Transfer learning approach for financial applications

Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject preexisting knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behavioural genetics approach proposed by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial data. Numerical evidence demonstrates the credibility of the new approach. We provide insights on the operation of transfer learning and highlight the benefits of using behavioural principles and selective breeding when tackling a set of diverse financial applications problems

Motor-sensory biases are associated with cognitive and social abilities in humans

Across vertebrates, adaptive behaviors, like feeding and avoiding predators, are linked to lateralized brain function. The presence of the behavioral manifestations of these biases are associated with increased task success. Additionally, when an individual’s direction of bias aligns with the majority of the population, it is linked to social advantages. However, it remains unclear if behavioral biases in humans correlate with the same advantages. This large-scale study (N = 313–1661, analyses dependent) examines whether the strength and alignment of behavioral biases associate with cognitive and social benefits respectively in humans. To remain aligned with the animal literature, we evaluate motor-sensory biases linked to motor-sequencing and emotion detection to assess lateralization. Results reveal that moderate hand lateralization is positively associated with task success and task success is, in turn, associated with language fluency, possibly representing a cascade effect. Additionally, like other vertebrates, the majority of our human sample possess a ‘standard’ laterality profile (right hand bias, left visual bias). A ‘reversed’ profile is rare by comparison, and associates higher self-reported social difficulties and increased rate of autism and/or attention deficit hyperactivity disorder. We highlight the importance of employing a comparative theoretical framing to illuminate how and why different laterization profiles associate with diverging social and cognitive phenotypes

Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment

Dementia has a large negative impact on the global healthcare and society. Diagnosis is rather challenging as there is no standardised test. The purpose of this paper is to conduct an analysis on ADNI data and determine its effectiveness for building classification models to differentiate the categories Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Dementia (DEM), based on tuning three Deep Learning models: two Multi-Layer Perceptron (MLP1 and MLP2) models and a Convolutional Bidirectional Long Short-Term Memory (ConvBLSTM) model. The results show that the MLP1 and MLP2 models accurately distinguish the DEM, MCI and CN classes, with accuracies as high as 0.86 (SD 0.01). The ConvBLSTM model was slightly less accurate but was explored in view of comparisons with the MLP models, and for future extensions of this work that will take advantage of time-related information. Although the performance of ConvBLSTM model was negatively impacted by a lack of visit code data, opportunities were identified for improvement, particularly in terms of pre-processing

Motor-sensory biases are associated with cognitive and social abilities in humans

Across vertebrates, adaptive behaviors, like feeding and avoiding predators, are linked to lateralized brain function. The presence of the behavioral manifestations of these biases are associated with increased task success. Additionally, when an individual’s direction of bias aligns with the majority of the population, it is linked to social advantages. However, it remains unclear if behavioral biases in humans correlate with the same advantages. This large-scale study (N = 313–1661, analyses dependent) examines whether the strength and alignment of behavioral biases associate with cognitive and social benefits respectively in humans. To remain aligned with the animal literature, we evaluate motor-sensory biases linked to motor-sequencing and emotion detection to assess lateralization. Results reveal that moderate hand lateralization is positively associated with task success and task success is, in turn, associated with language fluency, possibly representing a cascade effect. Additionally, like other vertebrates, the majority of our human sample possess a ‘standard’ laterality profile (right hand bias, left visual bias). A ‘reversed’ profile is rare by comparison, and associates higher self-reported social difficulties and increased rate of autism and/or attention deficit hyperactivity disorder. We highlight the importance of employing a comparative theoretical framing to illuminate how and why different laterization profiles associate with diverging social and cognitive phenotypes

Betti numbers for numerical semigroup rings

We survey results related to the magnitude of the Betti numbers of numerical semigroup rings and of their tangent cones.Comment: 22 pages; v2: updated references. To appear in Multigraded Algebra and Applications (V. Ene, E. Miller Eds.

The cloudUPDRS app: a medical device for the clinical assessment of Parkinson's Disease

Parkinson's Disease is a neurological condition distinguished by characteristic motor symptoms including tremor and slowness of movement. To enable the frequent assessment of PD patients, this paper introduces the cloudUPDRS app, a Class I medical device that is an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK. The app follows closely Part III of the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community unsupervised; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. The cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach based on Recurrent Convolutional Neural Networks, to identify failures to follow the movement protocol. We address the latter by developing a machine learning approach to personalize assessments by selecting those elements of the test that most closely match individual symptom profiles and thus offer the highest inferential power, hence closely estimating the patent's overall score

The CloudUPDRS smartphone software in Parkinson’s study: cross-validation against blinded human raters

Digital assessments of motor severity could improve the sensitivity of clinical trials and personalise treatment in Parkinson’s disease (PD) but have yet to be widely adopted. Their ability to capture individual change across the heterogeneous motor presentations typical of PD remains inadequately tested against current clinical reference standards. We conducted a prospective, dual-site, crossover-randomised study to determine the ability of a 16-item smartphone-based assessment (the index test) to predict subitems from the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III) as assessed by three blinded clinical raters (the reference-standard). We analysed data from 60 subjects (990 smartphone tests, 2628 blinded video MDS-UPDRS III subitem ratings). Subject-level predictive performance was quantified as the leave-one-subject-out cross-validation (LOSO-CV) accuracy. A pre-specified analysis classified 70.3% (SEM 5.9%) of subjects into a similar category to any of three blinded clinical raters and was better than random (36.7%; SEM 4.3%) classification. Post hoc optimisation of classifier and feature selection improved performance further (78.7%, SEM 5.1%), although individual subtests were variable (range 53.2–97.0%). Smartphone-based measures of motor severity have predictive value at the subject level. Future studies should similarly mitigate against subjective and feature selection biases and assess performance across a range of motor features as part of a broader strategy to avoid overly optimistic performance estimates