14,352 research outputs found
Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar
To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms
Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.
Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics
Epac, Rap and Rab3 act in concert to mobilize calcium from sperm's acrosome during exocytosis
BACKGROUND: Exocytosis of sperm's single secretory granule or acrosome (acrosome reaction, AR) is a highly regulated event essential for fertilization. The AR begins with an influx of calcium from the extracellular milieu and continues with the synthesis of cAMP and the activation of its target Epac. The cascade bifurcates into a Rab3-GTP-driven limb that assembles the fusion machinery and a Rap-GTP-driven limb that mobilizes internal calcium.
RESULTS: To understand the crosstalk between the two signaling cascades, we applied known AR inhibitors in three experimental approaches: reversible, stage-specific blockers in a functional assay, a far-immunofluorescence protocol to detect active Rab3 and Rap, and single cell-confocal microscopy to visualize fluctuations in internal calcium stores. Our model system was human sperm with their plasma membrane permeabilized with streptolysin O and stimulated with external calcium. The inhibition caused by reagents that prevented the activation of Rap was reversed by mobilizing intracellular calcium pharmacologically, whereas that caused by AR inhibitors that impeded Rab3's binding to GTP was not. Both limbs of the exocytotic cascade joined at or near the stage catalyzed by Rab3 in a unidirectional, hierarchical connection in which the intra-acrosomal calcium mobilization arm was subordinated to the fusion protein arm; somewhere after Rab3, the pathways became independent.
CONCLUSIONS: We delineated the sequence of events that connect an external calcium signal to internal calcium mobilization during exocytosis. We have taken advantage of the versatility of the sperm model to investigate how cAMP, calcium, and the proteinaceous fusion machinery coordinate to accomplish secretion. Because the requirement of calcium from two different sources is not unique to sperm and fusion proteins are highly conserved, our findings might contribute to elucidate mechanisms that operate in regulated exocytosis in other secretory cell types.Fil: Ruete, MarĂa Celeste. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mendoza. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas MĂ©dicas. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Lucchesi, Ornella. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mendoza. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas MĂ©dicas. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Bustos, Matias Alberto. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mendoza. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas MĂ©dicas. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Tomes, Claudia Nora. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Mendoza. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas MĂ©dicas. Instituto de HistologĂa y EmbriologĂa de Mendoza Dr. Mario H. Burgos; Argentin
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
Functional Profiling of Transcription Factor Genes in Neurospora crassa.
Regulation of gene expression by DNA-binding transcription factors is essential for proper control of growth and development in all organisms. In this study, we annotate and characterize growth and developmental phenotypes for transcription factor genes in the model filamentous fungus Neurospora crassa We identified 312 transcription factor genes, corresponding to 3.2% of the protein coding genes in the genome. The largest class was the fungal-specific Zn2Cys6 (C6) binuclear cluster, with 135 members, followed by the highly conserved C2H2 zinc finger group, with 61 genes. Viable knockout mutants were produced for 273 genes, and complete growth and developmental phenotypic data are available for 242 strains, with 64% possessing at least one defect. The most prominent defect observed was in growth of basal hyphae (43% of mutants analyzed), followed by asexual sporulation (38%), and the various stages of sexual development (19%). Two growth or developmental defects were observed for 21% of the mutants, while 8% were defective in all three major phenotypes tested. Analysis of available mRNA expression data for a time course of sexual development revealed mutants with sexual phenotypes that correlate with transcription factor transcript abundance in wild type. Inspection of this data also implicated cryptic roles in sexual development for several cotranscribed transcription factor genes that do not produce a phenotype when mutated
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
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