On Prediction Using Variable Order Markov Models

This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems

Handwritten digit recognition by bio-inspired hierarchical networks

The human brain processes information showing learning and prediction abilities but the underlying neuronal mechanisms still remain unknown. Recently, many studies prove that neuronal networks are able of both generalizations and associations of sensory inputs. In this paper, following a set of neurophysiological evidences, we propose a learning framework with a strong biological plausibility that mimics prominent functions of cortical circuitries. We developed the Inductive Conceptual Network (ICN), that is a hierarchical bio-inspired network, able to learn invariant patterns by Variable-order Markov Models implemented in its nodes. The outputs of the top-most node of ICN hierarchy, representing the highest input generalization, allow for automatic classification of inputs. We found that the ICN clusterized MNIST images with an error of 5.73% and USPS images with an error of 12.56%

Quantized Densely Connected U-Nets for Efficient Landmark Localization

In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused across the stacked U-Nets. This dense connectivity largely improves the information flow, yielding improved localization accuracy. However, a vanilla dense design would suffer from critical efficiency issue in both training and testing. To solve this problem, we first propose order-K dense connectivity to trim off long-distance shortcuts; then, we use a memory-efficient implementation to significantly boost the training efficiency and investigate an iterative refinement that may slice the model size in half. Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers. We validate our approach in two tasks: human pose estimation and face alignment. The results show that our approach achieves state-of-the-art localization accuracy, but using ~70% fewer parameters, ~98% less model size and saving ~75% training memory compared with other benchmark localizers. The code is available at https://github.com/zhiqiangdon/CU-Net.Comment: ECCV201

Four Work-Ins by Australian Journalists, 1944-80

During industrial disputes with employers between 1944 and 1980 the Australian Journalist's Association occasionally turned to the tactic of the work-in, producing wild cat newspapers during strikes in Sydney. These newspapers (The News, and The Clarion) exemplified problematic elements of the work-in as a working-class strategy. While single incident studies of the work-in have been conducted in Australia, the Australian Journalist Association work-ins present a time series of struggle. This time series allows for a broader evaluation of the radical content of the work-in and indicates that the tactic can become systematised, less radical, and less participatory when not connected to a broader generation of workplace radical behaviour by workers. In short: the work-in, much like the strike or go slow, can become a tame cat tactic – it is not inherently transgressive or opposed to capitalist production. Expectedly, the first work-ins were more radical in scope, presenting a newspaper which fully duplicated the commodity produced under capitalist control and in some ways exceeded the scope presented by capitalist organised journalism in both a material and a cultural sense. However, this radical economic potential dissipated by the end of the time series of work-ins. Instead of providing an alternative commodity fit for market, the tactic produced propaganda pieces aimed primarily at the members of the community who would be predisposed to favour the journalist's case. The 1980s Clarion was not a daily newspaper of news, sport, racing, women's interest, classifieds, and general opinion. This change will be explained in terms of human causes such as skills loss, production process causes such as computerisation and wire services, and broader social causes such as the changing role of the newspaper in Australian society.The symposium is organised on behalf of AAHANZBS by the Business and Labour History Group, The University of Sydney, with the financial support of the University’s Faculty of Economics and Business

Algorithms for learning parsimonious context trees

Parsimonious context trees, PCTs, provide a sparse parameterization of conditional probability distributions. They are particularly powerful for modeling context-specific independencies in sequential discrete data. Learning PCTs from data is computationally hard due to the combinatorial explosion of the space of model structures as the number of predictor variables grows. Under the score-and-search paradigm, the fastest algorithm for finding an optimal PCT, prior to the present work, is based on dynamic programming. While the algorithm can handle small instances fast, it becomes infeasible already when there are half a dozen four-state predictor variables. Here, we show that common scoring functions enable the use of new algorithmic ideas, which can significantly expedite the dynamic programming algorithm on typical data. Specifically, we introduce a memoization technique, which exploits regularities within the predictor variables by equating different contexts associated with the same data subset, and a bound-and-prune technique, which exploits regularities within the response variable by pruning parts of the search space based on score upper bounds. On real-world data from recent applications of PCTs within computational biology the ideas are shown to reduce the traversed search space and the computation time by several orders of magnitude in typical cases.Peer reviewe

Targeting the substrate preference of a type I nitroreductase to develop antitrypanosomal quinone-based prodrugs.

