arXiv subjects

Kai Jin

Publications and source records attributed to Kai Jin.

At least 37 records · Page 2Linked to original sources

arXiv2023

Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation

Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited fully-annotated data and abundant point-annotated data. Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations. The label trust maps provide an indication of the reliability of the pseudo-labels. Furthermore, we propose the Confident Learning Guided Label Refinement (CLGLR) module identifies error information in the pseudo-labels and leads to further refinement. Experiments on the RETOUCH dataset show that we are able to reduce the need for fully-annotated data by 94.22\%, closing the gap with the best fully supervised baselines to a mean IoU of only 2\%. Furthermore, We constructed a private 2D OCT fluid segmentation dataset for evaluation. Compared with other methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation.

cs.CV↗

arXiv2023

First sodium laser guide star asterism launching platform in China on 1.8m telescope at Gaomeigu Observatory

The application of sodium laser guide star is the key difference between modern adaptive optics system and traditional adaptive optics system. Especially in system like multi-conjugate adaptive optics, sodium laser guide star asterism which is formed by several laser guide stars in certain pattern is required to probe more atmospheric turbulence in different directions. To achieve this, a sodium laser guide star asterism launching platform is required. In this paper, we will introduce the sodium laser guide star asterism launching platform built and tested on the 1.8m telescope of the Gaomeigu Observatory. The platform has two functions: one is to compare the performance of sodium laser guide stars generated by different lasers at the same place; the other is to generate sodium laser guide star asterism with adjustable shape. The field test results at the beginning of 2021 verify the important role of the platform, which is also the first time to realize sodium laser guide star asterism in China.

astro-ph.IM↗

arXiv2023

Prediction of PEV Adoption with Agent-Based Parameterized Bass Network Diffusion Model

Although the growing electric vehicle (EV) population is leading us into a more sustainable world, it is also bringing challenges for the manufacturers's production planning, the charging facility providers's expansion plan, and the energy system's adaption to greater electricity demand. To tackle these challenges, a model to predict EV growth in geographical scope would be helpful. In this study, an agent-based parameterized bass network diffusion model was developed for EV population data in Washington. The model included income levels and number of neighbors adopted as two key factors in determining EV diffusion probabilities. With the parameters estimated from simulation, the resulting model achieve a high estimation accuracy for EV adoption in Washington in both temporal and geographical scopes. This model could be used to predict EV growth in Washington, and to be adopted to other geographical areas.

physics.soc-ph↗

arXiv2023

Architected Materials for Mechanical Compression: Design via Simulation, Deep Learning, and Experimentation

Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between architected structure and resultant properties remains an open field of great interest to many fields, from aerospace to civil to automotive applications. Here, we focus on properties related to mechanical compression, and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress. To do so, we employ a combination of techniques in a singular workflow, starting with molecular dynamics simulation of the forward design problem, augmenting with data-driven artificial intelligence models to address the inverse design problem, and verifying the behavior of de novo structures with experimentation of additively manufactured samples. We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.

cond-mat.mtrl-sci↗

arXiv2022

Sum-of-Max Partition under a Knapsack Constraint

Sequence partition problems arise in many fields, such as sequential data analysis, information transmission, and parallel computing. In this paper, we study the following partition problem variant: given a sequence of $n$ items $1,\ldots,n$, where each item $i$ is associated with weight $w_i$ and another parameter $s_i$, partition the sequence into several consecutive subsequences, so that the total weight of each subsequence is no more than a threshold $w_0$, and the sum of the largest $s_i$ in each subsequence is minimized. This problem admits a straightforward solution based on dynamic programming, which costs $O(n^2)$ time and can be improved to $O(n\log n)$ time easily. Our contribution is an $O(n)$ time algorithm, which is nontrivial yet easy to implement. We also study the corresponding tree partition problem. We prove that the problem on the tree is NP-complete and we present an $O(w_0 n^2)$ time ($O(w_0^2n^2)$ time, respectively) algorithm for the unit weight (integer weight, respectively) case.

cs.DS↗

arXiv2022

Linear time Minimum Area All-flush Triangles Circumscribing a Convex Polygon

We study the problem of computing the minimum area triangle that circumscribes a given $n$-sided convex polygon touching edge-to-edge. In other words, we compute the minimum area triangle that is the intersection of 3 half-planes out of $n$ half-planes defined by a given convex polygon. Building on the Rotate-and-Kill technique {Arxiv:1707.04071}, we propose an algorithm that solves the problem in $O(n)$ time, improving the best-known $O(n\log n)$ time algorithms given in [A. Aggarwal et. al. DCG94; B. Schieber. SODA95}. Our algorithm computes all the locally minimal area circumscribing triangles touching edge-to-edge.

