Algebraic shortcuts for leave-one-out cross-validation in supervised network inference

Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings. In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models

Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to classify in the wild videos into three categories: Positive, Neutral and Negative. Recent deep learning models have shown tremendous advances in analyzing interactions between people, predicting human behavior and affective evaluation. Nonetheless, their performance comes from individual-based analysis, which means summing up and averaging scores from individual detections, which inevitably leads to some privacy issues. In this research, we investigated a frugal approach towards a model able to capture the global moods from the whole image without using face or pose detection, or any individual-based feature as input. The proposed methodology mixes state-of-the-art and dedicated synthetic corpora as training sources. With an in-depth exploration of neural network architectures for group-level emotion recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF test set (eleventh place of the challenge). Given that the analysis is unimodal based only on global features and that the performance is evaluated on a real-world dataset, these results are promising and let us envision extending this model to multimodality for classroom ambiance evaluation, our final target application

FormlSlicer: A Model Slicing Tool for Feature-rich State-machine Models

A model of the feature-oriented requirements of a software system usually contains a large number of non-trivial features; each feature may have unintended interactions with other features. It may be difficult to comprehend or verify such a model. Model slicing is a useful approach to overcome such a challenge by enabling views of models of individual features that preserve feature interactions. Model slicing evolves from traditional program slicing; it is a technique to extract a sub-model from the original model with respect to a slicing criterion. In this thesis we focus on one type of model: state-based models (SBMs). Because of the difference in granularity between programs and SBMs, as well as the difficulty of maintaining well-formedness of a sliced SBM, SBM slicing is much more challenging than program slicing. Among a diverse range of slicing approaches, dependence-based slicing is the most popular; it relies on the computation of dependence relations among states and transitions in order to determine which model elements of the original model must be in the slice and which can be omitted. We present a workflow and tool for automatically constructing a feature-based slice from a feature-oriented state-machine model of the requirements of a software system. Each feature in the model is modeled as a complete state-transition machine called a feature-oriented state machine (FOSM). The workflow consists of two tasks—a preprocessing task and a slicing task. The preprocessing task mainly computes three types of dependences: hierarchy dependence (HD), which represents the state hierarchy relation among states in the original model; data dependence (DD), which captures the define-use relationship among transitions with respect to a variable; and control dependence (CD), which captures the notion of whether one state can affect the execution of another state or transition. The slicing task forks off multiple slicing processes; each process considers one of the FOSMs as the feature of interest (FOI)—which is the slicing criterion—and the rest of FOSMs as the rest of the system (ROS)—which is to be sliced. Each slicing process constructs a sliced model to preserve the portion of the ROS that interacts with the FOI. The construction process is multi-staged; it firstly identifies an initial set of transitions in the ROS that directly affect the FOI; it then finds more states and transitions in the transitive closure of dependences; and it eventually restructures the model to further reduce the model size and maintain its well-formedness property. We provide a correctness proof that shows that the resulting sliced models simulate the original model, by proving that an execution step of a given execution trace in the original model can always be projected to an execution step of at least one execution trace in the sliced model. Our proposed slicing workflow has been implemented in a tool called FormlSlicer. We conducted an empirical evaluation that demonstrates that, on average, the ROS of a sliced model has 23.0% of states, 15.7% of transitions, 32.8% of regions and 19.3% of variables of the ROS of the original model

Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this paper, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches across all standard evaluation settings

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability