Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset.

Our work focuses on training RL agents on multiple visually diverse environments to improve observational generalization performance. In prior methods, policy and value networks are separately optimized using a disjoint network architecture to avoid interference and obtain a more accurate value function. We identify that a value network in the multi-environment setting is more challenging to optimize and prone to memorizing the training data than in the conventional single-environment setting. In addition, we find that appropriate regularization on the value network is necessary to improve both training and test performance. To this end, we propose Delayed-Critic Policy Gradient (DCPG), a policy gradient algorithm that implicitly penalizes value estimates by optimizing the value network less frequently with more training data than the policy network. This can be implemented using a single unified network architecture. Furthermore, we introduce a simple self-supervised task that learns the forward and inverse dynamics of environments using a single discriminator, which can be jointly optimized with the value network. Our proposed algorithms significantly improve observational generalization performance and sample efficiency on the Procgen Benchmark.

Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-toend design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and communication. In the proposed NeuroComm system, each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio (IR) transmitter with multiple antennas. Transmission takes place over a shared fading channel to a receiver equipped with a multi-antenna impulse radio receiver and with an SNN. In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations. The proposed system is shown to significantly improve over conventional frame-based digital solutions, as well as over alternative non-adaptive training methods, in terms of time-to-accuracy and energy consumption metrics. The work of Osvaldo Simeone and Nicolas Skatchkovsky was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, grant agreement No. 725731, by an Open Fellowship of the EPSRC with reference EP/W024101/1, and by the European Union through project CENTRIC (101096379), and the work by Jiechen Chen was funded by the China Scholarship Council and King's College London for their Joint Full-Scholarship (K-CSC) under Grant CSC202108440223.

Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency. Neural network quantization can further reduce the communication overhead during model uploading. To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed. Unfortunately, existing analysis is loose and does not consider model quantization. In this paper, we derive a tighter convergence bound for hierarchical FL with quantization. The convergence result leads to practical guidelines for important design problems such as the client-edge aggregation and edge-client association strategies. Based on the obtained analytical results, we optimize the two aggregation intervals and show that the client-edge aggregation interval should slowly decay while the edge-cloud aggregation interval needs to adapt to the ratio of the client-edge and edge-cloud propagation delay. Simulation results shall verify the design guidelines and demonstrate the effectiveness of the proposed aggregation strategy.

Natixis is a French multinational financial services firm specialized in asset & wealth management, corporate & investment banking, insurance and payments. A subsidiary of Groupe BPCE, the second-largest banking group in France through its two retail banking networks, Banque Populaire and Caisse d'Epargne, Natixis counts nearly 16,000 employees across 38 countries. As Natixis Innovation Hub, Natixis in Portugal mission is to transform traditional banking by developing innovative solutions for the bank's business, operations and work culture, being a key driver of the company's culture of agility and innovation. Teams of IT and Banking Support Activities work in an integrated, inclusive and transversal way, supporting all the business lines and country platforms. We will only accept English CVs.

'The Five' panelists reacts to a new artificial intelligence bot, ChatGPT, that's capable of writing essays, books, poems and even computer code upon request. The New York City Department of Education has reportedly banned access to the popular artificial intelligence tool ChatGPT over fears it would harm students' education and in order to help prevent cheating. The controversial free writing tool can generate paragraphs of human-like text. ""Due to concerns about negative impacts on student learning, and concerns regarding the safety and accuracy of content, access to ChatGPT is restricted on New York City Public Schools' networks and devices," Education Department spokesperson Jenna Lyle first told Chalkbeat. "While the tool may be able to provide quick and easy answers to questions, it does not build critical-thinking and problem-solving skills, which are essential for academic and lifelong success."

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Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a specific task through experience. It is an increasingly important field with a wide range of applications, from image and speech recognition to natural language processing and decision-making. So, nowadays we can do anything using machine learning as long as we have data available for the job at hand. One of the key advantages of machine learning is its ability to automatically improve and adapt to new data. This allows it to be used in dynamic and complex systems, such as in healthcare, finance, and transportation, where traditional rule-based systems may not be sufficient.

Will robots take over our jobs? This Cornell University and King's College London collaboration examines how artificial intelligence (AI) has influenced major knowledge-intensive services sectors, such as telecommunications and health care -- and how governments, employers and workers have responded to the challenges that smart technologies pose for the world of work. Taking the United States and the United Kingdom as our case studies, we will explore a wide range of emerging issues and countervailing forces (e.g., public policies, professional associations, vocational training systems, licensing bodies and laws, unions and labor market regulation). The study aims to be the first to systematically map these issues in the United States and the United Kingdom, with the goal of launching a mixed-methods project that covers a broader set of country cases. In so doing, the collaboration leverages the interdisciplinary expertise of our institutions to inform policy debates at the intersection of AI and work.

Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks. Various neural network models are proposed to learn from tree-structured program representations, e.g., abstract syntax tree (AST) and concrete syntax tree (CST). However, most neural architectures either fail to capture long-range dependencies which are ubiquitous in programs, or cannot learn effective representations for syntax tree nodes, making them incapable of performing the node-level prediction tasks, e.g., bug localization. In this paper, we propose Tree-Transformer, a novel recursive tree-structured neural network to learn the vector representations for source codes. We propose a multi-head attention mechanism to model the dependency between siblings and parent-children node pairs. Moreover, we propose a bi-directional propagation strategy to allow node information passing in two directions, bottom-up and top-down along trees. In this way, Tree-Transformer can learn the information of the node features as well as the global contextual information. The extensive experimental results show that our Tree-Transformer significantly outperforms the existing tree-based and graph-based program representation learning approaches in both the tree-level and node-level prediction tasks.