Miracle-dz/UniFAR-FTUs huggingface dataset:
Homological orthogonality of "symplectic" and "lagrangian"

In this remark we discuss a relationship between (co)homology classes of a symplectic manifold realized by symplectic and lagrangian objects. We establish some transversality condition for the classes, realized by symplectic divisors and smooth lagrangian tori. As a consequence this leads to a formulation of some ”symplectic Nakai criterion”. Consider a Kahler manifold X which is a real even dimensional manifold equipped with an integrable complex structure I and a nondegenerate closed 2 form ω, compatible with I. Thus one can consider X as a manifold equipped with a complex and a symplectic structures. The structures distinguish subsets in the (co)homology group of X, realizable by either holomorphic objects or lagrangian (isotropic) objects or holomorphic lagrangian objects (f.e. if X is hyper Kahler). We discuss here the simplest case: holomorphic realization by holomorphic line bundles of 2cohomology classes and lagrangian realization by smooth orientable lagrangian submanifolds of middle dimension homology classes. Then it is not hard to show that Proposition For a Kahler manifold if c ∈ H(X, Z) is realized by a holomorphic line bundle L with c1(L) = c and [S] ∈ Hn(X, Z) is realized by a smooth lagrangian submanifold S then < c; [S] >∈ Hn−2(X, Z) is trivial. ∗BLTP JINR (Dubna), e-mail: ntyurin@theor.jinr. ru
Review: the predictability of the extra-tropical stratosphere on monthly timescales and its impact on the skill of tropospheric forecasts

Extreme variability of the winter-and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond 5 days and into the sub-seasonal range (0–30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecast skill by 5–7% in the extratropics on sub-seasonal time-scales. Thus understanding what limits stratospheric predictability is of signiﬁcant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context.
The mechanics of suspensions

Suspension mechanics—the flow of a fluid with small fragments of solid material suspended in it—is an area of wide applicability in both industry and nature. Examples include the transport of silt in rivers, the manufacture of toothpaste, and inkjet printing where pigments remain solid within the ink. One widespread method to simulate these flows is Stokesian Dynamics, a truncated multipole expansion of the Stokes equations. It is computationally efficient while making a reasonable approximation to the hydrodynamic interactions between particles; however, all particles are identical spheres and the background matrix must be Newtonian. This project extends Stokesian Dynamics to include differently-sized spheres. This allows us to study a variety of previously inaccessible suspension problems. In many suspensions, e.g. toothpaste, the suspending fluid itself is non-Newtonian and exhibits viscoelastic properties. We have extended Stokesian Dynamics to incorporate a simple model of viscoelasticity by using the small spheres as 'beads' in bead--spring dumbbells. Different spring laws are then tested in shear, and their rheological behaviour is compared to continuum constitutive models. Next, we replicate experiments in which a large sphere is dropped through a suspension of neutrally buoyant smaller spheres undergoing oscillatory shear flow. We qualitatively replicate the principal experimental observation—that at the moment of shear reversal, the suspension microstructure hinders the falling; while at the instant of fastest shear, it enhances the falling. We propose a physical mechanism explaining the observations. Finally, we extend Stokesian Dynamics to properly implement interparticle frictional contact. Contact forces are a critical component of shear thickening in suspensions such as cornflour, yet are usually implemented in an ad hoc way, resulting in inaccurate predictions or high computational cost. The new method allows us to investigate contact models quickly and efficiently, and suggests an important factor in models of strongly shear-thickening fluids.
Book Review Lost in Transmission : The Role of Attrition and Input in Heritage Language Development

As the title of this edited volume suggests, the book is organised around two main concepts: heritage language development and language attrition. Heritage languagesare getting increased importance and attention in modern societies with the development of language rights of minorities and diasporas. A heritage language is defined as the language which differs from thede facto language of the given country (e.g. English is thede facto language in the United States, whereas other languages are considered to be heritage languages). Polinsky (2007; 2008) uses the concept of heritage language to define the language learned in childhood and ceased to be dominant as a consequence of emigration.Benmamoun, Montreal &Polinsky (2013:133) give the following definition of a heritage speaker:
Delft University of Technology Conditioning of unsteady cross-flow instability modes using dielectric barrier discharge plasma actuators

