arXiv subjects

Jin Sun

Publications and source records attributed to Jin Sun.

At least 91 records · Page 5Linked to original sources

arXiv2019

Testing the Wyart-Cates model for non-Brownian shear thickening using bidisperse suspensions

There is a growing consensus that shear thickening of concentrated dispersions is driven by the formation of stress-induced frictional contacts. The Wyart-Cates (WC) model of this phenomenon, in which the microphysics of the contacts enters solely via the fraction $f$ of contacts that are frictional, can successfully fit flow curves for suspensions of weakly polydisperse spheres. However, its validity for "real-life", polydisperse suspensions has yet to be seriously tested. By performing systematic simulations on bidisperse mixtures of spheres, we show that the WC model applies only in the monodisperse limit and fails when substantial bidispersity is introduced. We trace the failure of the model to its inability to distinguish large-large, large-small and small-small frictional contacts. By fitting our data using a polydisperse analogue of $f$ that depends separately on the fraction of each of these contact types, we show that the WC picture of shear thickening is incomplete. Systematic experiments on model shear-thickening suspensions corroborate our findings, but highlight important challenges in rigorously testing the WC model with real systems. Our results prompt new questions about the microphysics of thickening for both monodisperse and polydisperse systems.

cond-mat.soft↗

arXiv2019

$θ_{23}=π/4$ and $δ=-π/2$ In Neutrino Mixing, Which Convention?

Considerable information has been obtained about neutrino mixing matrix. Present data show that in the particle data group (PDG) parameterization, the 2-3 mixing angle and the CP violating phase are consistent with $θ_{23} = π/4$ and $δ_{PDG} = -π/2$, respectively. A lot of efforts have been devoted to constructing models in realizing a mixing matrix with these values. However, the particular angles and phase are parameterization convention dependent. The meaning about the specific values for mixing angle and phase needs to be clarified. Using the well known 9 independent ways of parameterizing the mixing matrix, we show in detail how the mixing angles and phase change with conventions even with the 2-3 mixing angle to be $π/4$ and the CP violating phase to be $-π/2$. The original Kaobayashi-Maskawa and an additional one belong to such a category. The other 6 parameterizations have mixing angles and phase very different values from those in the PDG parameterization although the physical effects are the same. Therefore one should give the specific parameterization convention when making statements about values for mixing angles and phase.

hep-ph↗

arXiv2019

Leveraging Vision Reconstruction Pipelines for Satellite Imagery

Reconstructing 3D geometry from satellite imagery is an important topic of research. However, disparities exist between how this 3D reconstruction problem is handled in the remote sensing context and how multi-view reconstruction pipelines have been developed in the computer vision community. In this paper, we explore whether state-of-the-art reconstruction pipelines from the vision community can be applied to the satellite imagery. Along the way, we address several challenges adapting vision-based structure from motion and multi-view stereo methods. We show that vision pipelines can offer competitive speed and accuracy in the satellite context.

cs.CV↗

arXiv2019

Fair Pricing of Variable Annuities with Guarantees under the Benchmark Approach

In this paper we consider the pricing of variable annuities (VAs) with guaranteed minimum withdrawal benefits. We consider two pricing approaches, the classical risk-neutral approach and the benchmark approach, and we examine the associated static and optimal behaviors of both the investor and insurer. The first model considered is the so-called minimal market model, where pricing is achieved using the benchmark approach. The benchmark approach was introduced by Platen in 2001 and has received wide acceptance in the finance community. Under this approach, valuing an asset involves determining the minimum-valued replicating portfolio, with reference to the growth optimal portfolio under the real-world probability measure, and it both subsumes classical risk-neutral pricing as a particular case and extends it to situations where risk-neutral pricing is impossible. The second model is the Black-Scholes model for the equity index, where the pricing of contracts is performed within the risk-neutral framework. Crucially, we demonstrate that when the insurer prices and reserves using the Black-Scholes model, while the insured employs a dynamic withdrawal strategy based on the minimal market model, the insurer may be underestimating the value and associated reserves of the contract.

