Miracle-dz/UniFAR-FTUs huggingface dataset:
Push versus pull-based loop fusion in query engines

Abstract Database query engines use pull-based or push-based approaches to avoid the materialization of data across query operators. In this paper, we study these two types of query engines in depth and present the limitations and advantages of each engine. Similarly, the programming languages community has developed loop fusion techniques to remove intermediate collections in the context of collection programming. We draw parallels between databases (DB) and programming language (PL) research by demonstrating the connection between pipelined query engines and loop fusion techniques. Based on this connection, we propose a new type of pull-based engine, inspired by a loop fusion technique, which combines the benefits of both approaches. Then, we experimentally evaluate the various engines, in the context of query compilation, for the first time in a fair environment, eliminating the biasing impact of ancillary optimizations that have traditionally only been used with one of the approaches. We show that for realistic analytical workloads, there is no considerable advantage for either form of pipelined query engine, as opposed to what recent research suggests. Also, by using micro-benchmarks, which demonstrate certain edge cases on which one approach or the other performs better, we show that our proposed engine dominates the existing engines by combining the benefits of both.
The Impact of Gender Composition on Team Performance and Decision Making: Evidence from the Field

We investigate whether the gender composition of teams affects their economic performance. We study a large business game, played in groups of three, in which each group takes the role of a general manager. There are two parallel competitions, one involving undergraduates and the other involving MBA students. Our analysis shows that teams formed by three women are significantly outperformed by all other gender combinations, both at the undergraduate and MBA levels. Looking across the performance distribution, we find that for undergraduates, three-women teams are outperformed throughout, but by as much as 0.47 of a standard deviation of the mean at the bottom and by only 0.09 at the top. For MBA students, at the top, the best performing group is two men and one woman. The differences in performance are explained by differences in decision making. We observe that three-women teams are less aggressive in their pricing strategies, invest less in research and development, and invest more in social sustainability initiatives than does any other gender combination. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.
Evaluating model performance and parameter behavior for varying levels of land surface model complexity

This paper investigates model performance and parameter behavior for a range of land surface model (LSM) complexity across a variety of vegetated surfaces. Although LSMs are used routinely in regional and global climate (and weather) prediction, there has been limited rigorous testing of these models across a range of biomes. A systems‐based approach is used to compare five commonly used LSMs (BUCKET, CHASM, BATS1e, BATS2, and Noah) across five different vegetated sites (pasture, short grass, cropland, tropical rain forest, and semiarid desert). Results indicate that there is no “perfect” model and that additional complexity (defined as additional physical representation in a model) does not necessarily equate to improved performance. In general, the medium complexity BATS1e model has the most consistent performance, with overall lower errors across the sites. Results also indicate that prescribed parameter meanings are not consistent across the various LSM formulations. A comparison of BATS1e and BATS2 parameters reveals significant differences in behavior across the study sites. These findings have key implications for general application of a single model across a range of global biomes and for model intercomparison studies where parameters are preassigned to participating models.
Tomographic blur identification using image edges

A method for estimating the point spread function of a remotely sensed image is described. The technique developed is based on locating and measuring blurred linear features in the imagery and tomographically reconstructing the point spread function. Image features able to be modeled by a single step function or by a combination of two steps are located. For the latter cases a form of blind deconvolution is applied to extract the estimate of the line spread function (equivalent to the projection of the point spread function in the direction of the feature). The process is unsupervised and requires only that the image contains suitable linear features. Examples are given of the estimation of blur in satellite images.
Early results from AIRS on the EOS

The Atmospheric IR Sounder (AIRS) on the EOS Aqua spacecraft is an IR spectrometer/radiometer which covers the 650-2700 cm-1 region of the spectrum with 2378 spectral channels. The EOS Aqua was launched on 4 May 2002 from Vandenburg AFB, California, in to a 705km high, sun synchronous orbit. First test of the radiometric calibration using the analysis of (observed-calculated) for data from a single, relatively cloud free 2500×2500 km area of the subtropical Atlantic ocean confirm absolute radiometric accuracy of better than 0.5K. The spectral information in the data also suggests that the analyzed region contained more moisture than the NCEP analysis. Based on these results we expect that the assimilation of AIRS data into the forecast to result in major medium range weather forecast improvements and that the data set recorded by AIRS during its nominal seven year lifetime will be a major resource for climate studies.
Inversion of field data in fault tectonics to obtain the regional stress—II. Using conjugate fault sets within heterogeneous families for computing palaeostress axes

