Miracle-dz/UniFAR-FTUs huggingface dataset:
Missing snowmelt runoff following drought explained by root-zone storage deﬁcits D R A F T Missing snowmelt runoff following drought explained by root-zone storage deficits Supplementary Information for 2 Missing snowmelt runoff following drought explained by root-zone storage deficits

Water resources management in mountainous regions hinges on forecasting runoff during annual snowmelt periods. However, extreme droughts are altering snowpack-runoff relationships. The current megadrought in the Western United States provides a case in point: in 2021 in California, the historically reliable relationship between April 1 snowpack and runoff failed—much less streamflow arrived than was predicted. Several factors have been proposed to account for this ‘missing’ streamflow, including changes in: evapotranspiration, rainfall, snowmelt rate, and subsurface moisture conditions. Here, we use hydrologic data from 13 minimally disturbed basins and 6 water supply basins in the Sierra Nevada to demonstrate that the root-zone storage deficit (i.e., the net depletion of plant-accessible water from soil and weathered bedrock via evapotranspiration) is the dominant driver of snowmelt runoff reductions in years following drought. Transpiration in excess of precipitation in drought years generates large deficits that must be met prior to significant streamflow generation. We introduce a mass balance model that captures this effect to motivate model structures for forecasting models. By accounting for deficits in models for snowmelt runoff, overprediction of 2021 streamflow decreased from a total of ≈ 100% to < 12% at study sites in the Sierra Nevada. Our findings indicate that the relationship between snowpack and runoff will evolve as plant ecosystems respond to climate change and alter subsurface water storage dynamics. Through this climatic transition, root-zone storage deficits will play an essential role in snowmelt runoff prediction, and this study provides a framework for adapting forecasting models with readily-available data.
An Indexable Time Series Dimensionality Reduction Method for Maximum Deviation Reduction and Similarity Search

Similarity search over time series is essential in many applications. However, it may cause “the curse of dimensionality” due to the high dimensionality of time series. Various dimensionality reduction methods have been developed. Some of them sacrifice maximum deviation to get faster dimensionality reduction. The Adaptive Piecewise Linear Approximation ( 𝐴𝑃𝐿𝐴 ) method uses guaranteed error bounds for the best maximum deviation, but it takes a long time for dimensionality reduction. We propose an adaptive-length dimensionality reduction method, called Self Adaptive Piecewise Linear Approximation ( 𝑆𝐴𝑃𝐿𝐴 ). It consists of 1) initialization; 2) split & merge iteration; and 3) segment end-point movement iteration. Increment Area, Reconstruction Area, and several equations are applied to prune redundant computations. Experiments show that our method outperforms 𝐴𝑃𝐿𝐴 by 𝑛 times with a minor maximum deviation loss, where 𝑛 is the length of the time series. We also propose a lower bound distance measure between time series to guarantee lower bounding lemma and tightness for adaptive-length dimensionality reduction methods. Moreover, we propose a Distance-Based Covering with Convex Hull (DBCH ) structure to improve 𝐴𝑃𝐶𝐴 𝑀𝐵𝑅 for adaptive-length dimensionality reduction methods. When mapping time series into a DBCH-tree, we split nodes and pick branches using the lower bounding distance. Our experimental evaluations on 117 datasets from the UCR2018 Archive demonstrate the efficiency and effectiveness of the proposed approaches.
Appendix for “Label consistency in overﬁtted generalized k -means”

Proposition 2 and 3 show that perfect refinement for sub-gaussian mixture-of-curves model can be achieved when the number of clusters grows as Ln = O( √ n log n). To the best of our knowledge, this is the first such result in the literature, that is, an upper bound on the minimum number of clusters needed to achieve a perfect refinement of the true clusters. What remains for future investigations to determine is how tight this bound is. Empirically, we have found examples of the mixture-of-curves model for which Ln 1 seems to enough, but also an example where Ln √ n log n seems to be the required scaling. Figure S1(a) shows a noisy circle-torus model (cf. Section D.1) with R = 10, r = 2 and σ = 1 that demonstrates the scaling Ln √ n log n. Here, we plot the average misclassification rate over 32 repetitions vs Ln/ √ n log n for various n. The fact that these plots coincide with each other for different n suggests that there is sharp threshold τn = C1 √ n log n such that with Ln > τn, perfect refinement recovery is possible and with Ln < τn, impossible. Figure S1(b) shows an example that exhibits Ln 1 threshold: A line-circle model with parameters δ = 4, σ = 1 and line standard deviation = 7.
R agonist psilocybin in the treatment of tobacco addiction 2 APilot study of the 5-HT