q-fin.PR↗

arXiv2019

Force chains and networks: wet suspensions through dry granular eyes

Recent advances in shear-thickening suspension rheology suggest a relation between (wet) suspension flow below jamming and (dry) granular physics. To probe this connection, we simulated the contact force networks in suspensions of non-Brownian spheres using the discrete element method (DEM), varying the particle friction coefficient and volume fraction. We find that force networks in these suspensions show quantitative similarities to those in jammed dry grains. As suspensions approach the jamming point, the extrapolated volume fraction and coordination number at jamming are similar to critical values obtained for isotropically compressed spheres. Similarly, the shape of the distribution of contact forces in flowing suspensions is remarkably similar to that found in granular packings, suggesting potential refinements for analytical mean field models for the rheology of shear thickening suspensions.

cond-mat.soft↗

arXiv2018

Siamese Networks for Semantic Pattern Similarity

Semantic Pattern Similarity is an interesting, though not often encountered NLP task where two sentences are compared not by their specific meaning, but by their more abstract semantic pattern (e.g., preposition or frame). We utilize Siamese Networks to model this task, and show its usefulness in determining SQL patterns for unseen questions in a database-backed question answering scenario. Our approach achieves high accuracy and contains a built-in proxy for confidence, which can be used to keep precision arbitrarily high.

cs.CL↗

arXiv2017

Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images

Lighting estimation from face images is an important task and has applications in many areas such as image editing, intrinsic image decomposition, and image forgery detection. We propose to train a deep Convolutional Neural Network (CNN) to regress lighting parameters from a single face image. Lacking massive ground truth lighting labels for face images in the wild, we use an existing method to estimate lighting parameters, which are treated as ground truth with unknown noises. To alleviate the effect of such noises, we utilize the idea of Generative Adversarial Networks (GAN) and propose a Label Denoising Adversarial Network (LDAN) to make use of synthetic data with accurate ground truth to help train a deep CNN for lighting regression on real face images. Experiments show that our network outperforms existing methods in producing consistent lighting parameters of different faces under similar lighting conditions. Moreover, our method is 100,000 times faster in execution time than prior optimization-based lighting estimation approaches.

cs.CV↗

arXiv2017

Semi-supervised Conditional GANs

We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the conditional distribution of the attributes given the data respectively. In the semi-supervised setting, the marginal distribution (which is often harder to learn) is learned from the labeled + unlabeled data, and the conditional distribution is learned purely from the labeled data. Our experimental results demonstrate that this model performs significantly better compared to existing semi-supervised conditional GAN models.

stat.ML↗

arXiv2017

A note on the impact of management fees on the pricing of variable annuity guarantees

Variable annuities, as a class of retirement income products, allow equity market exposure for a policyholder's retirement fund with electable additional guarantees to limit the downside risk of the market. Management fees and guarantee insurance fees are charged respectively for the market exposure and for the protection from the downside risk. We investigate the impact of management fees on the pricing of variable annuity guarantees under optimal withdrawal strategies. Two optimal strategies, from policyholder's and from insurer's perspectives, are respectively formulated and the corresponding pricing problems are solved using dynamic programming. Our results show that when management fees are present, the two strategies can deviate significantly from each other, leading to a substantial difference of the guarantee insurance fees. This provides a possible explanation of lower guarantee insurance fees observed in the market. Numerical experiments are conducted to illustrate our results.

