Sampled Weighted Min-Hashing for Large-Scale Topic Mining

We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to automatically mine topics from large-scale corpora. SWMH generates multiple random partitions of the corpus vocabulary based on term co-occurrence and agglomerates highly overlapping inter-partition cells to produce the mined topics. While other approaches define a topic as a probabilistic distribution over a vocabulary, SWMH topics are ordered subsets of such vocabulary. Interestingly, the topics mined by SWMH underlie themes from the corpus at different levels of granularity. We extensively evaluate the meaningfulness of the mined topics both qualitatively and quantitatively on the NIPS (1.7 K documents), 20 Newsgroups (20 K), Reuters (800 K) and Wikipedia (4 M) corpora. Additionally, we compare the quality of SWMH with Online LDA topics for document representation in classification.Comment: 10 pages, Proceedings of the Mexican Conference on Pattern Recognition 201

Deep Neural Networks - A Brief History

Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure

Adrenaline modulates the global transcriptional profile of Salmonella revealing a role in the antimicrobial peptide and oxidative stress resistance responses

<p>Abstract</p> <p>Background</p> <p>The successful interaction of bacterial pathogens with host tissues requires the sensing of specific chemical and physical cues. The human gut contains a huge number of neurons involved in the secretion and sensing of a class of neuroendocrine hormones called catecholamines. Recently, in <it>Escherichia coli </it>O157:H7, the catecholamines adrenaline and noradrenaline were shown to act synergistically with a bacterial quorum sensing molecule, autoinducer 3 (AI-3), to affect bacterial virulence and motility. We wished to investigate the impact of adrenaline on the biology of <it>Salmonella </it>spp.</p> <p>Results</p> <p>We have determined the effect of adrenaline on the transcriptome of the gut pathogen <it>Salmonella enterica </it>serovar Typhimurium. Addition of adrenaline led to an induction of key metal transport systems within 30 minutes of treatment. The oxidative stress responses employing manganese internalisation were also elicited. Cells lacking the key oxidative stress regulator OxyR showed reduced survival in the presence of adrenaline and complete restoration of growth upon addition of manganese. A significant reduction in the expression of the <it>pmrHFIJKLM </it>antimicrobial peptide resistance operon reduced the ability of <it>Salmonella </it>to survive polymyxin B following addition of adrenaline. Notably, both phenotypes were reversed by the addition of the β-adrenergic blocker propranolol. Our data suggest that the BasSR two component signal transduction system is the likely adrenaline sensor mediating the antimicrobial peptide response.</p> <p>Conclusion</p> <p><it>Salmonella </it>are able to sense adrenaline and downregulate the antimicrobial peptide resistance <it>pmr </it>locus through the BasSR two component signalling system. Through iron transport, adrenaline may affect the oxidative stress balance of the cell requiring OxyR for normal growth. Both adrenaline effects can be inhibited by the addition of the β-adrenergic blocker propranolol. Adrenaline sensing may provide an environmental cue for the induction of the <it>Salmonella </it>stress response in anticipation of imminent host-derived oxidative stress. However, adrenaline may also serve in favour of the host defences by lowering antimicrobial peptide resistance and hence documenting for the first time such a function for a hormone.</p

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

Transthoracic echocardiography reference values in juvenile and adult 129/Sv mice

Background In the recent years, the use of Doppler-echocardiography has become a standard non-invasive technique in the analysis of cardiac malformations in genetically modified mice. Therefore, normal values have to be established for the most commonly used inbred strains in whose genetic background those mutations are generated. Here we provide reference values for transthoracic echocardiography measurements in juvenile (3 weeks) and adult (8 weeks) 129/Sv mice. Methods Echocardiographic measurements were performed using B-mode, M-mode and Doppler-mode in 15 juvenile (3 weeks) and 15 adult (8 weeks) mice, during isoflurane anesthesia. M-mode measurements variability of left ventricle (LV) was determined. Results Several echocardiographic measurements significantly differ between juvenile and adult mice. Most of these measurements are related with cardiac dimensions. All B-mode measurements were different between juveniles and adults (higher in the adults), except for fractional area change (FAC). Ejection fraction (EF) and fractional shortening (FS), calculated from M-mode parameters, do not differ between juvenile and adult mice. Stroke volume (SV) and cardiac output (CO) were significantly different between juvenile and adult mice. SV was 31.93 ± 8.67 μl in juveniles vs 70.61 ± 24.66 μl in adults, ρ < 0.001. CO was 12.06 ± 4.05 ml/min in juveniles vs 29.71 ± 10.13 ml/min in adults, ρ < 0.001. No difference was found in mitral valve (MV) and tricuspid valve (TV) related parameters between juvenile and adult mice. It was demonstrated that variability of M-mode measurements of LV is minimal. Conclusions This study suggests that differences in cardiac dimensions, as wells as in pulmonary and aorta outflow parameters, were found between juvenile and adult mice. However, mitral and tricuspid inflow parameters seem to be similar between 3 weeks and 8 weeks mice. The reference values established in this study would contribute as a basis to future studies in post-natal cardiovascular development and diagnosing cardiovascular disorders in genetically modified mouse mutant lines.Peer Reviewe