Despite suggestive early findings on the therapeutic use of hallucinogens in the treatment of substance use disorders, rigorous follow-up has not been conducted. To determine the safety and feasibility of psilocybin as an adjunct to tobacco smoking cessation treatment we conducted an openlabel pilot study administering moderate (20 mg/70 kg) and high (30 mg/70 kg) doses of psilocybin within a structured 15-week smoking cessation treatment protocol. Participants were 15 psychiatrically healthy nicotine-dependent smokers (10 males; mean age of 51 years), with a mean of six previous lifetime quit attempts, and smoking a mean of 19 cigarettes per day for a mean of 31 years at intake. Biomarkers assessing smoking status, and self-report measures of smoking behavior demonstrated that 12 of 15 participants (80%) showed seven-day point prevalence abstinence at 6-month follow-up. The observed smoking cessation rate substantially exceeds rates commonly reported for other behavioral and/or pharmacological therapies (typically <35%). Although the open-label design does not allow for definitive conclusions regarding the efficacy of psilocybin, these findings suggest psilocybin may be a potentially efficacious adjunct to current smoking cessation treatment models. The present study illustrates a framework for future research on the efficacy and mechanisms of hallucinogen-facilitated treatment of addiction.
MM-infer: A Tool for Inference of Multi-Model Schemas

The v ariety feature of Big Data, represented by multi-model data , has brought a new dimension of complexity to data management. The need to process a set of distinct but interlinked models is a challenging task. In our demonstration, we present our prototype implementation MM-infer that ensures inference of a common schema of multi-model data. It supports popular data models and all three types of their mutual combinations, i.e., inter-model references, the embedding of models, and cross-model redundancy. Following the current trends, the implementation can efficiently process large amounts of data. To the best of our knowledge, ours is the first tool addressing schema inference in the world of multi-model databases.
Influence of L1 on the production of English voiceless plosive clusters by Norwegian speakers

The realisation of voiceless plosive sequences is investigated in six English and Norwegian clusters. Differences between the two languages are found in the frequency of the release of the first plosive in the cluster. While a burst is nearly always present in Norwegian, it is more variable in English, where it also depends on the cluster. In English, burst durations are shorter and burst intensities are lower than in Norwegian. The total duration of the whole cluster is much shorter in English than in Norwegian. For Norwegian L2 speakers of English, the cluster durations are intermediate between Norwegian and L1 English. The L2 speakers nearly always produce a release burst for first plosive in the cluster, as in Norwegian. The burst duration is somewhat shorter than in Norwegian, but longer than in English. Burst intensities are higher than in English.
Dissecting, Designing, and Optimizing LSM-based Data Stores

Log-structured merge (LSM) trees have emerged as one of the most commonly used disk-based data structures in modern data systems. LSM-trees employ out-of-place ingestion to support high throughput for writes, while their immutable file structure allows for good utilization of disk space. Thus, the log-structured paradigm has been widely adopted in state-of-the-art NoSQL, relational, spatial, and time-series data systems. However, despite their popularity, there is a lack of pedagogical textbook-like material on LSM designs. The goal of this tutorial is to present the fundamental principles of the LSM paradigm along with a digest of optimizations and new designs proposed in recent research and adopted by modern LSM engines. This will serve as introductory material for non-experts, and as a roadmap to cutting-edge LSM results for the LSM-aware researchers and practitioners. Toward this, we first discuss in detail the basic operations (inserts, updates, deletes, point and range queries), their access patterns, and their paths through the LSM data structure. We then dive into the details of recent research on optimizing each of those operations. We first discuss techniques and designs that optimize data ingestion in LSM-trees and the performance tradeoff constructed by writes and reads for the LSM engines. Finally, we present the rich design space of the log-structured paradigm and outline how to navigate it and tune LSM-based systems. We conclude with a discussion on open challenges on LSM systems. This will be a 1.5-hour tutorial.
York University at TREC 2021: Deep Learning Track

This is our ﬁrst time to participate in TREC Deep Learning track. We submitted three BERT-based runs “YorkU21a”, “YorkU21b” and “YorkU21c”, where YorkU21a has the best performance. Our main goal is to explore the possibility of combining deep learning with traditional BM25 retrieval method. In this paper, we discuss the results of using the summation method to combine the above two approaches and provide some illustrative analyses on the impact of di ﬀ erent retrieval strategies on the results.
Coordination of two opposite flagella

