Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation

The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions. In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software-defined radio (SDR) platforms, in which every function is programmed from scratch, our programmable architectures are based on a clear decoupling between elementary commands (hard-coded into the devices) and programmable protocol logic (injected into the devices) according to which the commands execution is scheduled. Our contribution is two-fold: first, we designed and implemented a cross-technology time division multiple access (TDMA) scheme devised to provide a global synchronization signal and allocate alternating channel intervals to WiFi and ZigBee programmable nodes; second, we used the OMF control framework to define an interference detection and adaptation strategy that in principle could work in independent and autonomous networks. Experimental results prove the benefits of the envisioned solution

Validating a method for the estimate of gait spatio-temporal parameters with IMUs data on healthy and impaired people from two clinical centers

Instrumented gait analysis offers objective clinical outcome assessment. To this purpose, inertial measurement units (IMUs) represent nowadays a very effective solution due to their limited cost, ease of use and improved wearability. The aim of this study was to apply a well-documented IMU-based method to measure gait spatio-temporal parameters in a large number of healthy and gait-impaired subjects, and evaluate its robustness and validity across two clinical centers. Overall, the results of this work represent a robust and reliable foundation for the clinical use of the proposed IMU based method for gait parameters estimation

How Many Templates for GW Chirp Detection? The Minimal-Match Issue Revisited

In a recent paper dealing with maximum likelihood detection of gravitational wave chirps from coalescing binaries with unknown parameters we introduced an accurate representation of the no-signal cumulative distribution of the supremum of the whole correlator bank. This result can be used to derive a refined estimate of the number of templates yielding the best tradeoff between detector's performance (in terms of lost signals among those potentially detectable) and computational burden.Comment: submitted to Class. Quantum Grav. Typing error in eq. (4.8) fixed; figure replaced in version

Eventos: ferramentas estratégicas de comunicação.

A Comunicação Organizacional na Empresa Brasileira de Pesquisa Agropecuária (Embrapa) é definida, em sua política, como um processo gerencial permanente e sistêmico, que integra as atividades de relacionamento entre organização e públicos, tendo como objetivo criar e manter os fluxos de informação. Nesse contexto a promoção e realização de eventos é uma das ferramentas de comunicação usadas estrategicamente com o objetivo de criar e reforçar relacionamentos entre empresa e públicos de interesse, bem como apoiar o processo de transferência de tecnologia na Embrapa

Structured lexical similarity via convolution Kernels on dependency trees

A central topic in natural language process-ing is the design of lexical and syntactic fea-tures suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical simi-larities. We define efficient and powerful ker-nels for measuring the similarity between de-pendency structures, whose surface forms of the lexical nodes are in part or completely dif-ferent. The experiments with such kernels for question classification show an unprecedented results, e.g. 41 % of error reduction of the for-mer state-of-the-art. Additionally, semantic role classification confirms the benefit of se-mantic smoothing for dependency kernels.

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models

While adversarial training has been extensively studied for ResNet architectures and low resolution datasets like CIFAR, much less is known for ImageNet. Given the recent debate about whether transformers are more robust than convnets, we revisit adversarial training on ImageNet comparing ViTs and ConvNeXts. Extensive experiments show that minor changes in architecture, most notably replacing PatchStem with ConvStem, and training scheme have a significant impact on the achieved robustness. These changes not only increase robustness in the seen $\ell_\infty$-threat model, but even more so improve generalization to unseen $\ell_1/\ell_2$-attacks. Our modified ConvNeXt, ConvNeXt + ConvStem, yields the most robust $\ell_\infty$-models across different ranges of model parameters and FLOPs, while our ViT + ConvStem yields the best generalization to unseen threat models.Comment: Accepted at NeurIPS 202

Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models

While a large amount of work has focused on designing adversarial attacks against image classifiers, only a few methods exist to attack semantic segmentation models. We show that attacking segmentation models presents task-specific challenges, for which we propose novel solutions. Our final evaluation protocol outperforms existing methods, and shows that those can overestimate the robustness of the models. Additionally, so far adversarial training, the most successful way for obtaining robust image classifiers, could not be successfully applied to semantic segmentation. We argue that this is because the task to be learned is more challenging, and requires significantly higher computational effort than for image classification. As a remedy, we show that by taking advantage of recent advances in robust ImageNet classifiers, one can train adversarially robust segmentation models at limited computational cost by fine-tuning robust backbones

MUN Chamber Choir, conductor: D.F. Cook (April 29-May 3, 1979)

MUN Chamber Choir, conductor D.F. Cook (April 29-May 3, 1979