MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Previous studies have investigated several machine and deep learning models for electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, conventional feature extractions derived from ECG signals, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete PQRST segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN, for ECG classification tasks. Our extensive experimental results on the PhysioNet Apnea-ECG dataset revealed that with the new feature extraction method, we achieved a per-segment accuracy up to 92.11 \% and a per-recording accuracy of 100\%. Moreover, it yielded the highest correlation compared to state-of-the-art methods, with a correlation coefficient of 0.989. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme

The Alcohol Environment Protocol: A new tool for alcohol policy

Introduction and Aim  To report data on the implementation of alcohol policies regarding availability and marketing, and drink driving, along with ratings of enforcement from two small high-income to three high-middle income countries, and one low-middle income country.  Method  This study uses the Alcohol Environment Protocol, an International Alcohol Control study research tool, which documents the alcohol policy environment by standardised collection of data from administrative sources, observational studies and interviews with key informants to allow for cross-country comparison and change over time.  Results  All countries showed adoption to varying extents of key effective policy approaches outlined in the World Health Organization Global Strategy to Reduce the Harmful Use of Alcohol (2010). High-income countries were more likely to allocate resources to enforcement. However, where enforcement and implementation were high, policy on availability was fairly liberal. Key Informants judged alcohol to be very available in both high- and middle-income countries, reflecting liberal policy in the former and less implementation and enforcement and informal (unlicensed) sale of alcohol in the latter. Marketing was largely unrestricted in all countries and while drink-driving legislation was in place, it was less well enforced in middle-income countries.  Conclusion  In countries with fewer resources, alcohol policies are less effective because of lack of implementation and enforcement and, in the case of marketing, lack of regulation. This has implications for the increase in consumption taking place as a result of the expanding distribution and marketing of commercial alcohol and consequent increases in alcohol-related harm

Кризис экономики роста как системы: причины и следствия

Актуальность данной работы продиктована кризисным состоянием современной модели экономики. В основе этой модели лежат постулаты о необходимости постоянного роста экономики, который обеспечивается за счет конечного потребления. В обеспечение же реализации такой модели положены монетарные подходы и методы стимулирования потребительского спроса как основного драйвера экономического роста. Венцом данной модели стала долговая экономика потребления обществ индивидуалистов, не имеющих системы традиционных ценностей, а ориентирующихся на иллюзорные временные цели краткосрочного периода. Так, эгоцентрическая модель экономики заняла главенствующее положение по отношению к экологической модели экономики, обнажив массу системообразующих противоречий. Будущее оказалось под угрозой… Целью данной работы является анализ исходных причин этих противоречий, факторов возникновения кризисных явлений и угроз, с которыми столкнулось человечество на современном этапе развития, а также возможных альтернатив устранения этих противоречий и угроз. The relevance of this work is dictated by a critical state of the modern model of the economy. The basis of this model is construed by postulates about the need for sustained economic growth, which is provided by end-use. Monetary approaches and methods of consumer demand stimulation as a main incentive of economic growth guarantee the model implementation. On the top of this model there is a debt consumer economy of a society of individualists who do not have a system of traditional values, but instead have illusory short-term goals. Thus, an egocentric model of the economy has taken a dominant position in relation to an ecological economic model, revealing a lot of systemic contradictions. The future was under threat... The aim of this work is the analysis of the causes of these conflicts, the factors of crisis phenomena, the threats, which mankind faced at this stage of development, and the possible ways of elimination of the contradictions and threats as well. Methods: comparative analysis and synthesis of theoretical fundamental economic sources and assumptions, practical research and personal experience of various Russian and foreign scientists and business practitioners, the results, observations and conclusions arising from the personal experience of the author and his own theoretical research and inventions, and analysis of statistical data

Joint Consolidation and Service-Aware Load Balancing for Datacenters

Abstract-Workload consolidation is an efficient approach to reduce the power consumption of datacenters, meanwhile load balancing can reduce the datacenter's user delay. Despite complexities of 1) the coupling between consolidation and load balancing methods for server allocation, and 2) the heterogeneity of server configurations, we address the joint consolidation and service-aware load balancing problem to minimize the operation cost of datacenters. We first formulate the joint optimization problem, which is NP-hard. We then solve this problem using the Gibbs sampling method. Furthermore, to improve the computation of our approach, we propose the JCL algorithm that combines Gibbs sampling and the ADMM method for parallel and distributed calculations. Simulation results also validate that our method not only reduces the power consumption and delay cost, but also balances the workload in heterogeneous servers

Alcohol environment protocol : a new tool for alcohol policy

Findings of the study show that in countries with fewer resources, alcohol policies are less effective because of lack of implementation and enforcement and, in the case of marketing, lack of regulation. This has implications for increases in consumption as a result of the expanding distribution and marketing of commercial alcohol and consequent increases in alcohol-related harm. This study uses the Alcohol Environment Protocol, an International Alcohol Control study research tool, which documents the alcohol policy environment by standardised collection of data from administrative sources, observational studies, and interviews with key informants to allow for cross-country comparison and change over time

Associations of Underlying Health Conditions With Anxiety and Depression Among Outpatients: Modification Effects of Suspected COVID-19 Symptoms, Health-Related and Preventive Behaviors

Objectives: We explored the association of underlying health conditions (UHC) with depression and anxiety, and examined the modification effects of suspected COVID-19 symptoms (S-COVID-19-S), health-related behaviors (HB), and preventive behaviors (PB).Methods: A cross-sectional study was conducted on 8,291 outpatients aged 18–85 years, in 18 hospitals and health centers across Vietnam from 14th February to May 31, 2020. We collected the data regarding participant's characteristics, UHC, HB, PB, depression, and anxiety.Results: People with UHC had higher odds of depression (OR = 2.11; p < 0.001) and anxiety (OR = 2.86; p < 0.001) than those without UHC. The odds of depression and anxiety were significantly higher for those with UHC and S-COVID-19-S (p < 0.001); and were significantly lower for those had UHC and interacted with “unchanged/more” physical activity (p < 0.001), or “unchanged/more” drinking (p < 0.001 for only anxiety), or “unchanged/healthier” eating (p < 0.001), and high PB score (p < 0.001), as compared to those without UHC and without S-COVID-19-S, “never/stopped/less” physical activity, drinking, “less healthy” eating, and low PB score, respectively.Conclusion: S-COVID-19-S worsen psychological health in patients with UHC. Physical activity, drinking, healthier eating, and high PB score were protective factors