Miracle-dz/UniFAR-FTUs huggingface dataset:
THE POTENTIAL VALUE OF EARLY LONG-TERM USE OF MEMANTINE TO DELAY THE ONSET OF ALZHEIMER’S DEMENTIA IN GENETICALLY LOADED HIGH RISK INDIVIDUALS TO DEVELOP ALZHEIMER’S DEMENTIA WITH AND WITHOUT CARDIOVASCULAR RISK FACTORS

Memantine is NMDA (N Methyl D Aspartate) antagonist used to manage dementia, it works by blocking the glutamate receptors that stimulate excite-toxic neuronal death that results from excessive calcium influx into the neurons which in turn causes mitochondrial suffocation and neuronal death. Memantine is an uncompetitive, low affinity, open-channel blocker that reduces the glutamate induced overstimulation of the receptors that results in neuronal damage. It is unlike potential neuroprotective agents that block virtually all NMDA receptor activity including blockade of those essential for normal neuronal functioning. On the other hand, the pathology of dementia we are tackling here appears several years and may be decades earlier in structural and functional MRI studies before the development of the full-blown clinical picture of Alzheimer’s Dementia. The use of Meantime in this paper is targeting the excite-toxic etiology mainly and the development of Alzheimer’s Dementia. We used the term and definitions of critical numbers in a synonymous way to talk about brain reserve and to hypothesize the potential mechanism of Memantine in delaying the onset of symptoms of Alzheimer’s Dementia if used as a monotherapy irrespective of other pathologies such as the neurofibrillary tangles and plaques formation. Meanwhile, in the same hypothesis we left a room for using memantine and other disease modifying agents to be combined and used to tackle more than one etiological factor in those individuals with genetic risk to develop Alzheimer’s Dementia with and without cardiovascular risk factors. ASEAN Journal of Psychiatry, Vol. 22 (5): July 2021: 1-7.
ENFES: ENsemble FEw-Shot Learning For Intelligent Fault Diagnosis with Limited Data

Fault diagnosis is a key component of predictive system maintenance. Big data collected from sensors helps create data-driven fault diagnosis methods. However, it may be extremely costly to label specific fault types in a collected dataset. Hence, prediction algorithms should perform well under limited supervision. Few-shot learning (FSL) can provide a great prediction performance using very limited labeled data by discovering similarity among input pairs. But selection of a single FSL method may be arduous due to changing working conditions. Ensemble FSL solves this problem by combining a variety of FSL methods systematically. We propose an ensemble FSL framework, ENFES, where we combine 5 different Siamese neural network architectures using an iterative majority voting classifier. Our transfer learning-oriented experiments show that ENFES can improve the best algorithm significantly while using very limited labeled data. We obtain up to 16.4% improvement over the best algorithm by only using 0.3% of the training data.
Evaluation of Automatic Indexing with a Conceptual Thesaurus

When assigning descriptor terms, or key-words, from a conceptual thesaurus to texts, evaluation is rather difficult because the question whether a descriptor is appropriate cannot simply be answered with yes or no. This paper discusses general issues regarding the evaluation of thesaurus indexing and it compares human and automatic evaluation of the results of descriptor assignment software. This automatic descriptor assignment software was carried out on the basis of descriptors that had previously been assigned manually. The fact that the human evaluation results are so much better indicates that the automatic procedure can only give a very rough indication of real performance. Presumably, this conclusion also holds for other natural language processing applications such as document retrieval, summarisation and classification.
Passing and Pressure Metrics in Ice Hockey

