Reviewerly/exHarmony huggingface dataset:
Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals to the classical ones, and a hybrid solution is the recommended approach at present.
Web-Scale Semantic Product Search with Large Language Models

Abstract Dense embedding-based semantic matching is widely used in e-commerce product search to address the shortcomings of lexical matching such as sensitivity to spelling variants. The recent advances in BERT-like language model encoders, have however, not found their way to realtime search due to the strict inference latency requirement imposed on e-commerce websites. While bi-encoder BERT architectures enable fast approximate nearest neighbor search, training them effectively on query-product data remains a challenge due to training instabilities and the persistent generalization gap with cross-encoders. In this work, we propose a four-stage training procedure to leverage large BERT-like models for product search while preserving low inference latency. We introduce query-product interaction pre-finetuning to effectively pretrain BERT bi-encoders for matching and improve generalization. Through offline experiments on an e-commerce product dataset, we show that a distilled small BERT-based model (75M params) trained using our approach improves the search relevance metric by up to 23% over a baseline DSSM-based model with similar inference latency. The small model only suffers a 3% drop in relevance metric compared to the 20x larger teacher. We also show using online A/B tests at scale, that our approach improves over the production model in exact and substitute products retrieved.
Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Abstract Modern computer systems can use different types of hardware acceleration to achieve massive performance improvements. Some accelerators like FPGA and dedicated GPU (dGPU) need optimized data structures for the best performance and often use dedicated memory. In contrast, APUs, which are a combination of a CPU and an integrated GPU (iGPU), support shared memory and allow the iGPU to work together with the CPU on pointer-based data structures. First, we develop an approach for dGPU to accelerate queries in libcuckoo and robin-map and when looking at accelerating insert, updates and erase operations in the original libcuckoo using OneAPI on an APU. We evaluate the dGPU against the CPU variants and our dGPU approach adapted for the CPU and also in a hybrid context by using longer keys on the CPU and shorter keys on the dGPU. In comparison with the original libcuckoo algorithm, our dGPU approach achieves a speed-up of 2.1, and in comparison with the robin-map a speed-up of 1.5. For hybrid workloads, our approach is efficient if long keys are processed on the CPU and short keys are processed on the dGPU. By processing a mixture of 20% long keys on the CPU and 80% short keys on dGPU, our hybrid approach has a 40% higher throughput than the CPU only approach. In addition, we develop a hybrid APU approach for insert, update and erase operations in the original libcuckoo structure focusing on shared memory with iGPU accelerated look-ups of the positions for insert, update and erase operations.
Perceptual Authentication Hashing for Digital Images With Contrastive Unsupervised Learning

In recent years, many perceptual image hashing schemes for content authentication have been proposed based on classical methods and deep learning. However, most existing schemes target specific and limited content-preserving manipulations and cannot provide satisfactory robustness to unknown manipulations. In this work, we propose a new perceptual authentication hashing model for digital images based on contrastive unsupervised learning. In detail, a contrastive augmentation structure is exploited, which can optimize the model through changing the types and strengths of sample augmentation. Also, an integrated loss function is designed by the weighted summing of two components, i.e., the contrastive loss and hash loss, which can help the model learn perceptual feature representation with an unlabeled dataset and effectively improve the robustness and discrimination. Experimental results show that the proposed scheme can achieve superior performance compared with some state-of-the-art schemes, especially robustness to unknown attacks.
Evaluating the Potential of Wavelet Pooling on Improving the Data Efficiency of Light-Weight CNNs

