Breakthroughs In Deep Learning Research Articles

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-consuming and labor-intensive. Several computational methods have been developed to predict antibody-antigen interaction while suffering from the lack of characterizing the underlying structure of the antibody. Beneficial from the recent breakthroughs in deep learning for antibody structure prediction, we propose a novel neural network architecture to predict antibody-antigen interaction. We first introduce AbAgIPA: an antibody structure prediction network to obtain the antibody backbone structure, where the structural features of antibodies and antigens are encoded into representation vectors according to the amino acid physicochemical features and Invariant Point Attention (IPA) computation methods. Finally, the antibody-antigen interaction is predicted by global max pooling, feature concatenation, and a fully connected layer. We evaluated our method on antigen diversity and antigen-specific antibody-antigen interaction datasets. Additionally, our model exhibits a commendable level of interpretability, essential for understanding underlying interaction mechanisms. Quantitative experimental results demonstrate that the new neural network architecture significantly outperforms the best sequence-based methods as well as the methods based on residue contact maps and graph convolution networks (GCNs). The source code is freely available on GitHub at https://github.com/gmthu66/AbAgIPA .

A Survey on Classifying Ocular Diseases Using Deep Learning and Machine Learning Techniques

In ophthalmology, using fundus images to identify ocular diseases early may pose challenges for clinicians. Manually diagnosing ocular conditions is time-consuming, difficult, and requires experimentation. As a result, technology was created to help computers differentiate between ocular diseases. It is possible to create a system of this type due to different learning algorithms based on visual capabilities. Recent breakthroughs in deep learning and machine learning have led to the development of intelligent systems that improve accuracy and efficiency in classifying eye diseases. The purpose of this study is to conduct a comprehensive survey of modern systems that classify ocular problems using different methods, including pre-trained deep learning networks, using the Ocular Disease Intelligent Recognition (ODIR) dataset. The goal is to build and train a model that can recognize and classify ocular disorders. Previous research indicates the increasing use of deep learning techniques. CNN-based methods have spread widely in this field, compared to traditional manual procedures, due to their outstanding results. The most prominent deep learning techniques are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and various learning methods for increasing and transferring data. The survey highlights the potential of these systems to enhance classification accuracy and sensitivity while addressing challenges such as data availability, interpretability, and integration with clinical practice.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits.

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates dis-continuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

Rotifer detection and tracking framework using deep learning for automatic culture systems

Although rotifers (Brachionus plicatilis sp. complex) are an important first feed source in marine fish aquaculture, their management is quite time-consuming because their populations and movements need to be monitored daily. This management is still performed manually, and automation is required. If we could make good use of the recent breakthroughs in deep learning, the automating a rotifer culture system could be realized. We propose a deep learning framework for detecting and tracking rotifers as a basis for such automation and carefully verify its accuracy. Experimental results showed that a mean average precision of 88.5 % was achieved for detection, and a higher-order tracking accuracy of 88.7 % was achieved for tracking, indicating the suitability of deep learning methods for predicting the state of rotifers. In addition, this research will contribute to the development of the field by releasing the trained model and code for visualizing the tracking results, as well as an annotated dataset with over 30,000 instances.

Foundation model of neural activity predicts response to new stimulus types and anatomy.

The complexity of neural circuits makes it challenging to decipher the brain's algorithms of intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brain's computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

Application of Target Detection Based on Deep Learning in Intelligent Mineral Identification

In contemporary society, rich in mineral resources, efficiently and accurately identifying and classifying minerals has become a prominent issue. Recent advancements in artificial intelligence, particularly breakthroughs in deep learning, have offered new solutions for intelligent mineral recognition. This paper introduces a deep-learning-based object detection model for intelligent mineral identification, specifically employing the YOLOv8 algorithm. The model was developed with a focus on seven common minerals: biotite, quartz, chalcocite, silicon malachite, malachite, white mica, and pyrite. During the training phase, the model learned to accurately recognize and classify these minerals by analyzing and annotating a large dataset of mineral images. After 258 rounds of training, a stable model was obtained with high performance on key indicators such as Precision, Recall, mAP50, and mAP50–95, with values stable at 0.91766, 0.89827, 0.94300, and 0.91696, respectively. In the testing phase, using samples provided by the Geological and Mineral Museum at the School of Earth Sciences and Engineering, Sun Yat-sen University, the model successfully identified all test samples, with 83% of them having a confidence level exceeding 87%. Despite some potential misclassifications, the results of this study contribute valuable insights and practical experience to the development of intelligent mineral recognition technologies.

