Miracle-dz/UniFAR-FTUs huggingface dataset:
Hummingbirds use wing inertial effects to improve maneuverability

Hummingbirds outperform other birds in terms of aerial agility at low flight speeds. To reveal the key mechanisms that enable such unparalleled agility, we reconstructed body and wing motion of hummingbird escape maneuvers from high-speed videos; then, we performed computational fluid dynamics modeling and flight mechanics analysis, in which each wingbeat was resolved. We found that the birds utilized the inertia of their wings to achieve peak body rotational acceleration within half a wingbeat before the aerodynamic forces became dominant. The aerodynamic forces instead counteracted the reversed inertial forces in the other half wingbeat, thereby to sustain body rotation, albeit at a lower acceleration. Thus, individual wingbeat cycles that generated body rotations can be split into an agility phase with rapid inertial acceleration, and a stability phase with counteracting aerodynamic and inertial acceleration. This mechanism involving inertial steering enables hummingbirds to generate instantaneous body acceleration at any phase of a wingbeat, and it is likely the key to understanding the unique dexterity distinguishing them from aircraft that solely rely on aerodynamics for maneuvering.
Characterization of Pik1 function in fission yeast reveals its conserved role in lipid synthesis and not cytokinesis

Phosphatidylinositol (PI)-4-phosphate (PI4P) is a lipid found at the plasma membrane (PM) and Golgi in cells from yeast to humans. PI4P is generated from PI by PI4-kinases and can be converted to PI-4,5-bisphosphate [PI(4,5)P2]. Schizosaccharomyces pombe have 2 essential PI4-kinases: Stt4 and Pik1. Stt4 localizes to the PM and its loss from the PM results in a decrease of PM PI4P and PI(4,5)P2. As a result, cells divide non-medially due to disrupted cytokinetic ring-PM anchoring. However, the localization and function of S. pombe Pik1 has not been thoroughly examined. Here, we found that Pik1 localizes exclusively to the trans-Golgi and is required for Golgi PI4P production. We determined that Ncs1 regulates Pik1, but unlike in other organisms, it is not required for Pik1 Golgi localization. When Pik1 function was disrupted, PM PI4P but not PI(4,5)P2 levels were reduced, a major difference with Stt4. We conclude that Stt4 is the chief enzyme responsible for producing the PI4P that generates PI(4,5)P2. Also, that cells with disrupted Pik1 do not divide asymmetrically highlights the specific importance of PM PI(4,5)P2 for cytokinetic ring-PM anchoring. Summary statement Fission yeast Pik1 localizes exclusively to the trans-Golgi independently of Ncs1, where it contributes to PI4P but not PI(4,5)P2 synthesis. Pik1 does not affect cytokinesis.
Biomedical application of optical fibre sensors

Optical fibre sensors (OFS), as a result of their unique properties such as small size, no interference with electromagnetic radiation, high sensitivity and the ability to design multiplexed or distributed sensing systems, have found applications ranging from structural health monitoring to biomedical and point of care instrumentation. While the former represents the main commercial application for OFS, there is body of literature concerning the deployment of this versatile sensing platform in healthcare. This paper reviews the different types of OFS and their most recent applications in healthcare. It aims to help clinicians to better understand OFS technology and also provides an overview of the challenges involved in the deployment of developed technology in healthcare. Examples of the application of OFS in healthcare are discussed with particular emphasis on recently (2015–2017) published works to avoid replicating recent review papers. The majority of the work on the development of biomedical OFS stops at the laboratory stage and, with a few exceptions, is not explored in healthcare settings. OFSs have yet to fulfil their great potential in healthcare and methods of increasing the adoption of medical devices based on optical fibres are discussed. It is important to consider these factors early in the device development process for successful translation of the developed sensors to healthcare practice.
Standard Framework for Comparison of Graph Partitioning Techniques

Graph Partitioning is used to distribute graph partitions across nodes for processing. It is very important in the pre-processing step for distributed graph processing. In Math and Computer Science, many different distributed graph processing solutions use different partitioning approaches. This research deals with the identification of issues associated with the different graph partitioning approaches. This research paper compared the different graph partitioning solution (GraphLab, ParMetis, PT-Scotch) by applying them on different real world datasets and obtained the I/O and partitioning variation between them using different technique. This paper describes the procedure of configuring the GraphLab on Ubuntu OS and applying partitioning and pagerank techniques on it. Pmetis and Kmetis are two graph partitioning algorithms used in ParMetis. These algorithms were on same graph for different numbers of partitions and obtained the I/O and partitioning comparison between Pmetis and Kmetis. Different vertex cut strategies are also discussed in this paper. In this paper, the behavior of PowerGraph and PT-Scotch was explored while working on a very large datasets.
Efficient Methods For Large-Scale Empirical Risk Minimization

