Learning Harmonic Molecular Representations on Riemannian Manifold

Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the prevailing method in the geometric deep learning community. However, the equivariance constraints and message passing in Euclidean space may limit the network expressive power. In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface. HMR offers a multi-resolution representation of molecular geometric and chemical features on 2D Riemannian manifold. We also introduce a harmonic message passing method to realize efficient spectral message passing over the surface manifold for better molecular encoding. Our proposed method shows comparable predictive power to current models in small molecule property prediction, and outperforms the state-of-the-art deep learning models for ligand-binding protein pocket classification and the rigid protein docking challenge, demonstrating its versatility in molecular representation learning.Comment: 25 pages including Appendi

Parsing is All You Need for Accurate Gait Recognition in the Wild

Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait representations. To achieve accurate gait recognition in the wild, this paper presents a novel gait representation, named Gait Parsing Sequence (GPS). GPSs are sequences of fine-grained human segmentation, i.e., human parsing, extracted from video frames, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the GPS representation, we propose a novel human parsing-based gait recognition framework, named ParsingGait. ParsingGait contains a Convolutional Neural Network (CNN)-based backbone and two light-weighted heads. The first head extracts global semantic features from GPSs, while the other one learns mutual information of part-level features through Graph Convolutional Networks to model the detailed dynamics of human walking. Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively evaluate our method and existing gait recognition methods. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait. The code and dataset are available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page: https://gait3d.github.io/gait3d-parsing-h

Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness

Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides to the surgeon with context-aware support and safety. The current go-to strategy for improving performance is the development of new network mechanisms. However, the performance of current state-of-the-art techniques is substantially lower than other surgical tasks. Why is this happening? This is the question that we address in this work. We present the first study to understand the failure of existing deep learning models through the lens of robustness and explainabilty. Firstly, we study current existing models under weak and strong $\delta-$perturbations via adversarial optimisation scheme. We then provide the failure modes via feature based explanations. Our study revels that the key for improving performance and increasing reliability is in the core and spurious attributes. Our work opens the door to more trustworthiness and reliability deep learning models in surgical science

Measuring Perceptual Color Differences of Smartphone Photographs

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this paper, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.Comment: 10 figures, 8 tables, 14 page

Traceable Identity-Based Group Signature

Group signature is a useful cryptographic primitive, which makes every group member sign messages on behalf of a group they belong to. Namely group signature allows that group member anonymously signs any message without revealing his/her specific identity. However, group signature may make the signers abuse their signing rights if there are no measures of keeping them from abusing signing rights in the group signature schemes. So, group manager must be able to trace (or reveal) the identity of the signer by the signature when the result of the signature needs to be arbitrated, and some revoked group members must fully lose their capability of signing a message on behalf of the group they belong to. A practical model meeting the requirement is verifier-local revocation, which supports the revocation of group member. In this model, the verifiers receive the group member revocation messages from the trusted authority when the relevant signatures need to be verified. With the rapid development of identity-based cryptography, several identity-based group signature (IBGS) schemes have been proposed. Compared with group signature based on public key cryptography, IBGS can simplify key management and be used for more applications. Although some identity-based group signature schemes have been proposed, few identity-based group signature schemes are constructed in the standard model and focus on the traceability of signature. In this paper, we present a fully traceable (and verifier-local revocation) identity-based group signature (TIBGS) scheme, which has a security reduction to the computational Diffie–Hellman (CDH) assumption. Also, we give a formal security model for traceable identity-based group signature and prove that the proposed scheme has the properties of traceability and anonymity

Realization of high-dynamic-range broadband magnetic-field sensing with ensemble nitrogen-vacancy centers in diamond

We present a new magnetometry method integrating an ensemble of nitrogen-vacancy (NV) centers in a single-crystal diamond with an extended dynamic range for monitoring the fast changing magnetic-field. The NV-center spin resonance frequency is tracked using a closed-loop frequency locked technique with fast frequency hopping to achieve a 10 kHz measurement bandwidth, thus, allowing for the detection of fast changing magnetic signals up to 0.723 T/s.This technique exhibits an extended dynamic range subjected to the working bandwidth of the microwave source. This extended dynamic range can reach up to 4.3 mT, which is 86 times broader than the intrinsic dynamic range. The essential components for NV spin control and signal processing such as signal generation, microwave frequency control, data processing and readout are integrated in a board-level system. With this platform, we demonstrate broadband magnetometry with an optimized sensitivity of 4.2 nT-Hz-1/2. This magnetometry method has the potential to be implemented in a multichannel frequency locked vector magnetometer suitable for a wide range of practical applications such as magnetocardiography and high-precision current sensors.Comment: 18 pages, 9 figure

MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening

Breast cancer is a major cause of cancer death among women, emphasising the importance of early detection for improved treatment outcomes and quality of life. Mammography, the primary diagnostic imaging test, poses challenges due to the high variability and patterns in mammograms. Double reading of mammograms is recommended in many screening programs to improve diagnostic accuracy but increases radiologists' workload. Researchers explore Machine Learning models to support expert decision-making. Stand-alone models have shown comparable or superior performance to radiologists, but some studies note decreased sensitivity with multiple datasets, indicating the need for high generalisation and robustness models. This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data. MammoDG leverages multi-view mammograms and a novel contrastive mechanism to enhance generalisation capabilities. Extensive validation demonstrates MammoDG's superiority, highlighting the critical importance of domain generalisation for trustworthy mammography analysis in imaging protocol variations

Bypass transition in a boundary layer flow induced by plasma actuators

Bypass transition in ow over a at plate triggered by a pair of Dielectric-barrier-discharge (DBD) plasma actuators mounted on the plate surface and aligned in the streamwise direction is investigated. A 4-species plasma-uid model is used to model the electrohydrodynamic (EHD) force generated by the plasma actuation. A pair of counter-rotating streamwise vortices is created downstream of the actuators, leading to the formation of a high-speed streak in the centre and two low-speed streaks on each side. As the length of actuators increases, more momentum is added to the boundary layer and eventually a turbulent wedge is generated at an almost fixed location. With large spanwise distance between the actuators (wide layout), direct numerical simulations (DNS) indicate that the low-speed streaks on both sides lose secondary stability via an inclined varicose-like mode simultaneously, leaving a symmetric perturbation pattern with respect to the centre of the actuators. Further downstream, the perturbations are tilted by the mean shear of the high- and low-speed streaks and consequently a `W' shape pattern is observed. When the pair of plasma actuators is placed closer (narrow layout) in the spanwise direction, the mean shear in the centre becomes stronger and secondary instability first occurs on the high-speed streak with an asymmetric pattern. Inclined varicose-like and sinuous-like instabilities coexist in the following breakdown of the negative streaks on the side and the perturbations remain asymmetric with respect to the centre. Here the tilting of disturbances is dominated by the mean shear in the centre and the perturbations display a `V' shape. Linear analysis techniques including biglobal stability and transient growth are performed to further examine the uid physics and the aforementioned phenomena at narrow and wide layouts, such as the secondary instabilities, `V' and `W' shapes, the symmetric and asymmetric breakdown, are all observed