Revolutionising Ai Deployment: Survey On Hardware Acceleration for Machine Learning Optimisation and Resilience

This compilation of research studies holds the utmost significance in hardware acceleration for machine learning. In our current era, characterised by the exponential growth of artificial intelligence (AI) applications, these studies tackle crucial challenges in optimising neural network accelerators' performance, energy efficiency, and resilience. The importance lies in their potential to revolutionise AI implementation across various domains. Efficient hardware accelerators are a cornerstone in unlocking the full potential of AI, enabling breakthroughs in deep learning, high-speed train fault detection and isolation, and numerous other applications. By improving memory management, facts placement, bus scheduling, and fault tolerance, that research paves the way for AI structures which are both powerful and sustainable, making AI accessible and impactful in a wide variety of fields. This research is important for fostering the growth and adoption of AI, ultimately remodelling how we interact with technology and facts in our daily lives.

An Analysis on Recent Approaches for Image Captioning

Image captioning is an interdisciplinary area that uses techniques from computer vision and natural language processing to provide a textual description of a picture. The Image captioning task is the process of understanding the scene present in the image by identifying objects and associated actions present to create a meaningful human-like caption which can be used for wide range of applications, including image retrieval, video indexing, assistive technology for the visually impaired, content-based image search, biomedicine, and autonomous cars. Formerly, Machine Learning was utilized for this purpose which will be extensive use of hand-crafted features such as Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), the Histogram of Oriented Gradients (HOG), and combinations of these features. Extracting handmade characteristics from huge datasets is not straightforward or viable. Many deep learning-based techniques were later proposed. Deep Learning retrieval and template-based approaches were presented; however, both had drawbacks such as losing crucial objects. Recent breakthroughs in deep learning and natural language processing have resulted in considerable increases in image captioning system performance which involves adopting attention mechanisms, transformer-based architectures, multi modal connections, Object-Detection based encoder-decoder and many others. In this survey will explore some of the most recent techniques for image captioning, the datasets and evaluation measures that have been employed in deep learning-based automatic image captioning. The ultimate intention of this study is to act as a guide for researchers by emphasizing future directions for research work. Index Terms: image captioning, computer vision, deep learning, Textual description, natural language processing.

Crossing the principle–practice gap in AI ethics with ethical problem-solving

AbstractThe past years have presented a surge in (AI) development, fueled by breakthroughs in deep learning, increased computational power, and substantial investments in the field. Given the generative capabilities of more recent AI systems, the era of large-scale AI models has transformed various domains that intersect our daily lives. However, this progress raises concerns about the balance between technological advancement, ethical considerations, safety measures, and financial interests. Moreover, using such systems in sensitive areas amplifies our general ethical awareness, prompting a re-emergence of debates on governance, regulation, and human values. However, amidst this landscape, how to bridge the principle–practice gap separating ethical discourse from the technical side of AI development remains an open problem. In response to this challenge, the present work proposes a framework to help shorten this gap: ethical problem-solving (EPS). EPS is a methodology promoting responsible, human-centric, and value-oriented AI development. The framework’s core resides in translating principles into practical implementations using impact assessment surveys and a differential recommendation methodology. We utilize EPS as a blueprint to propose the implementation of an Ethics as a Service Platform, currently available as a simple demonstration. We released all framework components openly and with a permissive license, hoping the community would adopt and extend our efforts into other contexts. Available in the following URL https://nkluge-correa.github.io/ethical-problem-solving/.

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement.

Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research.

AI on the Road: NVIDIA Jetson Nano-Powered Computer Vision-Based System for Real-Time Pedestrian and Priority Sign Detection

Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.

Prospective Role of Foundation Models in Advancing Autonomous Vehicles.

With the development of artificial intelligence and breakthroughs in deep learning, large-scale foundation models (FMs), such as generative pre-trained transformer (GPT), Sora, etc., have achieved remarkable results in many fields including natural language processing and computer vision. The application of FMs in autonomous driving holds considerable promise. For example, they can contribute to enhancing scene understanding and reasoning. By pre-training on rich linguistic and visual data, FMs can understand and interpret various elements in a driving scene, and provide cognitive reasoning to give linguistic and action instructions for driving decisions and planning. Furthermore, FMs can augment data based on the understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data collection. The enhancement can subsequently lead to improvement in the accuracy and reliability of autonomous driving systems. Another testament to the potential of FMs' applications lies in world models, exemplified by the DREAMER series, which showcases the ability to comprehend physical laws and dynamics. Learning from massive data under the paradigm of self-supervised learning, world models can generate unseen yet plausible driving environments, facilitating the enhancement in the prediction of road users' behaviors and the off-line training of driving strategies. In this paper, we synthesize the applications and future trends of FMs in autonomous driving. By utilizing the powerful capabilities of FMs, we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving, consequently advancing overall safety in this domain.