EFFICIENT METHODS FOR LARGE-SCALE EMPIRICAL RISK MINIMIZATION Aryan Mokhtari Alejandro Ribeiro Empirical risk minimization (ERM) problems express optimal classifiers as solutions of optimization problems in which the objective is the sum of a very large number of sample costs. An evident obstacle in using traditional descent algorithms for solving this class of problems is their prohibitive computational complexity when the number of component functions in the ERM problem is large. The main goal of this thesis is to study different approaches to solve these large-scale ERM problems. We begin by focusing on incremental and stochastic methods which split the training samples into smaller sets across time to lower the computation burden of traditional descent algorithms. We develop and analyze convergent stochastic variants of quasi-Newton methods which do not require computation of the objective Hessian and approximate the curvature using only gradient information. We show that the curvature approximation in stochastic quasi-Newton methods leads to faster convergence relative to first-order stochastic methods when the problem is ill-conditioned. We culminate with the introduction of an incremental method that exploits memory to achieve a superlinear convergence rate. This is the best known convergence rate for an incremental method. An alternative strategy for lowering the prohibitive cost of solving large-scale ERM problems is decentralized optimization whereby samples are separated not across time but across multiple nodes of a network. In this regime, the main contribution of this thesis is in incorporating second-order information of the aggregate risk corresponding to samples of all nodes in the network in a way that can be implemented in a distributed fashion. We also explore the separation of samples across both, time and space, to reduce the computational and communication cost for solving large-scale ERM problems. We study this path by introducing a decentralized stochastic method which incorporates the idea of stochastic averaging gradient leading to a low computational complexity method with a fast linear convergence rate. We then introduce a rethinking of ERM in which we consider not a partition of the training set as in the case of stochastic and distributed optimization, but a nested collection of subsets that we grow geometrically. The key insight is that the optimal argument associated with a training subset of a certain size is not that far from the optimal argument associated with a larger training subset. Based on this insight, we present adaptive sample size schemes which start with a small number of samples and solve the corresponding ERM
Collaborative sparse priors for multi-view ATR

Recent work has seen a surge of sparse representation based classification (SRC) methods applied to automatic target recognition problems. While traditional SRC approaches used l0 or l1 norm to quantify sparsity, spike and slab priors have established themselves as the gold standard for providing general tunable sparse structures on vectors. In this work, we employ collaborative spike and slab priors that can be applied to matrices to encourage sparsity for the problem of multi-view ATR. That is, target images captured from multiple views are expanded in terms of a training dictionary multiplied with a coefficient matrix. Ideally, for a test image set comprising of multiple views of a target, coefficients corresponding to its identifying class are expected to be active, while others should be zero, i.e. the coefficient matrix is naturally sparse. We develop a new approach to solve the optimization problem that estimates the sparse coefficient matrix jointly with the sparsity inducing parameters in the collaborative prior. ATR problems are investigated on the mid-wave infrared (MWIR) database made available by the US Army Night Vision and Electronic Sensors Directorate, which has a rich collection of views. Experimental results show that the proposed joint prior and coefficient estimation method (JPCEM) can: 1. ) enable improved accuracy when multiple views vs. a single one are invoked, and 2. ) outperform state of the art alternatives particularly when training imagery is limited.
Robust modelling of the relationship between CD4 and viral load for complex AIDS data

ABSTRACT CD4 and viral load play important roles in HIV/AIDS studies, and the study of their relationship has received much attention with well-known results. However, AIDS datasets are often highly complex in the sense that they typically contain outliers, measurement errors, and missing data. These data complications can greatly affect statistical analysis results, but much of the literature fail to address these issues in data analysis. In this paper, we re-visit the important relationship between CD4 and viral load and propose methods which simultaneously address outliers, measurement errors, and missing data. We find that the strength of the relationship may be severely mis-estimated if measurement errors and outliers are ignored. The proposed methods are general and can be used in other settings, where jointly modelling several different types of longitudinal data is required in the presence of data complications.
Capitalization on item calibration error in computer adaptive testing