Wavelet pooling (WP) in neural network architectures has recently demonstrated more discriminative power than traditional pooling methods.This is mainly because the latter suffer from spatial information loss while wavelet pooling harnesses the power of spectral information.However, the potential of WP in increasing the data efficiency and the extent of this potential have not been investigated yet.Data efficiency refers to the volume of training data required to attain a certain performance level during inference, e.g., recognition accuracy.In this research, we are concerned with evaluating the data efficiency of WP in light-weight architectures-MobileNets.Across a wide variety of seven datasets/applications including object recognition (CIFAR-10, STL-10, CINIC-10, and Intel Image Classification datasets) and diagnostic imaging (colon diseases, brain tumors, and malaria cell images datasets), and while considering classification accuracy as a performance metric, we show that WP achieves an average data saving that exceeds 30% compared to traditional pooling techniques.For other performance measures, namely, precision, recall, and F1-score, we report an average of 30% data saving for object recognition datasets and 22% saving for diagnostic imaging datasets.By focusing on a light-weight architecture, this research further emphasizes the significance of wavelet pooling in training and testing resources-challenged settings such as the applications of edge computing and green deep learning.
Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model

The identification of drug-drug and chemical-protein interactions is essential for understanding unpredictable changes in the pharmacological effects of drugs and mechanisms of diseases and developing therapeutic drugs. In this study, we extract drug-related interactions from the DDI (Drug-Drug Interaction) Extraction-2013 Shared Task dataset and the BioCreative ChemProt (Chemical-Protein) dataset using various transfer transformers. We propose BERTGAT that uses a graph attention network (GAT) to take into account the local structure of sentences and embedding features of nodes under the self-attention scheme and investigate whether incorporating syntactic structure can help relation extraction. In addition, we suggest T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the relation classification problem by removing the self-attention layer in the decoder block. Furthermore, we evaluated the potential of biomedical relation extraction of GPT-3 (Generative Pre-trained Transformer) using GPT-3 variant models. As a result, T5slim_dec, which is a model with a tailored decoder designed for classification problems within the T5 architecture, demonstrated very promising performances for both tasks. We achieved an accuracy of 91.15% in the DDI dataset and an accuracy of 94.29% for the CPR (Chemical-Protein Relation) class group in ChemProt dataset. However, BERTGAT did not show a significant performance improvement in the aspect of relation extraction. We demonstrated that transformer-based approaches focused only on relationships between words are implicitly eligible to understand language well without additional knowledge such as structural information.
New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images

This paper introduces a new method for real-time high-density impulsive noise elimination applied to medical images. A double process aimed at the enhancement of local data composed of Nested Filtering followed by a Morphological Operation (NFMO) is proposed. The major problem with heavily noisy images is the lack of color information around corrupted pixels. We show that the classic replacement techniques all come up against this problem, resulting in average restoration quality. We only focus on the corrupt pixel replacement phase. For the detection itself, we use the Modified Laplacian Vector Median Filter (MLVMF). To perform pixel replacement, two-window nested filtering is suggested. All noise pixels in the neighborhood scanned by the first window are investigated using the second window. This investigation phase increases the amount of useful information within the first window. The remaining useful information that the second window failed to produce in the case of a very strong connex noise concentration is then estimated using a morphological operation of dilatation. To validate the proposed method, NFMO is first evaluated on the standard image Lena with a range of 10% to 90% impulsive noise. Using the Peak Signal-to-Noise Ratio metric (PSNR), the image denoising quality obtained is compared to the performance of a wide variety of existing approaches. Several noisy medical images are subjected to a second test. In this test, the computation time and image-restoring quality of NFMO are assessed using the PSNR and the Normalized Color Difference (NCD) criteria. Finally, an optimized design for a field-programmable gate array (FPGA) is suggested to implement the proposed method for real-time processing. The proposed solution performs excellent quality restoration for images with high-density impulsive noise. When the proposed NFMO is used on the standard Lena image with 90% impulsive noise, the PSNR reaches 29.99 dB. Under the same noise conditions, NFMO completely restores medical images in an average time of 23 milliseconds with an average PSNR of 31.62 dB and an average NCD of 0.10.
Hand-Controlled User Interfacing for Head-Mounted Augmented Reality Learning Environments