Advancements in player tracking technology and analytics have revolutionized how professional sports are managed, played, and even consumed. However, these tracking systems have mainly failed to translate to ice rinks, leaving ice hockey to collect relatively coarse and offensively-biased data for player statistics. As a result, current artiﬁcial intelligence (AI) models for player valuation and optimal group formation for ice hockey are limited to comparing with offensively-biased data, reinforcing these biases in models. The National Hockey League (NHL) used a new tracking system in the 2020 Stanley Cup Playoffs, and we design a suite of novel analytics that add new insights into the performance and behaviour of players, groups of players, and teams. We calculate metrics for passing lanes and player movement, passing effectiveness, and pressure which have not previously been possible to compute in hockey. We expect our analytics to support future and more accurate performance and coalition models, and to be of direct interest to the ice hockey and AI communities.
The effectiveness of 5 weeks of high intensity interval training on cardiovascular endurance in karate players: An experimental study

Purpose: Karate is one of the most popularly practiced Combat Sports across the globe and it is characterized by high-intensity activity, including explosive and intermittent actions. Cardiovascular endurance plays an important role in Karate. It prevents fatigue between two bouts, between 2 matches and between the fights High Intensity Interval Training It is a form of interval training, a cardiovascular exercise strategy alternating short periods of intense exercise with less intense recovery periods. It is one effective way of improving the cardiovascular endurance. This research studies the effect of High Intensity Interval Training on Cardiovascular endurance in Karate Players. Method: In this study, 30 samples were collected according to the inclusion and exclusion Criteria. The exercise protocol was explained to them in details and ascent/consent was taken. The protocol was then administered for 5 weeks, on alternate days in each week. Pre and Post data collection was done using 20m Shuttle Run Test. Results: After 5 weeks of intervention, results showed that the HIIT had significant improvements in the scores of 20m Shuttle run test indicating a significant improvement in Cardiovascular Endurance of Karate Players (Difference 7.06 to 9.8, t=14.752, p=<0.001) Conclusion: This study concludes that High Intensity Interval Training significantly improves Cardiovascular Endurance in Karate Players.
Medicinal plant-derived compounds as potential phytotherapy forCOVID-19: Future perspectives

Currently, the human being faces a contagious virus called SARS-CoV-2 with a high transmission ability, infection, and a relatively high mortality rate, especially for the elderly. All research interest is directed to finding a drug or vaccine to save millions of people’s lives;some have succeeded and applied now in some countries. Medicinal plants represent a green treasure that renders plentiful primary and secondary metabolites characterized by potent biological activities. Interestingly, the secondary metabolites, alkaloids, flavonoids, tannins, and terpenoids, own a broad antiviral history. The plant antiviral capabilities may be due to inhibition of viral attachment to/penetration of the host cell;inhibition of the virus replication;suppressing RNA replication;downregulation of viral proteins translation;interfering with virus non-structural/structural protein;downregulation of virus-induced pro-inflammatory/inflammatory signaling pathways in the host cell, and antioxidant activity thus abating the virus-induced oxidative stress effect. Presently, as we are trapped with a surly virus, there are attempts to exploit the power of nature. Therefore, this review’s main objective is to refer to some medicinal plants or their derivatives that limit the virus’s virulence or alleviate the symptoms associated with this virus.
Ascending Subgraph Decomposition for Forests

It lias been conjectured that if a graph G iras ("ft) uag"", then the edge set of G can be partitioned into n graphs Ct,Gz,. ,G, such that G; has i edges (t ( i < n), and G; is isomorphic to a subglaph of G;11 (i < i < "). Such a graph G is said to have an oscending subgraph d.ecomposition (ASD). IL will be shown that aty Iorest with ("1t) "ag"r has an ASD such that all the graphs 1n the decornposition are star {orests.
Examining Power Use and the Privacy Paradox between Intention vs. Actual Use of Mobile Applications

