Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirical, Bayesian approach to modelling the impact of weather on crop yield: maize in the US

We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18-19$^\circ$C, corresponding to a daily maximum temperature of 24-25$^\circ$C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of $2^\circ$C, relative to 1981-2014, results in the mean yield decreasing by 8\%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.
The LDBC Social Network Benchmark: Business Intelligence Workload

The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC's "choke point"-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of "parameter curation" in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result - only audited results can use this trademarked term.
Address-Event Variable-Length Compression for Time-Encoded Data

Time-encoded signals, such as social network update logs and spiking traces in neuromorphic processors, are defined by multiple traces carrying information in the timing of events, or spikes. When time-encoded data is processed at a remote site with respect to the location in which it is produced, the occurrence of events needs to be encoded and transmitted in a timely fashion. The standard Address-Event Representation (AER) protocol for neuromorphic chips encodes the indices of the "spiking" traces in the payload of a packet produced at the same time the events are recorded. This implicitly encodes the events' timing in the timing of the packet (which is assumed to be correctly detected at the receiver). This paper investigates the potential bandwidth saving that can be obtained by carrying out variable-length compression of packets' payloads. Compression leverages both intra-trace and inter-trace correlations over time that are typical in applications such as social networks or neuromorphic computing. The approach is based on discrete-time Hawkes processes and entropy coding with conditional codebooks. Results from an experiment based on a real-world retweet dataset are also provided.
Self-gravitational Force Calculation of Second-order Accuracy Using the Multigrid Method on Nested Grids

We present a simple and effective multigrid-based Poisson solver of second-order accuracy in both gravitational potential and forces in terms of the one, two, and infinity norms. The method is especially suitable for numerical simulations using nested mesh refinement. The Poisson equation is solved from coarse to fine levels using a one-way interface scheme. We introduce antisymmetrically linear interpolation for evaluating the boundary conditions across the multigrid hierarchy. The spurious forces commonly observed at the interfaces between refinement levels are effectively suppressed. We validate the method using two- and three-dimensional density-force pairs that are sufficiently smooth for probing the order of accuracy.
Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints

Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and shape modeling from images in the wild bring this challenge to the fore. Although the recent advances on view synthesis and image-to-image translation, currently available formulations are limited to transfer solely style and do not take into account the character’s motion and shape, which are by nature intermingled to produce plausible human forms. In this paper, we propose a unifying formulation for transferring appearance and retargeting human motion from monocular videos that regards all these aspects. Our method synthesizes new videos of people in a different context where they were initially recorded. Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints. The evaluation is performed with several experiments using publicly available real videos containing hard conditions. Our method is able to transfer both human motion and appearance outperforming state-of-the-art methods, while preserving specific features of the motion that must be maintained (e.g., feet touching the floor, hands touching a particular object) and holding the best visual quality and appearance metrics such as Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS).
Deep Learning for Free-Hand Sketch: A Survey

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.
Canonical domains for coadjoint orbits

This paper describes two real analytic symplectomorphisms defined on appropriate dense open subsets of any coadjoint orbit of a compact semisimple Lie algebra. The first symplectomorphism sends the open dense subset to a bounded subset of a standard cotangent bundle. The second symplectomorphism has as target a bounded subset of a hyperbolic coadjoint orbit of an associated non‐compact semisimple Lie algebra. Therefore, coadjoint orbits of compact Lie algebras are symplectic compactifications of domains of cotangent bundles, and are in symplectic correspondence with hyperbolic orbits of non‐compact semisimple Lie algebras.
Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation

Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. Statistical predictors are not robust enough to correctly identify patients who would benefit from Transcatheter Aortic Valve Implantation (TAVI). This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI. We adopt a modern gradient boosting on decision trees algorithm, specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling to identify the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 TAVI cases, reaching an AUC of 0.83. Our approach outperforms several widespread prognostic risk scores, such as logistic EuroSCORE II, the STS risk score and the TAVI2-score, which are broadly adopted by cardiologists worldwide.
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning

This paper addresses the problem of short-term traffic prediction for signalized traffic operations management. Specifically, we focus on predicting sensor states in high-resolution (second-by-second). This contrasts with traditional traffic forecasting problems, which have focused on predicting aggregated traffic variables, typically over intervals that are no shorter than five minutes. Our contributions can be summarized as offering three insights: first, we show how the prediction problem can be modeled as a matrix completion problem. Second, we use a block-coordinate descent algorithm and demonstrate that the algorithm converges in sublinear time to a block coordinate-wise optimizer. This allows us to capitalize on the “bigness” of high-resolution data in a computationally feasible way. Third, we develop an ensemble learning (or adaptive boosting) approach to reduce the training error to within any arbitrary error threshold. The latter uses past days so that the boosting can be interpreted as capturing periodic patterns in the data. The performance of the proposed method is analyzed theoretically and tested empirically using both simulated data and a real-world high-resolution traffic data set from Abu Dhabi, United Arab Emirates. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.
A Group Norm Regularized LRR Factorization Model for Spectral Clustering