cs.CG↗

arXiv2022

String Rearrangement Inequalities and a Total Order Between Primitive Words

We study the following rearrangement problem: Given $n$ words, rearrange and concatenate them so that the obtained string is lexicographically smallest (or largest, respectively). We show that this problem reduces to sorting the given words so that their repeating strings are non-decreasing (or non-increasing, respectively), where the repeating string of a word $A$ refers to the infinite string $AAA\ldots$. Moreover, for fixed size alphabet $Σ$, we design an $O(L)$ time sorting algorithm of the words (in the mentioned orders), where $L$ denotes the total length of the input words. Hence we obtain an $O(L)$ time algorithm for the rearrangement problem. Finally, we point out that comparing primitive words via comparing their repeating strings leads to a total order, which can further be extended to a total order on the finite words (or all words).

cs.DS↗

arXiv2021

Dispensed Transformer Network for Unsupervised Domain Adaptation

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques.

cs.CV↗

arXiv2021

Photon return on-sky test for by-product Rayleigh plume of sodium laser guide star

The sodium laser guide star (sLGS) adaptive optics (AO) system has become an essential component for large ground-based optical/infrared telescope. The performance of the AO system can be significantly hindered if the on-sky spot profile of its sLGS is degraded by the turbulence along the laser launching uplink path. One effective method to overcome this problem is to perform real-time pre-correction on the laser before it is launched onto the sky to counter this turbulence effect especially for the lower altitude where turbulence is stronger. The by-product Rayleigh backscatter generated by large molecule and aerosols at low altitude when projecting sLGS is a perfect candidate for real time detection of low altitude turbulence. It is therefore important to evaluate whether this by-product of a sodium laser guide star could achieve suitable performance for wavefront sensing. In this paper, we attempt to answer the question regarding to the achievable photon return of such Rayleigh backscattered plume. A comparison between results from our field test and theoretical model of MSISE-90 was presented. We showed that the greatest differences is less than 20% which hints the applicability of using the defocused Rayleigh plume as wavefront sensing signal for different turbulence strengths. The result also showed that in case of strong (Fried parameter (r0) of 5 cm) or moderate (r0 of 10 cm) turbulence, the optimum center for range gating is 9 km, and the corresponding value of range gate depths are 3 km, 1.1 km, 0.56 km and 0.28 km when subaperture sizes are 3 cm, 5 cm, 7 cm and 10 cm, with the maximum pulse repetition frequency at 1500 Hz.

astro-ph.IM↗

arXiv2021

Short term minutes-time scale temporal variation statistics of sodium layer dynamics

The brightness and height of the sodium laser guide star of adaptive optics could vary significantly due to the temporal dynamics of sodium column density and the mean height of sodium layer. To measure these dynamics, an independent sodium Lidar is a necessity. Without such an instrument, it is almost impossible to discern the cause of the brightness variation of laser guide star from the sodium layer's dynamics or other factors from the laser itself. For applications such as characterizing the performance of sodium laser for sodium laser guide star generation, minutes scale short term statistics of the sodium layers' abundance and height is extremely helpful for estimating the contribution of sodium layer's variation to the variation of laser guide star's brightness. In this paper, we analyzed our previous measurement of sodium layer dynamics that has been gathered in two winters, and presented the temporal variation statistics of sodium column density and mean height within minute time scale based on our measurements.

astro-ph.IM↗

arXiv2020

A Generalization of Self-Improving Algorithms

Ailon et al. [SICOMP'11] proposed self-improving algorithms for sorting and Delaunay triangulation (DT) when the input instances $x_1,\cdots,x_n$ follow some unknown \emph{product distribution}. That is, $x_i$ comes from a fixed unknown distribution $\mathsf{D}_i$, and the $x_i$'s are drawn independently. After spending $O(n^{1+\varepsilon})$ time in a learning phase, the subsequent expected running time is $O((n+ H)/\varepsilon)$, where $H \in \{H_\mathrm{S},H_\mathrm{DT}\}$, and $H_\mathrm{S}$ and $H_\mathrm{DT}$ are the entropies of the distributions of the sorting and DT output, respectively. In this paper, we allow dependence among the $x_i$'s under the \emph{group product distribution}. There is a hidden partition of $[1,n]$ into groups; the $x_i$'s in the $k$-th group are fixed unknown functions of the same hidden variable $u_k$; and the $u_k$'s are drawn from an unknown product distribution. We describe self-improving algorithms for sorting and DT under this model when the functions that map $u_k$ to $x_i$'s are well-behaved. After an $O(\mathrm{poly}(n))$-time training phase, we achieve $O(n + H_\mathrm{S})$ and $O(nα(n) + H_\mathrm{DT})$ expected running times for sorting and DT, respectively, where $α(\cdot)$ is the inverse Ackermann function.

cs.CG↗

arXiv2020

An Impedance Sensing Platform for Monitoring Heterogeneous Connectivity and Diagnostics in Lab-on-a-Chip Systems

