Person re-identification (re-ID) via 3D skeletons is an important emerging topic with many merits. Existing solutions rarely explore valuable body-component relations in skeletal structure or motion, and they typically lack the ability to learn general representations with unlabeled skeleton data for person re-ID. This paper proposes a generic unsupervised Skeleton Prototype Contrastive learning paradigm with Multi-level Graph Relation learning (SPC-MGR) to learn effective representations from unlabeled skeletons to perform person re-ID. Specifically, we first construct unified multi-level skeleton graphs to fully model body structure within skeletons. Then we propose a multi-head structural relation layer to comprehensively capture relations of physically-connected body-component nodes in graphs. A full-level collaborative relation layer is exploited to infer collaboration between motion-related body parts at various levels, so as to capture rich body features and recognizable walking patterns. Lastly, we propose a skeleton prototype contrastive learning scheme that clusters feature-correlative instances of unlabeled graph representations and contrasts their inherent similarity with representative skeleton features ("skeleton prototypes") to learn discriminative skeleton representations for person re-ID. Empirical evaluations show that SPC-MGR significantly outperforms several state-of-the-art skeleton-based methods, and it also achieves highly competitive person re-ID performance for more general scenarios.

Edtech startup Mindjoy reached more than 1 200 children between the ages of 11 and 15 during the July and August winter breaks with its artificial intelligence (AI) hackathons. Hackathons were hosted by 14 schools in KwaZulu-Natal, Gauteng and the Western Cape, as well as virtually for children in Kenya and the Netherlands. Plans are in place to reach a further 10 schools by the end of September, in an effort to help learners come to grips with the Fourth Industrial Revolution (4IR) and what AI means for their future. Mindjoy's hackathons immerse students in a world of code and allow them to learn with some of the most advanced AI technology in the world – GPT-3 created by OpenAI. Students are given "kid-shaped" problems – such as doing their homework – to solve, and a technology to use to build solutions to the problems.

Incorrectly sized balloon catheters can lead to increased post-surgical complications, yet even with preoperative imaging, correct selection remains a challenge. With limited feedback during surgery, it is difficult to verify correct deployment. We propose the use of integrated impedance measurements and Electrical Impedance Tomography (EIT) imaging to assess the deformation of the balloon and determine the size and shape of the surrounding lumen. Previous work using single impedance measurements, or pressure data and analytical models, whilst demonstrating high sizing accuracy, have assumed a circular cross section. Here we extend these methods by adding a multitude of electrodes to detect elliptical and occluded lumen and obtain EIT images to localise deformations. Using a 14 Fr (5.3 mm) catheter as an example, numerical simulations were performed to find the optimal electrode configuration of two rings of 8 electrodes spaced 10 mm apart. The simulations predicted that the maximum detectable aspect ratio decreased from 0.9 for a 14mm balloon to 0.5 at 30mm. The sizing and ellipticity detection results were verified experimentally. A prototype robotic balloon catheter was constructed to automatically inflate a compliant balloon while simultaneously recording EIT and pressure data. Data were collected in experiments replicating stenotic vessels with an elliptical and asymmetrical profile, and the widening of a lumen during angioplasty. After calibration, the system was able to correctly localise the occlusion and detect aspect ratios of 0.75. EIT images further localised the occlusion and visualised the dilation of the lumen during balloon inflation.

Commonsense reasoning is an appealing topic in natural language processing (NLP) as it plays a fundamental role in supporting the human-like actions of NLP systems. With large-scale language models as the backbone, unsupervised pre-training on numerous corpora shows the potential to capture commonsense knowledge. Current pre-trained language model (PLM)-based reasoning follows the traditional practice using perplexity metric. However, commonsense reasoning is more than existing probability evaluation, which is biased by word frequency. This paper reconsiders the nature of commonsense reasoning and proposes a novel commonsense reasoning metric, Non-Replacement Confidence (NRC). In detail, it works on PLMs according to the Replaced Token Detection (RTD) pre-training objective in ELECTRA, in which the corruption detection objective reflects the confidence on contextual integrity that is more relevant to commonsense reasoning than existing probability. Our proposed novel method boosts zero-shot performance on two commonsense reasoning benchmark datasets and further seven commonsense question-answering datasets. Our analysis shows that pre-endowed commonsense knowledge, especially for RTD-based PLMs, is essential in downstream reasoning.