With the rapid expansion of technology and hardware availability within the field of Augmented Reality, building and deploying Augmented Reality learning environments has become more logistically viable than ever before. In this paper, we focus on the development of a new mobile learning experience for a museum by combining multiple technologies to provide additional Human–computer interaction possibilities. This is both to reduce barriers to entry for end-users as well as provide natural interaction methods. Using our method, we implemented a new approach to gesture-based interactions for Augmented Reality interactions by combining two devices, a Leap Motion and a Microsoft HoloLens (1st Generation), via an intermediary device with the use of local-area networking. This was carried out with the intention of comparing this method against alternative forms of Augmented Reality to determine which implementation has the largest impact on adult learners’ ability to retain information. A control group has been used to establish data on memory retention without the use of Augmented Reality technology, along with three focus groups to explore the different methods and locations. Results found that adult learners retain the most overall information when being educated through a traditional lecture, with a statistically significant difference between the methods; however, the use of Augmented Reality resulted in a slower rate of knowledge decay between testing intervals. This contrasts with existing research as adult learners did not respond to the technology in the same way that child and teenage audiences previously have, which suggests that prior research may not be generalisable to all audiences.
Survey of Optimization Algorithms in Modern Neural Networks

The main goal of machine learning is the creation of self-learning algorithms in many areas of human activity. It allows a replacement of a person with artificial intelligence in seeking to expand production. The theory of artificial neural networks, which have already replaced humans in many problems, remains the most well-utilized branch of machine learning. Thus, one must select appropriate neural network architectures, data processing, and advanced applied mathematics tools. A common challenge for these networks is achieving the highest accuracy in a short time. This problem is solved by modifying networks and improving data pre-processing, where accuracy increases along with training time. Bt using optimization methods, one can improve the accuracy without increasing the time. In this review, we consider all existing optimization algorithms that meet in neural networks. We present modifications of optimization algorithms of the first, second, and information-geometric order, which are related to information geometry for Fisher–Rao and Bregman metrics. These optimizers have significantly influenced the development of neural networks through geometric and probabilistic tools. We present applications of all the given optimization algorithms, considering the types of neural networks. After that, we show ways to develop optimization algorithms in further research using modern neural networks. Fractional order, bilevel, and gradient-free optimizers can replace classical gradient-based optimizers. Such approaches are induced in graph, spiking, complex-valued, quantum, and wavelet neural networks. Besides pattern recognition, time series prediction, and object detection, there are many other applications in machine learning: quantum computations, partial differential, and integrodifferential equations, and stochastic processes.
Optimized-Weighted-Speedy Q-Learning Algorithm for Multi-UGV in Static Environment Path Planning under Anti-Collision Cooperation Mechanism

With the accelerated development of smart cities, the concept of a “smart industrial park” in which unmanned ground vehicles (UGVs) have wide application has entered the industrial field of vision. When faced with multiple tasks and heterogeneous tasks, the task execution efficiency of a single UGV is inefficient, thus the task planning research under multi-UGV cooperation has become more urgent. In this paper, under the anti-collision cooperation mechanism for multi-UGV path planning, an improved algorithm with optimized-weighted-speedy Q-learning (OWS Q-learning) is proposed. The slow convergence speed of the Q-learning algorithm is overcome to a certain extent by changing the update mode of the Q function. By improving the selection mode of learning rate and the selection strategy of action, the relationship between exploration and utilization is balanced, and the learning efficiency of multi-agent in complex environments is improved. The simulation experiments in static environment show that the designed anti-collision coordination mechanism effectively solves the coordination problem of multiple UGVs in the same scenario. In the same experimental scenario, compared with the Q-learning algorithm and other reinforcement learning algorithms, only the OWS Q-learning algorithm achieves the convergence effect, and the OWS Q-learning algorithm has the shortest collision-free path for UGVS and the least time to complete the planning. Compared with the Q-learning algorithm, the calculation time of the OWS Q-learning algorithm in the three experimental scenarios is improved by 53.93%, 67.21%, and 53.53%, respectively. This effectively improves the intelligent development of UGV in smart parks.
A large depth-of-field virtual measurement network for non-cooperative 6DOF pose estimation in occlusion scenes

