Publications

Combining Parameter-efficient Modules for Task-level Generalisation

Edoardo Ponti

Alessandro Sordoni

Yoshua Bengio

Siva Reddy

2023-05-01

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (publié)

doi.org

CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds

David Budaghyan

Arsenii Gorin

Cem (Yusuf) Subakan

Charles Onu

This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is … (voir plus)a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.

2023-05-01

ArXiv (prépublication)

doi.org

arxiv.org

Distinct Social Behavior and Inter-Brain Connectivity in Dyads with autistic individuals

Quentin Moreau

Florence Brun

Anaël Ayrolles

Jacqueline Nadel

Guillaume Dumas

2023-05-01

bioRxiv (prépublication)

doi.org

Embracing Channel Estimation in Multi-Packet Reception of ZigBee

Zhe Wang

Linghe Kong

Xue (Steve) Liu

Guihai Chen

As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Sinc… (voir plus)e ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of

2023-05-01

IEEE Transactions on Mobile Computing (publié)

doi.org

Embracing Channel Estimation in Multi-Packet Reception of ZigBee

Zhe Wang

L. Kong

Xuemei Liu

Guihai Chen

As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Sinc… (voir plus)e ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of

2023-05-01

IEEE Transactions on Mobile Computing (published)

doi.org

Low-Complexity Sphere Decoding for Polar-Coded MIMO Systems

Huayi Zhou

Jian Zheng

Minhua Yang

Warren Gross

Xiaohu You

Chuan Zhang

For polar-coded MIMO systems, separate detection and decoding (SDD) is the traditional scheme. In SDD systems, sphere decoding (SD) is one o… (voir plus)f the competitive MIMO detection schemes. However, SD may not utilize the coding information sufficiently in SDD systems, causing an error-correction performance loss. The existed joint detection and decoding using breadth-first SD (BSD) improves the performance than SDD, whereas the limited search space still causes a performance loss. In this paper, we propose joint detection and decoding based on SD (SD JDD) for polar-coded MIMO systems to reach maximum likelihood (ML) bound. Subsequently, two approaches are further proposed to reduce the computational complexity. The first approach reduces the layers of the SD search tree by exploiting symbol synchro sets, which could accelerate the convergence of SD JDD. The second efficient approach performs multiple tree searches. A small initial radius of the sphere for the first search is assigned to reduce the search space. The ML optimality could be preserved by the following multiple tree searches with increasing radius. It is shown from the numerical results that the proposed JDD outperforms SDD by 3.1 dB at FER

2023-05-01

IEEE Transactions on Vehicular Technology (publié)

doi.org

MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting

Oscar Mañas

Pau Rodriguez

Saba Ahmadi

Aida Nematzadeh

Yash Goyal

Aishwarya Agrawal

Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We p… (voir plus)ropose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL’s modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/oscmansan/mapl.

2023-05-01

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (publié)

doi.org

arxiv.org

OC-0290 Investigation of the feasibility of selenium-75 as a viable brachytherapy source

J. Reid

Jonathan Kalinowski

J. Munro

A. Armstrong

Shirin A. Enger

2023-05-01

Radiotherapy and Oncology (publié)