Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaches for Heart Disease Detection by Using Hybridisation in Machine Learning

-Cardiac disorder treatment is the extremely challenging and inefficient operation to perform as it is known to be one of the most deadly diseases around the world today. Hospitals and healthcare centers are working tremendously hard to reduce the statistical growth of death due to cardiac disorder. The major cause of cardiac disorder is present lifestyle in which human beings are modified. The researchers and medical healthcare uses figures and facts of the patient which is suffering with this. Due to the inception of machine learning the task became much easier for them. As machine learning gives an easy platform. For anticipating cardiac illness without human impinging. This system will provide a prediction that will facilitate the researcher and the medical experts to diagnosis the disease is much easier stage and in time. In this paper we are dealing with the techniques of hybridization of machine learning which will provide high accuracy. Hybridization deals with the combination of two or more techniques of machine learning so that the accuracy should be high in predicting heart disease. The main aim of this paper is to calculate various research works done on heart disease prediction dealing with the hybridization of machine learning. These methods introduced a new pathway for predicting heart disease.
KrakenOnMem: a memristor-augmented HW/SW framework for taxonomic profiling

State-of-the-art taxonomic profilers that comprise the first step in larger-context metagenomic studies have proven to be computationally intensive, i.e., while accurate, they come at the cost of high latency and energy consumption. Table Lookup operation is a primary bottleneck of today's profilers. In this paper, we first propose TL-PIM, a hardware accelerator based on the processing-in-memory (PIM) paradigm to accelerate Table Lookup. TL-PIM leverages the in-memory compute capability of emerging memory technologies along with intelligent data mapping. Then, we integrate TL-PIM into Kraken2, a state-of-the-art metagenomic profiler, and build an HW/SW co-designed profiler, called KrakenOnMem. Results from a silicon-based prototype of our emerging memory validate the design and required operations on a smaller scale. Our large-scale calibrated simulations show that KrakenOnMem can provide an average of 61.3% speedup compared to original Kraken2 for end-to-end profiling. Additionally, our design improves the energy consumption by orders of magnitude compared to the original Kraken2 while incurring a negligible area overhead.
Handling heavy-tailed input of transformer inference on GPUs

Transformer-based models achieve superior accuracy in the field of natural language processing (NLP) and start to be widely deployed in production. As a popular deployment device, graphic processing units (GPUs) basically adopt the batch processing technique for inferring transformer-based models and achieving high hardware performance. However, as the input sequence lengths of NLP tasks are generally variable and in a heavy-tailed distribution, the batch processing will bring large amounts of redundant computation and hurt the practical efficiency. In this paper, we propose a unified solution for eliminating most redundant computation and gaining performance profit in handling heavy-tailed input of the transformer-based model inference on GPUs. In details, the unified solution includes three strategies for the self-attention module, the multilayer perceptron (MLP) module, and the entire transformer-based model respectively. For the self-attention module, we design a fine-grained strategy, which orchestrates fine-grained parallelism in the self-attention module by indexing the valid block matrix multiplication. For the MLP module, we take the common word-accumulation strategy, which places all sequences in a batch densely. For the entire model, we design a block-organized strategy to link up the fine-grained strategy and the word-accumulation strategy through organizing the data layout of the self-attention module in the grain of block. Applying our solution to eight corpora of the GLUE benchmark, there averagely achieves 63.9% latency reduction in the self-attention module and 28.1% latency reduction in the Bert-base model.
Content-agnostic backscatter from thin air

