Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA

Memory-augmented neural networks (MANNs) are designed for question-answering tasks. It is difficult to run a MANN effectively on accelerators designed for other neural networks (NNs), in particular on mobile devices, because MANNs require recurrent data paths and various types of operations related to external memory access. We implement an accelerator for MANNs on a field-programmable gate array (FPGA) based on a data flow architecture. Inference times are also reduced by inference thresholding, which is a data-based maximum inner-product search specialized for natural language tasks. Measurements on the bAbI data show that the energy efficiency of the accelerator (FLOPS/kJ) was higher than that of an NVIDIA TITAN V GPU by a factor of about 125, increasing to 140 with inference thresholdingComment: Accepted to DATE 201

Development of emergency nursing care competency scale for school nurses

Background School nurses perform vital student emergency services at school, and assessing their emergency nursing care competency is critical to the safety and quality of care students receive. The purpose of the study was to develop a scale for measuring school nurses competency. Methods This was an instrument development and validation study. It was conducted according to the revised DeVellis scale development process coupled with the application of the International Council of Nurses Nursing Care Continuum Competencies Framework. Eight experts specializing in school health and emergency care evaluated the content validity, while 386 school nurses evaluated the scale. The validity evaluation comprised factor analysis, discriminative validity analysis according to differences in school nurse experience, and criterion validity analysis. Scale internal consistency was analyzed using Cronbachs α value. Results The final scale comprises a self-reported 5-point Likert scale with 30 items based on three factors and three sub-factors. Both the convergent validity of the items by factor and the discriminative validity were both confirmed. The criterion validity was also found to be positively correlated with the Triage Competency Scale. Conclusion The scale may be used to identify factors influencing school nurses competency in emergency nursing care and contribute to research in competency-based education programs

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.Comment: Accepted to ICPP 2022. Code: https://github.com/hukla/fedclassav

Restoration of axon conduction and motor deficits by therapeutic treatment with glatiramer acetate.

Glatiramer acetate (GA; Copaxone) is an approved drug for the treatment of multiple sclerosis (MS). The underlying multifactorial anti-inflammatory, neuroprotective effect of GA is in the induction of reactive T cells that release immunomodulatory cytokines and neurotrophic factors at the injury site. These GA-induced cytokines and growth factors may have a direct effect on axon function. Building on previous findings that suggest a neuroprotective effect of GA, we assessed the therapeutic effects of GA on brain and spinal cord pathology and functional correlates using the chronic experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Therapeutic regimens were utilized based on promising prophylactic efficacy. More specifically, C57BL/6 mice were treated with 2 mg/mouse/day GA for 8 days beginning at various time points after EAE post-induction day 15, yielding a thorough, clinically relevant assessment of GA efficacy within the context of severe progressive disease. Therapeutic treatment with GA significantly decreased clinical scores and improved rotorod motor performance in EAE mice. These functional improvements were supported by an increase in myelinated axons and fewer amyloid precursor protein-positive axons in the spinal cords of GA-treated EAE mice. Furthermore, therapeutic GA decreased microglia/macrophage and T cell infiltrates and increased oligodendrocyte numbers in both the spinal cord and corpus callosum of EAE mice. Finally, GA improved callosal axon conduction and nodal protein organization in EAE. Our results demonstrate that therapeutic GA treatment has significant beneficial effects in a chronic mouse model of MS, in which its positive effects on both myelinated and non-myelinated axons results in improved axon function

Therapeutic laquinimod treatment decreases inflammation, initiates axon remyelination, and improves motor deficit in a mouse model of multiple sclerosis.

BackgroundTherapeutic strategies that induce effective neuroprotection and enhance intrinsic repair mechanisms are central goals for future treatment of multiple sclerosis (MS), as well as other diseases. Laquinimod (LQ) is an orally administered, central nervous system (CNS)-active immunomodulator with demonstrated efficacy in MS clinical trials and a favorable safety and tolerability profile.AimsWe aimed to explore the pathological, functional, and behavioral consequences of prophylactic and therapeutic (after presentation of peak clinical disease) LQ treatment in the chronic experimental autoimmune encephalomyelitis (EAE) mouse model of MS.Materials and methodsActive EAE-induced 8-week-old C57BL/6 mice were treated with 5 or 25 mg/kg/day LQ via oral gavage beginning on EAE post-immunization day 0, 8, or 21. Clinical scores and rotorod motor performance were assessed throughout the disease course. Immune analysis of autoantigen-stimulated splenocytes, electrophysiological conduction of callosal axons, and immunohistochemistry of white matter-rich corpus callosum and spinal cord were performed.ResultsProphylactic and therapeutic treatment with LQ significantly decreased mean clinical disease scores, inhibited Th1 cytokine production, and decreased the CNS inflammatory response. LQ-induced improvement in axon myelination and integrity during EAE was functional, as evidenced by significant recovery of callosal axon conduction and axon refractoriness and pronounced improvement in rotorod motor performance. These improvements correlate with LQ-induced attenuation of EAE-induced demyelination and axon damage, and improved myelinated axon numbers.DiscussionEven when initiated at peak disease, LQ treatment has beneficial effects within the chronic EAE mouse model. In addition to its immunomodulatory effects, the positive effects of LQ treatment on oligodendrocyte numbers and myelin density are indicative of significant, functional neuroprotective and neurorestorative effects.ConclusionsOur results support a potential neuroprotective, in addition to immunomodulatory, effect of LQ treatment in inhibiting ongoing MS/EAE disease progression

A three-pass establishment protocol for real-time multiparty communication

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references: p. 42-46.Issued also on microfiche from Lange Micrographics.The development of high-speed networks and global internetworking protocols enable new, multimedia-oriented, applications to emerge, such as teleconferencing and other collaborative applications, or video-on-demand. These new applications rely on the underlying communication infrastructure to be able to provide Quality of Service (QoS) guarantees. Appropriate admission control during connection establishment is necessary for the underlying communication infrastructure to satisfy the above QoS requirements. Admission control is used in conjunction with a resource reservation protocol that reserve resources at communication establishment time, which then will be used to transfer data. A number of resource reservation algorithms have been suggested in the literature. They are severely limited when it comes to reserve resource for multiparty communication environments, in particular for multicast connections. We propose a new protocol for connection establishment in a real-time one-to-many communication environment. This algorithm very effectively allocates resources for established connections along their multicast routes, and thus reduces call-blocking probabilities and increases resource utilization while providing QoS guarantees. This is illustrated with a suite of simulation experiments which Compares the performance of our approach with that of other exisiting protocols