Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascaded Semantic and Positional Self-Attention Network for Document Classification

Transformers have shown great success in learning representations for language modelling. However, an open challenge still remains on how to systematically aggregate semantic information (word embedding) with positional (or temporal) information (word orders). In this work, we propose a new architecture to aggregate the two sources of information using cascaded semantic and positional self-attention network (CSPAN) in the context of document classification. The CSPAN uses a semantic self-attention layer cascaded with Bi-LSTM to process the semantic and positional information in a sequential manner, and then adaptively combine them together through a residue connection. Compared with commonly used positional encoding schemes, CSPAN can exploit the interaction between semantics and word positions in a more interpretable and adaptive manner, and the classification performance can be notably improved while simultaneously preserving a compact model size and high convergence rate. We evaluate the CSPAN model on several benchmark data sets for document classification with careful ablation studies, and demonstrate the encouraging results compared with state of the art.
A Self-Decoupled 32 Channel Receive Array for Human Brain Magnetic Resonance Imaging at 10.5T

Purpose: Receive array layout, noise mitigation and B0 field strength are crucial contributors to signal-to-noise ratio (SNR) and parallel imaging performance. Here, we investigate SNR and parallel imaging gains at 10.5Tesla (T) compared to 7T using 32-channel receive arrays at both fields. Methods: A self-decoupled 32-channel receive array for human brain imaging at 10.5T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared to the performance of an industry-standard 32-channel receiver at 7T (7T-32Rx) via experimental measurements using a human head shaped phantom. Results: The 10.5T-32Rx provided 67% more central SNR and 87% more peripheral SNR compared to the 7T-32Rx. Minimum inverse g-factor value of the 10.5T-32Rx (min(1/g) = 0.56) was 51% higher than that of the 7T-32Rx (min(1/g) = 0.37) with R=4x4 2D acceleration, resulting in significantly enhanced parallel imaging performance at 10.5T compared to 7T. The g-factor values of 10.5T-32Rx were on par with those of a 64-channel receiver at 7T, e.g. 1.8 versus 1.9, respectively, with R=4x4 axial acceleration. Conclusion: Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel imaging performance at 10.5T compared to 7T.
Spatio-Temporal Top-k Similarity Search for Trajectories in Graphs

We study the problem of finding the $k$ most similar trajectories to a given query trajectory. Our work is inspired by the work of Grossi et al. [6] that considers trajectories as walks in a graph. Each visited vertex is accompanied by a time-interval. Grossi et al. define a similarity function that captures temporal and spatial aspects. We improve this similarity function to derive a new spatio-temporal distance function for which we can show that a specific type of triangle inequality is satisfied. This distance function is the basis for our index structures, which can be constructed efficiently, need only linear memory, and can quickly answer queries for the top-$k$ most similar trajectories. Our evaluation on real-world and synthetic data sets shows that our algorithms outperform the baselines with respect to indexing time by several orders of magnitude while achieving similar or better query time and quality of results.
Challenging β-VAE with β<1 for Disentanglement Via Dynamic Learning

This paper challenges the common assumption that the weight of $\beta$-VAE should be larger than $1$ in order to effectively disentangle latent factors. We demonstrate that $\beta$-VAE with $\beta \leq 1$ can not only obtain good disentanglement but significantly improve the reconstruction accuracy via dynamic control. The goal of this paper is to deal with the trade-off problem between reconstruction accuracy and disentanglement with unsupervised learning. The existing methods, such as $\beta$-VAE and FactorVAE, assign a large weight in the objective, leading to high reconstruction errors in order to obtain better disentanglement. To overcome this problem, ControlVAE is recently developed to dynamically tune the weight to achieve the trade-off between disentangling and reconstruction using control theory. However, ControlVAE cannot fully decouple disentanglement learning and reconstruction, because it suffers from overshoot problem of the designed controller and does not timely respond to the target KL-divergence at the beginning of model training. In this paper, we propose a novel DynamicVAE that leverages an incremental PI controller, a variant of proportional-integral-derivative controller (PID) controller, and moving average as well as hybrid annealing method to effectively decouple the reconstruction and disentanglement learning. We then theoretically prove the stability of the proposed approach. Evaluation results on benchmark datasets demonstrate that DynamicVAE significantly improves the reconstruction accuracy for the comparable disentanglement compared to the existing methods. More importantly, we discover that our method is able to separate disentanglement learning and reconstruction without introducing any conflict between them.
Effects of inoculation of arbuscular mycorrhizal fungi on growth of rabbiteye blueberry (Vaccinium ashei Reade) in south China

