IEEE TRANSACTIONS ON INTELLIGENT TRANSPORT A TION SYSTEMS 1 Road Traffic Sign Recognition Method Using Siamese Network Combining Efficient-CNN-Based Encoder Zhenghao Xi, Member, IEEE, Y uchao Shao, Y ang Zheng, Member, IEEE, Xiang Liu, Member, IEEE, Y aqi Liu, and Yitong Cai Abstract -- Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system. However, the noise of complex environment may lead to motion-blur or occlusion problems, which raise the tough challenge to real-time recognition with high accuracy and robust. In this article, we propose IECES-network which with improved encoders and Siamese net. The three-stage approach of our method includes Efficient-CNN based encoders, Siamese backbone and the fully-connected layers. We firstly use convolu-tional encoders to extract and encode the traffic sign features of augmented training samples and standard images. Then, we design the Siamese neural network with Efficient-CNN based encoder and contrastive loss function, which can be trained to improve the robustness of TSR problem when facing the samples of motion-blur and occlusion by computing the distance between inputs and templates. Additionally, the template branch of the proposed network can be stopped when executing the recognition tasks after training to raise the process speed of our real-time model, and alleviate the computational resource and parameter scale. Finally, we recombined the feature code and a fully-connected layer with SoftMax function to classify the codes of samples and recognize the category of traffic signs. The results of experiments on the Tsinghua-T encent 100K dataset and the German Traffic Sign Recognition Benchmark dataset demonstrate the performance of the proposed IECES-network. Compared with other state-of-the-art methods, in the case of motion-blur and occluded environment, the proposed method achieves competitive performance precision-recall and accuracy metric average is 88.1%, 86.43% and 86.1% with a 2.9M lightweight scale, respectively. Moreover, processing time of our model is 0.1s per frame, of which the speed is increased by 1.5 times compared with existing methods. Index T erms-- Traffic signs recognition, Siamese network, efficient-CNN based encoder . Received 11 September 2024; revised 25 November 2024; accepted 9 January 2025.

Nasopharyngeal carcinoma (NPC) is one of the understudied yet deadliest cancers in South East Asia. In Malaysia, the prevalence is identified mainly in Sarawak, among the ethnic of Bidayuh. NPC is often late-diagnosed because it is asymptomatic at the early stage. There are several tissue representations from the nasopharynx biopsy, such as nasopharyngeal inflammation (NPI), lymphoid hyperplasia (LHP), nasopharyngeal carcinoma (NPC) and normal tissue. This paper is our first initiative to identify the difference between NPC, NPI and normal cases. Seven whole slide images (WSIs) with gigapixel resolutions from seven different patients and two hospitals were experimented with using two test setups, consisting of a different set of images. The tissue regions are patched into smaller blocks and classified using DenseNet architecture with 21 dense layers. Two tests are carried out, each for proof of concept (Test 1) and real-test scenario (Test 2). The accuracy achieved for NPC class is 94.8% for Test 1 and 67.0% for Test 2. Keywords: Deep learning, Densenet, Whole slide image, Digital pathology, Nasopharyngeal carcinoma.

Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading 1 * Corresponding author: [Qishuo Cheng]. Email: [qishuoc@uchicago.edu]. 2 expert is trained by a model, and the trading decision is solely based on the prediction results of the model. Secondly, the data source used by the model is relatively simple, and only considers the data of the stock itself, ignoring the impact of the whole market risk on the stock. In this paper, the DQN algorithm is introduced into asset management portfolios in a novel and straightforward way, and the performance greatly exceeds the benchmark, which fully proves the effectiveness of the DRL algorithm in portfolio management. This also inspires us to consider the complexity of financial problems, and the use of algorithms should be fully combined with the problems to adapt. Finally, in this paper, the strategy is implemented by selecting the assets and actions with the largest Q value. Since different assets are trained separately as environments, there may be a phenomenon of Q value drift among different assets (different assets have different Q value distribution areas), which may easily lead to incorrect asset selection. Consider adding constraints so that the Q values of different assets share a Q value distribution to improve results.

This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries.

