One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when simulated on real IBMQ processors. As test images, we use binary images with simple patterns, greyscale MNIST numbers and MNIST fashion images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy centre (NVs) in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.

Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. Given a free-text reported term (RT) such as "pain of right thigh to the knee", the task is to identify the matching lowest-level term (LLT) - in this case "unilateral leg pain" - from a very large and continuously growing repository of standardized medical terms. However, automating this task is challenging due to a large number of LLT codes (as of writing over 80,000), limited availability of training data for long tail/emerging classes, and the general high accuracy demands of the medical domain. With this paper, we introduce the MC task, discuss its challenges, and present a novel approach called xTARS that combines traditional BERT-based classification with a recent zero/few-shot learning approach (TARS). We present extensive experiments that show that our combined approach outperforms strong baselines, especially in the few-shot regime. The approach is developed and deployed at Bayer, live since November 2021. As we believe our approach potentially promising beyond MC, and to ensure reproducibility, we release the code to the research community.

Accurate depth-sensing plays a crucial role in securing a high success rate of robotic harvesting in natural orchard environments. Solid-state LiDAR (SSL), a recently introduced LiDAR technique, can perceive high-resolution geometric information of the scenes, which can be potential utilised to receive accurate depth information. Meanwhile, the fusion of the sensory information from LiDAR and camera can significantly enhance the sensing ability of the harvesting robots. This work introduces a LiDAR-camera fusion-based visual sensing and perception strategy to perform accurate fruit localisation for a harvesting robot in the apple orchards. Two SOTA extrinsic calibration methods, target-based and targetless-based, are applied and evaluated to obtain the accurate extrinsic matrix between the LiDAR and camera. With the extrinsic calibration, the point clouds and color images are fused to perform fruit localisation using a one-stage instance segmentation network. Experimental shows that LiDAR-camera achieves better quality on visual sensing in the natural environments. Meanwhile, introducing the LiDAR-camera fusion largely improves the accuracy and robustness of the fruit localisation. Specifically, the standard deviations of fruit localisation by using LiDAR-camera at 0.5 m, 1.2 m, and 1.8 m are 0.245, 0.227, and 0.275 cm respectively. These measurement error is only one one fifth of that from Realsense D455. Lastly, we have attached our visualised point cloud to demonstrate the highly accurate sensing method.

Deep Learning Research Scientist at Engineroom Remote › Worldwide, 100% remote position (Posted Apr 30 2022) Job description You will work in our research team to extend the capabilities of neural networks applied to video / audio / text understanding and common sense reasoning. You will perform research on recurrent networks, attention policies, multi-task learning and scaling up training. You will have expert understanding of NIPS, ICLR, CVPR, PAMI. You will help define and shape a platform with practical real world client applications from Day 1 at the forefront of Deep Learning and AI. We have used our expertise to: Increase crop yields, predict credit defaults, identify dark matter, render blockbuster VFX, develop & simulate medical breakthroughs, calculate re-entry profiles for spacecraft, give machine vision to Robots and UAV's, and much more......... WHAT WE OFFER: Full time, Part-time, Contract Roles.

Marley Spoon is bringing delightful, market fresh and easy cooking back to the people, while re-inventing the global food supply chain to reduce unethical food waste. We are connecting consumers with quality producers, providing fresh ingredients directly to customer's homes and we are present in Europe, the US and Australia, shipping more than 140 000 food boxes every week. We are a lean team working closely with the Data Warehousing, Data Science and DevOps Teams. The main focus of this team is to build and provide Services and Infrastructure to the Data Science Team to move models into production while following best practices. As a data driven company this position is a vital one for the growth and success of Marley Spoon.

As with many of us, after three years of staying home, I realized a few months ago that I'd had it with the pandemic. Having traveled up to 70% for years before Covid-19, my initial reaction to being in the same time zone and same building and bed was pure gratitude, even bliss. I wanted to go out and experience the world again. During our last Omicron-initiated staycation over the Christmas holidays, my 14-year-old son stated in his very polished, diplomatic and convincing style that he was bored. As soon as we learned Omicron was manageable, and there would be a break from lockdowns and fewer travel restrictions, we decided to get on with it and book some memorable holidays. So came the trips to Costa Rica and the Dominican Republic.

Russia's war in Ukraine is becoming a testing ground for loitering ammunition. How is artificial intelligence changing the future of military deterrence? The Russian attack on Ukraine shows that wars of conquest are not an artifact of the past. This reversion to an outdated notion of territorial integrity of states, visible since 2014 at the latest, puts the concept of deterrence back on the political agenda of many democracies. The new German government now wants to make the contribution to NATO that the then U.S. President Donald Trump, for example, demanded with media attention a few years ago.

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences). We construct a novel dataset for focus, coverage, and inter-sentential coherence, and develop automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods, including question answering (QA) approaches, semantic textual similarity (STS), next-sentence prediction (NSP), and scores derived from 19 pre-trained language models. We then apply the developed metrics in evaluating a broad range of summarization models across two datasets, with some surprising findings.

Phishing attacks are cyber-attacks through which criminals trick users into sending them money and sensitive information, or into installing malware on their computer, by sending them deceptive emails or messages. As these attacks have become increasingly widespread, developers have been trying to develop more advanced tools to detect them and protect potential victims. Researchers at Monash University and CSIRO's Data61 in Australia have recently developed a machine learning-based approach that could help users to identify phishing emails, so that they don't inadvertently install malware or send sensitive data to cyber-criminals. This model was introduced in a paper pre-published on arXiv and set to be presented at AsiaCCS 2022, a cyber-security conference. "We have identified a gap in current phishing research, namely realizing that existing literature focuses on rigorous'black and white' methods to classify whether something is a phishing email or not," Tingmin (Tina) Wu, one of the researchers who carried out the study, told TechXplore.

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.