SUMMARY In a family of faults that have been neither reactivated nor reoriented, each fault whose direction and sense of movement are known can be assigned Pθ and Tθ axes. If the rocks are isotropic and the orientation of each fault is controlled only by the orientations of the active principal stress axes and the apparent angle of internal friction φ that controls the angle 2θ between conjugate faults, Pθ and Tθ are parallel to the maximum and minimum principal stress axes, respectively. In most cases, however, this relationship does not hold accurately. Defining probability density regions around Pθ and Tθ that take into account variations of fault geometry and using the mathematical process of convolution, one can estimate the principal stress axes associated with any family of faults, whether the family be homogeneous or heterogeneous. The probability regions around Pθ and Tθ are in the form of Gaussian functions. The orientations of principal stress axes determined using different values for the constant C and reasonable values for φ (and therefore θ), are in good agreement with other numerical methods. The only major requirement of the method is the presence of conjugate fault systems within the fault slip data set studied.
Taming GANs with Lookahead

Generative Adversarial Networks are notoriously challenging to train. The underlying minimax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field. We empirically demonstrate the effectiveness of the Lookahead meta-optimization method for optimizing games, originally proposed for standard minimization. The backtracking step of Lookahead naturally handles the rotational game dynamics, which in turn enables the gradient ascent descent method to converge on challenging toy games often analyzed in the literature. Moreover, it implicitly handles high variance without using large mini-batches, known to be essential for reaching state of the art performance. Experimental results on MNIST, SVHN, and CIFAR-10, demonstrate a clear advantage of combining Lookahead with Adam or extragradient, in terms of performance, memory footprint, and improved stability. Using 30-fold fewer parameters and 16-fold smaller minibatches we outperform the reported performance of the class-dependent BigGAN on CIFAR-10 by obtaining FID of $13.65$ \emph{without} using the class labels, bringing state-of-the-art GAN training within reach of common computational resources.
Clustering-based Joint Channel Estimation and Signal Detection for NOMA

We propose a joint channel estimation and signal detection approach for the uplink non-orthogonal multiple access using unsupervised machine learning. We apply the Gaussian mixture model to cluster the received signals, and accordingly optimize the decision regions to enhance the symbol error rate (SER). We show that, when the received powers of the users are sufﬁciently different, the proposed clustering-based approach achieves an SER performance on a par with that of the conventional maximum-likelihood detector with full channel state information. However, unlike the proposed approach, the maximum-likelihood detector requires the transmission of a large number of pilot symbols to accurately estimate the channel. The accuracy of the utilized clustering algorithm depends on the number of the data points available at the receiver. Therefore, there exists a tradeoff between accuracy and block length. We provide a comprehensive performance analysis of the proposed approach as well as deriving a theoretical bound on its SER performance as a function of the block length. Our simulation results corroborate the effectiveness of the proposed approach and verify that the calculated theoretical bound can predict the SER performance of the proposed approach well.
A quasi‐linear theory of non‐Fickian and Fickian subsurface dispersion: 2. Application to anisotropic media and the Borden site

When the quasi-linear theory developed in paper 1 is applied to anisotropic media it shows, in contrast to the isotropic case, that longitudinal and transverse dispersivities may become asymptotically proportional to σY when the log hydraulic conductivity variance σY2 is much smaller than 1. It further implies, among other phenomena, that when the mean seepage velocity vector μ is at an angle to the principal axes of statistical anisotropy, the long axis of a plume is generally offset toward the direction of the largest log hydraulic conductivity correlation scale; when μ is at 45° to the bedding in strongly stratified media, the longitudinal axis is nearly parallel to the bedding under non-Fickian conditions. As Fickian conditions are approached, the plume rotates toward μ and stabilizes asymptotically at a relatively small angle of deflection depending on σY2. Application of the quasi-linear theory to depth-averaged concentration data from a tracer experiment at Borden, Ontario, yields a consistent and improved fit to a two-dimensional model without any need for parameter adjustment. Three-dimensional models are shown to be in fundamental conflict with observed behavior at Borden and in other stratified formations; we show that, in principle, this conflict is easy to resolve by accounting for local hydraulic anisotropy.
Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics

