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The AI community has made significant strides in developing powerful foundation models, driven by large-scale multimodal datasets. However, in the audio representation learning community, the present audio-language datasets suffer from limitations such as insufficient volume, simplistic content, and arduous collection procedures. To tackle these challenges, we present an innovative and automatic audio caption generation pipeline based on a series of public tools or APIs, and construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.9M audio-text pairs. To demonstrate the effectiveness of the proposed dataset, we train popular models on our dataset and show performance improvement on various downstream tasks, namely, audio-language retrieval, audio captioning, environment classification. In addition, we establish a novel test set and provide a benchmark for audio-text tasks. The proposed dataset will be released at //auto-acd.github.io/.

The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment. Exploiting sparsity in the network's feature maps is one of the ways to reduce its inference latency. It is known that unstructured sparsity results in lower accuracy degradation with respect to structured sparsity but the former needs extensive inference engine changes to get latency benefits. To tackle this challenge, we propose a solution to induce semi-structured activation sparsity exploitable through minor runtime modifications. To attain high speedup levels at inference time, we design a sparse training procedure with awareness of the final position of the activations while computing the General Matrix Multiplication (GEMM). We extensively evaluate the proposed solution across various models for image classification and object detection tasks. Remarkably, our approach yields a speed improvement of $1.25 \times$ with a minimal accuracy drop of $1.1\%$ for the ResNet18 model on the ImageNet dataset. Furthermore, when combined with a state-of-the-art structured pruning method, the resulting models provide a good latency-accuracy trade-off, outperforming models that solely employ structured pruning techniques.

The primary task of a quantum repeater network is to deliver entanglement among end nodes. Most of existing entanglement distribution protocols do not consider purification, which is thus delegated to an upper layer. This is a major drawback since, once an end-to-end entangled connection (or a portion thereof) is established it cannot be purified if its fidelity (F) does not fall within an interval bounded by Fmin (greater than 0.5) and Fmax (less than 1). In this paper, we propose the Ranked Entanglement Distribution Protocol (REDiP), a connection-oriented protocol that overcomes the above drawback. This result was achieved by including in our protocol two mechanisms for carrying out jointly purification and entanglement swapping. We use simulations to investigate the impact of these mechanisms on the performance of a repeater network, in terms of throughput and fidelity. Moreover, we show how REDiP can easily be configured to implement custom entanglement swapping and purification strategies, including (but not restricted to) those adopted in two recent works.

Residual neural networks are widely used in computer vision tasks. They enable the construction of deeper and more accurate models by mitigating the vanishing gradient problem. Their main innovation is the residual block which allows the output of one layer to bypass one or more intermediate layers and be added to the output of a later layer. Their complex structure and the buffering required by the residual block make them difficult to implement on resource-constrained platforms. We present a novel design flow for implementing deep learning models for field programmable gate arrays optimized for ResNets, using a strategy to reduce their buffering overhead to obtain a resource-efficient implementation of the residual layer. Our high-level synthesis (HLS)-based flow encompasses a thorough set of design principles and optimization strategies, exploiting in novel ways standard techniques such as temporal reuse and loop merging to efficiently map ResNet models, and potentially other skip connection-based NN architectures, into FPGA. The models are quantized to 8-bit integers for both weights and activations, 16-bit for biases, and 32-bit for accumulations. The experimental results are obtained on the CIFAR-10 dataset using ResNet8 and ResNet20 implemented with Xilinx FPGAs using HLS on the Ultra96-V2 and Kria KV260 boards. Compared to the state-of-the-art on the Kria KV260 board, our ResNet20 implementation achieves 2.88X speedup with 0.5% higher accuracy of 91.3%, while ResNet8 accuracy improves by 2.8% to 88.7%. The throughputs of ResNet8 and ResNet20 are 12971 FPS and 3254 FPS on the Ultra96 board, and 30153 FPS and 7601 FPS on the Kria KV26, respectively. They Pareto-dominate state-of-the-art solutions concerning accuracy, throughput, and energy.

We propose the use of a lower or upper triangular sub-base matrix to replace the identity matrix in the source-check-channel-variable linking protomatrix of a double-protograph low-density parity-check joint-source-channel code (DP-LDPC JSCC). The elements along the diagonal of the proposed lower or upper triangular sub-base matrix are assigned as "1" and the other non-zero elements can take any non-negative integral values. Compared with the traditional DP-LDPC JSCC designs, the new designs show a theoretical channel threshold improvement of up to 0.41 dB and a simulated source symbol error rate improvement of up to 0.5 dB at an error rate of 1e-6.