The prevalence of smartphones in our society warrants more research on understanding the characteristics of users and their information privacy behaviors when using mobile apps. This paper investigates the antecedents and consequences of “power use” (i.e., the competence and desire to use technology to its fullest) in the context of informational privacy. In a study with 380 Android users, we examined how gender and users’ education level influence power use, how power use affects users’ intention to install apps and share information with them versus their actual privacy behaviors (i.e., based on the number of apps installed and the total number of “dangerous permission” requests granted to those apps). Our findings revealed an inconsistency in the effect of power use on users’ information privacy behaviors: While the intention to install apps and to share information with them increased with power use, the actual number of installed apps and dangerous permissions ultimately granted decreased with power use. In other words, although the self-reported intentions suggested the opposite, people who scored higher on the power use scale seemed to be more prudent about their informational privacy than people who scored lower on the power use scale. We discuss the implications of this inconsistency and make recommendations for reconciling smartphone users’ informational privacy intentions and behaviors.
N ov 2 01 1 Regulators , entropy and infinite determinants

In this note we describe instances where values of the K-theoretical regulator map evaluated on topological cycles equal entropies of topological actions by a group Γ. These entropies can also be described by determinants on the von Neumann algebra of Γ. There is no conceptual understanding why regulators should be related to entropies or to such determinants and it would be interesting to find an explanation. The relations were first observed for real regulators. The latter have p-adic analogues and both p-adic entropy and p-adic determinants were then defined so that similar relations hold as in the real case. We describe this p-adic theory in the second part of the paper. In fact there are two natural p-adic analogues for the relevant values of the real regulator. A purely local one which we use and a local-global one for which as yet no corresponding notions of p-adic entropy or p-adic infinite determinants exist.
To Be or Not to Be Stuck, or Is It a Continuum?

Players can get stuck in video games, which impedes their process to their goal and results in unfavorable outcomes like negative emotions, impediments of flow, and obstacles for learning. Currently, it is not easily possible to assess if a player is stuck, as no widely accepted definition of "being stuck" in games exists. We conducted 13 expert interviews and a systematic literature review with 104 relevant papers selected from 4022 candidates. We present a definition of "being stuck" that conceptualizes the state as a continuum and contextualize it within related concepts. Our stuck continuum can be applied to regulate the player's stuck level. We propose a taxonomy of measures that are useful for the detection of the level of stuckness and discuss the effectiveness of countermeasures. Our stuck concept is crucial for game developers creating an optimal player experience in games.
Supplement: On the Expressiveness of Approximate Inference in Bayesian Neural Networks

In this appendix, we show plots generated by placing two Gaussian clusters of data with centers randomly chosen on the sphere of radius √ D, where D = 5 denotes input dimension. We generate synthetic data by sampling from a wide-limit Gaussian process. For each plot, we show the predictive mean and 2 standard deviations along the line segment in input space joining the centres of these two clusters. For all plots, we choose σw = √ 2, σb = 1, networks of width 50 and dropout probability of p = 0.05 for MCDO. We set the observation noise standard deviation to 0.01, which is the ground truth value used to generate the synthetic data. The initialisation of MFVI and MCDO is the same as discussed in appendix F.
The Unmanned Systems Research Laboratory (USRL): A New Facility for UAV-Based Atmospheric Observations

The Unmanned Systems Research Laboratory (USRL) of the Cyprus Institute is a new mobile exploratory platform of the EU Research Infrastructure Aerosol, Clouds and Trace Gases Research InfraStructure (ACTRIS). USRL offers exclusive Unmanned Aerial Vehicle (UAV)-sensor solutions that can be deployed anywhere in Europe and beyond, e.g., during intensive field campaigns through a transnational access scheme in compliance with the drone regulation set by the European Union Aviation Safety Agency (EASA) for the research, innovation, and training. UAV sensor systems play a growing role in the portfolio of Earth observation systems. They can provide cost-effective, spatial in-situ atmospheric observations which are complementary to stationary observation networks. They also have strong potential for calibrating and validating remote-sensing sensors and retrieval algorithms, mapping close-to-the-ground emission point sources and dispersion plumes, and evaluating the performance of atmospheric models. They can provide unique information relevant to the short- and long-range transport of gas and aerosol pollutants, radiative forcing, cloud properties, emission factors and a variety of atmospheric parameters. Since its establishment in 2015, USRL is participating in major international research projects dedicated to (1) the better understanding of aerosol-cloud interactions, (2) the profiling of aerosol optical properties in different atmospheric environments, (3) the vertical distribution of air pollutants in and above the planetary boundary layer, (4) the validation of Aeolus satellite dust products by utilizing novel UAV-balloon-sensor systems, and (5) the chemical characterization of ship and stack emissions. A comprehensive overview of the new UAV-sensor systems developed by USRL and their field deployments is presented here. This paper aims to illustrate the strong scientific potential of UAV-borne measurements in the atmospheric sciences and the need for their integration in Earth observation networks.
IoT-based Cyber-security of Drones using the Naïve Bayes Algorithm

