Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions. To address this issue, we propose a method using the pretrained audio neural networks (PANNs) as the encoder and local information assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of our method is in the proposal of the LocalAFT decoder, which allows local information within an audio signal to be captured while retaining the global information. This enables the events of different duration, including short duration, to be captured for more precise caption generation. Experiments show that our method outperforms the state-of-the-art methods in Task 6 of the DCASE 2021 Challenge with the standard attention-based decoder for caption generation.

In recent years, there has been a surge in building and adopting machine learning (ML) tools. The use of GPUs to accelerate increasingly compute-intensive models has been a prominent trend. To increase user access, the Accelerated WEKA project provides an accessible entry point for using GPUs in well-known WEKA algorithms by integrating open-source RAPIDS libraries. In this post, you will be introduced to Accelerated WEKA and learn how to leverage GPU-accelerated algorithms with a graphical user interface (GUI) using WEKA software. This Java open-source alternative is suitable for beginners looking for a variety of ML algorithms from different environments or packages.

This editorial refers to'Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England', by N.R. Hill et al., https://doi.org/10.1093/ehjdh/ztac009. To see things in the seed, that is genius. Undiagnosed atrial fibrillation (AF) is an important cause of stroke.1 AF screening may enable prompt detection of AF and initiation of oral anticoagulation (OAC) to prevent stroke.2 The 2007 SAFE trial reported a roughly 50% increase in AF diagnosis with screening individuals aged 65 years using electrocardiography (ECG) with or without pulse palpation,3 resulting in a Class I recommendation from the European Society of Cardiology4 and the Cardiac Society of Australia and New Zealand5 for AF screening using ECG among individuals aged 65 years. However, more recent studies suggest that mass screening may not be effective.6,7

Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years. Despite the adaptability and simplicity, their scalability and the selection of suitable ans\"atzs remain key challenges. In this work, we report an algorithmic framework based on nested Monte-Carlo Tree Search (MCTS) coupled with the combinatorial multi-armed bandit (CMAB) model for the automated design of quantum circuits. Through numerical experiments, we demonstrated our algorithm applied to various kinds of problems, including the ground energy problem in quantum chemistry, quantum optimisation on a graph, solving systems of linear equations, and finding encoding circuit for quantum error detection codes. Compared to the existing approaches, the results indicate that our circuit design algorithm can explore larger search spaces and optimise quantum circuits for larger systems, showing both versatility and scalability.

We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. This popular game has an enormous game tree on the order of $10^{535}$ nodes, i.e., $10^{175}$ times larger than that of Go. It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes). Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. Episodes are long, with often hundreds of moves before a player wins, and situations in Stratego can not easily be broken down into manageably-sized sub-problems as in poker. For these reasons, Stratego has been a grand challenge for the field of AI for decades, and existing AI methods barely reach an amateur level of play. DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play. The Regularised Nash Dynamics (R-NaD) algorithm, a key component of DeepNash, converges to an approximate Nash equilibrium, instead of 'cycling' around it, by directly modifying the underlying multi-agent learning dynamics. DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly (2022) and all-time top-3 rank on the Gravon games platform, competing with human expert players.

The UK is set to ease data mining laws in a move designed to further boost its flourishing AI industry. We all know that data is vital to AI development. Tech giants are in an advantageous position due to either having existing large datasets or the ability to fund/pay for the data required. Most startups rely on mining data to get started. Europe has notoriously strict data laws.

Zscaler, Inc. (NASDAQ: ZS), the leader in cloud security, today announced newly advanced AI/ML innovations powered by the largest security cloud in the world for unparalleled user protection and digital experience monitoring. The new capabilities further enhance Zscaler's Zero Trust Exchange security platform to enable organizations to implement a Security Service Edge (SSE) that protects against the most advanced cyberattacks, while delivering an exceptional digital experience to users, and simplifying the adoption of a zero-trust architecture. Organizations are facing a 314 percent increase in cyberattacks on encrypted internet traffic and an 80 percent increase in ransomware with nearly a 120 percent increase in double extortion attacks. Phishing is also on the rise with industries like financial services, government, and retail seeing annual increases in attacks of over 100 percent in 2021. To combat advancing threats, organizations need to adapt their defenses to real-time changes in risk.

The surge in digital transformation initiatives across businesses and the heightened need for real-time insights has led to an explosion in data creation. But few organisations have a proper understanding of where all their data exists in the first place. Every company has different siloed data sets running on-premises and across multiple public and private clouds and various servers. A recent global survey commissioned by IBM with Morning Consult found 9 out of 10 IT professionals in India reporting that their company draws from 20 or more different data sources to inform its AI, BI, and analytics systems. "This has led to data silos and complexity and as a result most data remains unanalysed, inaccessible or untrusted," says Siddhesh Naik, Data, AI & Automation sales leader, IBM Technology Sales, IBM India/South Asia.

Independence testing plays a central role in statistical and causal inference from observational data. Standard independence tests assume that the data samples are independent and identically distributed (i.i.d.) but that assumption is violated in many real-world datasets and applications centered on relational systems. This work examines the problem of estimating independence in data drawn from relational systems by defining sufficient representations for the sets of observations influencing individual instances. Specifically, we define marginal and conditional independence tests for relational data by considering the kernel mean embedding as a flexible aggregation function for relational variables. We propose a consistent, non-parametric, scalable kernel test to operationalize the relational independence test for non-i.i.d. observational data under a set of structural assumptions. We empirically evaluate our proposed method on a variety of synthetic and semi-synthetic networks and demonstrate its effectiveness compared to state-of-the-art kernel-based independence tests.

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$ reduction in EMV travel time as well as an $23.5\%$ shorter average travel time compared with existing approaches.