We present CAB, a content-agnostic backscatter system that can demodulate both tag and ambient data from ambient backscattered WiFi alone. In contrast to prior ambient backscatter systems that use ambient data (content) as tag-data carriers, we focus on zero-subcarriers, which are invariant and independent for any ambient OFDM WiFi. The idea of using zero-subcarriers to convey tag data is simple and elegant. Not only does it for the first time remove the dependency of tag-data demodulation on ambient data, but it also significantly improves the practicality of ambient backscatter. We prototype CAB using off-the-shelf FPGAs and SDRs. Extensive experiments show CAB is universal as it can work with multi-band, multi-stream, and multi-user ambient traffic, including WiFi 3/4/5/6. CAB is also high-performing since it can deliver 340.9 Mbps aggregate throughput, reaching 97% Shannon capacity. Since CAB is general, we extend it to leverage ambient LTE traffic as excitations, and the achieved tag-data BER is below 0.002%. As the first content-agnostic backscatter that delivers near Shannon-capacity throughput, we believe CAB takes a curial step forward on ubiquitous battery-free IoTs.
Clairvoyant: a log-based transformer-decoder for failure prediction in large-scale systems

System failures are expected to be frequent in the exascale era such as current Petascale systems. The health of such systems is usually determined from challenging analysis of large amounts of unstructured & redundant log data. In this paper, we leverage log data and propose Clairvoyant, a novel self-supervised (i.e., no labels needed) model to predict node failures in HPC systems based on a recent deep learning approach called transformer-decoder and the self-attention mechanism. Clairvoyant predicts node failures by (i) predicting a sequence of log events and then (ii) identifying if a failure is a part of that sequence. We carefully evaluate Clairvoyant and another state-of-the-art failure prediction approach - Desh, based on two real-world system log datasets. Experiments show that Clairvoyant is significantly better: e.g., it can predict node failures with an average Bleu, Rouge, and MCC scores of 0.90, 0.78, and 0.65 respectively while Desh scores only 0.58, 0.58, and 0.25. More importantly, this improvement is achieved with faster training and prediction time, with Clairvoyant being about 25X and 15X faster than Desh respectively.
MegTaiChi: dynamic tensor-based memory management optimization for DNN training

In real applications, it is common to train deep neural networks (DNNs) on modest clusters. With the continuous increase of model size and batch size, the training of DNNs becomes challenging under restricted memory budget. The tensor partition and tensor rematerialization are two major memory optimization techniques to enable larger model size and batch size within the limited-memory constrain. However, the related algorithms failed to fully extract the memory reduction opportunity, because they ignored the invariable characteristics of dynamic computational graphs and the variation among the same size tensors at different memory locations. In this work, we propose MegTaiChi, a dynamic tensor-based memory management optimization module for the DNN training, which first achieves an efficient coordination of tensor partition and tensor rematerialization. The key feature of MegTaiChi is that it makes memory management decisions based on dynamic tensor access pattern tracked at runtime. This design is motivated by the observation that the access pattern to tensors is regular during training iterations. Based on the identified patterns, MegTaiChi exploits the total memory optimization space and achieves the heuristic, adaptive and fine-grained memory management. The experimental results show, MegTaiChi can reduce the memory footprint by up to 11% for ResNet-50 and 10.5% for GL-base compared with DTR. For the training of 6 representative DNNs, MegTaiChi outperforms MegEngine and Sublinear by 5X and 2.4X of the maximum batch sizes. Compared with FlexFlow, Gshard and ZeRo-3, MegTaiChi achieves 1.2X, 1.8X and 1.5X performance speedups respectively on average. For the million-scale face recognition application, Meg-TaiChi achieves 1.8X speedup compared with the optimal empirical parallelism strategy on 256 GPUs.
Urban visualisation: the role of situated technology interventions in facilitating engagement with local topics

Advances in computing have enabled the deployment of technology in public settings such as high streets, squares, and parks. The role such community technology can play in engaging and connecting people in the urban environment has become of increasing interest in recent years, amid widespread concerns that cities are becoming less socially connected. Till date, however, many of the studies have focused on displaying games, photos, and other entertainment content on public screens, with the aim of bringing people together through play. Less is known about the use of publicly situated installations as a tool for encouraging people to view the perspectives of others and to share their personal perceptions. This thesis explores the use of situated public input technology and visualisations, collectively coined urban visualisation interventions, as a means of fostering community engagement. People's responses to and interactions with different topics, input devices, and visualisations were studied in a series of in-the-wild deployments in residential neighbourhoods and at events. In addition to the presentation of the design and evaluation of these deployments, this thesis presents an urban visualisation framework that outlines the key design and contextual factors that affect engagement, such as: the impact of the visualisation's update frequency on sustaining the community's interest, the influence of the input mechanism on the contribution quality, and the importance of positioning to ensure participation by a diversity of people.
State-of-the-art of thermoelectric materials processing