q-fin.PR↗

arXiv2017

Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval

Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer the 3D structure that bridges images with users' text descriptions. However, direct inference of 3D structure from images requires learning from large scale annotated data. Since interactions between objects can be reduced to a limited set of atomic spatial relations in 3D, we study the possibility of inferring 3D structure from a text description rather than an image, applying physical relation models to synthesize holistic 3D abstract object layouts satisfying the spatial constraints present in a textual description. We present a generic framework for retrieving images from a textual description of a scene by matching images with these generated abstract object layouts. Images are ranked by matching object detection outputs (bounding boxes) to 2D layout candidates (also represented by bounding boxes) which are obtained by projecting the 3D scenes with sampled camera directions. We validate our approach using public indoor scene datasets and show that our method outperforms baselines built upon object occurrence histograms and learned 2D pairwise relations.

cs.CV↗

arXiv2017

Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing

Most of computer vision focuses on what is in an image. We propose to train a standalone object-centric context representation to perform the opposite task: seeing what is not there. Given an image, our context model can predict where objects should exist, even when no object instances are present. Combined with object detection results, we can perform a novel vision task: finding where objects are missing in an image. Our model is based on a convolutional neural network structure. With a specially designed training strategy, the model learns to ignore objects and focus on context only. It is fully convolutional thus highly efficient. Experiments show the effectiveness of the proposed approach in one important accessibility task: finding city street regions where curb ramps are missing, which could help millions of people with mobility disabilities.

cs.CV↗

arXiv2016

Tunable Shear Thickening in Suspensions

Shear thickening, an increase of viscosity with shear rate, is a ubiquitous phenomena in suspended materials that has implications for broad technological applications. Controlling this thickening behavior remains a major challenge and has led to empirical strategies ranging from altering the particle surfaces and shape to modifying the solvent properties. However, none of these methods allow for tuning of flow properties during shear itself. Here, we demonstrate that by strategic imposition of a high-frequency and low-amplitude shear perturbation orthogonal to the primary shearing flow, we can largely eradicate shear thickening. The orthogonal shear effectively becomes a regulator for controlling thickening in the suspension, allowing the viscosity to be reduced by up to two decades on demand. In a separate setup, we show that such effects can be induced by simply agitating the sample transversely to the primary shear direction. Overall, the ability of in situ manipulation of shear thickening paves a route towards creating materials whose mechanical properties can be controlled.

cond-mat.soft↗

arXiv2016

Linking particle properties to paste extrusion flow characteristics using discrete element simulations

Extrusion is a widely used process for forming pastes into designed shapes, and is central to the manufacture of many industrial products. The extrusion through a square-entry die of a model paste of non-Brownian spheres suspended in a Newtonian fluid is investigated using discrete element simulations, capturing individual particle contacts and hydrodynamic interactions. The simulations reveal inhomogeneous velocity and stress distributions, originating in the inherent microstructure formed by the constituent particles. Such features are shown to be relevant to generic paste extrusion behaviour, such as die swell. The pressure drop across the extruder is correlated with the extrudate velocity using the Benbow-Bridgwater equation, with the empirical parameters being linked directly to particle properties such as surface friction, and processing conditions such as extruder wall roughness. Our model and results bring recent advances in suspension rheology into an industrial setting, laying foundations for future model development, paste formulation and extrusion design.

cond-mat.soft↗

arXiv2015

Shear thickening regimes of dense non-Brownian suspensions

We propose a unifying rheological framework for dense suspensions of non-Brownian spheres, predicting the onsets of particle friction and particle inertia as distinct shear thickening mechanisms, while capturing quasistatic and soft particle rheology at high volume fractions and shear rates respectively. Discrete element method simulations that take suitable account of hydrodynamic and particle-contact interactions corroborate the model predictions, demonstrating both mechanisms of shear thickening, and showing that they can occur concurrently with carefully selected particle surface properties under certain flow conditions. Microstructural transitions associated with frictional shear thickening are presented. We find very distinctive divergences of both the microstructural and dynamic variables with respect to volume fraction in the thickened and non-thickened states.

cond-mat.soft↗

arXiv2015

Hydrodynamic and contact contributions to shear thickening in colloidal suspensions