Spectral clustering is a very important and classic graph clustering method. Its clustering results are heavily dependent on affine matrix produced by data. Solving Low-Rank Representation~(LRR) problems is a very effective method to obtain affine matrix. This paper proposes LRR factorization model based on group norm regularization and uses Augmented Lagrangian Method~(ALM) algorithm to solve this model. We adopt group norm regularization to make the columns of the factor matrix sparse, thereby achieving the purpose of low rank. And no Singular Value Decomposition~(SVD) is required, computational complexity of each step is great reduced. We get the affine matrix by different LRR model and then perform cluster testing on synthetic noise data and real data~(Hopkin155 and EYaleB) respectively. Compared to traditional models and algorithms, ours are faster to solve affine matrix and more robust to noise. The final clustering results are better. And surprisingly, the numerical results show that our algorithm converges very fast, and the convergence condition is satisfied in only about ten steps. Group norm regularized LRR factorization model with the algorithm designed for it is effective and fast to obtain a better affine matrix.
SGD with Hardness Weighted Sampling for Distributionally Robust Deep Learning

Distributionally Robust Optimization (DRO) has been proposed as an alternative to Empirical Risk Minimization (ERM) in order to account for potential biases in the training data distribution. However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread Stochastic Gradient Descent (SGD) based optimizers for deep learning with ERM. We propose SGD with Hardness weighted sampling, an efficient optimization method for machine learning with DRO with a focus on deep learning. In this work, we propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. We show that our optimization method can be interpreted as a principled Hard Example Mining strategy. Similar to an online hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning. It only requires adding a softmax layer and maintaining a history of the loss values for each training example to compute adaptive sampling probabilities. In contrast to typical ad hoc hard mining approaches, and exploiting recent theoretical results in deep learning optimization, we We also prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters. Preliminary results demonstrate the feasibility and usefulness of our approach.
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection

In scene understanding, robots benefit from not only detecting individual scene instances but also from learning their possible interactions. Human-Object Interaction (HOI) Detection infers the action predicate on a triplet. Contextual information has been found critical in inferring interactions. However, most works only use local features from single human-object pairs for inference. Few works have studied the disambiguating contribution of subsidiary relations made available via graph networks. Similarly, few have leveraged visual cues with the intrinsic semantic regularities embedded in HOIs. We contribute Visual-Semantic Graph Attention Networks (VS-GATs): a dual-graph attention network that effectively aggregates visual, spatial, and semantic contextual information dynamically from primary human-object relations as well as subsidiary relations through attention mechanisms for strong disambiguating power. We achieve competitive results on two benchmarks: V-COCO and HICO-DET. The code is available at https://github.com/birlrobotics/vs-gats.
Totally Real Flat Minimal Surface in Hyperquadric

In this paper, we study geometry of totally real minimal surfaces in the complex hyperquadric $Q_{N-2}$, and obtain some characterizations of the harmonic sequence generated by these minimal immersions. For totally real flat surfaces that are minimal in both $Q_{N-2}$ and $\mathbb{C}P^{N-1}$, we determine them for $N=4, 5, 6$, and give a classification theorem when they are Clifford solutions.
Needs Analysis, Physiological Response, and Program Guidelines for Gaelic Football

ABSTRACT GAELIC FOOTBALL IS A POPULAR IRISH SPORT THAT HAS INCREASED IN POPULARITY THROUGHOUT THE WORLD. THE PURPOSE OF THIS ARTICLE IS TO FAMILIARIZE THE STRENGTH AND CONDITIONING COACH (SCC) OF THE MOVEMENTS ASSOCIATED WITH GAELIC FOOTBALL. IN ADDITION, THE PHYSIOLOGICAL RESPONSE RELATED TO GAELIC FOOTBALL IS DISCUSSED, AND A SAMPLE TRAINING PROGRAM IS PROVIDED. A BETTER UNDERSTANDING OF THE NATURE OF THE SPORT AND THE DIFFERENCES THAT EXIST BETWEEN PLAYING POSITIONS WILL HELP THE SCC PREPARE PLAYERS FOR COMPETITION.
Short-term effects of environmental restoration on diversity and abundance of Carabids in three lowland ecosystems in Burkina Faso