Development of new thermoelectric materials has been rapid in past few years. The goal of the work appears to be to reduce the thermal conductivity while remaining the electrical conductivity at relatively high level. Different techniques have been demonstrated in laboratory scale utilizing nanoscale tailoring. The recent development in nanoscale tailoring has based mainly on utilization of silicon. However, silicon can not be considered to be a good thermoelectric material due to its low ZT value. Thus, one can assume that better performance can be achieved by finding more appropriate materials. Also the problem of manufacturing the nanostructures in industrial scale still prevails. Thus, it is suggested that the development work should be focused on development of new efficient thermoelectric materials suitable for mass manufacturing utilizing nanoscience. ISBN 978-951-38-7184-0 (URL: http://www.vtt.fi/publications/index.jsp) Series title and ISSN Project number VTT Working Papers 1459-7683 (URL: http://www.vtt.fi/publications/index.jsp) 35650 Date Language Pages June 2009 English 29 p. Name of project Commissioned by
HAWK1655 ACGIE Conference Proceedings_FA.indd

Introduction Until recently there was confidence that the impact of the Argentine stem weevil (ASW; Listronotus bonariensis (Kuschel) (Coleoptera: Curculionidae)) on Lolium sp. (ryegrass)-based pasture grasses had declined as a consequence of introducing a biocontrol agent, the parasitoid wasp Microctonus hyperodae Loan (Hymenoptera: Braconidae) and the development and adoption of novel ryegrass endophytes that protect plants from ASW attack (Goldson et al., 2014a; Goldson et al., 2014b; 2015). However, there is now mounting evidence that the effectiveness of M. hyperodae as a biocontrol agent may have decreased. This in part has been based on reports of the reappearance of notable ASW damage to pasture (e.g. Popay et al., 2011). Moreover, a meta-analysis of historical datasets of winter parasitism rates (that tend to be stable due to absences of both weevil population fluctuations and parasitoid oviposition activity) has shown that parasitism rates have declined by more than 50% in Loliumbased pastures since the parasitoid’s initial establishment and stabilisation in the first six years following its introduction in 1990 (e.g. Goldson et al., 2014a; Goldson et al., 2014b).
Association of the COVID-19 pandemic with bystander cardiopulmonary resuscitation for out-of-hospital cardiac arrest: a population-based analysis in Tokyo, Japan

Background The impact of the COVID-19 pandemic on bystander cardiopulmonary resuscitation (CPR) for out-of-hospital cardiac arrest (OHCA) is unclear. This study aimed to investigate whether rates of bystander CPR and patient outcomes changed during the initial state of emergency declared in Tokyo for the COVID-19 pandemic. Methods This retrospective study used data from a population-based database of OHCA maintained by the Tokyo Fire Department. By comparing data from the periods before (18 February to 6 April 2020) and during the declaration of a state of emergency (7 April 2020 to 25 May 2020), we estimated the change in bystander CPR rate, prehospital return of spontaneous circulation, and survival and neurological outcomes 1 month after OHCA, accounting for outcome trends in 2019. We performed a multivariate regression analysis to evaluate the potential mechanisms for associations between the state of emergency and these outcomes. Results The witnessed arrest rates before and after the declaration periods in 2020 were 42.5% and 45.1%, respectively, compared with 44.1% and 44.7% in the respective corresponding periods in 2019. The difference between the two periods in 2020 was not statistically significant when the trend in 2019 was considered. The bystander CPR rates before and after the declaration periods significantly increased from 34.4% to 43.9% in 2020, an 8.3% increase after adjusting for the trend in 2019. This finding was significant even after adjusting for patient and bystander characteristics and the emergency medical service response. There were no significant differences between the two periods in the other study outcomes. Conclusion The COVID-19 pandemic was associated with an improvement in the bystander CPR rate in Tokyo, while patient outcomes were maintained. Pandemic-related changes in patient and bystander characteristics do not fully explain the underlying mechanism; there may be other mechanisms through which the community response to public emergency increased during the pandemic.
NDT_A_229296 3041..3050

