Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Fingerprinting of Indian Medicinal Tree Sara Asoca using RAPD Markers

Saraca asoca is an important medicinal tree facing a serious problem of reduction from its instinctivetenancy in India.Before formulation of conservation strategies for geographical protection of S. asoca genotypes available in India, it is necessary to characterize them. In the current study, the RAPD markers have been utilized effectively for categorization of S. asoca collected from 15 diverse sites of India. An initial experiment on the amplification suitability of genomic DNA samples of four S. asoca was done with 35 RAPD primers. Among them only twenty sixproved their efficiency in two times repeat amplification.Total 146 bands were amplified and out of these 97 bands were found to be polymorphic. The average numbers of total band was 5.61 while average numbers of polymorphic bands was 3.73. The numbers of bands produced per primer ranged from 3 (OPE-15) to 8 (RUF205). Among all studied markers the highest percentage (100%) of polymorphism was demonstrated by only one marker (OPE06). The lowest percent of polymorphicm (20%) was demonstrated by marker RUF211. The average percentage of polymorphism was 66.44%. Cluster analysis grouped all the S. asoca genotypes under study into two groups. Grouping of genotypes according to their sites of collection demonstrates higher similarity among or between them. The results obtained in the current study may help to formulate conservation strategies for the conservation of S. asoca genotypes.
Extrinsic Performance Limitations of AlGaN/GaN Heterostructure Field Effect Transistors

Extrinsic effects on the DC output characteristics of AlGaN/GaN HFETs with 1 m gate lengths are examined. The devices investigated were fabricated on MOCVD-grown AlGaN/ GaN heterostructures on sapphire substrates. An analytical model that takes into account parasitic resistances and thermal effects is constructed, and its results are compared with experimental data. With parameters determined from characterization experiments on the same wafer and from independent theoretical results, the agreement between the data and the model predictions is found to be very good. The model is then applied to performance predictions for devices with improved series resistances and heat sinking. INTRODUCTION The group III-nitride compound semiconductors have considerable potential for the fabrication of high frequency/high power electronic devices. Progress in the growth and process technology of these materials has recently led to the demonstration of very impressive results for the output current density, the gain cut-off frequency, and the output power density of AlGaN/ GaN heterostructure field effect transistors (HFETs). [1,2,3] These encouraging results notwithstanding, the device design and the fabrication techniques are still far from optimized. For example, contact resistances are relatively large (compared to conventional III-V materials) and deleterious thermal effects are likely to limit the performance of the HFETs at high voltages and currents. This is particularly the case for devices fabricated on sapphire substrates, due to that material’s relatively low thermal conductivity. In this communication extrinsic effects that limit the performance of AlGaN/GaN HFETs are examined in the framework of an analytical model. The results are compared with experimental data from AlGaN/GaN HFETs. It is found that significant improvements in output current can be expected if the heat generated in the devices is removed effectively (for example by a flip-chip bonding technique, or by the use of a substrate with high thermal conductivity) and if the source and drain series resistances can be reduced.
New generations – new trends in the spa industry

Today's tourism market is a scene of rapid changes that equally affect the demand and the supply side. In the spa industry market, due to the presence of both wellness and medical services, a wide range of generations appear as consumers, with different expectations toward the service providers. Therefore, the changes taking place in the present and the upcoming years are worth exploring, which, in addition to the changes in consumer habits, visualize the expansion of consumer needs. Current article attempts to describe the expected spa services by examining the nature of generations and identifying future changes that can be predicted. The author primarily tries to summarize the market trends that contribute significant changes, using international and national secondary resources and the professional experience gained in the industry. Összefoglalás Napjaink turisztikai piaca gyors változások színtere, amelyek mind a keresleti, mind a kínálati oldalt érintik. A spa iparág piacán – a wellness és gyógyszolgáltatások együttes jelenléte miatt – a generációk széles köre jelenik meg fogyasztóként, más és más elvárást támasztva a szolgáltatókkal szemben. Vizsgálatra érdemesek tehát a jelen és elkövetkező években zajló változások, amelyek a fogyasztói szokások változása mellett a fogyasztói igények szélesedését vizionálják. Jelen cikk kísérletet tesz az egyes generációk sajátosságainak vizsgálatával azok elvárt spa szolgáltatásainak leírására valamint a jövőre előrevetíthető változások beazonosítására. A szerző elsősorban a témában fellelhető nemzetközi és hazai szekunder források és az iparágban szerzett szakmai tapasztalata felhasználásával igyekszik összefoglalni mindazokat a piaci tendenciákat, amelyek jelentős változásokat hoznak. 1 Contact Author. Tel. : +36 56 516066 E-mail address: mondok.anita@gk.uni-neumann.hu
Storage Benchmarking with Deep Learning Workloads

