Reducing the Energy Consumption of sEMG-Based Gesture Recognition at the Edge Using Transformers and Dynamic Inference.

Hand gesture recognition applications based on surface electromiographic (sEMG) signals can benefit from on-device execution to achieve faster and more predictable response times and higher energy efficiency. However, deploying state-of-the-art deep learning (DL) models for this task on memory-constrained and battery-operated edge devices, such as wearables, requires a careful optimization process, both at design time, with an appropriate tuning of the DL models' architectures, and at execution time, where the execution of large and computationally complex models should be avoided unless strictly needed. In this work, we pursue both optimization targets, proposing a novel gesture recognition system that improves upon the state-of-the-art models both in terms of accuracy and efficiency.

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.

Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic computing technologies. We present here a detailed comparison of available spike encoding techniques for the translation of time-varying signals into the event-based signal domain, tested on two different datasets both acquired through commercially available digital devices: the Free Spoken Digit dataset (FSD), consisting of 8-kHz audio files, and the WISDM dataset, composed of 20-Hz recordings of human activity through mobile and wearable inertial sensors.

Q-PPG: Energy-Efficient PPG-Based Heart Rate Monitoring on Wearable Devices.

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor.

A Deep Learning Approach to the Screening of Oncogenic Gene Fusions in Humans.

Gene fusions have a very important role in the study of cancer development. In this regard, predicting the probability of protein fusion transcripts of developing into a cancer is a very challenging and yet not fully explored research problem. To this date, all the available approaches in literature try to explain the oncogenic potential of gene fusions based on protein domain analysis, that is cancer-specific and not easy to adapt to newly developed information.

LAPSE: Low-Overhead Adaptive Power Saving and Contrast Enhancement for OLEDs.

Organic Light Emitting Diode (OLED) display panels are becoming increasingly popular especially in mobile devices; one of the key characteristics of these panels is that their power consumption strongly depends on the displayed image. In this paper we propose LAPSE, a new methodology to concurrently reduce the energy consumed by an OLED display and enhance the contrast of the displayed image, that relies on image-specific pixel-by-pixel transformations. Unlike previous approaches, LAPSE focuses specifically on reducing the overheads required to implement the transformation at runtime.

Unsupervised HEp-2 mitosis recognition in indirect immunofluorescence imaging.

Automated HEp-2 mitotic cell recognition in IIF images is an important and yet scarcely explored step in the computer-aided diagnosis of autoimmune disorders. Such step is necessary to assess the goodness of the HEp-2 samples and helps the early diagnosis of the most difficult or ambiguous cases. In this work, we propose a completely unsupervised approach for HEp-2 mitotic cell recognition that overcomes the problem of mitotic/non-mitotic class imbalance due to the limited number of mitotic cells.

VDJSeq-Solver: in silico V(D)J recombination detection tool.

In this paper we present VDJSeq-Solver, a methodology and tool to identify clonal lymphocyte populations from paired-end RNA Sequencing reads derived from the sequencing of mRNA neoplastic cells. The tool detects the main clone that characterises the tissue of interest by recognizing the most abundant V(D)J rearrangement among the existing ones in the sample under study. The exact sequence of the clone identified is capable of accounting for the modifications introduced by the enzymatic processes.

The Human Brain Project and neuromorphic computing.

Understanding how the brain manages billions of processing units connected via kilometers of fibers and trillions of synapses, while consuming a few tens of Watts could provide the key to a completely new category of hardware (neuromorphic computing systems). In order to achieve this, a paradigm shift for computing as a whole is needed, which will see it moving away from current "bit precise" computing models and towards new techniques that exploit the stochastic behavior of simple, reliable, very fast, lowpower computing devices embedded in intensely recursive architectures. In this paper we summarize how these objectives will be pursued in the Human Brain Project.

Gelsius: a literature-based workflow for determining quantitative associations between genes and biological processes.

An effective knowledge extraction and quantification methodology from biomedical literature would allow the researcher to organize and analyze the results of high-throughput experiments on microarrays and next-generation sequencing technologies. Despite the large amount of raw information available on the web, a tool able to extract a measure of the correlation between a list of genes and biological processes is not yet available. In this paper, we present Gelsius, a workflow that incorporates biomedical literature to quantify the correlation between genes and terms describing biological processes.

Acceleration of coarse grain molecular dynamics on GPU architectures.

Coarse grain (CG) molecular models have been proposed to simulate complex systems with lower computational overheads and longer timescales with respect to atomistic level models. However, their acceleration on parallel architectures such as graphic processing units (GPUs) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance.

Computer-aided techniques for chromogenic immunohistochemistry: status and directions.

Although immunohistochemistry (IHC) is a popular imaging technique, the quantitative analysis of IHC images via computer-aided methods is an emerging field that is gaining more and more importance thanks to the new developments in digital high-throughput scanners. In this paper, we discuss the main steps of IHC and review the techniques for computer-aided chromogenic IHC analysis, including methods to determine the location of interest of the antigens and quantify their activations. Moreover, we discuss the issues arising from the standardization of the immunostaining process, that are generally overlooked by the current literature, and finally provide requirements for reliable computer-aided IHC quantification.

Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model.

Motivation: Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model.

miREE: miRNA recognition elements ensemble.

Background: Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction.

A molecular dynamics study of a miRNA:mRNA interaction.

In this paper we present a methodology to evaluate the binding free energy of a miRNA:mRNA complex through molecular dynamics (MD)-thermodynamic integration (TI) simulations. We applied our method to the Caenorhabditis elegans let-7 miRNA:lin-41 mRNA complex-a validated miRNA:mRNA interaction-in order to estimate the energetic stability of the structure. To make the miRNA:mRNA simulation possible and realistic, the methodology introduces specific solutions to overcome some of the general challenges of nucleic acid simulations and binding free energy computations that have been discussed widely in many previous research reports.

Automated segmentation of cells with IHC membrane staining.

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference.

TOM: a web-based integrated approach for identification of candidate disease genes.

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases.

Automated DNA fragments recognition and sizing through AFM image processing.

This paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises.

Automatic intrinsic DNA curvature computation from AFM images.

Critical information on several biological processes such as DNA-protein interactions and DNA transcription can be derived from analysis of DNA curvature. Under thermal perturbation, the curvature is composed of static and dynamic contributions, thus, can be described as the sum of intrinsic curvature and a fluctuation contribution. Without considering thermal agitations, the DNA curvature is reducible to the intrinsic component, which is a function of the DNA nucleotide sequence only.