Semantic search for candidate retrieval is an important yet neglected problem in retrieval-based Chatbots, which aims to select a bunch of candidate responses efficiently from a large pool. The existing bottleneck is to ensure the model architecture having two points: 1) rich interactions between a query and a response to produce query-relevant responses; 2) ability of separately projecting the query and the response into latent spaces to apply efficiently in semantic search during online inference. To tackle this problem, we propose a novel approach, called Multitask-based Semantic Search Neural Network (MSSNN) for candidate retrieval, which accomplishes query-response interactions through multi-tasks. The method employs a Seq2Seq modeling task to learn a good query encoder, and then performs a word prediction task to build response embeddings, finally conducts a simple matching model to form the dot-product scorer. Experimental studies have demonstrated the potential of the proposed approach.

Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction. Previous methods that train DNNs in low-precision typically keep a copy of weights in high-precision during the weight updates. Directly training with low-precision weights leads to accuracy degradation due to complex interactions between the low-precision number systems and the learning algorithms. To address this issue, we develop a co-designed low-precision training framework, termed LNS-Madam, in which we jointly design a logarithmic number system (LNS) and a multiplicative weight update algorithm (Madam). We prove that LNS-Madam results in low quantization error during weight updates, leading to stable performance even if the precision is limited. We further propose a hardware design of LNS-Madam that resolves practical challenges in implementing an efficient datapath for LNS computations. Our implementation effectively reduces energy overhead incurred by LNS-to-integer conversion and partial sum accumulation. Experimental results show that LNS-Madam achieves comparable accuracy to full-precision counterparts with only 8 bits on popular computer vision and natural language tasks. Compared to FP32 and FP8, LNS-Madam reduces the energy consumption by over 90% and 55%, respectively.

A precise forecast for droughts is of considerable value to scientific research, agriculture, and water resource management. With emerging developments of data-driven approaches for hydro-climate modeling, this paper investigates an AutoML-based framework to forecast droughts in the U.S. Compared with commonly-used temporal deep learning models, the AutoML model can achieve comparable performance with less training data and time. As deep learning models are becoming popular for Earth system modeling, this paper aims to bring more efforts to AutoML-based methods, and the use of them as benchmark baselines for more complex deep learning models.

The European Union is likely to introduce among the first, most stringent, and most comprehensive AI regulatory regimes of the world's major jurisdictions. In this report, we ask whether the EU's upcoming regulation for AI will diffuse globally, producing a so-called "Brussels Effect". Building on and extending Anu Bradford's work, we outline the mechanisms by which such regulatory diffusion may occur. We consider both the possibility that the EU's AI regulation will incentivise changes in products offered in non-EU countries (a de facto Brussels Effect) and the possibility it will influence regulation adopted by other jurisdictions (a de jure Brussels Effect). Focusing on the proposed EU AI Act, we tentatively conclude that both de facto and de jure Brussels effects are likely for parts of the EU regulatory regime. A de facto effect is particularly likely to arise in large US tech companies with AI systems that the AI Act terms "high-risk". We argue that the upcoming regulation might be particularly important in offering the first and most influential operationalisation of what it means to develop and deploy trustworthy or human-centred AI. If the EU regime is likely to see significant diffusion, ensuring it is well-designed becomes a matter of global importance.

Vice President, Dr Mahamadu Bawumia has refuted claims by a section of the public that automation and digitalisation will create an undesirable era of joblessness where robots will replace manpower demands of industry. Conservatives have, over the years, argued that advancement in technology and widespread deployment of robots would put a chunk of the youthful working population out of work. But speaking at the inauguration of Academic City University College in Accra Wednesday, Dr. Bawumia said studies have shown that that argument is unfounded. He argued that rather, a highly digitalised establishment with high use of robots and other digital technology increased staff numbers compared to institutions with limited digitalisation. According to him, advancement in every civilized society according to the vice president will largely rely on advancement in Artificial intelligence and ICT.

Can an artificial intelligence (AI) system be an inventor? Stephen Thaler recently submitted two patent applications for which an artificial intelligence system named "DABUS" was listed as the sole inventor. Specifically, the first application was directed to a food or beverage container that facilitates stacking.1 The second application was directed to a light device including a neural flame that serves as a signal beacon for human detection.2 The USPTO denied the patent applications for failing to list any human as an inventor.