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Transcriptional control in the prereplicative phase of T4 development

Control of transcription is crucial for correct gene expression and orderly development. For many years, bacteriophage T4 has provided a simple model system to investigate mechanisms that regulate this process. Development of T4 requires the transcription of early, middle and late RNAs. Because T4 does not encode its own RNA polymerase, it must redirect the polymerase of its host, E. coli, to the correct class of genes at the correct time. T4 accomplishes this through the action of phage-encoded factors. Here I review recent studies investigating the transcription of T4 prereplicative genes, which are expressed as early and middle transcripts. Early RNAs are generated immediately after infection from T4 promoters that contain excellent recognition sequences for host polymerase. Consequently, the early promoters compete extremely well with host promoters for the available polymerase. T4 early promoter activity is further enhanced by the action of the T4 Alt protein, a component of the phage head that is injected into E. coli along with the phage DNA. Alt modifies Arg265 on one of the two α subunits of RNA polymerase. Although work with host promoters predicts that this modification should decrease promoter activity, transcription from some T4 early promoters increases when RNA polymerase is modified by Alt. Transcription of T4 middle genes begins about 1 minute after infection and proceeds by two pathways: 1) extension of early transcripts into downstream middle genes and 2) activation of T4 middle promoters through a process called sigma appropriation. In this activation, the T4 co-activator AsiA binds to Region 4 of σ70, the specificity subunit of RNA polymerase. This binding dramatically remodels this portion of σ70, which then allows the T4 activator MotA to also interact with σ70. In addition, AsiA restructuring of σ70 prevents Region 4 from forming its normal contacts with the -35 region of promoter DNA, which in turn allows MotA to interact with its DNA binding site, a MotA box, centered at the -30 region of middle promoter DNA. T4 sigma appropriation reveals how a specific domain within RNA polymerase can be remolded and then exploited to alter promoter specificity

The neural basis of auditory temporal discrimination in girls with fragile X syndrome

Fragile X syndrome (FXS) is a common genetic disorder in which temporal processing may be impaired. To our knowledge however, no studies have examined the neural basis of temporal discrimination in individuals with FXS using functional magnetic resonance imaging (fMRI). Ten girls with fragile X syndrome and ten developmental age-matched typically developing controls performed an auditory temporal discrimination task in a 3T scanner. Girls with FXS showed significantly greater brain activation in a left-lateralized network, comprising left medial frontal gyrus, left superior and middle temporal gyrus, left cerebellum, and left brainstem (pons), when compared to a developmental age-matched typically developing group of subjects who had similar in-scanner task performance. There were no regions that showed significantly greater brain activation in the control group compared to individuals with FXS. These data indicate that networks of brain regions involved in auditory temporal processing may be dysfunctional in FXS. In particular, it is possible that girls with FXS employ left hemispheric resources to overcompensate for relative right hemispheric dysfunction

Deep Online Hierarchical Unsupervised Learning for Pattern Mining from Utility Usage Data.

Non-intrusive load monitoring (NILM) has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While much work has focused on supervised algorithms, unsupervised approaches can be more interesting and of more practical use in real case scenarios. More specifically, they do not require labelled training data to be collected from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Deep Belief network (DBN) and online Latent Dirichlet Allocation (LDA). Firstly, the raw signals of the house utilities are fed into DBN to extract low-level generic features in an unsupervised fashion, and then the hierarchical Bayesian model, LDA, learns high-level features that capture the correlations between the low-level ones. Thus, the proposed method (DBN-LDA) harnesses the DBN’s ability of learning distributed hierarchies of features to extract sophisticated appliances specific features without the need of precise human-crafted input representations. The clustering power of the hierarchical Bayesian models helps further summarise the input data by extracting higher-level information representing the residents’ energy consumption patterns. Using Deep-Hierarchical models reduces the computational complexity since LDA is not directly applied to the raw data. The computational efficiency is crucial as our application involves massive data from different types of utility usages. Moreover, we develop a novel online inference algorithm to cope with this big data. Another novelty of this work is that the data is a combination of different utilities (e.g, electricity, water and gas) and some sensors measurements. Finally, we propose different methods to evaluate the results and preliminary experiments show that the DBN-LDA is promising to extract useful patterns