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Fei He

Publications and source records attributed to Fei He.

At least 73 records · Page 4Linked to original sources
arXiv2020

Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking

Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods. To solve this problem we develop C-FAR, a novel method for Fast, Automated and Reproducible assessment of multiple hierarchical clustering algorithms simultaneously. Our algorithm takes any number of hierarchical clustering trees as input, then strategically queries pairs for human feedback, and outputs an optimal clustering among those nominated by these trees. While it is applicable to large dataset in any domain that utilizes pairwise comparisons for assessment, our flagship application is the cluster aggregation step in spike-sorting, the task of assigning waveforms (spikes) in recordings to neurons. On simulated data of 96 neurons under adverse conditions, including drifting and 25\% blackout, our algorithm produces near-perfect tracking relative to the ground truth. Our runtime scales linearly in the number of input trees, making it a competitive computational tool. These results indicate that C-FAR is highly suitable as a model selection and assessment tool in clustering tasks.

stat.ML↗
arXiv2020

Gravitational waves detection with exceptional points in micro cavities

Here we propose a new gravitational waves(GWs) detector in broad frequency band, which is operated at exceptional points(EPs) in micro cavities. The detected signal is an eigenfrequency split of the mechanical modes caused by the spatial strain. Due to the complex square root topology near the EP, the splitting is greatly enhanced for sufficiently small perturbations. Compared to current strategies, it can be achieved at the room temperature and has advantages in micro device scale, wide frequency band and higher sensitivity.

quant-ph↗
arXiv2019

Regularity estimates for the gradient flow of a spinorial energy functional

In this note, we establish certain regularity estimates for the spinor flow introduced and initially studied in \cite{AWW2016}. Consequently, we obtain that the norm of the second order covariant derivative of the spinor field becoming unbounded is the only obstruction for long-time existence of the spinor flow. This generalizes the blow up criteria obtained in \cite{Sc2018} for surfaces to general dimensions. As another application of the estimates, we also obtain a lower bound for the existence time in terms of the initial data. Our estimates are based on an observation that, up to pulling back by a one-parameter family of diffeomorphisms, the metric part of the spinor flow is equivalent to a modified Ricci flow.

math.DG↗
arXiv2018

Weakly PIC1 manifolds with maximal volume growth

In this article we use Ricci flow to show that complete PIC1 manifolds with maximal volume growth are diffeomorphic to $\mathbb{R}^n$. One of the key ingredients is local estimates of curvature lower bounds on an initial time interval of the Ricci flow. As another application of these estimates we obtain pseudolocality type results related to the PIC1 condition.

math.DG↗
arXiv2018

Fast OBDD Reordering using Neural Message Passing on Hypergraph

Ordered binary decision diagrams (OBDDs) are an efficient data structure for representing and manipulating Boolean formulas. With respect to different variable orders, the OBDDs' sizes may vary from linear to exponential in the number of the Boolean variables. Finding the optimal variable order has been proved a NP-complete problem. Many heuristics have been proposed to find a near-optimal solution of this problem. In this paper, we propose a neural network-based method to predict near-optimal variable orders for unknown formulas. Viewing these formulas as hypergraphs, and lifting the message passing neural network into 3-hypergraph (MPNN3), we are able to learn the patterns of Boolean formula. Compared to the traditional methods, our method can find a near-the-best solution with an extremely shorter time, even for some hard examples.To the best of our knowledge, this is the first work on applying neural network to OBDD reordering.

cs.AI↗
arXiv2018

When Regression Verification Meets CEGAR

Software systems evolve throughout their life cycles. Many revisions are produced over time. Model checking each revision of the software is impractical. Regression verification suggests reusing intermediate results from the previous verification runs. This paper proposes a fully automatic regression verification technique in the context of CEGAR. Procedure summaries, which describe the input/output behaviors of a procedure, are proposed as the intermediate results to be reused. Procedure summaries are reasonably small to store, technically easy to process, and do not require much extra computation effort to be reused. Reusing procedure summaries saves much analysis effort on the corresponding procedures. By combining regression verification and CEGAR, we propose a technique that is able to reuse procedure summaries across different abstract precisions and different program revisions. We performed extensive experiments on a large number of industrial programs (534 revisions of 89 Linux kernel device drivers). The results show that our approach can significantly improve the performance of regression verification.