We address the problem of distributed resource allocation for multicast communication in device-to-device (D2D) enabled underlay cellular networks. The optimal resource allocation is crucial for maximizing the performance of such networks, which are limited by the severe co-channel interference between cellular users (CU) and D2D multicast groups. However, finding such optimal allocation for networks with large number of CUs and D2D users is challenging. Therefore, we propose a pragmatic scheme that allocates resources distributively, reducing signaling overhead and improving network scalability. Numerical simulations establish the efficacy of the proposed solution in improving the overall system throughout, compared to various existing schemes.

In multiple-input multiple-output (MIMO) wireless systems with frequency-flat channels, a single-antenna jammer causes receive interference that is confined to a one-dimensional subspace. Such a jammer can thus be nulled using linear spatial filtering at the cost of one degree of freedom. Frequency-selective channels are often transformed into multiple frequency-flat subcarriers with orthogonal frequency-division multiplexing (OFDM). We show that when a single-antenna jammer violates the OFDM protocol by not sending a cyclic prefix, the interference received on each subcarrier by a multi-antenna receiver is, in general, not confined to a subspace of dimension one (as a single-antenna jammer in a frequency-flat scenario would be), but of dimension L, where L is the jammer's number of channel taps. In MIMO-OFDM systems, a single-antenna jammer can therefore resemble an L-antenna jammer. Simulations corroborate our theoretical results. These findings imply that mitigating jammers with large delay spread through linear spatial filtering is infeasible. We discuss some (im)possibilities for the way forward.

Ordered sequences of data, specified with a join operation to combine sequences, serve as a foundation for the implementation of parallel functional algorithms. This abstract data type can be elegantly and efficiently implemented using balanced binary trees, where a join operation is provided to combine two trees and rebalance as necessary. In this work, we present a verified implementation and cost analysis of joinable red-black trees in $\textbf{calf}$, a dependent type theory for cost analysis. We implement red-black trees and auxiliary intermediate data structures in such a way that all correctness invariants are intrinsically maintained. Then, we describe and verify precise cost bounds on the operations, making use of the red-black tree invariants. Finally, we implement standard algorithms on sequences using the simple join-based signature and bound their cost in the case that red-black trees are used as the underlying implementation. All proofs are formally mechanized using the embedding of $\textbf{calf}$ in the Agda theorem prover.

Being able to harness the power of large datasets for developing cooperative multi-agent controllers promises to unlock enormous value for real-world applications. Many important industrial systems are multi-agent in nature and are difficult to model using bespoke simulators. However, in industry, distributed processes can often be recorded during operation, and large quantities of demonstrative data stored. Offline multi-agent reinforcement learning (MARL) provides a promising paradigm for building effective decentralised controllers from such datasets. However, offline MARL is still in its infancy and therefore lacks standardised benchmark datasets and baselines typically found in more mature subfields of reinforcement learning (RL). These deficiencies make it difficult for the community to sensibly measure progress. In this work, we aim to fill this gap by releasing off-the-grid MARL (OG-MARL): a growing repository of high-quality datasets with baselines for cooperative offline MARL research. Our datasets provide settings that are characteristic of real-world systems, including complex environment dynamics, heterogeneous agents, non-stationarity, many agents, partial observability, suboptimality, sparse rewards and demonstrated coordination. For each setting, we provide a range of different dataset types (e.g. Good, Medium, Poor, and Replay) and profile the composition of experiences for each dataset. We hope that OG-MARL will serve the community as a reliable source of datasets and help drive progress, while also providing an accessible entry point for researchers new to the field.

Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Moreover, it is a critical determinant in the consumer's decision-making process when considering a smartphone purchase. From the sustainability perspective, it becomes imperative to explore approaches aimed at mitigating the energy consumption of mobile devices, given the significant global consequences arising from the extensive utilisation of billions of smartphones, which imparts a profound environmental impact. Despite the existence of various energy-efficient programming practices within the Android platform, the dominant mobile ecosystem, there remains a need for documented machine learning-based energy prediction algorithms tailored explicitly for mobile app development. Hence, the main objective of this research is to propose a novel neural network-based framework, enhanced by a metaheuristic approach, to achieve robust energy prediction in the context of mobile app development. The metaheuristic approach here plays a crucial role in not only identifying suitable learning algorithms and their corresponding parameters but also determining the optimal number of layers and neurons within each layer. To the best of our knowledge, prior studies have yet to employ any metaheuristic algorithm to address all these hyperparameters simultaneously. Moreover, due to limitations in accessing certain aspects of a mobile phone, there might be missing data in the data set, and the proposed framework can handle this. In addition, we conducted an optimal algorithm selection strategy, employing 13 metaheuristic algorithms, to identify the best algorithm based on accuracy and resistance to missing values. The comprehensive experiments demonstrate that our proposed approach yields significant outcomes for energy consumption prediction.