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

Deep learning topology-preserving EEG-based images for autism detection in infants

Developing digital biomarkers that would enable reliable detection of autism-ASD early in life is challenging because of the variability in the presentation of the autistic disorder and the need for simple measurements that could be implemented routinely during checkups. Electroencephalography, widely known as EEG, is an electrophysiological monitoring method that has been explored as a potential clinical tool for monitoring atypical brain function. EEG measurements were collected from 101 infants, beginning at 12 to 15 months of age and continuing until 36 months of age. In contrast to previous work in the literature that analysed EEG signals, our approach considers EEG-as-an-image using an appropriate signal transformation that preserves the spatial location of the EEG signals to create RGB images. It employs Residual neural networks and transfer learning to detect atypical brain function. Prediction of the clinical diagnostic outcome of ASD or not ASD at 36 months was accurate from as early as 12 months of age. This shows that using end-to-end deep learning is a viable way of extracting useful digital biomarkers from EEG measurements for predicting autism in infants

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

PDKit: a data science toolkit for the digital assessment of Parkinson's Disease

PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson's Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license

Deep learning Parkinson's from smartphone data

The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely 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; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how 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 used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score

Recherche des interactions entre forme et fonction de la main chez les humains actuels dans la perspective de mieux apprécier l’évolution de la dextérité humaine

La main humaine a changé de forme et de fonction tout au long de notre évolution en raison, entre autres, de la bipédie terrestre, de l’utilisation d’outils et de l’asymétrie directionnelle telle que la préférence manuelle. Cependant, nous manquons d’informations sur la variabilité potentielle des facteurs clés pouvant affecter la force de préhension et la dextérité, en particulier la taille, la forme et la fonction quotidienne de la main. Nous avons mesuré la force de préhension par pincement et la dextérité manuelle dans un échantillon hétérogène et transversal d’humains actuels (n=556), pour tester les effets potentiels du sexe, de l’âge (17 à 82 ans), des asymétries manuelles, de la morphologie de la main, et des activités manuelles fréquemment pratiquées. Nous avons trouvé un effet significatif du sexe sur la force de pincement et la dextérité, avec les hommes plus forts mais les femmes meilleures pour l’exercice de précision, tandis que la dominance de la main a un effet significatif sur la dextérité mais pas sur la force de pincement. Les hommes droitiers étaient plus forts que les gauchers, pour les deux mains, mais pas plus précis, alors que les femmes gauchères étaient plus précises avec leur main non dominante que les femmes droitières. Les hommes et femmes avec de larges mains étaient plus forts, mais pas plus précis, que ceux avec de longues mains, alors que la taille des doigts n’a eu aucun effet. La pratique fréquente d’instruments de musique manuels a significativement augmenté la dextérité des femmes et cela pour les deux mains. Les résultats indiquent différents modèles d’asymétries manuelles et de fonction de la main, améliorant notre compréhension du lien entre forme et fonction pour les deux mains et apportant un contexte de référence crucial pour mieux comprendre l’évolution humaine de la dextérité.</p

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.</p