34 research outputs found

    Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

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    Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.Comment: Accepted at IEEE BigData 202

    The local translation of KNa in dendritic projections of auditory neurons and the roles of KNa in the transition from hidden to overt hearing loss

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    Local and privileged expression of dendritic proteins allows segregation of distinct functions in a single neuron but may represent one of the underlying mechanisms for early and insidious presentation of sensory neuropathy. Tangible characteristics of early hearing loss (HL) are defined in correlation with nascent hidden hearing loss (HHL) in humans and animal models. Despite the plethora of causes of HL, only two prevailing mechanisms for HHL have been identified, and in both cases, common structural deficits are implicated in inner hair cell synapses, and demyelination of the auditory nerve (AN). We uncovered that N

    Sodium-activated potassium channels shape peripheral auditory function and activity of the primary auditory neurons in mice

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    Potassium (K+) channels shape the response properties of neurons. Although enormous progress has been made to characterize K+ channels in the primary auditory neurons, the molecular identities of many of these channels and their contributions to hearing in vivo remain unknown. Using a combination of RNA sequencing and single molecule fluorescent in situ hybridization, we localized expression of transcripts encoding the sodium-activated potassium channels K(Na)1.1(SLO2.2/Slack) and K(Na)1.2 (SLO2.1/Slick) to the primary auditory neurons (spiral ganglion neurons, SGNs). To examine the contribution of these channels to function of the SGNs in vivo, we measured auditory brainstem responses in K(Na)1.1/1.2 double knockout (DKO) mice. Although auditory brainstem response (wave I) thresholds were not altered, the amplitudes of suprathreshold responses were reduced in DKO mice. This reduction in amplitude occurred despite normal numbers and molecular architecture of the SGNs and their synapses with the inner hair cells. Patch clamp electrophysiology of SGNs isolated from DKO mice displayed altered membrane properties, including reduced action potential thresholds and amplitudes. These findings show that K(Na)1 channel activity is essential for normal cochlear function and suggest that early forms of hearing loss may result from physiological changes in the activity of the primary auditory neurons

    Altered outer hair cell mitochondrial and subsurface cisternae connectomics are candidate mechanisms for hearing loss in mice

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    Organelle crosstalk is vital for cellular functions. The propinquity of mitochondria, ER, and plasma membrane promote regulation of multiple functions, which include intracellular C

    A Double-Edged Sword: The Two Faces of PARylation

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    Poly ADP-ribosylation (PARylation) is a post-translational modification process. Following the discovery of PARP-1, numerous studies have demonstrated the role of PARylation in the DNA damage and repair responses for cellular stress and DNA damage. Originally, studies on PARylation were confined to PARP-1 activation in the DNA repair pathway. However, the interplay between PARylation and DNA repair suggests that PARylation is important for the efficiency and accuracy of DNA repair. PARylation has contradicting roles; however, recent evidence implicates its importance in inflammation, metabolism, and cell death. These differences might be dependent on specific cellular conditions or experimental models used, and suggest that PARylation may play two opposing roles in cellular homeostasis. Understanding the role of PARylation in cellular function is not only important for identifying novel therapeutic approaches; it is also essential for gaining insight into the mechanisms of unexplored diseases. In this review, we discuss recent reports on the role of PARylation in mediating diverse cellular functions and homeostasis, such as DNA repair, inflammation, metabolism, and cell death

    Automatic RTL Generation Tool of FPGAs for DNNs

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    With the increasing use of multi-purpose artificial intelligence of things (AIOT) devices, embedded field-programmable gate arrays (FPGA) represent excellent platforms for deep neural network (DNN) acceleration on edge devices. FPGAs possess the advantages of low latency and high energy efficiency, but the scarcity of FPGA development resources challenges the deployment of DNN-based edge devices. Register-transfer level programming, hardware verification, and precise resource allocation are needed to build a high-performance FPGA accelerator for DNNs. These tasks present a challenge and are time consuming for even experienced hardware developers. Therefore, we propose an automated, collaborative design process employing an automatic design space exploration tool; an automatic DNN engine enables the tool to reshape and parse a DNN model from software to hardware. We also introduce a long short-term memory (LSTM)-based model to predict performance and generate a DNN model that suits the developer requirements automatically. We demonstrate our design scheme with three FPGAs: a zcu104, a zcu102, and a Cyclone V SoC (system on chip). The results show that our hardware-based edge accelerator exhibits superior throughput compared with the most advanced edge graphics processing unit
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