Automatic Segmentation of Oracle Bone Inscriptions Using YOLOv8

Oracle bones inscription is an important part of Chinese cultural heritage, and the automatic segmentation technology of oracle bones is of great significance to promote the research of oracle bones. The main challenges for automatic segmentation of oracle bone inscriptions include the interference of complex backgrounds, the extraction of text regions of tiny sizes, and the problems of noise and cracks in images, which are hard to deal with using traditional image processing and machine learning techniques. With the development of artificial intelligence technology, especially the breakthrough of deep learning and convolutional neural network (CNN) in image recognition, new solutions are provided for automatic segmentation of oracle bone inscriptions. In this paper, we introduce the YOLOv8 model to the automatic segmentation task of oracle bone images. The experimental results show that the YOLOv8 model achieves satisfactory performance in this task, which verifies the effectiveness of advanced deep learning models, and may provide technical support for the digitization and cultural inheritance of oracle bones in future work.

Assessing the Impact of Communicative Artificial Intelligence Based Accounting Information Systems on Small and Medium Enterprises

Recent breakthroughs in deep learning have led to the development of cutting-edge industrial applications of Communicative Artificial Intelligence (AI), making it indispensable for businesses aiming to maintain a competitive edge. Consequently, artificial intelligence is no longer exclusive to large corporations; it now impacts businesses of all sizes, including small and medium-sized enterprises (SMEs), serving as a tool for command in the production and communication of crucial business aspects. This article delves into the extent of Communicative AI adoption by SMEs in Indonesia, shedding light on issues related to implementing industrial AI applications. To achieve this, a sample of SMEs participated in a structured online survey. Currently, AI adoption among SMEs in Indonesia is minimal. The reluctance is primarily attributed to high costs, extended duration, and inherent risks of developing proprietary applications. Instead, SMEs are heavily relying on AI-as-a-service and other cloud-based solutions. Various factors contribute to businesses' hesitancy. The slow progress in SME implementation indicates misunderstandings related to data and a lack of knowledge, influencing how these enterprises perceive the obstacles they encounter.

Complex-Valued Convolution and Frequency Global Filter for Automatic Modulation Recognition

Automatic modulation recognition (AMR) plays an important role in cognitive radio and dynamic spectrum access, which has been widely applied in military and civilian applications. Due to the breakthroughs in deep learning (DL), DL-based AMR methods are becoming extremely popular. However, most existing DL-based methods are unable to deal with complex format data, and learning the mappings from the time series or its transformed representation to the true modulation type directly is difficult. To address these difficulties, this letter presents a complex-valued convolution and frequency global filter unit (CGFU), and proposes a hybrid neural network, namely CGF-HNN, which can efficiently exploit features from different domains. We evaluate the recognition performance of the proposed model on two well-known datasets, i.e., RML2016.10a and RML2018.01a. Simulation results show that the proposed model outperforms the existing state-of-the-art models.

AI-Bazaar: A Cloud-Edge Computing Power Trading Framework for Ubiquitous AI Services

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

A unified microstructure segmentation approach via human-in-the-loop machine learning

Microstructure segmentation, a technique for extracting structural statistics from microscopy images, is an essential step for establishing quantitative structure–property relationships in a broad range of materials research areas. However, the task is challenging due to the morphological complexity and diversity of structural constituents. While recent breakthroughs in deep learning have led to significant progress in microstructure segmentation, there remain two fundamental challenges to its further diffusion: prohibitive annotation costs and an unreliable decision-making process. To tackle these challenges, we propose a human-in-the-loop machine learning framework for unified microstructure segmentation, which leverages recent advances in both weakly supervised learning and active learning techniques. The key idea behind our approach lies in the integration of human and machine capabilities to make not only precise but also reliable microstructure segmentation at minimal annotation costs. Extensive experiments demonstrate the generality of our framework across different material classes, structural constituents, and microscopic imaging modalities.

A knowledge-inherited learning for intelligent metasurface design and assembly

Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes. Inspired by object-oriented C++ programming, we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design. Each inherited neural network carries knowledge from the “parent” metasurface and then is freely assembled to construct the “offspring” metasurface; such a process is as simple as building a container-type house. We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces, with accuracies that reach 86.7%. Furthermore, we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities. Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices.

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system's capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.

Intelligent identification of pavement cracks based on PSA-Net

The identification of pavement cracks is critical for ensuring road safety. Currently, manual crack detection is quite time-consuming. To address this issue, automated pavement crack-detecting technology is required. However, automatic pavement crack recognition remains challenging, owing to the intensity heterogeneity of cracks and the complexity of the backdrop, e.g., low contrast of damages and backdrop may have shadows with comparable intensity. Motivated by breakthroughs in deep learning, we present a new network architecture combining the feature pyramid with the attention mechanism (PSA-Net). In a feature pyramid, the network integrates spatial information and underlying features for crack detection. During the training process, it improves the accuracy of automatic road crack recognition by nested sample weighting to equalize the loss caused by simple and complex samples. To verify the effectiveness of the suggested technique, we used a dataset of real road cracks to test it with different crack detection methods.