As more private data is stored and shared digitally, researchers are exploring new ways to protect data against attacks from bad actors. Current silicon technology exploits microscopic differences between computing components to create secure keys, but artificial intelligence (AI) techniques can be used to predict these keys and gain access to data. Now, Penn State researchers have designed a way to make the encrypted keys harder to crack. Led by Saptarshi Das, assistant professor of engineering science and mechanics, the researchers used graphene--a layer of carbon one atom thick--to develop a novel low-power, scalable, reconfigurable hardware security device with significant resilience to AI attacks. They published their findings in Nature Electronics today (May 10).

Nielsen (NLSN) and the University of Oxford today announced a two-year collaboration to advance the use of artificial intelligence (AI) to identify and classify consumer packaged goods (CPG) products on shelves in retail stores. Facilitated between Nielsen's Image Recognition group and the Visual Geometry Group (VGG) at the University of Oxford, this partnership brings together the world's largest pool of product reference data with industry-leading brainpower around AI technology to yield greater accuracy in product identification and discovery. Through this partnership, Nielsen is working directly with University of Oxford Professors Andrew Zisserman and Andrea Vedaldi (Department of Engineering Science), world-renowned computer scientists and pioneers in image recognition and AI research. Zisserman, Vedaldi and their team of research scientists will work together with Nielsen to more precisely and quickly identify and classify in-store products based on product images captured through Nielsen's eCollection solution. The Oxford researchers will focus on building and enhancing the eCollection algorithms with increasingly advanced deep learning capabilities, enabling a more automatic detection of store products, promotions and prices without the need for manual intervention.

We live in ever-changing times, so information we can trust is more important than ever before, and it's not always where our academics agree that's most revealing, but where they disagree. Futuremakers is the fly on the wall to that debate. You may already have read a hundred articles about artificial intelligence and the future of society, but these longer conversations – featuring four of our academics at the cutting edge of research and at the forefront of their profession – explore each topic in detail, from the automation of jobs to the inherent bias of algorithms. In 2013 two Oxford academics published a paper titled'The Future of Employment: How Susceptible Are Jobs to Computerisation?' estimating that 47% of US jobs were at risk of automation. Since then, numerous studies have emerged, arriving at very different conclusions.

Artificial intelligence is split as "narrow AI", designed to perform specific tasks inside a website, and "general AI", which may learn and perform tasks anyplace. Machine learning because the development of latest statistics-based algorithms and models in engineering science is stated as "narrow AI". As such, ML involves procedure statistics, applied computing, and mathematical optimization, whereas AI attracts upon several sciences and technologies: engineering science, mathematics, psychology, linguistics, philosophy, neurobiology, natural philosophy, engineering, etc. AI is regarding making intelligent systems [that will apprehend, learn, reason, plan, perceive, method linguistic communication, act], involving machine intelligence, artificial consciousness, and intelligent communities. ML is simply machine-controlled feature engineering, feature learning or knowledge illustration learning, to mechanically discover the representations required for feature detection or ...

The initiative focuses on joint, integrative research approaches in the social sciences and the engineering sciences. Against the background of the current and emerging developments in the field of "Artificial Intelligence" the Foundation wishes to support projects dealing with the development of new perspectives and insights with a view to shaping the future of society as well as technology. The aim is to enable novel project constellations and interdisciplinary cooperations in a scientific and socially highly topical area through a shift in thinking towards new perspectives and solutions. An essential challenge and at the same time a special opportunity for the participating disciplines lies in the joint development of a specific topic and the associated research issues as well as in the necessary expansion of the respective range of methods. The funding initiative is aimed primarily at postdoctoral researchers and professors at all career levels in the social and engineering sciences who devote themselves to the challenges of artificial intelligence and society in interdisciplinary research constellations.

As part of this development, OMI is becoming part of the University's Department of Engineering Science from 1 August 2016. The development of the OMI's focus will create a hub for machine learning and data analysis at Eagle House, the current home of the OMI and Man AHL's Oxford research lab. The OMI's researchers will be joined by the Department of Engineering Science's Machine Learning Group, a body of around 20 leading machine learning researchers who will relocate to Eagle House. The aim is to foster a stimulating environment composed of researchers focused on machine learning techniques, whereby machine learning and data analytics expertise can be shared and leveraged. In addition, the intention is for the OMI to appoint two new Senior Research Fellows/Associate Professors in machine learning with a specific focus on quantitative finance.