Recent advancements in drone technology are opening new opportunities and applications: in various fields of life especially in the form of small drones. However, these advancements are also causing new challenges in terms of security, adaptability, and consistency. Small drones are proving to be a new opportunity for the civil and military industries. The small drones are suffering from architectural issues and the definition of security and safety issues. The rapid growth of the Internet of things opens new dimensions for drone technology but posing new threats as well. The tiny flying intelligent devices are challenging for the security and privacy of data. The design of these small drones is yet not matured to fulfill the domain requirements. The basic design issues also need security mechanisms, privacy mechanisms, and data transformations. The aspects like intrusion and interception in the domain of the Internet of Drones (IoD) need to be investigated to make these timely drones more secure and more adaptable. In this paper, we have used intelligent machine learning approach to design an IoT aided drone. This approach will provide intelligent cyber security system which will help in detecting network security threats
REEFT-360: Real-time Emulation and Evaluation Framework for Tile-based 360 Streaming under Time-varying Conditions

With 360° video streaming, the user's field of view (a.k.a. viewport) is at all times determined by the user's current viewing direction. Since any two users are unlikely to look in the exact same direction as each other throughout the viewing of a video, the frame-by-frame video sequence displayed during a playback session is typically unique. This complicates the direct comparison of the perceived Quality of Experience (QoE) using popular metrics such as the Multiscale-Structural Similarity (MS-SSIM). Furthermore, there is an absence of light-weight emulation frameworks for tiled-based 360° video streaming that allow easy testing of different algorithm designs and tile sizes. To address these challenges, we present REEFT-360, which consists of (1) a real-time emulation framework that captures tile-quality adaptation under time-varying bandwidth conditions and (2) a multi-step evaluation process that allows the calculation of MS-SSIM scores and other frame-based metrics, while accounting for the user's head movements. Importantly, the framework allows speedy implementation and testing of alternative head-movement prediction and tile-based prefetching solutions, allows testing under a wide range of network conditions, and can be used either with a human user or head-movement traces. The developed software tool is shared with the paper. We also present proof-of-concept evaluation results that highlight the importance of including a human subject in the evaluation.
Bispecific Antibodies in Multiple Myeloma: Present and Future.

Despite many recent advances in therapy, there is still no plateau in overall survival curves in multiple myeloma. Bispecific antibodies are a novel immunotherapeutic approach designed to bind antigens on malignant plasma cells and cytotoxic immune effector cells. Early-phase clinical trials targeting B-cell maturation antigen (BCMA), GPRC5D, and FcRH5 have demonstrated a favorable safety profile, with mainly low-grade cytokine release syndrome, cytopenias, and infections. Although dose escalation is ongoing in several studies, early efficacy data show response rates in the most active dose cohorts between 61% and 83% with many deep responses; however, durability remains to be established. Further clinical trial data are eagerly anticipated. Significance Overall survival of triple-class refractory multiple myeloma remains poor. Bispecific antibodies are a novel immunotherapeutic modality with a favorable safety profile and impressive preliminary efficacy in heavily treated patients. Although more data are needed, bispecifics will likely become an integral part of the multiple myeloma treatment paradigm in the near future. Studies in earlier lines of therapy and in combination with other active anti-multiple myeloma agents will help further define the role of bispecifics in multiple myeloma.
Improving harvest efficiency of maize varieties via accumulated temperature in a certain planting area

