Modified Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours

Detecting distracted driving behaviours is important to reduce millions of deaths and injuries occurring worldwide. Distracted or anomalous driving behaviours are deviations from the normal driving that need to be identified correctly to alert the driver. However, these driving behaviours do not comprise of one specific type of driving style and their distribution can be different during training and testing phases of a classifier. We formulate this problem as a supervised contrastive learning approach to learn a visual representation to detect normal, and seen and unseen anomalous driving behaviours. We made a change to the standard contrastive loss function to adjust the similarity of negative pairs to aid the optimization. Normally, the (self) supervised contrastive framework contains an encoder followed by a projection head, which is omitted during testing phase as the encoding layers are considered to contain general visual representative information. However, we assert that for supervised contrastive learning task, including projection head will be beneficial. We showed our results on a Driver Anomaly Detection dataset that contains 783 minutes of video recordings of normal and anomalous driving behaviours of 31 drivers from various from top and front cameras (both depth and infrared). We also performed an extra step of fine tuning the labels in this dataset. Out of 9 video modalities combinations, our modified contrastive approach improved the ROC AUC on 7 in comparison to the baseline models (from 3.12% to 8.91% for different modalities); the remaining two models also had manual labelling. We performed statistical tests that showed evidence that our modifications perform better than the baseline contrastive models. Finally, the results showed that the fusion of depth and infrared modalities from top and front view achieved the best AUC ROC of 0.9738 and AUC PR of 0.9772.

Superconductivity of Light-Elements Doped H$ {}_{3} $S

Pressurized hydrogen-rich compounds, which could be viewed as precompressed metallic hydrogen, exhibit high superconductivity, thereby providing a viable route toward the discovery of high-temperature superconductors. Of particular interest is to search for high-temperature superconductors with low stable pressure in terms of pressure-stabilized hydrides. In this work, with the aim of obtaining high-temperature superconducting compounds at low pressure, we attempt to study the doping effects for high-temperature superconductive $ mathrm{H_3S} $ with supercells up to 64 atoms using first principle electronic structure simulations. As a result of various doping, we found that Na doping for $ mathrm{H_3S} $ could lower the dynamically stable pressure by 40 GPa. The results also indicate P doping could enhance the superconductivity of $ mathrm{H_3S} $ system, which is in agreement with previous calculations. Moreover, our work proposed an approach that could reasonably estimate the superconducting critical temperature ($ T_{c} $) of a compound containing a large number of atoms, saving the computational cost significantly for large-scale elements-doping superconductivity simulations.

Throughput Maximization of UAV Networks

In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying $K$ UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the $K$ UAVs is connected. We then propose a $frac{1-1/e}{lfloor sqrt{K} rfloor}$-approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is $frac{1-1/e}{5( sqrt{K} +1)}$), where $e$ is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms.

MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge

Recent years have seen a rapid growth of utilizing graph neural networks (GNNs) in the biomedical domain for tackling drug-related problems. However, like any other deep architectures, GNNs are data hungry. While requiring labels in real world is often expensive, pretraining GNNs in an unsupervised manner has been actively explored. Among them, graph contrastive learning, by maximizing the mutual information between paired graph augmentations, has been shown to be effective on various downstream tasks. However, the current graph contrastive learning framework has two limitations. First, the augmentations are designed for general graphs and thus may not be suitable or powerful enough for certain domains. Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks. Therefore, in this paper, we study graph contrastive learning in the context of biomedical domain, where molecular graphs are present. We propose a novel framework called MoCL, which utilizes domain knowledge at both local- and global-level to assist representation learning. The local-level domain knowledge guides the augmentation process such that variation is introduced without changing graph semantics. The global-level knowledge encodes the similarity information between graphs in the entire dataset and helps to learn representations with richer semantics. The entire model is learned through a double contrast objective. We evaluate MoCL on various molecular datasets under both linear and semi-supervised settings and results show that MoCL achieves state-of-the-art performance.

Energy-preserving fully-discrete schemes for nonlinear stochastic wave equations with multiplicative noise

In this paper, we focus on constructing numerical schemes preserving the averaged energy evolution law for nonlinear stochastic wave equations driven by multiplicative noise. We first apply the compact finite difference method and the interior penalty discontinuous Galerkin finite element method to discretize space variable and present two semi-discrete schemes, respectively. Then we make use of the discrete gradient method and the Pade approximation to propose efficient fully-discrete schemes. These semi-discrete and fully-discrete schemes are proved to preserve the discrete averaged energy evolution law. In particular, we also prove that the proposed fully-discrete schemes exactly inherit the averaged energy evolution law almost surely if the considered model is driven by additive noise. Numerical experiments are given to confirm theoretical findings.