In this study, experiments are performed towards the identification and measurement of unsteady modes occurring in a transitional swept wing boundary layer. These modes are generated by the interaction between the primary stationary and travelling cross-flow instabilities or by secondary instability mechanisms of the stationary cross-flow vortices and have a crucial role in the laminar-to-turbulent breakdown process. Detailed hot-wire measurements were performed at the location of stationary instability amplitude-saturation. In order to deterministically capture the spatio-temporal evolution of the unsteady modes, measurements were phaseand frequency-conditioned using concurrent forcing by means of a dielectric barrier discharge plasma actuator mounted upstream of the measurement domain. The actuator effect, when positioned sufficiently upstream the secondary modes onset, was tuned such to successfully condition the high-frequency type-I and the low-frequency type-III modes without modifying the transition evolution. Two primary stationary cross-flow vortices of different amplitude were measured, revealing the effect of base-flow variations on the growth of travelling instabilities. The response of these two stationary waves to the naturally occurring and forced fluctuations was captured at different chordwise positions. Additionally, the deterministic conditioning of the instability phase to the phase of the actuation allowed phase-averaged reconstruction of the spatio-temporal evolution of the unsteady structures providing valuable insight on their topology. Finally, the effect of locating the actuator at a more downstream position, closer to the secondary instability branch-I, resulted in laminar-to turbulent breakdown for the high-frequency actuation while the low-frequency forcing showed milder effects on the transition evolution.
Large-eddy simulation of Rayleigh-Bénard convection for extreme Rayleigh numbers

We adopt the stretched spiral vortex sub-grid model for large-eddy simulation (LES) of turbulent convection at extreme Rayleigh numbers. We simulate Rayleigh-Bénard convection (RBC) for Rayleigh numbers ranging from 10 6 to 10 13 and for Prandtl numbers 0.768 and 1. We choose a box of dimensions 1:1:10 to reduce computational cost. Our LES yields Nusselt and Reynolds numbers that are in good agreement with the direct-numerical simulation (DNS) results of Iyer et al. (2020), albeit with a smaller grid size and at signiﬁcantly reduced computational expense. For example, in our simulations at 𝑅𝑎 = 10 13 , we use grids that are 1/120 times the grid-resolution as that of the DNS (Iyer et al. 2020). The Reynolds numbers in our simulations range from 1,000 to 200,000. Consistent with the literature, we obtain scaling relations for Nusselt and Reynolds numbers as 𝑁𝑢 ∼ 𝑅𝑎 0 . 322 and Re ∼ 𝑅𝑎 0 . 502 . We also perform LES of RBC with periodic side-walls, for which we obtain the corresponding scaling exponents as 0 . 343 and 0 . 477 respectively. Our LES is a promising tool to push simulations of thermal convection to extreme Rayleigh numbers, and hence enable us to test the transition to ultimate convection regime. Key Authors The eﬀect of side-walls are then eliminated by repeating the numerical experiments of §4.1 but with periodic boundary conditions in the two horizontal directions. We analyse the modiﬁcations in the scaling of 𝑁𝑢 and Re triggered by removing the adiabatic side-walls in §4.2. Finally we summarize and conclude our ﬁndings in §5.
Part-level Car Parsing and Reconstruction from a Single Street View

Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction from street views leveraging the part information. There are two major contributions in this paper. Firstly, we make the first attempt to build a framework to simultaneously estimate shape, translation, orientation, and semantic parts of cars in 3D space from a single street view. As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views. For pose and shape estimation, we propose a novel network structure that utilizes both part features and 3D losses. Secondly, we are the first to construct a high-quality dataset that contains 348 different car models with physical dimensions and part-level annotations based on global and local deformations. Given these models, we further generate 60K synthesized images with randomization of orientation, illumination, occlusion, and texture. Our results demonstrate that our part segmentation performance is significantly improved after applying our implicit transfer approach. Our network for pose and shape estimation achieves the state-of-the-art performance on the ApolloCar3D dataset and outperforms 3D-RCNN and DeepMANTA by 12.57 and 8.91 percentage points in terms of mean A3DP-Abs [17, 3, 30].
Assessing the potential of ride-sharing using mobile and social data: a tale of four cities

This paper assesses the potential of ride-sharing for reducing traffic in a city -- based on mobility data extracted from 3G Call Description Records (CDRs), for the cities of Madrid and Barcelona (BCN), and from OSNs, such as Twitter and Foursquare (FSQ), collected for the cities of New York (NY) and Los Angeles (LA). First, we analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. Then, we develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints, including social distance. The solution provides an upper bound to the potential decrease in the number of cars in a city that can be achieved by ride-sharing. Our results indicate that this decrease can be as high as 31%, when users are willing to ride with friends of friends.
Comparison of Linear Predictability of Surface Wind Components from Observations with Simulations from RCMs and Reanalysis

