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Researcher profile

Chi Xu

Chi Xu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionMachine LearningArtificial IntelligenceComputation and Languagecond-mat.mtrl-scics.CYmath.APphysics.app-phphysics.optics

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Published work

8 published item(s)

preprint2026arXiv

Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Direct Modulation of Electrically Pumped Coupled Microring Lasers

We demonstrate how the presence of gain-loss contrast between two coupled identical resonators can be used as a new degree of freedom to enhance the modulation frequency response of laser diodes. An electrically pumped microring laser system with a bending radius of 50 μm is fabricated on an InAlGaAs/InP MQW p-i-n structure. The room temperature continuous wave (CW) laser threshold current of the device is 27 mA. By adjusting the ratio between the injection current levels in the two coupled microrings, our experimental results clearly show a bandwidth improvement by up to 1.63 times the fundamental resonant frequency of the individual device. This matches well with our rate equation simulation model.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Asymptotic behavior of a quasilinear Keller--Segel system with signal-suppressed motility

This paper is concerned with the density-suppressed motility model: $u_{t}=Δ(\displaystyle\frac{u^m}{v^α}) +βuf(w), v_{t}=DΔv-v+u, w_{t}=Δw-uf(w)$ in a smoothly bounded convex domain $Ω\subset {\mathbb{R}}^2$, where $m>1$, $α>0, β>0$ and $D>0$ are parameters, the response function $f$ satisfies $f\in C^1([0,\infty)), f(0)=0, f(w)>0$ in $(0,\infty)$. This system describes the density-suppressed motility of Eeshcrichia coli cells in process of spatio-temporal pattern formation via so-called self-trapping mechanisms. Based on the duality argument, it is shown that for suitable large $D$ the problem admits at least one global weak solution $(u,v,w)$ which will asymptotically converge to the spatially uniform equilibrium $(\overline{u_0}+β\overline{w_0},\overline{u_0}+β\overline{w_0},0)$ with $\overline{u_0}=\frac1{|Ω|}\int_Ωu(x,0)dx $ and $\overline{w_0}=\frac1{|Ω|}\int_Ωw(x,0)dx $ in $L^\infty(Ω)$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

3D Human Shape Reconstruction from a Polarization Image

This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing surface normal of the objects of interest. Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images. A dedicated two-stage deep learning approach, SfP, is proposed: given a polarization image, stage one aims at inferring the fined-detailed body surface normal; stage two gears to reconstruct the 3D body shape of clothing details. Empirical evaluations on a synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate the qualitative and quantitative performance of our approach in estimating human poses and shapes. This indicates polarization camera is a promising alternative to the more conventional color or depth imaging for human shape estimation. Further, normal maps inferred from polarization imaging play a significant role in accurately recovering the body shapes of clothed people.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Critical behavior of the insulator-to-metal transition in Te-hyperdoped Si

Hyperdoping Si with chalcogens is a topic of great interest due to the strong sub-bandgap absorption exhibited by the resulting material, which can be exploited to develop broadband room-temperature infrared photodetectors using fully Si-compatible technology. Here, we report on the critical behavior of the impurity-driven insulator-to-metal transition in Te-hyperdoped Si layers fabricated via ion implantation followed by nanosecond pulsed-laser melting. Electrical transport measurements reveal an insulator-to-metal transition, which is also confirmed and understood by density functional theory calculations. We demonstrate that the metallic phase is governed by a power law dependence of the conductivity at temperatures below 25 K, whereas the conductivity in the insulating phase is well described by a variable-range hopping mechanism with a Coulomb gap at temperatures in the range of 2-50 K. These results show that the electron wave-function in the vicinity of the transition is strongly affected by the disorder and the electron-electron interaction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Direct-Current Generator Based on Dynamic Water-Semiconductor Junction with Polarized Water as Moving Dielectric Medium

There is a rising prospective in harvesting energy from water droplets, as microscale energy is required for the distributed sensors in the interconnected human society. However, achieving a sustainable direct-current generating device from water flow is rarely reported, and the quantum polarization principle of the water molecular remains uncovered. Herein, we propose a dynamic water-semiconductor junction with moving water sandwiched between two semiconductors as a moving dielectric medium, which outputs a sustainable direct-current voltage of 0.3 V and current of 0.64 uA with low internal resistance of 390 kilohm. The sustainable direct-current electricity is originating from the dynamic water polarization process in water-semiconductor junction, in which water molecules are continuously polarized and depolarized driven by the mechanical force and Fermi level difference, during the movement of the water on silicon. We further demonstrated an encapsulated portable power-generating device with simple structure and continuous direct-current voltage, which exhibits its promising potential application in the field of wearable electronic generators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network

Most recent existing aspect-term level sentiment analysis (ATSA) approaches combined various neural network models with delicately carved attention mechanisms built upon given aspect and context to generate refined sentence representations for better predictions. In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However, the annotated aspect term might be unavailable in real-world scenarios which may challenge the applicability of the existing methods. In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown. We access this goal by proposing a capsule network based model named CAPSAR. In CAPSAR, sentiment categories are denoted by capsules and aspect term information is injected into sentiment capsules through a sentiment-aspect reconstruction procedure during the training. As a result, coherent patterns between aspects and sentimental expressions are encapsulated by these sentiment capsules. Experiments on three widely used benchmarks demonstrate these patterns have potential in exploring aspect terms from test sentence when only feeding the sentence to the model. Meanwhile, the proposed CAPSAR can clearly outperform SOTA methods in standard ATSA tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Polarization Human Shape and Pose Dataset

Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest. Meanwhile, inspired by the recent breakthroughs in human shape estimation from a single color image, we attempt to investigate the new question of whether the geometric cues from polarization camera could be leveraged in estimating detailed human body shapes. This has led to the curation of Polarization Human Shape and Pose Dataset (PHSPD), our home-grown polarization image dataset of various human shapes and poses.

0 citations0 reviewsNo code linkedOpen access