important technique in organising and categorising web scale documents. The main challenges faced in clustering the billions of documents available on the web are the processing power required and the sheer size of the datasets available. More importantly, it is nigh impossible to generate the labels for a general web document collection containing billions of documents and a vast taxonomy of topics. However, document clusters are most commonly evaluated by comparison to a ground truth set of labels for documents. This paper presents a clustering and labeling solution where the Wikipedia is clustered and hundreds of millions of web documents in ClueWeb12 are mapped on to those clusters. This solution is based on the assumption that the Wikipedia contains such a wide range of diverse topics that it represents a small scale web. We found that it was possible to perform the web scale document clustering and labeling process on one desktop computer under a couple of days for the Wikipedia clustering solution containing about 1000 clusters. It takes longer to execute a solution with finer granularity clusters such as 10,000 or 50,000. These results were evaluated using a set of external data.

Facial emotion processing in schizophrenia : no evidence for a deficit specific to negative emotions in a differential deficit design

People with schizophrenia perform poorly when recognising facial expressions of emotion, particularly negative emotions such as fear. This finding has been taken as evidence of a “negative emotion specific deficit”, putatively associated with a dysfunction in the limbic system, particularly the amygdala. An alternative explanation is that greater difficulty in recognising negative emotions may reflect a priori differences in task difficulty. The present study uses a differential deficit design to test the above argument. Facial emotion recognition accuracy for seven emotion categories was compared across three groups. Eighteen schizophrenia patients and one group of healthy age- and gender-matched controls viewed identical sets of stimuli. A second group of 18 age- and gender-matched controls viewed a degraded version of the same stimuli. The level of stimulus degradation was chosen so as to equate overall level of accuracy to the schizophrenia patients. Both the schizophrenia group and the degraded image control group showed reduced overall recognition accuracy and reduced recognition accuracy for fearful and sad facial stimuli compared with the intact-image control group. There were no differences in recognition accuracy for any emotion category between the schizophrenia group and the degraded image control group. These findings argue against a negative emotion specific deficit in schizophrenia.

Functional neuroanatomy of auditory mismatch processing : an event-related fMRI study of duration-deviant oddballs

This study was designed to identify the neural networks underlying automatic auditory deviance detection in 10 healthy subjects using functional magnetic resonance imaging. We measured blood oxygenation level-dependent contrasts derived from the comparison of blocks of stimuli presented as a series of standard tones (50 ms duration) alone versus blocks that contained rare duration-deviant tones (100 ms) that were interspersed among a series of frequent standard tones while subjects were watching a silent movie. Possible effects of scanner noise were assessed by a “no tone” condition. In line with previous positron emission tomography and EEG source modeling studies, we found temporal lobe and prefrontal cortical activation that was associated with auditory duration mismatch processing. Data were also analyzed employing an event-related hemodynamic response model, which confirmed activation in response to duration-deviant tones bilaterally in the superior temporal gyrus and prefrontally in the right inferior and middle frontal gyri. In line with previous electrophysiological reports, mismatch activation of these brain regions was significantly correlated with age. These findings suggest a close relationship of the event-related hemodynamic response pattern with the corresponding electrophysiological activity underlying the event-related “mismatch negativity” potential, a putative measure of auditory sensory memory.

Conclusion

This monograph provides an overview of recruitment learning approaches from a computational perspective. Recruitment learning is a unique machine learning technique that: (1) explains the physical or functional acquisition of new neurons in sparsely connected networks as a biologically plausible neural network method; (2) facilitates the acquisition of new knowledge to build and extend knowledge bases and ontologies as an artificial intelligence technique; (3) allows learning by use of background knowledge and a limited number of observations, consistent with psychological theory.

Breaking the DNA-vaccine bottleneck

Monash University in Australia has developed a new approach towards DNA vaccine development that has the potential to cut the time it takes to produce a vaccine from up to nine months to four weeks or less. The university has designed and filed a patent on a commercially viable, single-stage technology for manufacturing DNA molecules. The technology was used to produce malaria and measles DNA vaccines, which were tested to be homogeneous supercoiled DNA, free from RNA and protein contaminations and meeting FDA regulatory standards for DNA vaccines. The technique is based on customized, smart, polymeric, monolithic adsorbents that can purify DNA very rapidly. The design criteria of solid-phase adsorbent include rapid adsorption and desorption kinetics, physical composition, and adequate selectivity , capacity and recovery. The new show technology significantly improved binding capacities, higher recovery, drastically reduced use of buffers and processing time, less clogging, and higher yields of DNA.

Dewatering of microalgal cultures : a major bottleneck to algae-based fuels

Microalgae dewatering is a major obstruction to industrial-scale processing of microalgae for biofuel prodn. The dil. nature of harvested microalgal cultures creates a huge operational cost during dewatering, thereby, rendering algae-based fuels less economically attractive. Currently there is no superior method of dewatering microalgae. A technique that may result in a greater algal biomass may have drawbacks such as a high capital cost or high energy consumption. The choice of which harvesting technique to apply will depend on the species of microalgae and the final product desired. Algal properties such as a large cell size and the capability of the microalgae to autoflocculate can simplify the dewatering process. This article reviews and addresses the various technologies currently used for dewatering microalgal cultures along with a comparative study of the performances of the different technologies.