AbstractThis study compares the predictability of surface wind components by linear statistical downscaling using data from both observations and comprehensive models [regional climate models (RCM) and NCEP-2 reanalysis] in three domains: North America (NAM), Europe–Mediterranean Basin (EMB), and East Asia (EAS). A particular emphasis is placed on predictive anisotropy, a phenomenon referring to unequal predictability of surface wind components in different directions. Simulated predictability by comprehensive models is generally close to that found in observations in flat regions of NAM and EMB, but it is overestimated relative to observations in mountainous terrain. Simulated predictability in EAS shows different structures. In particular, there are regions in EAS where predictability simulated by RCMs is lower than that in observations. Overestimation of predictability by comprehensive models tends to occur in regions of low predictability in observations and can be attributed to small-scale physical p.
Model ShapeNet Annotation Interface Initial Pose ( 1 ) human pose annotation Mask-RCNN DeepLab Extract Segment Render Mask Instance Segmentation Semantic Segmentation Initial Segment Segmentation Reference 3 D Model Iterative Local Search Final Pose

Existing 3D pose datasets of object categories are limited to generic object types and lack of fine-grained information. In this work, we introduce a new largescale dataset that consists of 409 fine-grained categories and 31,881 images with accurate 3D pose annotation. Specifically, we augment three existing fine-grained object recognition datasets (StanfordCars, CompCars and FGVCAircraft) by finding a specific 3D model for each subcategory from ShapeNet and manually annotating each 2D image by adjusting a full set of 7 continuous perspective parameters. Since the fine-grained shapes allow 3D models to better fit the images, we further improve the annotation quality by initializing from the human annotation and conducting local search of the pose parameters with the objective of maximizing the IoUs between the projected mask and the segmentation reference estimated from state-of-theart deep Convolutional Neural Networks (CNNs). We provide a full statistics of the annotations with qualitative and quantitative comparisons suggesting that our dataset can be a complementary source for studying 3D pose estimation. The dataset can be downloaded at http://users. umiacs.umd. edu/ ̃wym/3dpose.html.
Rossby Wave Packets on the Midlatitude Waveguide—A Review

Rossby wave packets (RWPs) are Rossby waves for which the amplitude has a local maximum and decays to smaller values at larger distances. This review focuses on upper-tropospheric transient RWPs along the midlatitude jet stream. Their central characteristic is the propagation in the zonal direction as well as the transfer of wave energy from one individual trough or ridge to its downstream neighbor, a process called “downstream development. ” These RWPs sometimes act as long-range precursors to extreme weather and presumably have an influence on the predictability of midlatitude weather systems. The paper reviews research progress in this area with an emphasis on developments during the last 15 years. The current state of knowledge is summarized including a discussion of the RWP life cycle as well as Rossby waveguides. Recent progress in the dynamical understanding of RWPs has been based, in part, on the development of diagnostic methods. These methods include algorithms to identify and track RWPs in an automated manner, which can be used to extract the climatological properties of RWPs. RWP dynamics have traditionally been investigated using the eddy kinetic energy framework; alternative approaches based on potential vorticity and wave activity fluxes are discussed and put into perspective with the more traditional approach. The different diagnostics are compared to each other and the strengths and weaknesses of individual methods are highlighted. A recurrent theme is the role of diabatic processes, which can be a source for forecast errors. Finally, the paper points to important open research questions and suggests avenues for future research.
3-Phosphoinositide–Dependent Protein Kinase-1 Regulates Proliferation and Survival of Cancer Cells with an Activated Mitogen-Activated Protein Kinase Pathway

Engagement of cell surface receptor tyrosine kinases by insulin and growth factors activates phosphatidylinositol 3-kinase (PI3K) and generates the second messenger, phosphatidylinositol 3,4,5-trisphosphate. This second messenger leads to the recruitment of 3-phosphoinositide–dependent protein kinase-1 (PDK1) to the proximal side of the plasma membrane, which results in the activation of AKT kinase. In addition, PDK1 can phosphorylate numerous other kinases, including p90RSK, a kinase downstream of mitogen-activated protein kinase (MAPK) that is important for cell proliferation and survival. Previous studies have shown that the loss of PDK1 sensitizes tumor cells to chemotherapeutic agents and radiation but have not focused on delineating the contribution of PDK1 to pathway-specific mutations associated with various cancers other than the PI3K/AKT pathway. In this study, we show that the reduction of PDK1 by RNAi in melanoma and colon cancer cell lines activated in the MAPK pathway results in significant cell growth inhibition and apoptosis. Furthermore, PDK1 reduction in tumor cells resulted in impaired PAK kinase signaling, altered actin polymerization, and reduced cell migration. These studies show that PDK1 plays a pivotal role in MAPK and PI3K signaling in tumor cells. Mol Cancer Res; 8(3); 421–32
Growing by the Masses: Revisiting the Link between Firm Size and Market Power