cs.SE↗
arXiv2017

Doubly Robust Data-Driven Distributionally Robust Optimization

Data-driven Distributionally Robust Optimization (DD-DRO) via optimal transport has been shown to encompass a wide range of popular machine learning algorithms. The distributional uncertainty size is often shown to correspond to the regularization parameter. The type of regularization (e.g. the norm used to regularize) corresponds to the shape of the distributional uncertainty. We propose a data-driven robust optimization methodology to inform the transportation cost underlying the definition of the distributional uncertainty. We show empirically that this additional layer of robustification, which produces a method we called doubly robust data-driven distributionally robust optimization (DD-R-DRO), allows to enhance the generalization properties of regularized estimators while reducing testing error relative to state-of-the-art classifiers in a wide range of data sets.

stat.ML↗
arXiv2017

DTNC: A New Server-side Data Cleansing Framework for Cellular Trajectory Services

It is essential for the cellular network operators to provide cellular location services to meet the needs of their users and mobile applications. However, cellular locations, estimated by network-based methods at the server-side, bear with {\it high spatial errors} and {\it arbitrary missing locations}. Moreover, auxiliary sensor data at the client-side are not available to the operators. In this paper, we study the {\em cellular trajectory cleansing problem} and propose an innovative data cleansing framework, namely \underline{D}ynamic \underline{T}ransportation \underline{N}etwork based \underline{C}leansing (DTNC) to improve the quality of cellular locations delivered in online cellular trajectory services. We maintain a dynamic transportation network (DTN), which associates a network edge with a probabilistic distribution of travel times updated continuously. In addition, we devise an object motion model, namely, {\em travel-time-aware hidden semi-Markov model} ({\em TT-HsMM}), which is used to infer the most probable traveled edge sequences on DTN. To validate our ideas, we conduct a comprehensive evaluation using real-world cellular data provided by a major cellular network operator and a GPS dataset collected by smartphones as the ground truth. In the experiments, DTNC displays significant advantages over six state-of-the-art techniques.

cs.NI↗
arXiv2016

Existence, Lifespan and Transfer Rate of Ricci Flows on Manifolds with Small Ricci Curvature

We show that in dimension 4 and above, the lifespan of Ricci flows depends on the relative smallness of the Ricci curvature compared to the Riemann curvature on the initial manifold. We can generalize this lifespan estimate to the local Ricci flow, using which we prove the short-time existence of Ricci flow solutions on noncompact Riemannian manifolds with at most quadratic curvature growth, whose Ricci curvature and its first two derivatives are sufficiently small in regions where the Riemann curvature is large. Those Ricci flow solutions may have unbounded curvature. Moreover, our method implies that, under some appropriate assumptions, the spatial transfer rate (the rate at which high curvature regions affect low curvature regions) of the Ricci flow resembles that of the heat equation.

math.DG↗
arXiv2015

Time-resolved shadowgraphs of transient plasma induced by spatiotemporally focused femtosecond laser pulses in fused silica glass

We report on experimental observations of formation and evolution of transient plasma produced in fused silica glass with spatiotemporally focused (STF) femtosecond laser pulses using a pump-probe shadow imaging technique. Surprisingly, the observation shows that the track of the plasma is significantly curved, which is attributed to an asymmetric density distribution of the transient plasma produced in the focal volume caused by the pulse front tilt of the STF laser field.

physics.optics↗
arXiv2015

Spatiotemporal focusing does not always improve axial intensity localization

We report on an experimental comparison on critical intensities of nonlinear self-focusing in air with conventional focusing and spatiotemporal focusing schemes. Our results show that the conventional focusing with the focal lens completely filled with the incident beam allows for the strongest axial intensity confinement against the self-focusing effect. This is because that in the high-numerical-aperture condition, the focal spot will have a compact size which results in a high focal intensity. Meanwhile, the Rayleigh length of the focused beam will be substantially shortened which efficiently postpones the onset of self-focusing.

physics.optics↗
arXiv2015

Porous LSCF/Dense 3YSZ Interface Fracture Toughness Measured by Single Cantilever Beam Wedge Test