The pot experiment was conducted to study the effects of inoculation of two arbuscular mycorrhizal fungi(AMF) on growth of rabbiteye blueberry(Vaccinium ashei). The results showed that AMF inoculation by Glomus versiforme and Glomus mosseae significantly enhanced the root length,root area,fresh mass of aboveground parts,branch length and branch number of V.ashei compared to the un-inoculated control.G. versiforme displayed higher colonication rate and provided more growth stimulation.The AMF inoculation also increased P2O5content in rhizosphere soil of V.ashei. In conclusion,mycorrhizal inoculation is important for introduction and cultivation of V.ashei in south China.
The road from reality: The universe exists as indu- bitable structured information

This manuscript describes the most fundamental description of reality I believe to be possible. Specifically, my proposal is sufficiently powerful to inherit the coveted indubitable property of the Cartesian universal doubt method then to carry it forward such that the structure of reality, in the form of the laws of physics, is inherited indubitably from pure reason, whilst avoiding the usual pitfall at the mind-body problem. To derive the laws of physics, the strategy is to construct a purely mathematical model of science whose domain is the set of all indubitable statements, then to solve it for the laws of physics by maximizing the entropy of said statements. In the model, reality is described as a set of realized experiments (the set of what "I" has proven), and the laws of physics are those that remain universally valid for all permutations or rearrangements of experiments constrained only by nature, which we define as the group of all possible transformations of the set. At the most fundamental level, physics is —quite simply— the probability measure that makes reality maximally informative (to "I"/ the observer).
Evaluating Energy Forward Dynamics Modeled as a Subordinated Hilbert-Space Linear Functional

In this study, we evaluate energy forward dynamics modeled as time-change Hilbert-space of linear functional. The energy forward is represented as an element of Hilbert-space of function. Representing energy forward and futures contracts as a time-changing stochastic process in a Hilbert-space of functions shows clearly, that an arbitrage-free forward price can be derived from the buy-and hold strategy in the energy market thereby enabling investors in the market willing to be salvage from the market uncertainties as well as Arrow-Debreu situations to execute a spot or forward contracts depending on the time and place the market becomes favorable. With a clock measuring speed of evolution or data frequency for the energy stock market, the distribution of the increments of the Levy process with the subordinator is subordinated to the distribution of increments of the Levy process and the results are utilized to price forward contracts of a sample electricity commodity.
Comparative Pollen and Dye Transfer by Pollinators of Delphinium nelsonii

The perennial herb Delphinium nelsonii Greene is pollinated by hummingbirds and bumble-bees in Colorado, USA. Birds and bees were induced to forage in an aviary and the resulting carryover of pollen and fluorescent dye powder from an initial male-phase donor flower to emasculated female-phase recipients was compared. Bumble-bee visitation caused dye transfer whose properites closely resembled those of pollen transfer on average, while hummingbird visitation caused more variable deposition of dye and pollen on recipients and longer distances of pollen than of dye movement on average. The last result implies that pollen dispersal and genetic neighbourhood area are more extensive than estimated previously from dye dispersal in D. nelsonii populations. Pollen transfer in this species also is comparable to that in two sympatric herbs, Ipomopsis aggregata (Pursh) V. Grant and Erythronium grandiflorum Pursh. Key-words: Bumble-bees, Colorado, Delphinium, genetic neighbourhood, hummingbirds, pollination, pollen dispersal
Grit and chiropractic students' academic performance: a cross-sectional study.

OBJECTIVE Previous investigations have studied the relationship between grit and academic performance, and it has been reported that grittier students perform better academically. The objectives of this study are to measure chiropractic students' grittiness and to explore the correlation between grit and academic performance. METHODS We distributed the Short Grit Scale (Grit-S) questionnaire to chiropractic students in electronic form. We included questions about their previous grade point average and the number of times they had retaken examinations. We scored the overall Grit-S scale and the Consistency of Interest and Perseverance of Effort subscales. A 2-tailed t test and 1-way analysis of variance were used to determine differences between groups. RESULTS The response rate was 87% (n = 110). The mean grit score (3.44 ± 0.60) was similar to the general population and slightly lower than other healthcare professionals. The students who had a grade point average between <80% but less than 90% obtained significantly higher grit scores compared to those who had a grade point average <60% but less than 70%. Similarly, students who had no examination retakes had higher grit scores compared to those who took 4 or more exam retakes. We observed these differences in the overall and subscales scores. No other group showed any difference. CONCLUSION The results of this research showed that the grittier students performed better academically than the less gritty students. Grit scores can potentially be used to identify the students at risk of failing or dropping out. The role and potential application of grit in chiropractic education, student support, and admission procedures should be further evaluated.
Quantifying uncertainties of climate signals in chemistry climate models related to the 11-year solar cycle – Part 1: Annual mean response in heating rates, temperature, and ozone