Phytophthora species cause diseases in a large variety of plants and represent a 15 serious agricultural threat, leading, every year, to multibillion dollar losses. Infection occurs when 16 these biflagellated zoospores move across the soil at their characteristic high speed and reach 17 the roots of a host plant. Despite the relevance of zoospore spreading in the epidemics of plant 18 diseases, characteristics of individual swimming of zoospores have not been fully investigated. It 19 remains unknown about the characteristics of two opposite beating flagella during translation 20 and turning, and the roles of each flagellum on zoospore swimming. Here, combining 21 experiments and modeling, we show how these two flagella contribute to generate thrust when 22 beating together, and identify the mastigonemes-attached anterior flagellum as the main source 23 of thrust. Furthermore, we find that turning involves a complex active process, in which the 24 posterior flagellum temporarily stops, while the anterior flagellum keeps on beating and changes 25 its gait from sinusoidal waves to power and recovery strokes, similar to Chlamydomonas’s 26 breaststroke, to reorient its body to a new direction. Our study is a fundamental step towards a 27 better understanding of the spreading of plant pathogens’ motile forms, and shows that the 28 motility pattern of these biflagellated zoospores represents a distinct eukaryotic version of the 29 celebrated “run-and-tumble" motility class exhibited by peritrichous bacteria. 30
The European Union’s handling of Russian disinformation

The European Union’s democratic features, such as open and fact-based public deliberation processes and free and fair election processes and principles, are threatened by anti-EU and pro-Kremlin disinformation. This thesis explores the European Union’s motivations to handle Russian disinformation. By employing the case study method and conducting a qualitative content analysis on official European Union documents and three elite interviews. This thesis employs the theory of realism and social constructivism to examine the empirical material. This thesis focuses on the EU’s initiatives after 2015 – the year of the establishment of the East StratCom Task Force, which was the first major initiative taken by the European Union to handle Russian disinformation. This thesis found that the European Union’s main motivations to handle disinformation are to protect public deliberation and the democratic processes within the European Union from being meddled by foreign actors. Most of the initiative launched by the European Union provides solutions to the issue by undertaking internal actions and does not seek to implement offensive sanctions
DataGossip: A Data Exchange Extension for Distributed Machine Learning Algorithms

Distributed, data parallel machine learning algorithms frequently exchange the gradients of their locally trained models to converge to a consistent global model. This distributed training works reasonably well if the training classes are partitioned evenly. In practice, however, the data partitions are often skewed for various reasons. The local models then strive for different local optima and start to compete rather than collaborate, which spoils the global model. The general solution to this problem is data shuffling, but due to predetermined data placements or physical constraints re-allocating almost all training records in the cluster for some machine learning task is often not possible. To minimize the negative impact of skewed data distributions, we propose DataGossip, a novel extension for distributed machine learning algorithms. DataGossip runs as a background process and dynamically exchanges relevant training instances between the different cluster nodes. We investigate how many training instances a cluster node needs to exchange, which training instances it should select, and how much foreign training instances need to be emphasized to impact the training positively. Our evaluation shows that DataGossip can improve the accuracy of models that have been trained on skewed data distributions. It also motivates further research on asynchronous and reactive data exchange systems for distributed learning scenarios.
Design and implementation of an inductive power transfer system for wireless charging of future electric transportation

iii Acknowledgements . iv List of Figures . ix List of Tables . xiii List of Symbols. xiv Introduction . 17 1.1 Rationale for Adopting Wireless Charging system for EVs . 18 1.2 A Brief History of Development of RIPT System for EV Charging . 20 1.3 Working Principle and Components of RIPT System . 23 1.4 Wireless Charging Standards for Electric Vehicle . 27 1.5 Research Goals and Objectives . 29 Design Considerations for Resonant Inductive Link . 33 2.1 Brief Overview of Different Coil Shapes Employed In Wireless Charger . 33 2.2 Electrical Equivalent Circuit for Series-Series Compensated RIPT System . 36 2.2.1 Quality Factor of an SS-RIPT System . 38 2.2.2 Bifurcation Phenomena In an SS-RIPT System. 41 2.3 Calculation of Electrical Parameters for Bifurcation Free Operation . 47 2.4 Analytical Design of Archimedean Spiral Coils . 49 2.4. 1 Analytical Model of Self-Inductance of Coils . 50 2.4. 2 Analytical Model of Mutual Inductance between Coils. 52 2.5 Finding Coil-Pair Least Sensitive to Misalignment. 54 2.5. 1 Calculation of Electrical Parameters for 500 W setup. 55 2.5. 2 Calculation of Geometric Parameters for 500 W setup. 60 2.5. 3 Verification of Analytical Expressions . 63 2.5. 4 Mutual Inductance Profile of Coil-Pairs. 67 2.6 Summary of Chapter 2 . 72 Design of 3.6 kW Wireless Charger. 73
Debt structure and its determinants: an analysis of large Brazilian manufacturing companies