Lijun Zhang Jianyang Liu Yingbin Ge Meiyan Liu 1Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing 100029, People’s Republic of China; 2Department of Physiology, Nanjing Medical University, Nanjing, Jiangsu 211166, People’s Republic of China Background: Depression has been shown to share an extremely high comorbidity with heart failure (HF). Ginkgo biloba extract (GBE) is a widely used traditional Chinese medicine in cardiac disease. However, its potential therapeutic effect on depressive symptoms following HF largely remains unknown. In this article, we aimed to investigate its effects in reducing depressive behaviors of a HF mouse model. Moreover, we also discussed whether its effects are associated with changes in neural inflammation and 5-hydroxytryptamine (5-HT) signaling. Methods: Mice were randomly divided into three groups: sham, HF+saline and HF+GBE (150 mg/kg/d) (n=10 per group). Systolic heart failure was induced by ligating the left anterior descending coronary artery. Cardiac functions together with depressive-like behaviors were measured after 4 weeks’ treatment. Levels of brain natriuretic peptide (BNP), 5HT, 5-HT receptor 2A (5-HT2AR), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), vascular endothelial growth factor (VEGF), hypoxia inducible factor-1 (HIF-1), (cleaved) caspase-3, Bax and Bcl-2 were analyzed by Western blot, Elisa and immunohistochemistry at the end of the experiments. Results: GBE benefited antidepressant-like behaviors and improved cardiac functions in mice with heart failure. Levels of TNF-α, IL-1β and 5-HTwere reduced in the hippocampus after the administration of GBE. Further experiments revealed that GBE also blocked the release of serotonin in the peripheral blood and triggered HIF-1 induced anti-apoptotic pathways. Conclusion: GBE has potential therapeutic effects in relieving depressive status of patients with HF.
Avitabile, Daniele and Hoyle, Rebecca and Samaey, Giovanni (2014) Noise reduction in coarse bifurcation analysis of stochastic agent-based models: an example

We investigate coarse equilibrium states of a fine-scale, stochastic agent-based model of consumer lock-in in a duopolistic market. In the model, agents decide on their next purchase based on a combination of their personal preference and their neighbours’ opinions. For agents with independent identically-distributed parameters and all-to-all coupling, we derive an analytic approximate coarse evolution-map for the expected average purchase. We then study the emergence of coarse fronts when spatial segregation is present in the relative perceived quality of products. We develop a novel Newton-Krylov method that is able to compute accurately and efficiently coarse fixed points when the underlying fine-scale dynamics is stochastic. The main novelty of the algorithm is in the elimination of the noise that is generated when estimating Jacobian-vector products using time-integration of perturbed initial conditions. We present numerical results that demonstrate the convergence properties of the numerical method, and use the method to show that macroscopic fronts in this model destabilise at a coarse symmetry-breaking bifurcation.
Commentaries on “The Future History of Geriatrics: Geriatrics at the Crossroads” and Author Response Geriatrics at the Crossroads—or Simply Early in the Journey?