Reliable hardware connectivity is vital in heterogeneous integrated systems. For example, in digital microfluidics lab-on-a-chip systems, there are hundreds of physical connections required between a micro-electro-mechanical fabricated device and the driving system that can be remotely located on a printed-circuit-board. Unfortunately, the connection reliability cannot be checked or monitored by vision-based detection methods that are commonly used in the semiconductor industry. Therefore, a sensing platform that can be seamlessly integrated into existing digital microfluidics systems and provide real-time monitoring of multi-connectivity is highly desired. Here, we report an impedance sensing platform that can provide fast detection of a single physical connection in timescales of milli-seconds. Once connectivity is established, the same set-up can be used to determine droplet location. The sensing system can be scaled up to support multiple channels or applied to other heterogeneously integrated systems that require real-time monitoring and diagnostics of multi-connectivity systems.

eess.SY↗

arXiv2020

A floating top-electrode electrowetting-on-dielectric system

Herein, we describe a novel device configuration for double-plate electrowetting-on-dielectric system with a floating top-electrode. As conventional double-plate EWOD device requires a grounded electrode on top-plate, it will cause additional fabrication complicity and cost during the encapsulation process. In this work, we found that by carefully designing electrode arrangement and configuring driving electronic circuit, the droplet driving force can be maintained with a floating electrode on the top-plate. This can provide the possibilities to integrate additional electrical or electrochemical sensing functions on the top-plate. We use both finite element analysis and fabricated system to validate the theory, and the results indicate that floating top-electrode EWOD systems are highly reliable and reproducible once the design considerations are fully met.

physics.app-ph↗

arXiv2019

A Geometric Structure Associated with the Convex Polygon

We propose a geometric structure induced by any given convex polygon $P$, called $Nest(P)$, which is an arrangement of $Θ(n^2)$ line segments, each of which is parallel to an edge of $P$, where $n$ denotes the number of edges of $P$. We then deduce six nontrivial properties of $Nest(P)$ from the convexity of $P$ and the parallelism of the line segments in $Nest(P)$. Among others, we show that $Nest(P)$ is a subdivision of the exterior of $P$, and its inner boundary interleaves the boundary of $P$. They manifest that $Nest(P)$ has a surprisingly good interaction with the boundary of $P$. Furthermore, we study some computational problems on $Nest(P)$. In particular, we consider three kinds of location queries on $Nest(P)$ and answer each of them in (amortized) $O(\log^2n)$ time. Our algorithm for answering these queries avoids an explicit construction of $Nest(P)$, which would take $Ω(n^2)$ time. By applying the aforementioned six properties altogether, we find that the geometric optimization problem of finding the maximum area parallelogram(s) in $P$ can be reduced to answering $O(n)$ aforementioned location queries, and thus be solved in $O(n\log^2n)$ time. This application will be reported in a subsequent paper.

cs.CG↗

arXiv2019

Extensions of Self-Improving Sorters

Ailon et al. (SICOMP 2011) proposed a self-improving sorter that tunes its performance to an unknown input distribution in a training phase. The input numbers $x_1,x_2,\ldots,x_n$ come from a product distribution, that is, each $x_i$ is drawn independently from an arbitrary distribution ${\cal D}_i$. We study two relaxations of this requirement. The first extension models hidden classes in the input. We consider the case that numbers in the same class are governed by linear functions of the same hidden random parameter. The second extension considers a hidden mixture of product distributions.

cs.DS↗

arXiv2019

On 1-factorizations of Bipartite Kneser Graphs

It is a challenging open problem to construct an explicit 1-factorization of the bipartite Kneser graph $H(v,t)$, which contains as vertices all $t$-element and $(v-t)$-element subsets of $[v]:=\{1,\ldots,v\}$ and an edge between any two vertices when one is a subset of the other. In this paper, we propose a new framework for designing such 1-factorizations, by which we solve a nontrivial case where $t=2$ and $v$ is an odd prime power. We also revisit two classic constructions for the case $v=2t+1$ --- the \emph{lexical factorization} and \emph{modular factorization}. We provide their simplified definitions and study their inner structures. As a result, an optimal algorithm is designed for computing the lexical factorizations. (An analogous algorithm for the modular factorization is trivial.)

cs.DM↗

arXiv2019

A note on self-improving sorting with hidden partitions

We study self-improving sorting with hidden partitions. Our result is an optimal algorithm which runs in expected time O(H(π(I)) + n), where I is the given input which contains n elements to be sorted, π(I) is the output which are the ranks of all element in I, and H(π(I)) denotes the entropy of the output.

cs.CG↗

arXiv2019

On the Complexity of Computing the Topology of Real Algebraic Space Curves

In this paper, we present a deterministic algorithm to find a strong generic position for an algebraic space curve. We modify our existing algorithm for computing the topology of an algebraic space curve and analyze the bit complexity of the algorithm. It is $\tilde{\mathcal {O}} (N^{20})$, where $N=\max\{d,τ\}$, $d, τ$ are the degree bound and the bit size bound of the coefficients of the defining polynomials of the algebraic space curve. To our knowledge, this is the best bound among the existing work. It gains the existing results at least $N^2$.

cs.SC↗