Stochastic logarithmic Schrodinger equations: energy regularized approach

In this paper, we prove the global existence and uniqueness of the solution of the stochastic logarithmic Schrodinger (SlogS) equation driven by additive noise or multiplicative noise. The key ingredient lies on the regularized stochastic logarithmic Schrodinger (RSlogS) equation with regularized energy and the strong convergence analysis of the solutions of (RSlogS) equations. In addition, temporal Holder regularity estimates and uniform estimates in energy space $mathbb H^1(mathcal O)$ and weighted Sobolev space $L^2_{alpha}(mathcal O)$ of the solutions for both SlogS equation and RSlogS equation are also obtained.

Learning Deep Neural Networks under Agnostic Corrupted Supervision

Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Unlike many existing approaches that quantify the quality of the data points (e.g., based on their individual loss values), and filter them accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the average gradient. Even when a corrupted data point failed to be excluded by our algorithm, the data point will have a very limited impact on the overall loss, as compared with state-of-the-art filtering methods based on loss values. Extensive experiments on multiple benchmark datasets have demonstrated the robustness of our algorithm under different types of corruption.

Exploring charm decays with missing energy in leptoquark models

We investigate the possibility that scalar leptoquarks generate consequential effects on the flavor-changing neutral-current decays of charmed hadrons into final states with missing energy ($ ot!!E$) carried away by either standard model or sterile neutrinos. We focus on scenarios involving the $R_2$, $tilde R_2$, and $bar S_1$ leptoquarks and take into account various pertinent constraints, learning that meson-mixing ones and those inferred from collider searches can be of significance. We find in particular that the branching fractions of charmed meson decays $Dto M! ot!!E$, $M=pi,rho$, and $D_sto K^{(*)}! ot!!E$ and singly charmed baryon decays $Lambda_c^+to p! ot!!E$ and $Xi_ctoSigma! ot!!E$ are presently allowed to attain the $10^{-7}$-$10^{-6}$ levels if induced by $R_2$ and that the impact of $tilde R_2$ is comparatively much less. In contrast, the contributions of $bar S_1$, which couples to right-handed up-type quarks and the sterile neutrinos, could lead to branching fractions as high as order $10^{-3}$. This suggests that these charmed hadron decays might be within reach of the BESIII and Belle II experiments or future super charm-tau factories and could serve as potentially promising probes of leptoquark interactions with sterile neutrinos.

Robust Collaborative Learning with Noisy Labels

Learning with curriculum has shown great effectiveness in tasks where the data contains noisy (corrupted) labels, since the curriculum can be used to re-weight or filter out noisy samples via proper design. However, obtaining curriculum from a learner itself without additional supervision or feedback deteriorates the effectiveness due to sample selection bias. Therefore, methods that involve two or more networks have been recently proposed to mitigate such bias. Nevertheless, these studies utilize the collaboration between networks in a way that either emphasizes the disagreement or focuses on the agreement while ignores the other. In this paper, we study the underlying mechanism of how disagreement and agreement between networks can help reduce the noise in gradients and develop a novel framework called Robust Collaborative Learning (RCL) that leverages both disagreement and agreement among networks. We demonstrate the effectiveness of RCL on both synthetic benchmark image data and real-world large-scale bioinformatics data.

Effect of deep-defect excitation on mechanical energy dissipation of single-crystal diamond

The ultra-wide bandgap of diamond distinguishes it from other semiconductors, in that all known defects have deep energy levels that are inactive at room temperature. Here, we present the effect of deep defects on the mechanical energy dissipation of single-crystal diamond experimentally and theoretically up to 973 K. Energy dissipation is found to increase with temperature and exhibits local maxima due to the interaction between phonons and deep defects activated at specific temperatures. A two-level model with deep energies is proposed to well explain the energy dissipation at elevated temperatures. It is evident that the removal of boron impurities can substantially increase the quality factor of room-temperature diamond mechanical resonators. The deep-energy nature of nitrogen bestows single-crystal diamond with outstanding low-intrinsic energy dissipation in mechanical resonators at room temperature or above.