The ripening and drying of maize (Zea mays L.) grain are closely related to temperature. In accordance with maize grain drying characteristics, regional accumulated temperature (AT00°C) distribution is of great significance for a rational allocation of maize varieties, thus reducing grain moisture content (MC) to improve maize harvest efficiency. From 2016 to 2018, a multi-site trial was carried out in the spring maize production area of Northeastern China. In this study, under a guaranteed rate of 80% for AT0, this area was divided into 15 accumulated temperature zones (ATZs) with an interval of 100°C based on climatic data of 78 local weather stations. Then the AT0 demand of different maize varieties during different growth stages was calculated by combining experimental records with the established prediction model of MC, and then, the spatial partition for different types of maize varieties under different MCs was analyzed. The results showed that all the tested varieties could not reach physiological maturity (PM) at ATZs 13-15, hence, where maize planting is risky. With the increasing accumulated temperature demand of different types of maize varieties from planting to PM, to the MC of 25% and to the MC of 20%, the unplantable areas were gradually expanded from south to north while the region where the maize varieties could be harvested under different MCs was also moved southwardly. Additionally, at 1-2 ATZs, it is entirely possible to achieve mechanical kernel harvesting under the MC of 20%, even though the AT0 requirements of the varieties are relatively high. Conclusively, on the grounds of AT0 demand law of maize varieties and heat resource distribution in Northeastern China, the layout optimization for achieving different harvesting scenarios is conducive to providing a basis not only for selecting suitable varieties but also for promoting mechanical kernel harvesting in the spring maize production area of this region. Keywords: grain, moisture content, accumulated temperature zone, cultivars’ layout, Northeastern China DOI: 10.25165/j.ijabe. 20211404.6337 Citation: Huang Z F, Hou L Y, Xue J, Wang K R, Xie R Z, Hou P, et al. Improving harvest efficiency of maize varieties via accumulated temperature in a certain planting area. Int J Agric & Biol Eng, 2021; 14(4): 175–181.
Sunflower Theorems in Monotone Circuit Complexity

Alexander Razborov (1985) developed the approximation method to obtain lower bounds on the size of monotone circuits deciding if a graph contains a clique. Given a "small" circuit, this technique consists in finding a monotone Boolean function which approximates the circuit in a distribution of interest, but makes computation errors in that same distribution. To prove that such a function is indeed a good approximation, Razborov used the sunflower lemma of Erd\H{o}s and Rado (1960). This technique was improved by Alon and Boppana (1987) to show lower bounds for a larger class of monotone computational problems. In that same work, the authors also improved the result of Razborov for the clique problem, using a relaxed variant of sunflowers. More recently, Rossman (2010) developed another variant of sunflowers, now called "robust sunflowers", to obtain lower bounds for the clique problem in random graphs. In the following years, the concept of robust sunflowers found applications in many areas of computational complexity, such as DNF sparsification, randomness extractors and lifting theorems. Even more recent was the breakthrough result of Alweiss, Lovett, Wu and Zhang (2020), which improved Rossman's bound on the size of hypergraphs without robust sunflowers. This result was employed to obtain a significant progress on the sunflower conjecture. In this work, we will show how the recent progress in sunflower theorems can be applied to improve monotone circuit lower bounds. In particular, we will show the best monotone circuit lower bound obtained up to now, breaking a 20-year old record of Harnik and Raz (2000). We will also improve the lower bound of Alon and Boppana for the clique function in a slightly more restricted range of clique sizes. Our exposition is self-contained. These results were obtained in a collaboration with Benjamin Rossman and Mrinal Kumar.