In test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee’s ability estimate, such as the test information function in computerized adaptive testing. But it leads to the nontrivial problem of how to realize a set of content constraints on the test—a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of their future consequences. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test, and the latter, a projection of the test itself. The pros and cons of these methods were analyzed. An empirical comparison between the WDM and STA was also conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM.
Effect of Parameter Estimation on Fertilizer Optimization

It is important to optimize the amount of fertilizer applied to a crop, both to improve profitability and to reduce environmental damage. Previous work has concentrated mainly on modelling the response of production to fertilizer. Here we complement this work by concentrating on the effect of parameter estimation on the value of the objective function. A simple expression for confidence intervals for the value of the true optimal strategy is obtained. Also, a risk function for the effect of using an estimated optimal strategy is proposed, and a simple estimator derived. The results are applied first to fertilizer optimization for a single field, and then for a population of fields.
Excitation of Higher Order Modes of Cylindrical Dielectric Resonator Antenna using Dual-slot feed

Excitation of the higher order modes (HOM) of a cylindrical dielectric resonator antenna(DRA) of high relative permittivity, using dual feed scheme is investigated. The feed scheme uses a pair of narrow slots and is chosen on the basis of the field distribution of the desired DRA modes. Numerical studies using ANSYS HFSS show that the dual-feed excited a combination of two HOMs, which are identified as HEM21δ and TM01δ. The mixed-up nature is further verified through studying the radiation pattern of the DRA which shows azimuthal asymmetry and low gain. It is suggested that if one of the HOM is suppressed, better antenna performance can be achieved.
StripMaker: Perception-driven Learned Vector Sketch Consolidation

Artist sketches often use multiple overdrawn strokes to depict a single intended curve. Humans effortlessly mentally consolidate such sketches by detecting groups of overdrawn strokes and replacing them with the corresponding intended curves. While this mental process is near instantaneous, manually annotating or retracing sketches to communicate this intended mental image is highly time consuming; yet most sketch applications are not designed to handle overdrawing and can only operate on overdrawing-free, consolidated sketches. We propose StripMaker, a new and robust learning based method for automatic consolidation of raw vector sketches. We avoid the need for an unsustainably large manually annotated learning corpus by leveraging observations about artist workflow and perceptual cues viewers employ when mentally consolidating sketches. We train two perception-aware classifiers that assess the likelihood that a pair of stroke groups jointly depicts the same intended curve: our first classifier is purely local and only accounts for the properties of the evaluated strokes; our second classifier incorporates global context and is designed to operate on approximately consolidated sketches. We embed these classifiers within a consolidation framework that leverages artist workflow: we first process strokes in the order they were drawn and use our local classifier to arrive at an approximate consolidation output, then use the contextual classifier to refine this output and finalize the consolidated result. We validate StripMaker by comparing its results to manual consolidation outputs and algorithmic alternatives. StripMaker achieves comparable performance to manual consolidation. In a comparative study participants preferred our results by a 53% margin over those of the closest algorithmic alternative (67% versus 14%, other/neither 19%).
Juxtaform: interactive visual summarization for exploratory shape design

We present juxtaform, a novel approach to the interactive summarization of large shape collections for conceptual shape design. We conduct a formative study to ascertain design goals for creative shape exploration tools. Motivated by a mathematical formulation of these design goals, juxtaform integrates the exploration, analysis, selection, and refinement of large shape collections to support an interactive divergence-convergence shape design workflow. We exploit sparse, segmented sketch-stroke visual abstractions of shape and a novel visual summarization algorithm to balance the needs of shape understanding, in-situ shape juxtaposition, and visual clutter. Our evaluation is three-fold: we show that existing shape and stroke clustering algorithms do not address our design goals compared to our proposed shape corpus summarization algorithm; we compare juxtaform against a structured image gallery interface for various shape design and analysis tasks; and we present multiple compelling 2D/3D applications using juxtaform.
ETER: Elastic Tessellation for Real-Time Pixel-Accurate Rendering of Large-Scale NURBS Models