Abstract. Variations in the solar spectral irradiance (SSI) with the 11-year sunspot cycle have been shown to have a significant impact on temperatures and the mixing ratios of atmospheric constituents in the stratosphere and mesosphere. Uncertainties in modelling the effects of SSI variations arise from uncertainties in the empirical models reconstructing the prescribed SSI data set as well as from uncertainties in the chemistry–climate model (CCM) formulation. In this study CCM simulations with the ECHAM/MESSy Atmospheric Chemistry (EMAC) model and the Community Earth System Model 1 (CESM1)–Whole Atmosphere Chemistry Climate Model (WACCM) have been performed to quantify the uncertainties of the solar responses in chemistry and dynamics that are due to the usage of five different SSI data sets or the two CCMs. We apply a two-way analysis of variance (ANOVA) to separate the influence of the SSI data sets and the CCMs on the variability of the solar response in shortwave heating rates, temperature, and ozone. The solar response is derived from climatological differences of time slice simulations prescribing SSI for the solar maximum in 1989 and near the solar minimum in 1994. The SSI values for the solar maximum of each SSI data set are created by adding the SSI differences between November 1994 and November 1989 to a common SSI reference spectrum for near-solar-minimum conditions based on ATLAS-3 (Atmospheric Laboratory of Applications and Science-3). The ANOVA identifies the SSI data set with the strongest influence on the variability of the solar response in shortwave heating rates in the upper mesosphere and in the upper stratosphere–lower mesosphere. The strongest influence on the variability of the solar response in ozone and temperature is identified in the upper stratosphere–lower mesosphere. However, in the region of the largest ozone mixing ratio, in the stratosphere from 50 to 10 hPa, the SSI data sets do not contribute much to the variability of the solar response when the Spectral And Total Irradiance REconstructions-T (SATIRE-T) SSI data set is omitted. The largest influence of the CCMs on variability of the solar responses can be identified in the upper mesosphere. The solar response in the lower stratosphere also depends on the CCM used, especially in the tropics and northern hemispheric subtropics and mid-latitudes, where the model dynamics modulate the solar responses. Apart from the upper mesosphere, there are also regions where the largest fraction of the variability of the solar response is explained by randomness, especially for the solar response in temperature.
Testimony and Interpretation

Testimony can be a source of knowledge. Work on the epistemology of testimony largely assumes that hearers understand what a speaker has testified to. This paper relaxes that assumption, and examines how misinterpretation, or the risk of it, can prevent a hearer from acquiring knowledge. Because unreliability in interpretation can arise in many ways, section 2 considers a variety of such cases. Section 3 sketches some desiderata for a successful account of the role of interpretation in testimony, on which interpretation needn’t proceed inferentially through knowledgeable belief about what is said. Finally, section 4 offers a safety-theoretic account of reliable interpretation which explains how and when misinterpretation prevents knowledge.
The feeding ecology and survival of pheasant chicks on arable farmland

(1) Pheasant broods were radio-tracked for approximately 20 days after hatching and their diet determined from faecal samples collected from nocturnal roost sites during the first 12 days. (2) Broods which moved over a large range suffered heavier mortality than those which remained within a relatively small area. (3) Broods preferred to forage in weedy areas, rough grass and winter barley, which all had a high arthropod biomass. Broods selected against woodland. (4) Carabid beetles and the larvae of sawfly and Lepidoptera were preferred food items. Hens ate similar arthropods to their chicks but in less quantity. (5) Variation in arthropod densities in the home range of radio-tagged broods explained 75% of the variation in chick survival. Chick survival was highest in those broods which had ingested the highest biomass of insects. (6) Carabid beetles, chrysomelid beetles and the larvae of sawfly and Lepidoptera, explained 67% of the between-year variation in chick survival rate. The effects of weather are difficult to explain because of the intercorrelations of weather with the densities of some arthropods.
Texture analysis in colorectal liver metastases

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Sentiment analysis of the agenda of the Spanish political parties on Twitter during the 2018 motion of no confidence . A compositional data approach

