End-to-end Incremental Learning

Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from (catastrophic forgetting), a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model---a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees.This work has been funded by project TIC-1692 (Junta de Andalucía), TIN2016-80920R (Spanish Ministry of Science and Technology) and Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Irregular alignment of arbitrarily long DNA sequences on GPU

The use of Graphics Processing Units to accelerate computational applications is increasingly being adopted due to its affordability, flexibility and performance. However, achieving top performance comes at the price of restricted data-parallelism models. In the case of sequence alignment, most GPU-based approaches focus on accelerating the Smith-Waterman dynamic programming algorithm due to its regularity. Nevertheless, because of its quadratic complexity, it becomes impractical when comparing long sequences, and therefore heuristic methods are required to reduce the search space. We present GPUGECKO, a CUDA implementation for the sequential, seed-and-extend sequence-comparison algorithm, GECKO. Our proposal includes optimized kernels based on collective operations capable of producing arbitrarily long alignments while dealing with heterogeneous and unpredictable load. Contrary to other state-of-the-art methods, GPUGECKO employs a batching mechanism that prevents memory exhaustion by not requiring to fit all alignments at once into the device memory, therefore enabling to run massive comparisons exhaustively with improved sensitivity while also providing up to 6x average speedup w.r.t. the CUDA acceleration of BLASTN.Funding for open access publishing: Universidad Málaga/CBUA /// This work has been partially supported by the European project ELIXIR-EXCELERATE (grant no. 676559), the Spanish national project Plataforma de Recursos Biomoleculares y Bioinformáticos (ISCIII-PT13.0001.0012 and ISCIII-PT17.0009.0022), the Fondo Europeo de Desarrollo Regional (UMA18-FEDERJA-156, UMA20-FEDERJA-059), the Junta de Andalucía (P18-FR-3130), the Instituto de Investigación Biomédica de Málaga IBIMA and the University of Málaga

Real-Time Unsupervised Object Localization on the Edge for Airport Video Surveillance.

Object localization is vital in computer vision to solve object detection or classification problems. Typically, this task is performed on expensive GPU devices, but edge computing is gaining importance in real-time applications. In this work, we propose a real-time implementation for unsupervised object localization using a low-power device for airport video surveillance. We automatically find regions of objects in video using a region proposal network (RPN) together with an optical flow region proposal (OFRP) based on optical flow maps between frames. In addition, we study the deployment of our solution on an embedded architecture, i.e. a Jetson AGX Xavier, using simultaneously CPU, GPU and specific hardware accelerators. Also, three different data representations (FP32, FP16 and INT8) are employed for the RPN. Obtained results show that optimizations can improve up to 4.1× energy consumption and 2.2× execution time while maintaining good accuracy with respect to the baseline model.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Multimodal Human Pose Feature Fusion for Gait Recognition.

Gait recognition allows identifying people at a distance based on the way they walk (i.e. gait) in a non-invasive approach. Most of the approaches published in the last decades are dominated by the use of silhouettes or other appearance-based modalities to describe the Gait cycle. In an attempt to exclude the appearance data, many works have been published that address the use of the human pose as a modality to describe the walking movement. However, as the pose contains less information when used as a single modality, the performance achieved by the models is generally poorer. To overcome such limitations, we propose a multimodal setup that combines multiple pose representation models. To this end, we evaluate multiple fusion strategies to aggregate the features derived from each pose modality at every model stage. Moreover, we introduce a weighted sum with trainable weights that can adaptively learn the optimal balance among pose modalities. Our experimental results show that (a) our fusion strategies can effectively combine different pose modalities by improving their baseline performance; and, (b) by using only human pose, our approach outperforms most of the silhouette-based state-of-the-art approaches. Concretely, we obtain 92.8% mean Top-1 accuracy in CASIA-B.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Effect of Rough Surface Platforms on the Mucosal Attachment and the Marginal Bone Loss of Implants: A Dog Study

The preservation of peri-implant tissues is an important factor for implant success. This study aimed to assess the influence of the surface features of a butt-joint platform on soft-tissue attachment and bone resorption after immediate or delayed implant placement. All premolars and first molars of eight Beagle dogs were extracted on one mandible side. Twelve-weeks later, the same surgery was developed on the other side. Five implants with different platform surface configurations were randomly inserted into the post-extracted-sockets. On the healed side, the same five different implants were randomly placed. Implants were inserted 1 mm subcrestal to the buccal bony plate and were connected to abutments. The primary outcome variables were the supracrestal soft tissue (SST) adaptation and the bone resorption related to the implant shoulder. The SST height was significantly larger in immediate implants (IC95% 3.9–4.9 mm) compared to delayed implants (IC95% 3.1–3.5 mm). Marginal bone loss tended to be higher in immediate implants (IC95% 0.4–0.9 mm) than in delayed implants (IC95% 0.3–0.8 mm). Linear-regression analysis suggested that the SST height was significantly affected by the configuration of the platform (0.3–1.9 mm). Roughened surface platforms resulted in higher SST height when compared to machined surface platforms. Marginal bone loss was less pronounced in roughened designs

Concurrent Calculations on Reconfigurable Logic Devices Applied to the Analysis of Video Images

This paper presents the design and implementation on FPGA devices of an algorithm for computing similarities between neighboring frames in a video sequence using luminance information. By taking advantage of the well-known flexibility of Reconfigurable Logic Devices, we have designed a hardware implementation of the algorithm used in video segmentation and indexing. The experimental results show the tradeoff between concurrent sequential resources and the functional blocks needed to achieve maximum operational speed while achieving minimum silicon area usage. To evaluate system efficiency, we compare the performance of the hardware solution to that of calculations done via software using general-purpose processors with and without an SIMD instruction set

Long-Term Clinical Outcomes of Treatment with Dental Implants with Acid Etched Surface

Implant dentistry constitutes a therapeutic modality in the prosthodontic treatment of partially and totally edentulous patients. This study reports a long-term evaluation of treatment by the early loading of acid-etched surface implants. Forty-eight partially and totally edentulous patients were treated with 169 TSA Defcon® acid-etched surface implants for prosthodontic rehabilitation. Implants were loaded after a healing free-loading period of 6-8 weeks in mandible and maxilla, respectively. Implant and prosthodontic clinical findings were followed during at least 17 years. Clinical results indicate a survival and success rate of implants of 92.9%, demonstrating that acid-etched surface achieves and maintains successful osseointegration. Five implants in three patients were lost during the healing period. Sixty-five prostheses were placed in 45 patients over the remaining 164 implants, 30 single crowns, 21 partially fixed bridges, 9 overdentures, and 5 full-arch fixed rehabilitations. A total of 12 implants were lost during the follow-up period. Mean marginal bone loss was 1.91 ± 1.24 mm, ranging from 1.1 to 3.6 mm. The most frequent complication was prosthetic technical complications (14.2%), followed by peri-implantitis (10.6%). The mean follow-up was of 214.4 months (208-228 months). Prosthodontic rehabilitation with an early-loading protocol over acid-etched surface implants is a successful implant treatment