Sandwich specimens were prepared by firing a thin inter-layer of porous La0.6Sr0.4Co0.2Fe0.8O3-d (LSCF) to bond a thin tetragonal yttria-stabilised zirconia (YSZ) beam to a thick YSZ substrate. Fracture of the joint was evaluated by introducing a wedge between the two YSZ adherands so that the stored energy in the thin YSZ cantilever beam drives a stable crack in the adhesive bond and allows the critical energy release rate for crack extension (fracture toughness) to be measured. The crack path in most specimens showed a mixture of adhesive failure (at the YSZ-LSCF interface) and cohesive failure (within the LSCF). It was found that the extent of adhesive fracture increased with firing temperature and decreased with LSCF layer thickness. The adhesive failures were mainly at the interface between the LSCF and the thin YSZ beam and FEM modelling revealed that this is due to asymmetric stresses in the LSCF. Within the firing temperature range of 1000-1150C, the bonding fracture toughness appears to have a strong dependence on firing temperature. However, the intrinsic adhesive fracture toughness of the LSCF/YSZ interface was estimated to be 11 Jm2 and was not firing temperature dependent within the temperature range investigated.

cond-mat.mtrl-sci↗
arXiv2014

Quasi-monoenergetic Electron Beams from Laser-plasma Acceleration by Ionization-induced Injection in Low- density Pure Nitrogen

We report a laser wakefield acceleration of electron beams up to 130 MeV from laser-driven 4-mm long nitrogen gas jet. By using a moderate laser intensity (3.5*10^18 W.cm^(-2)) and relatively low plasma densities (0.8*10^18 cm^(-3) to 2.7*10^18 cm^(-3)) we have achieved a stable regime for laser propagation and consequently a stable generation of electron beams. We experimentally studied the dependence of the drive laser energy on the laser-plasma channel and electron beam parameters. The quality of the generated electron beams is discussed within the framework of the ionization-induced injection mechanism.

physics.plasm-ph↗
arXiv2014

Enhanced electron yield from a laser-plasma accelerator using high-Z gas jet targets

An investigation of the multi-hundred MeV electron beam yield (charge) form helium, nitrogen, neon and argon gas jet plasmas in a laser-plasma wakefield acceleration experiment was carried out. The charge measurement has been made via imaging the electron beam intensity profile on a fluorescent screen into a 14-bit charge coupled device (CCD) which was cross-calibrated with nondestructive electronics-based method. Within given laser and plasma parameters, we found that laser-driven low Z- gas jet targets generate high-quality and well-collimated electron beams with reasonable yields at the level of 10-100 pC. On the other hand, filamentary electron beams which were observed from high-Z gas jets at higher densities reached much higher yield. Evidences for cluster formation were clearly observed in high-Z gases, especially in the argon gas jet target where we received the highest yield of ~ 3 nC

physics.plasm-ph↗
arXiv2014

Interferometric characterization of pulse front tilt of spatiotemporally focused femtosecond laser pulses for three-dimensional micromachining application

We report on an experimental measurement of the pulse front tilt (PFT) of spatiotemporally focused femtosecond laser pulses in the focal plane in both air and bulk transparent materials, which is achieved by examination of the interference pattern between the spatiotemporally focused pulse and a conventional focused reference pulse as a function of time delay between the two pulses. Our simulation results agree well with the experimental observations.

physics.optics↗
arXiv2014

Beyond $χ^2$ Difference: Learning Optimal Metric for Boundary Detection

This letter focuses on solving the challenging problem of detecting natural image boundaries. A boundary usually refers to the border between two regions with different semantic meanings. Therefore, a measurement of dissimilarity between image regions plays a pivotal role in boundary detection of natural images. To improve the performance of boundary detection, a Learning-based Boundary Metric (LBM) is proposed to replace $χ^2$ difference adopted by the classical algorithm mPb. Compared with $χ^2$ difference, LBM is composed of a single layer neural network and an RBF kernel, and is fine-tuned by supervised learning rather than human-crafted. It is more effective in describing the dissimilarity between natural image regions while tolerating large variance of image data. After substituting $χ^2$ difference with LBM, the F-measure metric of mPb on the BSDS500 benchmark is increased from 0.69 to 0.71. Moreover, when image features are computed on a single scale, the proposed LBM algorithm still achieves competitive results compared with \emph{mPb}, which makes use of multi-scale image features.

cs.CV↗