We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorithm to achieve pixel accuracy, measured by the screen space error between the exact surface and its triangulation. To resolve a bottleneck in NURBS rendering, we present a novel evaluation method based on uniform sampling grids and accelerated by GPU Tensor Cores. Compared to evaluation based on hardware tessellation, our method has achieved a significant speedup of 2.9 to 16.2 times depending on the degrees of the patches. We develop an efficient crack-filling algorithm based on conservative rasterization and visibility buffer to fill the tessellation-induced cracks while greatly reducing the jagged effect introduced by conservative rasterization. We integrate all our novel algorithms, implemented in CUDA, into a GPU NURBS rendering pipeline based on Mesh Shaders and hybrid software/hardware rasterization. Our performance data on a commodity GPU show that the rendering pipeline based on ETER is capable of rendering up to 3.7 million patches (0.25 billion tessellated triangles) in real-time (30FPS). With its advantages in performance, scalability, and visual quality in rendering large-scale NURBS models, a real-time tessellation solution based on ETER can be a powerful alternative or even a potential replacement for the existing pre-tessellation solution in CAD systems.
Second-order Stencil Descent for Interior-point Hyperelasticity

In this paper, we present a GPU algorithm for finite element hyperelastic simulation. We show that the interior-point method, known to be effective for robust collision resolution, can be coupled with non-Newton procedures and be massively sped up on the GPU. Newton's method has been widely chosen for the interior-point family, which fully solves a linear system at each step. After that, the active set associated with collision/contact constraints is updated. Mimicking this routine using a non-Newton optimization (like gradient descent or ADMM) unfortunately does not deliver expected accelerations. This is because the barrier functions employed in an interior-point method need to be updated at every iteration to strictly confine the search to the feasible region. The associated cost (e.g., per-iteration CCD) quickly overweights the benefit brought by the GPU, and a new parallelism modality is needed. Our algorithm is inspired by the domain decomposition method and designed to move interior-point-related computations to local domains as much as possible. We minimize the size of each domain (i.e., a stencil) by restricting it to a single element, so as to fully exploit the capacity of modern GPUs. The stencil-level results are integrated into a global update using a novel hybrid sweep scheme. Our algorithm is locally second-order offering better convergence. It enables simulation acceleration of up to two orders over its CPU counterpart. We demonstrate the scalability, robustness, efficiency, and quality of our algorithm in a variety of simulation scenarios with complex and detailed collision geometries.
Sliding contact on the interface of elastic body and rigid surface using a single block Burridge-Knopoff model

Burridge and Knopoff proposed a mass-spring model to explore interface dynamics along a fault during an earthquake. The Burridge and Knopoff (BK) model is composed of a series of blocks of equal mass connected to each other by springs of same stiffness. The blocks also are attached to a rigid driver via another set of springs that pulls them at a constant velocity against a rigid substrate. They studied dynamics of interface for an especial case with ten blocks and a specific set of fault properties. In our study effects of Coulomb and rate-state dependent friction laws on the dynamics of a single block BK model is investigated. The model dynamics is formulated as a system of coupled nonlinear ordinary differential equations in state-space form which lends itself to numerical integration methods, e.g. Runge-Kutta procedure for solution. The results show that the rate and state dependent friction law has the potential of triggering dynamic patterns that are different from those under Coulomb law.
Deployable strip structures

We introduce the new concept of C-mesh to capture kinetic structures that can be deployed from a collapsed state. Quadrilateral C-meshes enjoy rich geometry and surprising relations with differential geometry: A structure that collapses onto a flat and straight strip corresponds to a Chebyshev net of curves on a surface of constant Gaussian curvature, while structures collapsing onto a circular strip follow surfaces which enjoy the linear-Weingarten property. Interestingly, allowing more general collapses actually leads to a smaller class of shapes. Hexagonal C-meshes have more degrees of freedom, but a local analysis suggests that there is no such direct relation to smooth surfaces. Besides theory, this paper provides tools for exploring the shape space of C-meshes and for their design. We also present an application for freeform architectural skins, namely paneling with spherical panels of constant radius, which is an important fabrication-related constraint.
Efficient Embeddings in Exact Arithmetic

We provide a set of tools for generating planar embeddings of triangulated topological spheres. The algorithms make use of Schnyder labelings and realizers. A new representation of the realizer based on dual trees leads to a simple linear time algorithm mapping from weights per triangle to barycentric coordinates and, more importantly, also in the reverse direction. The algorithms can be implemented so that all coefficients involved are 1 or -1. This enables integer computation, making all computations exact. Being a Schnyder realizer, mapping from positive triangle weights guarantees that the barycentric coordinates form an embedding. The reverse direction enables an algorithm for fixing flipped triangles in planar realizations, by mapping from coordinates to weights and adjusting the weights (without forcing them to be positive). In a range of experiments, we demonstrate that all algorithms are orders of magnitude faster than existing robust approaches.