Many human activities affect arthropod diversity and may threaten environmental health. Some insects may act as biological indicators based on their presence or absence in an ecosystem. The present study was conducted from 2006 to 2009 to evaluate changes in the diversity and abundance of ground Carabids following restoration activities conducted in three lowlands ecosystems that had previously experienced high levels of human pressure in Burkina Faso. Three micro-catchments were identified in the regions of Soum, Sanmatenga, and Kompienga, using a hydrographic chart of Burkina Faso. The diversity and abundance of carabids collected in pitfall traps at the beginning and end of the rainy season were compared using 2006 as a sampling reference year with restoration activities (abandonment of farming, forestation and scarification) occurring in 2007. Post-treatment surveys were conducted in the same seasons of 2008 and 2009. Forty-nine species of Carabids have been identified based on monitoring of 1800 pitfall traps. The number of species generally increased in 2008 and 2009, from 9 to 41 species in the three micro-catchments. Species richness and insect abundance definitely increased where human pressure decreased and restoration measures were successfully conducted. However, Carabid diversity increased only in Soum micro-catchment; this site seemed to be more affected by restoration. Carabids are likely to be good indicator species; reports can be prepared relatively quickly. The results are discussed in relation to the goal of developing ecologically sound tools based on bio-indicator insects that can be used for environmental management.
Dosage de la cocaïne et de ses metabolites dans les cheveux, la salive et les urines Analysis of cocaine and metabolites in hair, oral fluid and urine

Des révisions proposées aux directives fédérales incluant l'utilisation potentielle des cheveux, de la salive ainsi que des urines pour le dépistage des conduites addictives en entreprise ont été publiées en 2004. Cette étude a été conçue pour déterminer le taux de positivité de différents types d'échantillons, dans une population toxicomane et dans une autre niant sa consommation de cocaïne. Cette étude a été menée sur 200 sujets, la moitié admettant une consommation de cocaïne, l'autre moitié la niant. Chaque sujet a donné un échantillon d'urine, de salive et de cheveux prélevés au moment de l'entrevue. Pour chaque sujet, des informations sur leur consommation de stupéfiants, incluant le moment de la dernière prise et la fréquence de consommation, leurappartenance ethnique, leur âge, leur sexe et la couleur de leurs cheveux ont été enregistrés. Les échantillons ont été analysés pour la cocaïne et/ou ses metabolites selon la matrice. Les résultats sont présentés ci-dessous. Les cheveux ont identifié le plus grand nombre de toxicoSUMMARY
Caprine responsiveness towards gastrointestinal nematode infection

Studies were conducted using Scottish cashmere goats which were segregated into responsive and non-responsive individuals on the basis of ranked faecal egg counts following artificial and natural gastrointestinal nematode infection. These studies demonstrate that capnne responsiveness to gastrointestinal nematode infection is a relatively stable and heritable characteristic, largely unaffected by season and mode or site of infection. Initial comparative studies showed does to be considerably more susceptible to mixed artificial Teladorsagia circumcincta and Trichostrongylus vitrinus infection than were ewes or worm-naïve lambs. This was reflected in distinct differences in mucosal mast cell (M1MC) and globule leukocyte (GL) populations, the does having more GLs but many fewer MMCs than ewes. These differences together with the very low sheep mast cell proteinase concentrations recovered from doe tissue suggest that there are important functional differences in the mast cell responses of sheep and goats. The responses of breeding male and female goats were very consistent, with individuals occupying the same position of relative responsiveness while on pasture and after artificial challenge. Differences in the susceptibility of responders and nonresponders were apparent in egg count following natural and artificial infection and selection was largely supported by worm burdens recovered after artificial challenge. There was a tendency for enhanced responsiveness to be associated with increased tissue eosinophil and GL numbers though this relationship was not very strong. However, responders were able to mount a more rapid and vigorous peripheral
Motivational interviewing and its application in the management of coronary heart disease

Objective This review paper explores the concept, spirit, principles, and core skills of motivational interviewing (MI) as a psychological approach applied to CHD patients. Methods Literature reviews based on English and Chinese articles. Results The English and Chinese literature provided strong evidence for the effectiveness of MI in terms of behavior changes, blood pressure control, hyperglycemia and hyperlipidemia control, compliance improvement, and improving quality of life amongst chronic heart failure patients. The Happy Life Club (HLC) is the only randomized controlled trial (RCT) that examined the effects of MI on behavior changes and self-management improvements in Chinese patients with type 2 diabetes mellitus. The HLC pilot revealed positive outcomes, and RCTs will provide further evidence. Conclusion MI has been expanded in its application to a wider range of lifestyle-related chronic diseases, including CHD. At the time of this review, MI represents a new concept and approach in China for improving physical and mental health (body and mind) in patients with CHD. In order to integrate MI into medical practice, the authors suggest improving the medical education curriculum and strengthening vocational training, while conducting further research initiatives to assess the innovation and inform practice management.
A mixed blessing? Using email for counselling and psychotherapy at a distance

A new area of practice and inquiry, online counselling and supervision, is developing in a range of settings and in a variety of formats. The purpose of this article is to inform practitioners and researchers about a recent study of student counselling in New Zealand. The study is based on counselling practice which uses the most common mode of delivery for online counselling at this time: asynchronous text-based email and synchronous chat. The article reports evaluative, descriptive research from a student counselling service which was the first in New Zealand to offer online therapy using email. The implications of the study are that some students who have traditionally not used face-to-face counselling are using online counselling and that some presenting issues are different in online therapy.