Resumen This paper provides a sentiment analysis of the political parties’ discussion on Twitter about the motion of no confidence in the Spanish government in 2018. In particular, 2824 tweets from the official accounts of the 13 political parties represented in the Congress of Deputies were extracted and analysed. The methodology applied compositional data analysis and its visualisation employing Biplot (a visualisation tool that enables contrasting the relative importance of the elements under study). Unlike traditional approaches, this study emphasises the relative importance of the issues within the agenda while incorporating a third category, the sentiment analysis. The main findings concern the reliability of the method to represent compositionally the agenda and the agenda-setters, as well as the sentiment analysis. This confirms that the issues most notably associated with certain parties also reflect their projection on sentiment. In sum, this analysis sheds light on sentiment representation in the agenda-setters (attribute agenda), especially in the field of political communication. Este artículo presenta el análisis de sentimiento de la discusión de partidos políticos en Twitter, en el contexto de la Moción de Censura al gobierno español de 2018. En particular, extrajimos y analizamos 2824 tweets de las cuentas oficiales de las 13 formaciones políticas representadas en el Congreso de los Diputados. En su desarrollo metodológico aplicamos el análisis composicional de datos y su visualización a través del biplot (una herramienta de visualización que permite contrastar la importancia relativa de los elementos en estudio). A diferencia de los enfoques tradicionales, nuestro estudio enfatiza la importancia relativa de los temas dentro de la agenda, a la vez que incorpora un tercer componente, el análisis de sentimiento. La investigación concluye sobre la fiabilidad del método para representar composicionalmente la agenda y los agenda-setters, así como el análisis de sentimiento, constatando que los temas que se asocian de forma más notable con determinados partidos, también lo hacen con su proyección sobre los sentimientos. El análisis arroja luz sobre la representación de los sentimientos en los agenda-setters (agenda de atributos), especialmente en el campo de la comunicación política.
Variable Binding for Sparse Distributed Representations: Theory and Applications

Variable binding is a cornerstone of symbolic reasoning and cognition. But how binding can be implemented in connectionist models has puzzled neuroscientists, cognitive psychologists, and neural network researchers for many decades. One type of connectionist model that naturally includes a binding operation is vector symbolic architectures (VSAs). In contrast to other proposals for variable binding, the binding operation in VSAs is dimensionality-preserving, which enables representing complex hierarchical data structures, such as trees, while avoiding a combinatoric expansion of dimensionality. Classical VSAs encode symbols by dense randomized vectors, in which information is distributed throughout the entire neuron population. By contrast, in the brain, features are encoded more locally, by the activity of single neurons or small groups of neurons, often forming sparse vectors of neural activation. Following Laiho et al. (2015), we explore symbolic reasoning with a special case of sparse distributed representations. Using techniques from compressed sensing, we first show that variable binding in classical VSAs is mathematically equivalent to tensor product binding between sparse feature vectors, another well-known binding operation which increases dimensionality. This theoretical result motivates us to study two dimensionality-preserving binding methods that include a reduction of the tensor matrix into a single sparse vector. One binding method for general sparse vectors uses random projections, the other, block-local circular convolution, is defined for sparse vectors with block structure, sparse block-codes. Our experiments reveal that block-local circular convolution binding has ideal properties, whereas random projection based binding also works, but is lossy. We demonstrate in example applications that a VSA with block-local circular convolution and sparse block-codes reaches similar performance as classical VSAs. Finally, we discuss our results in the context of neuroscience and neural networks.
Incentive-compatible mechanisms for continuous resource allocation in mobility-as-a-service: Pay-as-You-Go and Pay-as-a-Package

Mobility as a Service (MaaS) has recently received a significant attention from researchers, industry stakeholders, and the public sector. The vast majority of existing MaaS paradigms are articulated based on the traditional segmentation of travel modes, e.g. private vehicle, public transportation (bus, metro, light rail) and shared mobility (car/bike/ride-sharing, ride-sourcing). In the context of `Everything-as-a-Service' (XaaS), service providers have evolved from product-based models towards less segmented representations in which resources are priced in a continuous fashion. Yet, such continuous resource allocation formulations are inexistent for MaaS systems. This study attempts to address this gap by introducing innovative MaaS mechanisms that allocate mobility resources to users without any form of travel mode segmentation. We introduce an online auction framework where travelers have the possibility to bid for continuous mobility resources based on their requirements and willingness to pay. We propose two MaaS mechanisms, Pay-as-You-Go (PAYG) and Pay-as-a-Package (PAAP), which allow travelers to either pay for the immediate use of mobility services or to subscribe to mobility service packages for a more protracted usage. Both MaaS mechanisms are based on mixed-integer or linear programming formulations designed to maximize social welfare. We show that the proposed PAYG and PAAP mechanisms are incentive-compatible, develop efficient online primal-dual algorithms to implement the proposed MaaS mechanisms and derive theoretical bounds on the worst-case performance of these algorithms. Moreover, we design a rolling horizon framework to incorporate booking flexibility. Numerical results on extensive problem instances generated from realistic mobility data highlight the benefits of the proposed MaaS mechanisms, and quantify the trade-offs among the proposed approaches.