Shear thickening is a widespread phenomenon in suspension flow that, despite sustained study, is still the subject of much debate. The longstanding view that shear thickening is due to hydrodynamic clusters has been challenged by recent theory and simulations suggesting that contact forces dominate, not only in discontinuous, but also in continuous shear thickening. Here, we settle this dispute using shear reversal experiments on micron-sized silica and latex colloidal particles to measure directly the hydrodynamic and contact force contributions to shear thickening. We find that contact forces dominate even continuous shear thickening. Computer simulations show that these forces most likely arise from frictional interactions.

cond-mat.soft↗

arXiv2015

Two-scale evolution during shear reversal in dense suspensions

We use shear reversal simulations to explore the rheology of dense, non-Brownian suspensions, resolving lubrication forces between neighbouring particles and modelling particle surface contacts. The transient stress response to an abrupt reversal of the direction of shear shows rate-independent, nonmonotonic behaviour, capturing the salient features of the corresponding classical experiments. Based on analyses of the hydrodynamic and particle contact stresses and related contact networks, we demonstrate distinct responses at small and large strains, associated with contact breakage and structural re-orientation, respectively, emphasising the importance of particle contacts. Consequently, the hydrodynamic and contact stresses evolve over disparate strain scales and with opposite trends, resulting in nonmonotonic behaviour when combined. We further elucidate the roles of particle roughness and repulsion in determining the microstructure and hence the stress response at each scale.

cond-mat.soft↗

arXiv2015

Managing Hybrid Main Memories with a Page-Utility Driven Performance Model

Hybrid memory systems comprised of dynamic random access memory (DRAM) and non-volatile memory (NVM) have been proposed to exploit both the capacity advantage of NVM and the latency and dynamic energy advantages of DRAM. An important problem for such systems is how to place data between DRAM and NVM to improve system performance. In this paper, we devise the first mechanism, called UBM (page Utility Based hybrid Memory management), that systematically estimates the system performance benefit of placing a page in DRAM versus NVM and uses this estimate to guide data placement. UBM's estimation method consists of two major components. First, it estimates how much an application's stall time can be reduced if the accessed page is placed in DRAM. To do this, UBM comprehensively considers access frequency, row buffer locality, and memory level parallelism (MLP) to estimate the application's stall time reduction. Second, UBM estimates how much each application's stall time reduction contributes to overall system performance. Based on this estimation method, UBM can determine and place the most critical data in DRAM to directly optimize system performance. Experimental results show that UBM improves system performance by 14% on average (and up to 39%) compared to the best of three state-of-the-art mechanisms for a large number of data-intensive workloads from the SPEC CPU2006 and Yahoo Cloud Serving Benchmark (YCSB) suites.

cs.AR↗

arXiv2014

Flow regime transitions in dense non-Brownian suspensions: rheology, microstructural characterisation and constitutive modelling

Shear flow of dense, non-Brownian suspensions is simulated using the discrete element method, taking particle contact and hydrodynamic lubrication into account. The resulting flow regimes are mapped in the parametric space of solid volume fraction, shear rate, fluid viscosity and particle stiffness. Below a critical volume fraction $ϕ_c$, the rheology is governed by the Stokes number, which distinguishes between viscous and inertial flow regimes. Above $ϕ_c$, a quasistatic regime exists for low and moderate shear rates. At very high shear rates, the $ϕ$ dependence is lost and soft particle rheology is explored. The transitions between rheological regimes are associated with the evolving contribution of lubrication to the suspension stress. Transitions in microscopic phenomena such as inter-particle force distribution, fabric and correlation length are found to correspond to those in the macroscopic flow. Motivated by the bulk rheology, a constitutive model is proposed combining a viscous pressure term with a dry granular model presented by Chialvo, Sun and Sundaresan [Phys. Rev. E. \textbf{85}, 021305 (2012)]. The model is shown to successfully capture the flow regime transitions.

cond-mat.soft↗