Nitroheterocyclic prodrugs are used to treat infections caused by Trypanosoma cruzi and Trypanosoma brucei. A key component in selectivity involves a specific activation step mediated by a protein homologous with type I nitroreductases, enzymes found predominantly in prokaryotes. Using data from determinations based on flavin cofactor, oxygen-insensitive activity, substrate range, and inhibition profiles, we demonstrate that NTRs from T. cruzi and T. brucei display many characteristics of their bacterial counterparts. Intriguingly, both enzymes preferentially use NADH and quinones as the electron donor and acceptor, respectively, suggesting that they may function as NADH:ubiquinone oxidoreductases in the parasite mitochondrion. We exploited this preference to determine the trypanocidal activity of a library of aziridinyl benzoquinones against bloodstream-form T. brucei. Biochemical screens using recombinant NTR demonstrated that several quinones were effective substrates for the parasite enzyme, having K(cat)/K(m) values 2 orders of magnitude greater than those of nifurtimox and benznidazole. In tests against T. brucei, antiparasitic activity mirrored the biochemical data, with the most potent compounds generally being preferred enzyme substrates. Trypanocidal activity was shown to be NTR dependent, as parasites with elevated levels of this enzyme were hypersensitive to the aziridinyl agent. By unraveling the biochemical characteristics exhibited by the trypanosomal NTRs, we have shown that quinone-based compounds represent a class of trypanocidal compound

Expression profiling identifies novel candidate genes for ethanol sensitivity QTLs

The Inbred Long Sleep (ILS) and Inbred Short Sleep (ISS) mouse strains have a 16-fold difference in duration of loss of the righting response (LORR) following administration of a sedative dose of ethanol. Four quantitative trait loci (QTLs) have been mapped in these strains for this trait. Underlying each of these QTLs must be one or more genetic differences (polymorphisms in either gene coding or regulatory regions) influencing ethanol sensitivity. Because prior studies have tended to focus on differences in coding regions, genome-wide expression profiling in cerebellum was used here to identify candidate genes for regulatory region differences in these two strains. Fifteen differentially expressed genes were found that map to the QTL regions and polymorphisms were identified in the promoter regions of four of these genes by direct sequencing of ILS and ISS genomic DNA. Polymorphisms in the promoters of three of these genes, Slc22a4, Rassf2, and Tax1bp3, disrupt putative transcription factor binding sites. Slc22a4 and another candidate, Xrcc5, have human orthologs that map to genomic regions associated with human ethanol sensitivity in genetic linkage studies. These genes represent novel candidates for the LORR phenotype and provide new targets for future studies into the neuronal processes underlying ethanol sensitivity

Training Genetic Counsellors to Deliver an Innovative Therapeutic Intervention: their views and experience of facilitating multi-family discussion groups

Innovations in clinical genetics have increased diagnosis, treatment and prognosis of inherited genetic conditions (IGCs). This has led to an increased number of families seeking genetic testing and / or genetic counselling and increased the clinical load for genetic counsellors (GCs). Keeping pace with biomedical discoveries, interventions are required to support families to understand, communicate and cope with their Inherited Genetic Condition. The Socio-Psychological Research in Genomics (SPRinG) collaborative have developed a new intervention, based on multi-family discussion groups (MFDGs), to support families affected by IGCs and train GCs in its delivery. A potential challenge to implementing the intervention was whether GCs were willing and able to undergo the training to deliver the MFDG. In analysing three multi-perspective interviews with GCs, this paper evaluates the training received. Findings suggests that MFDGs are a potential valuable resource in supporting families to communicate genetic risk information and can enhance family function and emotional well-being. Furthermore, we demonstrate that it is feasible to train GCs in the delivery of the intervention and that it has the potential to be integrated into clinical practice. Its longer term implementation into routine clinical practice however relies on changes in both organisation of clinical genetics services and genetic counsellors' professional development

Efficacy of Fumaric Acid Esters in the R6/2 and YAC128 Models of Huntington's Disease

Huntington's disease (HD) is an autosomal dominantly inherited progressive neurodegenerative disease. The exact sequel of events finally resulting in neurodegeneration is only partially understood and there is no established protective treatment so far. Some lines of evidence speak for the contribution of oxidative stress to neuronal tissue damage. The fumaric acid ester dimethylfumarate (DMF) is a new disease modifying therapy currently in phase III studies for relapsing-remitting multiple sclerosis. DMF potentially exerts neuroprotective effects via induction of the transcription factor “nuclear factor E2-related factor 2” (Nrf2) and detoxification pathways. Thus, we investigated here the therapeutic efficacy of DMF in R6/2 and YAC128 HD transgenic mice which mimic many aspects of HD and are characterized by an enhanced generation of free radicals in neurons. Treatment with DMF significantly prevented weight loss in R6/2 mice between postnatal days 80–90. At the same time, DMF treatment led to an attenuated motor impairment as measured by the clasping score. Average survival in the DMF group was 100.5 days vs. 94.0 days in the placebo group. In the histological analysis on day 80, DMF treatment resulted in a significant preservation of morphologically intact neurons in the striatum as well as in the motor cortex. DMF treatment resulted in an increased Nrf2 immunoreactivity in neuronal subpopulations, but not in astrocytes. These beneficial effects were corroborated in YAC128 mice which, after one year of DMF treatment, also displayed reduced dyskinesia as well as a preservation of neurons. In conclusion, DMF may exert beneficial effects in mouse models of HD. Given its excellent side effect profile, further studies with DMF as new therapeutic approach in HD and other neurodegenerative diseases are warranted