Six degrees of freedom (6DOF) measurement capable of determining the rigid transformation is a vital task in aerospace exploration and autonomous navigation. In this paper, a large depth-of-field virtual measurement network is proposed based on a moving rotating-prism-embedded camera to achieve 6DOF pose estimation for non-cooperative partially occluded targets. It includes stereo recovery using dense beam localization and accurate absolute orientation based on dual quaternions and adaptive scale factors. The former allows the three-dimensional (3D) extraction by multiview large-parallax imaging, while the latter enables the decoupling of translation and orientation without initial value estimation to avoid error accumulation. Compared to the mainstream methods, our method provides high-quality pose metrology, especially for depth extraction. The experiments show that the architecture allows the 3D reconstruction and non-cooperative 6DOF estimation with a large depth of field and object occlusion, solving the problem of difficult compromise between long-baseline imaging and compact configuration.
Efficient Isogeny Proofs Using Generic Techniques

Generating supersingular elliptic curves of unknown endomorphism ring has been a problem vexing isogeny-based cryptographers for several years. A recent development has proposed a trusted setup protocol to generate such a curve, where each participant generates and proves knowledge of an isogeny. Thus, the construction of efficient proofs of knowledge of isogeny has developed new interest. Historically, the isogeny community has assumed that obtaining isogeny proofs of knowledge from generic proof systems, such as zkSNARKs, was not a practical approach. We contribute the first concrete result in this area by applying Aurora (EUROCRYPT’19), Ligero (CCS’17) and Limbo (CCS’21) to an isogeny path relation, and comparing their performance to a state-of-the-art, tailor-made protocol for the same relation. In doing so, we show that modern generic proof systems are competitive when applied to isogeny assumptions, and provide an order of magnitude ( $$3\text {-}10\times $$ ) improvement to proof and verification times, with similar proof sizes. In addition, these proofs provide a stronger notion of soundness, and statistical zero-knowledge; a property that has only recently been achieved in isogeny PoKs. Independently, this technique shows promise as a component in the design of future isogeny-based or other post-quantum protocols.
Panoptic quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology

Panoptic Quality (PQ), designed for the task of "Panoptic Segmentation" (PS), has been used in several digital pathology challenges and publications on cell nucleus instance segmentation and classification (ISC) since its introduction in 2019. Its purpose is to encompass the detection and the segmentation aspects of the task in a single measure, so that algorithms can be ranked according to their overall performance. A careful analysis of the properties of the metric, its application to ISC and the characteristics of nucleus ISC datasets, shows that is not suitable for this purpose and should be avoided. Through a theoretical analysis we demonstrate that PS and ISC, despite their similarities, have some fundamental differences that make PQ unsuitable. We also show that the use of the Intersection over Union as a matching rule and as a segmentation quality measure within PQ is not adapted for such small objects as nuclei. We illustrate these findings with examples taken from the NuCLS and MoNuSAC datasets. The code for replicating our results is available on GitHub ( https://github.com/adfoucart/panoptic-quality-suppl ).
MPool: Motif-Based Graph Pooling

Recently, Graph Neural Networks (GNNs) have emerged as a powerful technique for various graph-related tasks. Current GNN models apply different graph pooling methods that reduce the number of nodes and edges to learn the higher-order structure of the graph in a hierarchical way. However, these methods primarily rely on the one-hop neighborhood and do not consider the higher-order structure of the graph. To address this issue, in this work, we propose a multi-channel Motif-based Graph Pooling method named (MPool) that captures the higher-order graph structure with motif and also considers the local and global graph structure through a combination of selection and clustering-based pooling operations. In the first channel, we develop node selection-based graph pooling by designing a node ranking model considering the motif adjacency of nodes. In the second channel, we develop cluster-based graph pooling by designing a spectral clustering model using motif adjacency. Finally, the result of each channel is aggregated into the final graph representation. We perform extensive experiments and demonstrate that our proposed method outperforms the baseline methods for graph classification tasks on eight benchmark datasets.
Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation

Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein GANs (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple Fully Connected Neural Network (FCN) benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a Convolutional Neural Network (CNN) can be improved slightly. However, this is not the case for a Vision Transformer (ViT).