How are a firm’s size and market power related to one another? Combining micro-data about producers and consumers, we document that while firms mainly grow by selling to more customers, their markups are only associated with their average sales per customer. To study the macroeconomic implications of these facts, we develop a model of firm dynamics with endogenous customer acquisition and variable markups. Relative to a model without customer acquisition, our model generates higher concentration at the top, but a lower aggregate markup. Our quantitative analysis reveals large welfare and efficiency losses due to (mis)allocation of customers across firms. By increasing market concentration among the most productive firms, the efficient allocation achieves 11% higher aggregate productivity and 15% higher output.
Deep Interpretable Non-rigid Structure from Motion

Current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle. This has hampered the practical utility of NRSfM for many applications within vision. Deep Neural Networks (DNNs) are an obvious candidate to help with such issue. However, their use has not been explored in recovering poses and 3D shapes from an ensemble of vector-based 2D landmarks. In this proposal, we present a novel deep neural network to recover camera poses and 3D points solely from an ensemble of 2D image coordinates. The proposed neural network is built upon our prior work on compressible structure form motion – extending the original single-layer sparsity constraint to a multi-layer one. The network architecture is mathematically interpretable as a multi-layer block sparse dictionary learning problem. Our network is capable of handling problems of unprecedented scale in terms of samples and parameterization – allowing us to effectively recover 3D shapes deemed too complex by previous state-of-the-art. We further propose a generalization measure (based on the network weights) for guiding training to efficiently avoid overfitting, circumventing the need for 3D ground-truth. Once the networks weights are estimated (for a non-rigid object) we show how our approach can be used to recover 3D shape from a single image without 3D supervision. We shall propose how to extend our current framework to handle missing data, and identify new applications – such as Structure from Category (SfC) – where our approach can have substantial impact within computer vision. November 26, 2018 DRAFT
Dense Adaptive Cascade Forest: A Densely Connected Deep Ensemble for Classification Problems

Recent research has shown that deep ensemble for forest can achieve a huge increase in classification accuracy compared with the general ensemble learning method. Especially when there are only few training data. In this paper, we decide to take full advantage of this observation and introduce the Dense Adaptive Cascade Forest (daForest), which has better performance than the original one named Cascade Forest. And it is particularly noteworthy that daForest has a powerful ability to handle high-dimensional sparse data without any preprocessing on raw data like PCA or any other dimensional reduction methods. Our model is distinguished by three major features: the first feature is the combination of the SAMME.R boosting algorithm in the model, boosting gives the model the ability to continuously improve as the number of layer increases, which is not possible in stacking model or plain cascade forest. The second feature is our model connects each layer to its subsequent layers in a feed-forward fashion, to some extent this structure enhances the ability of the model to resist degeneration. When number of layers goes up, accuracy of model goes up a little in the first few layers then drop down quickly, we call this phenomenon degeneration in training stacking model. The third feature is that we add a hyper-parameter optimization layer before the first classification layer in the proposed deep model, which can search for the optimal hyper-parameter and set up the model in a brief period and nearly halve the training time without having too much impact on the final performance. Experimental results show that daForest performs particularly well on both high-dimensional low-order features and low-dimensional high-order features, and in some cases, even better than neural networks and achieves state-of-the-art results.
Extrusion and Death of DPP/BMP-Compromised Epithelial Cells in the Developing Drosophila Wing

During animal development, epithelial cell fates are specified according to spatial position by extracellular signaling pathways. Among these, the transforming growth factor β/bone morphogenetic protein (TGF-β/BMP) pathways are evolutionarily conserved and play crucial roles in the development and homeostasis of a wide range of multicellular tissues. Here we show that in the developing Drosophila wing imaginal epithelium, cell clones deprived of the BMP-like ligand Decapentaplegic (DPP) do not die as previously thought but rather extrude from the cell layer as viable cysts exhibiting marked abnormalities in cell shape and cytoskeletal organization. We propose that in addition to assigning cell fates, a crucial developmental function of DPP/BMP signaling is the position-specific control of epithelial architecture.
A Draft Sequence for the Genome of the Domesticated Silkworm (Bombyx mori)

We report a draft sequence for the genome of the domesticated silkworm (Bombyx mori), covering 90.9% of all known silkworm genes. Our estimated gene count is 18,510, which exceeds the 13,379 genes reported for Drosophila melanogaster. Comparative analyses to fruitfly, mosquito, spider, and butterfly reveal both similarities and differences in gene content.