N his provocative thought-piece, Dr. Kane suggests that geriatrics is at a crossroads (1). He does acknowledge that attempts have been made to “gerontologize” other fields of medicine but suggests that geriatrics has now reached the point where the next stage in its journey requires redirection to a focus on chronic disease as its defining niche. From my perspective, however, for geriatrics to move for-ward would not entail redirection but simply reinforcement of our determination to disseminate widely our traditional expertise in chronic disease and related domains—including frailty and other geriatric syndromes. Our geriatric diaspora should spread ever more widely and become integrated as all of internal medicine
EB_A_277760 139..167

1Faculty of Medicine, Queen’s University, Kingston, Canada; 2Faculty of Medicine, University of Toronto, Toronto, Canada; 3Faculty of Science, University of Toronto, Scarborough, Ontario, Canada; 4Department of Ophthalmology and Vision Sciences and Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada; 5Kensington Vision and Research Centre, Toronto, Canada Objective: Immune checkpoint inhibitors (ICIs) are novel cancer therapies that may be associated with immune-related adverse events (IRAEs) and come to the attention of neuroophthalmologists. This systematic review aims to synthesize the reported ICI-associated IRAEs relevant to neuro-ophthalmologists to help in the diagnosis and management of these conditions. Methods: A systematic review of the literature indexed by MEDLINE, Embase, CENTRAL, and Web of Science databases was searched from inception to May 2020. Reporting followed the Preferred Reporting Items for Systematic Review and Metaanalysis (PRISMA) guidelines. Primary studies on ICIs and neuro-ophthalmic complications were included. Outcomes included number of cases and incidence of neuro-ophthalmic IRAEs. Results: Neuro-ophthalmic complications of ICIs occurred in 0.46% of patients undergoing ICI and may affect the afferent and efferent visual systems. Afferent complications include optic neuritis (12.8%), neuroretinitis (0.9%), and giant cell arteritis (3.7%). Efferent complications include myasthenia gravis (MG) (45.0%), thyroid-like eye disease (11.9%), orbital myositis (13.8%), general myositis with ptosis (7.3%), internuclear ophthalmoplegia (0.9%), opsoclonus-myoclonus-ataxia syndrome (0.9%), and oculomotor nerve palsy (0.9%). Pembrolizumab was the most common causative agent for neuro-ophthalmic complications (32.1%). Mortality was highest for MG (19.8%). Most patients (79.8%) experienced improvement or complete resolution of neuro-ophthalmic symptoms due to cessation of ICI and immunosuppression with systemic corticosteroids. Conclusion: While incidence of neuro-ophthalmic IRAEs is low, clinicians involved in the care of cancer patients must be aware of their presentation to facilitate prompt recognition and management. Collaboration between oncology and neuro-ophthalmology teams is required to effectively manage patients and reduce morbidity and mortality.
Acute mechanical, physiological and perceptual responses in older men to traditional-set or different cluster-set configuration resistance training protocols

Purpose The aims of this study were to compare mechanical outputs (i.e. power and impulse), physiological (i.e. heart rate) and perceptual (i.e. effort and fatigue) responses in older men to traditional-set or different cluster-set configuration resistance training protocols. Methods In a randomized cross-over design, 20 healthy old men (aged 67.2 ± 2.1 years) completed four resistance training sessions using the back squat exercise loaded with optimal power loads. Training configurations were: traditional (TRA), three sets of six repetitions with 120-s rest between each set; Cluster-set 1 (CLU1), 24 single-repetition clusters with 10 s of rest after every cluster; Cluster-set 2 (CLU2), 12 double-repetition clusters with 20-s rest after every cluster; and Cluster-set 4 (CLU4), 6 quadruple-repetition clusters with 40-s rest after every cluster. Results Cluster-set configurations resulted in greater power outputs compared to traditional-set configuration (range 2.6– 9.2%, all p ≤ 0.07 for main effect and protocol × set interactions). CLU1 and CLU2 induced higher heart rate (range 7.1– 10.5%, all p < 0.001 for main effect and protocol × set interactions), lower rating of perceived exertion (range − 1.3 to − 3.2 AU, all p ≤ 0.006 for pairwise comparisons) and lower ratings of fatigue (range − 0.15 to − 4 AU, all p ≤ 0.012 for pairwise comparisons) compared to TRA and CLU4. Finally, an absolute preference for CLU2 was reported. Conclusions Findings presented here support the prescription of CLU2 as an optimal resistance training configuration for trained older men using the back squat. Keyword Elderly · Health · Resistance training · Strength · Power