Driving dynamic colloidal assembly using eccentric self-propelled colloids

Designing protocols to dynamically direct the self-assembly of colloidal particles has become an important direction in soft matter physics because of the promising applications in fabrication of dynamic responsive functional materials. Here using computer simulations, we found that in the mixture of passive colloids and eccentric self-propelled active particles, when the eccentricity and self-propulsion of active particles are high enough, the eccentric active particles can push passive colloids to form a large dense dynamic cluster, and the system undergoes a novel dynamic demixing transition. Our simulations show that the dynamic demixing occurs when the eccentric active particles move much faster than the passive particles such that the dynamic trajectories of different active particles can overlap with each other while passive particles are depleted from the dynamic trajectories of active particles. Our results suggest that this is in analogy to the entropy driven demixing in colloid-polymer mixtures, in which polymer random coils can overlap with each other while deplete the colloids. More interestingly, we find that by fixing the passive colloid composition at certain value, with increasing the density, the system undergoes an intriguing re-entrant mixing, and the demixing only occurs within certain intermediate density range. This suggests a new way of designing active matter to drive the self-assembly of passive colloids and fabricate dynamic responsive materials.Comment: Accepted in Soft Matter. Supplementary information can found at https://www.dropbox.com/sh/xb3u5iaoucc2ild/AABFUyqjXips7ewaie2rFbj_a?dl=

Triplet-based Deep Similarity Learning for Person Re-Identification

In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.Comment: ICCV Workshops 201

Source Mechanism and Rupture Directivity of the 18 May 2009 M_W 4.6 Inglewood, California, Earthquake

On 18 May 2009, an M_w 4.6 earthquake occurred beneath Inglewood, California, and was widely felt. Though source mechanism and its location suggest that the Newport–Inglewood fault (NIF) may be involved in generating the earthquake, rupture directivity must be modeled to establish the connection between the fault and the earthquake. We first invert for the event’s source mechanism and depth with the cut-and-paste method in the long-period band (>5 s). Because of the low velocity shallow sediments in the Los Angeles (LA) basin, we use two velocity models in the inversion for stations inside and outside the LA basin. However, little difference is observed in the resolved source mechanism (M_w 4.6, strike 246°/145°, dip 50°/77°, rake 17°/138°) and depth (7 to ~9 km), compared to an inversion using the standard southern Calfornia model. With the resolved source parameters, we calibrate the amplitude anomaly of the short-period (0.5–2 Hz) P waves with amplitude adjustment factors (AAF). These AAFs are used as corrections when retrieving source mechanisms of the smaller aftershocks using short-period P waves alone. Most of the aftershocks show similar source mechanisms as that of the mainshock, providing ideal empirical Green’s functions (EGFs) for studying its rupture process. We use a forward modeling approach to retrieve rupture directivity of the mainshock, consistent with movement on the NIF with rupture toward the southeast. Although we focus on P waves for analyzing rupture directivity, the resolved unilateral pattern is also confirmed with the azimuthal variation of the duration of SH waves observed in the basin. The high rupture velocity near the shear velocity and relatively low stress drop are consistent with the hypothesis of rupture on a mature fault

Earthquake Centroid Locations Using Calibration from Ambient Seismic Noise

Earthquakes occur in complex geology, making it difficult to determine their source parameters and locations because of uncertainty in path effects. We can avoid some of these problems by applying the cut-and-paste (CAP) method, which allows for timing shifts between phases, assuming a 1D model, and determines source parameters. If the travel times or lags of the phases due to path effects are known relative to a reference model, we can locate the events’ centroid with surface waves without knowledge of the 3D velocity structure. Here, we use ambient seismic noise for such a calibration. We cross correlate the seismic stations near the earthquake with stations 100–300 km away to obtain the 10–100-s surface wave Green’s functions. The new method is tested in southern California to locate the 2008 Chino Hills earthquake, which proves consistent with the epicenter location from P waves. It appears possible to use the location offset between the high-frequency P-wave onset relative to the centroid to provide a fast estimate of directivity

An SEM-DSM three-dimensional hybrid method for modelling teleseismic waves with complicated source-side structures

Despite recent advances in High Performance Computing (HPC), numerical simulation of high frequency (e.g. 1 Hz or higher) seismic wave propagation at the global scale is still prohibitive. To overcome this difficulty, we propose a hybrid method to efficiently compute teleseismic waveforms with 3-D source-side structures. By coupling the Spectral Element Method (SEM) with the Direct Solution Method (DSM) based on the representation theorem, we are able to limit the costly SEM simulation to a small source-side region and avoid computation over the entire space of the Earth. Our hybrid method is benchmarked against 1-D DSM synthetics and 3-D SEM synthetics. We also discuss numerical difficulties in the implementation, including slow DSM convergence near source depth, discretization error, Green’s function interpolation and local 3-D wavefield approximations. As a case study, we apply our hybrid method to two subduction earthquakes and show its advantage in understanding 3-D source-side effects on teleseismic P-waves. Our hybrid method reduces computational cost by more than two orders of magnitude when only source-side 3-D complexities are of concern. Thus our hybrid method is useful for a series of problems in seismology, such as imaging 3-D structures of a subducting slab or a mid-ocean ridge and studying source parameters with 3-D source-side complexities using teleseismic waveforms

An evolutionary algorithm with double-level archives for multiobjective optimization

Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed

Retrieval of Moho-reflected shear wave arrivals from ambient seismic noise

Theoretical studies on ambient seismic noise (ASN) predict that complete Green's function between seismic stations can be retrieved from cross correlation. However, only fundamental mode surface waves emerge in most studies involving real data. Here we show that Moho-reflected body wave (SmS) and its multiples can be identified with ASN for station pairs near their critical distances in the short period band (1–5 s). We also show that an uneven distribution of noise sources, such as mining activity and wind–topography interaction, can cause surface wave precursors, which mask weaker body wave phases