arXiv subjects

J. Xue

Publications and source records attributed to J. Xue.

7 recordsLinked to original sources

arXiv2026

Approximating Convex Hulls via Range Queries

Recently, motivated by the rapid increase of the data size in various applications, Monemizadeh [APPROX'23] and Driemel, Monemizadeh, Oh, Staals, and Woodruff [SoCG'25] studied geometric problems in the setting where the only access to the input point set is via querying a range-search oracle. Algorithms in this setting are evaluated on two criteria: (i) the number of queries to the oracle and (ii) the error of the output. In this paper, we continue this line of research and investigate one of the most fundamental geometric problems in the oracle setting, i.e., the convex hull problem. Let $P$ be an unknown set of points in $[0,1]^d$ equipped with a range-emptiness oracle. Via querying the oracle, the algorithm is supposed to output a convex polygon $C \subseteq [0,1]^d$ as an estimation of the convex hull $CH(P)$ of $P$. The error of the output is defined as the volume of the symmetric difference $C \oplus CH(P) = (C \backslash CH(P)) \cup (CH(P) \backslash C)$. We prove tight and near-tight tradeoffs between the number of queries and the error of the output for different variants of the problem, depending on the type of the range-emptiness queries and whether the queries are non-adaptive or adaptive. - Orthogonal emptiness queries in $d$-dimensional space: We show that the minimum error a deterministic algorithm can achieve with $q$ queries is $\Theta(q^{-1/d})$ if the queries are non-adaptive, and $\Theta(q^{-1/(d-1)})$ if the queries are adaptive. In particular, in 2D, the bounds are $\Theta(1/\sqrt{q})$ and $\Theta(1/q)$ for non-adaptive and adaptive queries, respectively. - Halfplane emptiness queries in 2D: We show that the minimum error a deterministic algorithm can achieve with $q$ queries is $\Theta(1/\sqrt{q})$ if the queries are non-adaptive, and $\widetilde{\Theta}(1/q^2)$ if the queries are adaptive. Here $\widetilde{\Theta}(\cdot)$ hides logarithmic factors.

cs.CG↗

arXiv2025

Experimental Results from Early Non-Planar NI-HTS Magnet Prototypes for the Columbia Stellarator eXperiment (CSX)

The Columbia Stellarator eXperiment (CSX) is an upgrade of the Columbia Non-neutral Torus (CNT) that aims to demonstrate a university-scale, quasi-axisymmetric stellarator using high-temperature superconducting (HTS) technology at an on-axis magnetic field target of 0.5 T. Due to the strain sensitivity of ReBCO (Rare-earth Barium Copper Oxides), adapting it to non-planar stellarator geometries requires new winding, structural, and cooling strategies. We report on the results of a staged prototype program (P1, P2, P3) employing 3D-printed, sectional aluminum coil frames with winding channels, gimballed constant-tension winding mechanics, and solder potting for radial current redistribution and passive quench mitigation. The first prototype, P1 (planar elliptical, double-pancake) tested additive manufacture, sectional joining and baseline winding, achieving predicted fields at 77 K. P2 (non-planar, higher strain) was wound to 42 turns, energized at 30-40 K to produce expected magnetic fields, and studied thermal gradients and resistance at up to 4.5 kAt. Design evolution in P3 introduces concave geometry with dual double-pancakes, 200 turns, and approaches the 70 kAt target at 20 K. In parallel, sub-microhm lap joints have been developed. Together, these results de-risk manufacturing, cooling interfaces, quench management, and diagnostics, paving the way for full-size non-planar HTS stellarator coils for CSX.

physics.ins-det↗

arXiv2025

Reliable Clutter Suppression for Slow-Moving Weak Target Radar Detection

Reliable slow-moving weak target detection in complicated environments is challenging due to the masking effects from the surrounding strong reflectors. The traditional Moving Target Indication (MTI) may suppress the echoes from not only the static interference objects (IOs), but also the desired slow-moving weak target. According to the low-rank and sparse properties of the range-velocity maps across different radar scans, a novel clutter suppression scheme based on the Go decomposition (Godec) framework is proposed in this paper. The simulation results show that with the existence of masking effects, the target detection scheme based on Godec clutter suppression can reliably detect the slow-moving weak target, compared to the traditional MTI-based scheme. Besides, the time consumption comparison is conducted, demonstrating that the proposed solution is one that sacrifices time complexity in exchange for enhanced reliability. Additionally, the tradeoffs among the number of false alarm cells, the detection probability and the iteration times for convergence have been revealed, guiding parameter settings of the proposed solution in practical applications. Experiment validation is also conducted to verify the proposed solution, providing further insight into the scenarios where the solution is most applicable.

eess.SP↗

arXiv2025

Embedding Graphs as Euclidean kNN-Graphs

Let G = (V, E) be a directed graph on n vertices where each vertex has out-degree k. We say that G is kNN-realizable in d-dimensional Euclidean space if there exists a point set P = {p1, p2, ..., pn} in R^d along with a one-to-one mapping phi: V -> P such that for any u, v in V, u is an out-neighbor of v in G if and only if phi(u) is one of the k nearest neighbors of phi(v); we call the map phi a "kNN realization" of G in R^d. The kNN realization problem, which aims to compute such a mapping in R^d, is known to be NP-hard already for d = 2 and k = 1 (Eades and Whitesides, Theoretical Computer Science, 1996), and to the best of our knowledge, has not been studied in dimension d = 1. The main results of this paper are the following: (1) For any fixed dimension d >= 2, we can efficiently compute an embedding realizing at least a (1 - epsilon) fraction of G's edges, or conclude that G is not kNN-realizable in R^d. (2) For d = 1, we can decide in O(kn) time whether G is kNN-realizable and, if so, compute a realization in O(n^{2.5} * polylog(n)) time.

cs.CG↗

arXiv2022

Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data

Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has shown that mobility patterns across different waves exhibit varying relationships with fluctuations in infection cases. Therefore, to predict the multiwave pandemic, we propose a Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay of symptom-related web search frequency to capture the changes in public awareness across multiple waves. Our model combines GNN and LSTM to model the complex relationships among urban districts, inter-district mobility patterns, web search history, and future COVID-19 infections. We train our model to predict future pandemic outbreaks in the Tokyo area using its mobility and web search data from April 2020 to May 2021 across four pandemic waves collected by Yahoo Japan Corporation under strict privacy protection rules. Results demonstrate our model outperforms state-of-the-art baselines such as ST-GNN, MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3 layers and 10 hidden neurons), the proposed model enables public agencies to anticipate and prepare for future pandemic outbreaks.

cs.SI↗

arXiv2021

CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems

In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this paper, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios.

cs.IT↗

arXiv2000

Charge-Density-Wave induced modifications to the quasiparticle self-energy in 2H TaSe2

The self-energy of the photo-hole in 2H-TaSe2 is measured by angle-resolved photoemission spectroscopy (ARPES) as a function of binding energy and temperature. In the charge-density wave (CDW) state, a structure in the self- energy is detected at ~ 65 meV that can not be explained by electron-phonon scattering. A reduction in the scattering rates below this energy indicates the collapse of a major scattering channel with the formation of the CDW state, accompanying the appearance of a bosonic "mode" in the excitation spectrum of the system.

cond-mat.str-el↗