Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

Mechanisms underlying neurocognitive dysfunctions in recurrent major depression

Recent work shows that depression is intimately associated with changes in cognitive functioning, including memory, attention, verbal fluency, and other aspects of higher-order cognitive processing. Changes in cognitive functioning are more likely to occur when depressive episodes are recurrent and to abate to some degree during periods of remission. However, with accumulating frequency and duration of depressive episodes, cognitive deficits can become enduring, being evident even when mood improves. Such changes in cognitive functioning give depression links to mild cognitive impairment and thereby with neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, schizophrenia, and multiple sclerosis. Depression may then be conceptualized on a dimension of depression - mild cognitive impairment - dementia. The biological underpinnings of depression have substantial overlaps with those of neurodegenerative conditions, including reduced neurogenesis, increased apoptosis, reactive oxygen species, tryptophan catabolites, autoimmunity, and immune-inflammatory processes, as well as decreased antioxidant defenses. These evolving changes over the course of depressive episodes drive the association of depression with neurodegenerative conditions. As such, the changes in cognitive functioning in depression have important consequences for the treatment of depression and in reconceptualizing the role of depression in wider neuroprogressive conditions. Here we review the data on changes in cognitive functioning in recurrent major depression and their association with other central conditions.

Challenge demands, hindrance demands, and psychological need satisfaction: their influence on employee engagement and emotional exhaustion

The job demands-resources (JD-R) model provides a well-validated account of how job resources and job demands influence work engagement, burnout, and their constituent dimensions. The present study aimed to extend previous research by including challenge demands not widely examined in the context of the JD-R. Furthermore, and extending self-determination theory, the research also aimed to investigate the potential mediating effects that employees' need satisfaction as regards their need for autonomy, need for belongingness, need for competence, and need for achievement, as components of a higher order needs construct, may have on the relationships between job demands and engagement. Structural equations modeling across two independent samples generally supported the proposed relationships. Further research opportunities, practical implications, and study limitations are discussed.

Self-assembled peptide nanostructures for the fabrication of cell scaffolds

The fabrication of artificial scaffolds that effectively mimic the host environment of the cell have exciting potential for the treatment of many diseases in regenerative medicine. In particular, appropriately designed scaffolds have the capacity to support, replace, and mediate the transplantation of therapeutic cells in order to regenerate damaged or diseased tissues. To achieve these goals for regeneration, the engineering of an environment structurally similar to the native extracellular matrix (ECM) is necessary in order to closely match the chemical and physical conditions found within the extracellular niche. Recently, self-assembled peptide (SAP) hydrogels have shown great potential for such biological applications due to their inherent biocompatibility, propensity to form higher order structures, rich chemical functionality and ease of synthesis. Importantly, it is possible to control the organization and properties of the target materials as the chemical structure is determined by amino acid sequence. Here, the development of SAP hydrogels as functional cell scaffolds and useful tools in tissue engineering is reviewed.

Tensor-variate restricted Boltzmann machines

Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance.

Derivações de ordem superior em anéis primos e semiprimos

Nesta tese estudamos as derivações de ordem superior (DOS) em anéis não-comutativos. Inicialmente, mostramos que toda derivação tripla de Jordan de ordem superior em um anel semiprimo livre de 2-torção é uma DOS. Em particular, toda derivação de Jordan de ordem superior (DJOS) num anel deste tipo é uma DOS. Estendemos também o resultado a ideais de Lie U, provando que se R é um anel primo livre de 2-torção e D é uma DJOS de U em R onde U ct Z(R) é tal que U2E U para todo u E U, então D é uma DOS de U em R. Nestas condições, se U C Z(R), então o resultado não é válido. Estudamos ainda as DOS cujas componentes satisfazem relações de dependência linear sobre R ou Q (o anel de quocientes à direita de M artindale de R). Caracterizamos tais DOS, e mostramos que as relações de dependência linear são preservadas ao estendermos uma DOS de R a Q.

A family of autoregressive conditional duration models

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter λ to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and β-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power λ of the duration process. Finally, we assess the practical usefulness of our family of ACD models using NYSE transactions data, with special attention to IBM price durations. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks.

A family of autoregressive conditional duration models

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter λ to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and β-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power λ of the duration process. Finally, we assess the practical usefulness of our family of ACD models using NYSE price duration data on the IBM stock. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks.

Testing the optimality of aggregate consumption decisions: is there rule-of-thumb behavior?

Consumption is an important macroeconomic aggregate, being about 70% of GNP. Finding sub-optimal behavior in consumption decisions casts a serious doubt on whether optimizing behavior is applicable on an economy-wide scale, which, in turn, challenge whether it is applicable at all. This paper has several contributions to the literature on consumption optimality. First, we provide a new result on the basic rule-of-thumb regression, showing that it is observational equivalent to the one obtained in a well known optimizing real-business-cycle model. Second, for rule-of-thumb tests based on the Asset-Pricing Equation, we show that the omission of the higher-order term in the log-linear approximation yields inconsistent estimates when lagged observables are used as instruments. However, these are exactly the instruments that have been traditionally used in this literature. Third, we show that nonlinear estimation of a system of N Asset-Pricing Equations can be done efficiently even if the number of asset returns (N) is high vis-a-vis the number of time-series observations (T). We argue that efficiency can be restored by aggregating returns into a single measure that fully captures intertemporal substitution. Indeed, we show that there is no reason why return aggregation cannot be performed in the nonlinear setting of the Pricing Equation, since the latter is a linear function of individual returns. This forms the basis of a new test of rule-of-thumb behavior, which can be viewed as testing for the importance of rule-of-thumb consumers when the optimizing agent holds an equally-weighted portfolio or a weighted portfolio of traded assets. Using our setup, we find no signs of either rule-of-thumb behavior for U.S. consumers or of habit-formation in consumption decisions in econometric tests. Indeed, we show that the simple representative agent model with a CRRA utility is able to explain the time series data on consumption and aggregate returns. There, the intertemporal discount factor is significant and ranges from 0.956 to 0.969 while the relative risk-aversion coefficient is precisely estimated ranging from 0.829 to 1.126. There is no evidence of rejection in over-identifying-restriction tests.