Robust Phoneme Recognition with Little Data

A common belief in the community is that deep learning requires large datasets to be effective. We show that with careful parameter selection, deep feature extraction can be applied even to small datasets.We also explore exactly how much data is necessary to guarantee learning by convergence analysis and calculating the shattering coefficient for the algorithms used. Another problem is that state-of-the-art results are rarely reproducible because they use proprietary datasets, pretrained networks and/or weight initializations from other larger networks. We present a two-fold novelty for this situation where a carefully designed CNN architecture, together with a knowledge-driven classifier achieves nearly state-of-the-art phoneme recognition results with absolutely no pretraining or external weight initialization. We also beat the best replication study of the state of the art with a 28% FER. More importantly, we are able to achieve transparent, reproducible frame-level accuracy and, additionally, perform a convergence analysis to show the generalization capacity of the model providing statistical evidence that our results are not obtained by chance. Furthermore, we show how algorithms with strong learning guarantees can not only benefit from raw data extraction but contribute with more robust results

Mieloradiculite por CMV e HSV2 em paciente infectado pelo HIV

While CMV myeloradiculitis is a known complication in AIDS patients with severe immunosuppression, HSV-2 necrotizing myeloradiculitis is rare and often associated with disabling a fatal outcome. We hereby describe a 46 year-old HIV infected patient with profound and sustained immunosuppression who presented with an acute ascending paraparesis and urinary retention. Lumbar spine MRI showed contrast enhancement at the conus medullaris and cauda equine, and both CMV and HSV-2 CSF PCR were positive. Despite treatment, the patient died 20 days later. We review the main diagnostic and therapeutic aspects of herpes virus associated myeloradiculitis and discuss the approach in similar cases.Enquanto a mieloradiculite pelo CMV é complicação conhecida em pacientes com SIDA e imunossupressão grave, a mieloradiculite necrosante por HSV-2 é rara e muitas vezes associada a sequelas ou desfecho fatal. Descrevemos um paciente de 46 anos de idade, infectado pelo HIV com imunossupressão profunda e sustentada que apresentou paraparesia aguda ascendente e retenção urinária. A RM de coluna lombar mostrou o realce de contraste no cone medular e cauda equina e ambos PCR para CMV e HSV-2 no LCR foram positivos. Apesar do tratamento, o paciente morreu 20 dias depois. Revisamos os principais aspectos diagnósticos e terapêuticos da mieloradiculite associada aos herpesvírus e discutimos a abordagem em casos semelhantes

Canopy Height and Its Relationship with Leaf Area Index and Light Interception of Tropical Grasses

Photosynthetic tissues, mainly green leaves, are the major component of forage growth and development. The amount of these tissues in a forage plant is influenced directly by the cutting management, which is based on cutting frequency and stubble height. It is usual to recommend as a management practice to cut (or graze) the forage whenever it reaches a given stubble height. Brougham (1956) stated that, when the forage canopy is intercepting 95% of the photosynthetic active radiation, this is the critical leaf area index (LAI), which means the forage is near its maximum growth rate without shading itself. There is also the optimum LAI, where the forage reaches the maximum point of mass accumulation, indicating time to start grazing or cut. Generally the critical and optimum LAI have close values, but they are not necessarily the same (Brown and Blaser, 1968). This trial evaluated the relationship among canopy height, leaf area index, and light interception in ten different tropical grasses

Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP's proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.Comment: 10 pages, 11 figure