In this AI-driven era, while compute infrastructure is often the focus, storage is equally important. Loading many batches of sample randomly is the common workload for deep learning (DL). Those iterative small random reads impose nontrivial I/O pressure on storage systems. Therefore, we are interested in exploring the optimal storage system and data format for storing DL data and the possible trade-offs. In the meantime, object storage is usually preferred because of its high scalability, rich metadata, competitive cost, and the ability to store unstructured data. This motivates us to benchmark two object storage systems: MinIO and Ceph. As a comparison, we also benchmark three popular key-value storage databases: MongoDB, Redis, and Cassandra. We explore the impact of different parameters, including storage location, storage disaggregation granularity, access pattern, and data format. For each parameter, we summarize the benchmark results and give some suggestions. Overall, although the optimal storage system is workload-specific, our benchmarks provide some insights on how to reach it.
FIRE WEATHER INDEX

Forest fires are a major environmental issue, creating economical and ecological damage while dangering human lives. The investigation and survey for forest fire had been done in Aek Godang, Northern Sumatera, Indonesia. There is 26 hotspot in 2017 close to Aek Godang, North Sumatera, Indonesia. In this study, we use a data mining approach to train and test the data of forest fire and the Fire Weather Index (FWI) from meteorological data. The aim of this study to predict the burned area and identify the forest fire in Aek Godang areas, North Sumatera. The result of this study indicated that Fire fighting and prevention activity may be one reason for the observed lack of correlation. The fact that this dataset exists indicates that there is already some effort going into fire prevention.
Cation–π interaction: a case for macrocycle–cation π-interaction by its ureidoarene counteranion

We report the solid state structures of homocomplex [1·K]+[I]− and of heterocomplexes [1·K]+[3·I]− and [1·K]+[4·I]. In the [1·K]+[I]− complex the apical position of the K+ in the macrocycle is occupied by a –CH2– moiety of a neighboring 18-crown-6, consistent with a close contact and corresponding to ‘agostic’ interactions between K+ and –CH2– moieties. In the [1·K]+[3·I]− complex the second apical site of the cation is coordinated by a bridging water molecule which is simultaneously H-bonded to both the phenol hydroxyl and the iodide anion. In the [1·K]+[4·I]− complex the second apical site of the cation is occupied by the indole moiety. Moreover, the new heterocomplex system [1·K]+[4·I]− presented here, despite the multiple possibilities of K+–π contacts with the sterically available phenyl, phenyl-indole and pyrrole-indole rings of 4, shows that the pyrrolo C2C3 double bond is a versatile π-donor. 1H NMR results led us to conclude that the complexes adopt similar conformations in solution to those observed in the solid state.
Endometriosis-Related Pain Reduction During Bleeding and Nonbleeding Days in Women Treated with Elagolix

Objective In this post hoc analysis, we evaluated the impact of elagolix on dysmenorrhea and nonmenstrual pelvic pain across menstrual period (bleeding days) and nonmenstrual (nonbleeding) days. Methods Data from two randomized, 6-month, placebo-controlled trials (Elaris Endometriosis (EM)-I and EM-II) of elagolix (150 mg once daily (QD) and 200 mg twice daily (BID)) in premenopausal women with moderate to severe endometriosis-associated pain (N = 1686) were pooled. Women recorded the presence of menstrual period and severity of dysmenorrhea or nonmenstrual pelvic pain in a daily electronic diary. Results At baseline, women in the placebo group and both elagolix treatment groups reported moderate or severe dysmenorrhea, on average, 81% of their menstrual period days and moderate/severe nonmenstrual pelvic pain, on average, 56% of their nonmenstrual (nonbleeding) days. Compared with placebo at month 6, elagolix-treated women had a significantly lower mean (standard deviation (SD)) percentage of menstrual period days with moderate or severe dysmenorrhea (elagolix 150 mg QD = 52.4 (38.9), p = 0.002; elagolix 200 mg BID = 38.5 (43.6), p < 0.001, placebo = 61.3 (33.7)) and a significantly lower mean (SD) percentage of nonmenstrual (nonbleeding) days with moderate or severe nonmenstrual pelvic pain (elagolix 150 mg QD = 31.1 (35.8), p < 0.001; elagolix 200 mg BID = 19.7 (29.9), p < 0.001; placebo = 35.6 (33.9)). Conclusion Following 6 months of elagolix treatment, women who still menstruated had a lower proportion of menstrual period days with moderate or severe dysmenorrhea compared with placebo, demonstrating pain reduction despite continued menses. Additionally, pain did not shift from dysmenorrhea to nonmenstrual pelvic pain, as the percentage of days with moderate or severe nonmenstrual pelvic pain was also reduced for elagolix-treated women compared with placebo. Trial Registration The Elaris EM-I study is registered with the US National Library of Medicine, www.ClinicalTrials. gov, NCT01620528. The Elaris EM-II study is registered with the US National Library of Medicine, www.ClinicalTrials. gov, NCT01931670. Both studies are registered with the EU Clinical Trial Register, www.clinicaltrialsregister. ed, 2011-004295-11.
Sleep quality monitoring in individual sports athletes: parameters and deﬁnitions by systematic review