Rapid production of a plasmid DNA encoding a malaria vaccine candidate via amino-functionalized poly(GMA-co-EDMA) monolith

Malaria is a global health problem; an effective vaccine is urgently needed. Due to the relative poverty and lack of infrastructure in malaria endemic areas, DNA-based vaccines that are stable at ambient temperatures and easy to formulate have great potential. While attention has been focused mainly on antigen selection, vector design and efficacy assessment, the development of a rapid and commercially viable process to manufacture DNA is generally overlooked. We report here a continuous purification technique employing an optimized stationary adsorbent to allow high-vaccine recovery, low-processing time, and, hence, high-productivity. A 40.0 mL monolithic stationary phase was synthesized and functionalized with amino groups from 2-Chloro-N,N- diethylethylamine hydrochloride for anion-exchange isolation of a plasmid DNA (pDNA) that encodes a malaria vaccine candidate, VR1020-PyMSP4/5. Physical characterization of the monolithic polymer showed a macroporous material with a modal pore diameter of 750 nm. The final vaccine product isolated after 3 min elution was homogeneous supercoiled plasmid with gDNA, RNA and protein levels in keeping with clinical regulatory standards. Toxicological studies of the pVR1020-PyMSP4/5 showed a minimum endotoxin level of 0.28 EU/m.g pDNA. This cost-effective technique is cGMP compatible and highly scalable for the production of DNA-based vaccines in commercial quantities, when such vaccines prove to be effective against malaria. © 2008 American Institute of Chemical Engineers.

The suitability of DEAE-Cl active groups on customized poly(GMA-co-EDMA) continuous stationary phase for fast enzyme-free isolation of plasmid DNA

The creation of a commercially viable and a large-scale purification process for plasmid DNA (pDNA) production requires a whole-systems continuous or semi-continuous purification strategy employing optimised stationary adsorption phase(s) without the use of expensive and toxic chemicals, avian/bovine-derived enzymes and several built-in unit processes, thus affecting overall plasmid recovery, processing time and economics. Continuous stationary phases are known to offer fast separation due to their large pore diameter making large molecule pDNA easily accessible with limited mass transfer resistance even at high flow rates. A monolithic stationary sorbent was synthesised via free radical liquid porogenic polymerisation of ethylene glycol dimethacrylate (EDMA) and glycidyl methacrylate (GMA) with surface and pore characteristics tailored specifically for plasmid binding, retention and elution. The polymer was functionalised with an amine active group for anion-exchange purification of pDNA from cleared lysate obtained from E. coli DH5α-pUC19 pellets in RNase/protease-free process. Characterization of the resin showed a unique porous material with 70% of the pores sizes above 300 nm. The final product isolated from anion-exchange purification in only 5 min was pure and homogenous supercoiled pDNA with no gDNA, RNA and protein contamination as confirmed with DNA electrophoresis, restriction analysis and SDS page. The resin showed a maximum binding capacity of 15.2 mg/mL and this capacity persisted after several applications of the resin. This technique is cGMP compatible and commercially viable for rapid isolation of pDNA.

Towards the design of a scalable and commercially viable technique for plasmid purification using a methacrylate monolithic stationary phase

A monolithic stationary phase was prepared via free radical co-polymerization of ethylene glycol dimethacrylate (EDMA) and glycidyl methacrylate (GMA) with pore diameter tailored specifically for plasmid binding, retention and elution. The polymer was functionalized. with 2-chloro-N,N-diethylethylamine hydrochloride (DEAE-Cl) for anion-exchange purification of plasmid DNA (pDNA) from clarified lysate obtained from E. coli DH5α-pUC19 culture in a ribonuclease/ protease-free environment. Characterization of the monolithic resin showed a porous material, with 68% of the pores existing in the matrix having diameters above 300 nm. The final product isolated from a single-stage 5 min anion-exchange purification was a pure and homogeneous supercoiled (SC) pDNA with no gDNA, RNA and protein contamination as confirmed by ethidium bromide agarose gel electrophoresis (EtBr-AGE), enzyme restriction analysis and sodium dodecyl sulfate-polyacrylamide gel electrophoresis. This non-toxic technique is cGMP compatible and highly scalable for production of pDNA on a commercial level.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

Instructional objectives to core-gameplay : a serious game design technique

This paper explores a gap within the serious game design research. That gap is the ambiguity surrounding the process of aligning the instructional objectives of serious games with their core-gameplay i.e. the moment-to-moment activity that is the core of player interaction. A core-gameplay focused design framework is proposed that can work alongside existing, more broadly focused serious games design frameworks. The framework utilises an inquiry-based approach that allows the serious game designer to use key questions as a means to clearly outline instructional objectives with the core-gameplay. The use of this design framework is considered in the context of a small section of gameplay from an educational game currently in development. This demonstration of the framework brings shows how instructional objectives can be embedded into a serious games core-gameplay.

Injury narrative text classification using factorization model

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.