—Well-calibrated probabilistic regression models are a crucial learning component in robotics applications as datasets grow rapidly and tasks become more complex. Classical regression models are usually either probabilistic kernel machines with a ﬂexible structure that does not scale gracefully with data or deterministic and vastly scalable automata, albeit with a restrictive parametric form and poor regularization. In this paper, we consider a probabilistic hierarchical modeling paradigm that combines the beneﬁts of both worlds to deliver computationally efﬁcient representations with inherent complexity regularization. The presented approaches are probabilistic interpretations of local regression techniques that approximate nonlinear functions through a set of local linear or polynomial units. Importantly, we rely on principles from Bayesian nonparametrics to formulate ﬂexible models that adapt their complexity to the data and can potentially encompass an inﬁnite number of components. We derive two efﬁcient variational inference techniques to learn these representations and highlight the advantages of hierarchical inﬁnite local regression models, such as dealing with non-smooth functions, mitigating catastrophic forgetting, and enabling parameter sharing and fast predictions. Finally, we validate this approach on a set of large inverse dynamics datasets and test the learned models in real-world control scenarios.
Strategy, ICT Investment, BPR And Business Performance: An Empirical Investigation

The study on the links between strategy, structure and performance has been for long time a fundamental and highly important research topic for the management researchers and practitioners. In this direction this paper presents a ‘holistic’ empirical investigation of the whole network of relations between business strategy (at a first level), information and communication technologies (ICT) investment, non-ICT investment and BPR (at a second level), and finally business performance (at a third level). It is based on firm-level data from 271 Greek firms, which are used for the estimation of structural equation models (SEM) connecting the above variables, theoretically based on the CobbDouglas Production Function. It is concluded that none of the three generic business strategies defined by M. Porter (cost leadership, differentiation and focus) has a significant effect on ICT and non-ICT investment; on the contrary, particular strategic choices (differentiation and focus strategy) have been found to drive process change. Also, it has been found that all the investigated internal factors, ICT investment, non-ICT investment and BPR, have a positive impact on business performance. Concerning their interrelations, ICT investment affects positively BPR, which indicates that BPR is a partial mediator in the relationship between ICT and performance; on the contrary, this does not happen with non-ICT investment, indicating an important difference between these two types of capital investment as to their relation with process change.
Remote sensing observation and numerical modeling of an upwelling jet in Guangdong coastal water

[1] An upwelling event that occurred in Guangdong coastal water on July 14–16, 2003 is observed by using satellite multisensor data including the Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) and QuikSCAT ocean surface winds. Successive MODIS SST images reveal expansion process of a jet-like upwelling cold water body in surface layer. The regional ocean model system (ROMS) is used to explore the upwelling dynamics. The modeling successfully reproduces the jet-like shape of the surface upwelling water as well as the upwelling developing process. Analyses of modeling momentums reveal that the large offshore transport appeared on the west side of Honghai Bay as a result of high alongshore pressure gradient and nonlinear advections, and the enhanced horizontal advection also played an important role in developing the prominent upwelling in Honghai Bay. A numerical experiment is conducted to confirm that it was the wind driven upwelling rather than the wind-induced vertical turbulent mixing that induced the surface cold water. Further numerical analyses suggest that strong internal tides occurred in Honghai Bay caused by the local bottom topography. The interaction between the upwelling and internal tides enhances the bottom water uplifting. The offshore expansion of the upwelling water is controlled by the cross-shore topography slope: a gentle and offshore extended slope helps the bottom water climbing up to the surface in a wide range in cross-shore direction, whereas a steep and narrow slope restricts the expansion of the upwelling water and confines the cold water in a narrow band along the shore.
Saliency-Guided Mutual Learning Network for Few-shot Fine-grained Visual Recognition

—Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature focus on addressing this challenge through global-based or local-based representation approaches. The former employs global feature representations for recog-nization, which may lack ﬁne-grained information. The latter captures local relationships with complex structures, possibly leading to high model complexity. To address the above challenges, this article proposes a novel framework called SGML-Net for few-shot ﬁne-grained visual recognition. SGML-Net incorporates auxiliary information via saliency detection to guide discriminative representation learning, achieving high performance and low model complexity. Speciﬁcally, SGML-Net utilizes the saliency detection model to emphasize the key regions of each sub-category, providing a strong prior for representation learning. SGML-Net transfers such prior with two independent branches in a mutual learning paradigm. To achieve effective transfer, SGML-Net leverages the relationships among different regions, making the representation more informative and thus providing better guidance. The auxiliary branch is excluded upon the transfer’s completion, ensuring low model complexity in deployment. The proposed approach is empirically evaluated on three widely-used benchmarks, demonstrating its superior performance.
An MILP-based approach to tree partitioning with minimal power flow disruption and generator coherency constraints

Tree partitioning has recently been proposed in the power systems literature as a less severe alternative to controlled islanding. In this paper, we formulate an optimization problem to tree partition a network with minimal power flow disruption and generator coherency constraints. We propose a single-stage MILP formulation to compute optimal solutions. Numerical experiments show that our MILP-based approach drastically decreases the power flow disruption when compared to an earlier proposed two-stage approach based on spectral clustering. Moreover, using a search space reduction procedure based on the Steiner Tree problem, the MILP-based approach computes near-optimal tree partitions in sub-second time for instances up to 500 buses.
An analytic solution to two quaternion attitude estimation problems

This paper presents solutions to the following two common quaternion attitude estimation problems: (i) estimation of attitude using measurement of two reference vectors, and (ii) estimation of attitude using rate measurement and measurement of a single reference vector. Both these problems yield to a direct geometric analysis and solution. The former problem already has a well established analytic solution in literature using linear algebraic methods. This note shows how the solution may also be obtained using geometric methods, which are not only more intuitive, but also amenable to unconventional extensions. With respect to the latter problem, existing solutions typically involve filters and observers and use a mix of differential-geometric and control systems methods. However, no analytic solution has yet been reported to this problem. In this note, both the problems are formulated as optimization problems, which can be solved analytically to obtain a unique closed-form solution. The analytic attitude estimates are (i) instantaneous with respect to the measurements, thus overcoming the latency inherent in solutions based upon negative feedback upon an error, which can at best show asymptotic convergence, (ii) exact, thus overcoming errors in solutions based upon linear methods, and (iii) geometry-based, thus enabling imposition of geometric inequality constraints.
Formalizing Chemical Theory using the Lean Theorem Prover

Interactive theorem provers are computer programs that check whether mathematical statements are correct. We show how the mathematics of chemical theories can be written in the language of the Lean theorem prover, allowing chemical theory to be made even more rigorous and providing insight into the mathematics behind a theory. We use Lean to precisely deﬁne the assumptions and derivations of the Langmuir [1] and BET [2] theories of adsorption. We can also go further and create a network of deﬁnitions that build off of each other. This allows us to deﬁne a common basis for equations of motion or thermodynamics and derive many statements about them, like the kinematic equations of motion or gas laws such as Boyle’s Law. This approach could be extended beyond chemistry, and we propose the creation of a library of formally-proven theories in all ﬁelds of science.
Application of a roughness‐length representation to parameterize energy loss in 3‐D numerical simulations of large rivers

Recent technological advances in remote sensing have enabled investigation of the morphodynamics and hydrodynamics of large rivers. However, measuring topography and flow in these very large rivers is time consuming and thus often constrains the spatial resolution and reach‐length scales that can be monitored. Similar constraints exist for computational fluid dynamics (CFD) studies of large rivers, requiring maximization of mesh‐ or grid‐cell dimensions and implying a reduction in the representation of bedform‐roughness elements that are of the order of a model grid cell or less, even if they are represented in available topographic data. These “subgrid” elements must be parameterized, and this paper applies and considers the impact of roughness‐length treatments that include the effect of bed roughness due to “unmeasured” topography. CFD predictions were found to be sensitive to the roughness‐length specification. Model optimization was based on acoustic Doppler current profiler measurements and estimates of the water surface slope for a variety of roughness lengths. This proved difficult as the metrics used to assess optimal model performance diverged due to the effects of large bedforms that are not well parameterized in roughness‐length treatments. However, the general spatial flow patterns are effectively predicted by the model. Changes in roughness length were shown to have a major impact upon flow routing at the channel scale. The results also indicate an absence of secondary flow circulation cells in the reached studied, and suggest simpler two‐dimensional models may have great utility in the investigation of flow within large rivers.