In the present review, we identify which instruments and parameters are used for sleep quality monitoring in individual sport athletes and which definitions were used for sleep quality parameters in this literature field. Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews. The initial search returned 3316 articles. After the removal of duplicate articles, eligibility assessment, 75 studies were included in this systematic review. Our main findings were that the most widely used measurement instruments were Actigraphy (25%), Rating Likert Scales (16%) and Sleep Diary (13%). On sleep quality parameters (Sleep duration = 14%; Wake after sleep onset = 14%; Sleep Quality = 12%; Sleep Effciency = 11% and Sleep Latency = 9%), the main point is that there are different definitions for the same parameters in many cases reported in the literature. We conclude that the most widely used instruments for monitoring sleep quality were Actigraphy, Likert scales and Sleep diary. Moreover, the definitions of sleep parameters are inconsistent in the literature, hindering the understanding of the sleep-sport performance relationship.
Monitoring transcellular fluid shifts during episodes of intradialytic hypotension using bioimpedance spectroscopy

Abstract Background Transcellular fluid shifts during dialysis treatment could be related to the frequency and severity of intradialytic hypotension (IDH). We investigated that (i) in addition to ultrafiltration, extracellular fluid (ECF) is further depleted by transcellular fluid shifts and (ii) changes in intracellular fluid (ICF), which have been overlooked so far, or if they were considered, are not understood, might be due to these fluid shifts. Methods Thirty-six patients were categorized as haemodynamically stable, asymptomatic IDH or unstable (symptomatic IDH) according to their changes in systolic blood pressure and associated clinical symptoms. Their intradialytic changes in body fluids were studied using bioimpedance spectroscopy measurements and compared among groups. Results For IDH-prone patients, data showed a rapid drop in ECF that was more than expected from the ultrafiltration rate (UFR) profile and was associated with a significant increase in ICF (P = 0.001). Study of accumulative loss profiles of ECF revealed a loss in ECF up to 300 ml, more than that predicted from UFR for unstable patients. Conclusions The considerable discrepancy between the expected and measured loss in ECF might provide evidence of transcellular fluid shifts possibly induced by changes in plasma osmolarity due to haemodialysis. Moreover, the results suggest a pattern of fluid removal in IDH-prone patients that significantly differs from that in haemodynamically stable patients.
Basic chronobiology: what do sleep physicians need to know?

Sleep is an essential physiological process, which profoundly affects a wide range of biological activities. It is now known that sleep supports myriad vital functions in the central nervous system. This includes neural plasticity, learning, memory, cognition and emotional regulation. Additionally, it affects basic processes such as cardiovascular, immunological and metabolic activity. Evidence from multiple lines of research has thus shown that good quality of sleep is essential for both survival and optimal functioning of life. Considerable evidence also supports the conclusion that even minimal dysfunctions in circadian regulation can significantly disrupt sleep and broadly affect body physiology. As a consequence, it is now appreciated that the therapy of sleep disorders is more complex than was once thought. At present, several clinical disciplines have recognized the significance of the biological clock in health and illness, and are incorporating this knowledge into treatment programs. Recent decades have seen the emergence of chronotherapies, i.e., treatment strategies that are aimed at producing adjustments in the circadian clock. The final objective of these approaches is to affect basic cellular and physiological processes, which in turn may be at the root of disorders such as physiological aging, immune functioning, metabolic activity, and psychiatric disturbance. It is suggested that the integration of chronobiological perspectives into many mainstream medical disciplines would be of significant benefit, both for the reduction of the prevalence of diseases and their treatment. This review considers the physiology of sleep and the importance of timekeeping mechanisms in the regulation of overall health.
Effective and Scalable Clustering on Massive Attributed Graphs

Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same cluster share similar topological and attribute characteristics, while those in different clusters are dissimilar. This problem is challenging on massive graphs, e.g., with millions of nodes and billions of attribute values. For such graphs, existing solutions either incur prohibitively high costs, or produce clustering results with compromised quality. In this paper, we propose , an efficient approach to k-AGC that yields high-quality clusters with costs linear to the size of the input graph G. The main contributions of are twofold: (i) a novel formulation of the k-AGC problem based on an attributed multi-hop conductance quality measure custom-made for this problem setting, which effectively captures cluster coherence in terms of both topological proximities and attribute similarities, and (ii) a linear-time optimization solver that obtains high quality clusters iteratively, based on efficient matrix operations such as orthogonal iterations, an alternative optimization approach, as well as an initialization technique that significantly speeds up the convergence of in practice. Extensive experiments, comparing 11 competitors on 6 real datasets, demonstrate that consistently outperforms all competitors in terms of result quality measured against ground truth labels, while being up to orders of magnitude faster. In particular, on the Microsoft Academic Knowledge Graph dataset with 265.2 million edges and 1.1 billion attribute values, outputs high-quality results for 5-AGC within 1.68 hours using a single CPU core, while none of the 11 competitors finish within 3 days.
The Implicit Biases of Stochastic Gradient Descent on Deep Neural Networks with Batch Normalization

Deep neural networks with batch normalization (BN-DNNs) are invariant to weight rescaling due to their normalization operations. However, using weight decay (WD) beneﬁts these weight-scale-invariant networks, which is often attributed to an increase of the effective learning rate when the weight norms are decreased. In this paper, we demonstrate the insufﬁciency of the previous explanation and investigate the implicit biases of stochastic gradient descent (SGD) on BN-DNNs to provide a theoretical explanation for the efﬁcacy of weight decay. We identity two implicit biases of SGD on BN-DNNs: 1) the weight norms in SGD training remain constant in the continuous-time domain and keep increasing in the discrete-time domain; 2) SGD optimizes weight vectors in fully-connected networks or convolution kernels in convolution neural networks by updating components lying in the input feature span, while leaving those components orthogonal to the input feature span unchanged. Thus, SGD without WD accumulates weight noise orthogonal to the input feature span, and cannot eliminate such noise. Our empirical studies corroborate the hypothesis that weight decay suppresses weight noise that is left untouched by SGD. Furthermore, we pro-pose to use weight rescaling (WRS) instead of weight decay to achieve the same regularization effect, while avoiding performance degradation of WD on some momentum-based optimizers. Our empirical results on image recognition show that regardless of optimization methods and network architectures, training BN-DNNs using WRS achieves similar or better performance
Convolutional Neural Network Interpretability with General Pattern Theory

Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefits, such as more accountable usage, better algorithm maintenance and improvement. The complexity of dataset structure may contribute to the difficulty in solving interpretability problem arising from DNN's black-box mechanism. Thus, we propose to use pattern theory formulated by Ulf Grenander, in which data can be described as configurations of fundamental objects that allow us to investigate convolutional neural network's (CNN) interpretability in a component-wise manner. Specifically, U-Net-like structure is formed by attaching expansion blocks (EB) to ResNet, allowing it to perform semantic segmentation-like tasks at its EB output channels designed to be compatible with pattern theory's configurations. Through these modules, some heatmap-based explainable artificial intelligence (XAI) methods will be shown to extract explanations w.r. t individual generators that make up a single data sample, potentially reducing the impact of dataset's complexity to interpretability problem. The MNIST-equivalent dataset containing pattern theory's elements is designed to facilitate smoother entry into this framework, along which the theory's generative aspect is naturally presented.
Prepivoted permutation tests

We present a general approach to constructing permutation tests that are both exact for the null hypothesis of equality of distributions and asymptotically correct for testing equality of parameters of distributions while allowing the distributions themselves to differ. These robust permutation tests transform a given test statistic by a consistent estimator of its limiting distribution function before enumerating its permutation distribution. This transformation, known as prepivoting, aligns the unconditional limiting distribution for the test statistic with the probability limit of its permutation distribution. Through prepivoting, the tests permute one minus an asymptotically valid p-value for testing the null of equality of parameters. We describe two approaches for prepivoting within permutation tests, one directly using asymptotic normality and the other using the bootstrap. We further illustrate that permutation tests using bootstrap prepivoting can provide improvements to the order of the error in rejection probability relative to competing transformations when testing equality of parameters, while maintaining exactness under equality of distributions. Simulation studies highlight the versatility of the proposal, illustrating the restoration of asymptotic validity to a wide range of permutation tests conducted when only the parameters of distributions are equal.
Design of Polar Code Lattices of Small Dimension

Polar code lattices are formed from binary polar codes using Construction D. In this paper, we propose a design technique for finite-dimension polar code lattices. The dimension $n$ and target probability of decoding error are parameters for this design. To select the rates of the Construction D component codes, rather than using the capacity as in past work, we use the explicit finite-length properties of the polar code. Under successive cancellation decoding, density evolution allows choosing code rates that satisfy the equal error probability rule. At an error-rate of $10^{-4}$, a dimension $n=128$ polar code lattice achieves a VNR of 2.5 dB, within 0.2 dB of the best-known BCH code lattice, but with significantly lower decoding complexity.