This study analyzes the determinants of financing sources of the largest Brazilian manufacturing companies, focusing on privately held companies. We conduct the analysis considering a single sample of 153 companies, 64 publicly traded and 89 privately held. Moreover, we classify the corporate debt into five categories, namely: a) bank debt; b) subsidized debt; c) capital market debt; d) leasing debt; and e) others. According to the results, the determinants of financing sources vary depending on the source and the type of company analyzed (listed or privately held). In addition, our results also show that most of the variables identified in the literature as determinants of the capital structure cannot explain the variation in the financing sources of the privately held companies.
Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks

Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52 % and 77.57 % accuracy results with speaker-independent (SI) performance, 66.90 % and 82.26 % accuracy results with speaker-dependent (SD) experiment in final.
CFD analysis of airflow patterns and heat transfer in small, medium, and large structures

Designing buildings to use energy more efficiently can lead to lower energy costs, while maintaining comfort for occupants. Computational fluid dynamics (CFD) can be utilized to visualize and simulate expected flows in buildings and structures. CFD gives architects and designers the ability to calculate the velocity, pressure, and heat transfer within a building. Previous research has not modeled natural ventilation situations that challenge common design rules of thumb used for cross-ventilation and single-sided ventilation. The current study uses a commercial code (FLUENT) to simulate cross-ventilation in simple structures and analyzes the flow patterns and heat transfer in the rooms. In the Casa Giuliana apartment and the Affleck house, this study simulates passive cooling in spaces well-designed for natural ventilation. Heat loads, human models, and electronics are included in the apartment to expand on prior research into natural ventilation in a full-scale building. Two different cases were simulated. The first had a volume flow rate similar to the ambient conditions, while the second had a much lower flow rate that had an ACH of 5, near the minimum recommended value Passive cooling in the Affleck house is simulated and has an unorthodox ventilation method; a window in the floor that opens to an exterior basement is opened along with windows and doors of the main floor to create a pressure difference. In the Affleck house, two different combinations of window and door openings are simulated to model different scenarios. Temperature contours, flow patterns, and the air changes per hour (ACH) are explored to analyze the ventilation of these structures.
The role of storytelling marketing in the customer's choice of the trademark of telecom companies in Iraq

This study aimed to clarify the role of storytelling marketing in the trademark marketing of telecommunications companies in Iraq, and it is logical that the role can be negative or favorable namely: value harmony, trademark image, trademark love, trademark loyalty. The researcher used the descriptive approach with reference to the literature of the study and previous studies, then used the questionnaire as a method for collecting primary data, and after processing and analysing the data, the researcher reached a number of conclusions, the most important of which are: Oral marketing did not lose its lustre and did not lose its impact on the customer's choice of the Iraqi telecom companies' trademark. Details of conclusions and recommendations at the end of the paper.
Influence of Hydraulic Shock Absorber Characteristic Parameters on the Critical Speed of High-speed Trains

In order to obtain the influence of hydraulic shock absorber characteristic parameters on the critical speed and wheel-rail wear of high-speed train, take CRH380B as an example, a vehicle dynamics model is established using SIMPACK soft-ware based on the vehicle dynamics theory.Select the rubber joint stiffness, damping characteristics of secondary lateral shock absorber and yaw shock absorber to research. The results showed that: rubber joint stiffness of yaw shock absorber optimal ranges is 10-12 MN/m. When the stiffness is less than this value, the critical speed decreased significantly, the wheel-rail wear slowly increase. When stiffness is greater than this value, the critical speed slow down, but a sharp increase in wheel-rail wear. rubber joint stiffness of secondary lateral shock absorber have little effect on the critical speed and wheel-rail wear, optimal value is 4.25 MN/m. The damping characteristics of yaw shock absorber and secondary lateral shock absorber have some influence on the critical speed and wheel-rail wear.
NEONATAL GROWTH IN CAPTIVE DUSKY RATTLESNAKES (CROTALUS TRISERIATUS) FROM TWO POPULATIONS IN CENTRAL MÉXICO CRECIMIENTO NEONATAL DE LA SERPIENTE DE CASCABEL TRANSVOLCÁNICA (CROTALUS TRISERIATUS) CAUTIVAS DE DOS POBLACIONES DEL CENTRO DE MÉXICO

— Body size has important effects on snake ecology, and in turn, body size is dependent on the rate at which individuals grow. In this study, we analyzed first year captive growth of individuals from two populations of the Mexican dusky rattlesnake (Crotalus triseriatus). We used linear models to estimate individual growth rates as well as to compare growth rates between populations and sexes. Birth body size was greater in males than in females, and this pattern remained throughout the experiment. Average growth rate for all individuals was 7.66 mm/month. While the average absolute change was 79.8 mm. No statistical differences were detected in growth rates between populations or sexes. This could indicate that the variation in this attribute may be greater within populations than between populations.