Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one

Neural-Symbolic Temporal Decision Trees for Multivariate Time Series Classification

Multivariate time series classification is a widely known problem, and its applications are ubiquitous. Due to their strong generalization capability, neural networks have been proven to be very powerful for the task, but their applicability is often limited by their intrinsic black-box nature. Recently, temporal decision trees have been shown to be a serious alternative to neural networks for the same task in terms of classification performances, while attaining higher levels of transparency and interpretability. In this work, we propose an initial approach to neural-symbolic temporal decision trees, that is, an hybrid method that leverages on both the ability of neural networks of capturing temporal patterns and the flexibility of temporal decision trees of taking decisions on intervals based on (possibly, externally computed) temporal features. While based on a proof-of-concept implementation, in our experiments on public datasets, neural-symbolic temporal decision trees show promising results

PINK1 Defect Causes Mitochondrial Dysfunction, Proteasomal Deficit and α-Synuclein Aggregation in Cell Culture Models of Parkinson's Disease

Mutations in PTEN induced kinase 1 (PINK1), a mitochondrial Ser/Thr kinase, cause an autosomal recessive form of Parkinson's disease (PD), PARK6. Here, we report that PINK1 exists as a dimer in mitochondrial protein complexes that co-migrate with respiratory chain complexes in sucrose gradients. PARK6 related mutations do not affect this dimerization and its associated complexes. Using in vitro cell culture systems, we found that mutant PINK1 or PINK1 knock-down caused deficits in mitochondrial respiration and ATP synthesis. Furthermore, proteasome function is impaired with a loss of PINK1. Importantly, these deficits are accompanied by increased α-synclein aggregation. Our results indicate that it will be important to delineate the relationship between mitochondrial functional deficits, proteasome dysfunction and α-synclein aggregation

Interactionwise Semantic Awareness in Visual Relationship Detection

Visual Relationship Detection (VRD) is a relatively young research area, where the goal is to develop prediction models for detecting the relationships between objects depicted in an image. A relationship is modeled as a subject-predicate-object triplet, where the predicate (e.g an action, a spatial relation, etc. such as “eat”, “chase” or “next to”) describes how the subject and the object are interacting in the given image. VRD can be formulated as a classification problem, but suffers from the effects of having a combinatorial output space; some of the major issues to overcome are long-tail class distribution, class overlapping and intra-class variance. Machine learning models have been found effective for the task and, more specifically, many works proved that combining visual, spatial and semantic features from the detected objects is key to achieving good predictions. This work investigates on the use of distributional embeddings, often used to discover/encode semantic information, in order to improve the results of an existing neural network-based architecture for VRD. Some experiments are performed in order to make the model semantic-aware of the classification output domain, namely, predicate classes. Additionally, different word embedding models are trained from scratch to better account for multi-word objects and predicates, and are then fine-tuned on VRD-related text corpora. We evaluate our methods on two datasets. Ultimately, we show that, for some set of predicate classes, semantic knowledge of the predicates exported from trained-fromscratch distributional embeddings can be leveraged to greatly improve prediction, and it’s especially effective for zero-shot learning

Experimental Investigation of Coupled Conduction and Laminar Convection in a Circular Tube

Wall heat conduction effects on laminar flow heat transfer are experimentally investigated. The steady flow of water through a uniformly heated copper pipe is considered in the experiment, which covers a range of Reynolds numbers from 500 to 1900. The thermal behaviour of the test section is simulated numerically and the influence of conduction along the pipe wall is therefore accounted for in the reduction of the data. Fully developed flow results satisfactorily compare with predictions by a theoretical method previously developed by the authors [Heat Technol. 2,72 (1984)]. Results are also reported for the case where the velocity profile is partially developed at the inlet of the heat transfer section. The combined effects on heat transfer of flow development and of wall axial heat conduction are discussed

Indagine sperimentale sullo scambio termico laminare in condotti cilindrici: modellazione dell'apparato mediante tecnica numerica

Si presenta la tecnica di modellazione dell'apparato sperimentale impiegato per lo studio dello scambio termico coniugato in condotti. In particolare si illustra il metodo di discretizzazione agli elementi finiti della parete riscaldante, modellata come costituita da due strati coassiali, l'uno generativo e l'altro puramente conduttivo

Thermal Coupling in Laminar Double Pipe Heat Exchangers

Thermal interaction between the streams of laminar flow double-pipe heat exchangers is investigated theoretically by accounting for axial conduction along the wall separating the fluids. In a countercurrent arrangement, thermal coupling is demonstrated to have a definite influence on all the more important heat transfer parameters, such as the wall temperature, the heat flux density, the local entropy production rate, and the Nusselt number distributions. The overall performance of the device is considered under a second law point of view, and a complete parametric study is carried out

Conjugated Heat Transfer in a Circular Duct with Uniform and non-Uniform Wall Thickness

The interaction between convection and axial heat conduction along a pipe is analysed assuming third kind boundary conditions at the outer face of the duct. Wall thickness is assumed to be either uniform or having periodic step variations in the axial direction. Axial heat conduction is found to have a definite influence on the heat flux and the Nusselt number distributions. However, the overall heat flux can accurately be predicted by ordinary methods disregarding axial conduction in the wall

A method to solve conjugate heat transfer problems: the case of fully developed laminar flow in a pipe

A simple fast and general technique combining the superposition principle with a finite element method is proposed to deal with conjugate heat transfer problems. The method is employed to consider the wall conduction effect on heat transfer to fully developed laminar flow through a pipe whose exterior boundary is uniformly heated along a finite length. Results are given for two values of each of the four parameters determining the relative importance of axial conduction: the Péclet number, the wall to fluid conductivity ratio, and the dimensionless thick ness and length of the heated section of the pipe

Della potestà e della politia della Chiesa : trattati due contro le nuove opinioni di Pietro Giannone...

Port. a dos tintas, con grab. xil.Esc. xil. en p. 548.Sign.: []2, a-c4, A-3X4, 3Y