BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Mo Li

Mo Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
physics.app-phphysics.opticscond-mat.mtrl-sciApplicationsArtificial IntelligenceComputation and LanguageComputer Visioncond-mat.mes-hallHuman-Computer InteractionMachine LearningMethodologyNetworking and Internet Architecturequant-ph

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
9works
0followers
13topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

9 published item(s)

preprint2026arXiv

AutoTour: Automatic Photo Tour Guide with Smartphones and LLMs

We present AutoTour, a system that enhances user exploration by automatically generating fine-grained landmark annotations and descriptive narratives for photos captured by users. The key idea of AutoTour is to fuse visual features extracted from photos with nearby geospatial features queried from open matching databases. Unlike existing tour applications that rely on pre-defined content or proprietary datasets, AutoTour leverages open and extensible data sources to provide scalable and context-aware photo-based guidance. To achieve this, we design a training-free pipeline that first extracts and filters relevant geospatial features around the user's GPS location. It then detects major landmarks in user photos through VLM-based feature detection and projects them into the horizontal spatial plane. A geometric matching algorithm aligns photo features with corresponding geospatial entities based on their estimated distance and direction. The matched features are subsequently grounded and annotated directly on the original photo, accompanied by large language model-generated textual and audio descriptions to provide an informative, tour-like experience. We demonstrate that AutoTour can deliver rich, interpretable annotations for both iconic and lesser-known landmarks, enabling a new form of interactive, context-aware exploration that bridges visual perception and geospatial understanding.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

OpenCompass: A Universal Evaluation Platform for Large Language Models

In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs). With the rapid iteration of LLMs, objective, quantitative, and comprehensive evaluation of their capabilities has become a critical link in advancing technological development. Currently, the mainstream static benchmark dataset-based evaluation methods face challenges such as the diversity of task types, inconsistent evaluation criteria, and fragmentation of data and processing workflows, making it difficult to efficiently conduct cross-domain and large-scale model evaluation. To address the aforementioned issues, this paper proposes and open-sources OpenCompass, a one-stop, scalable, and high-concurrency-supported general-purpose LLM evaluation platform. Adhering to the design philosophy of modularization and component decoupling, the platform boasts three core advantages: high compatibility, flexibility, and high concurrency. The core architecture of OpenCompass comprises five key components: the Configuration System, Task Partitioning Module, Execution and Scheduling Module, Task Execution Unit, and Result Visualization Module. Its workflow provides rule-based, LLM-as-a-Judge, and cascaded evaluators to adapt to the requirements of different task scenarios. Supporting mainstream benchmark datasets across multiple domains, including knowledge, reasoning, computation, science, language, code, etc., the platform offers a unified and efficient LLM evaluation tool for both academia and industry, facilitating the accurate identification of strengths and weaknesses of LLMs as well as their subsequent optimization.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

R-Estimation with Right-Censored Data

This paper considers the problem of directly generalizing the R-estimator under a linear model formulation with right-censored outcomes. We propose a natural generalization of the rank and corresponding estimating equation for the R-estimator in the case of the Wilcoxon (i.e., linear-in-ranks) score function, and show how it can respectively be exactly represented as members of the classes of estimating equations proposed in Ritov (1990) and Tsiatis (1990). We then establish analogous results for a large class of bounded nonlinear-in-ranks score functions. Asymptotics and variance estimation are obtained as straightforward consequences of these representation results. The self-consistent estimator of the residual distribution function, and the mid-cumulative distribution function (and, where needed, a generalization of it), play critical roles in these developments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Long-range transport of 2D excitons with acoustic waves

Excitons are elementary optical excitation in semiconductors. The ability to manipulate and transport these quasiparticles would enable excitonic circuits and devices for quantum photonic technologies. Recently, interlayer excitons in 2D semiconductors have emerged as a promising candidate for engineering excitonic devices due to their long lifetime, large exciton binding energy, and gate tunability. However, the charge-neutral nature of the excitons leads to weak response to the in-plane electric field and thus inhibits transport beyond the diffusion length. Here, we demonstrate the directional transport of interlayer excitons in bilayer WSe2 driven by the propagating potential traps induced by surface acoustic waves (SAW). We show that at 100 K, the SAW-driven excitonic transport is activated above a threshold acoustic power and reaches 20 mm, a distance at least ten times longer than the diffusion length and only limited by the device size. Temperature-dependent measurement reveals the transition from the diffusion-limited regime at low temperature to the acoustic field-driven regime at elevated temperature. Our work shows that acoustic waves are an effective, contact-free means to control exciton dynamics and transport, promising for realizing 2D materials-based excitonic devices such as exciton transistors, switches, and transducers up to room temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Programmable black phosphorus image sensor for broadband optoelectronic edge computing

Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors are uniquely advantageous in realizing such intelligent visionary sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT's electrical conductance and photoresponsivity can be locally or remotely programmed with high precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated multispectral infrared imaging and in-sensor computing with the black phosphorous optoelectronic sensor array can be scaled up to build a more complex visionary neural network, which will find many promising applications for distributed and remote multispectral sensing.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Attack-Aware Data Timestamping in Low-Power Synchronization-Free LoRaWAN

Low-power wide-area network technologies such as LoRaWAN are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This paper considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN's one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices' clocks at all times. However, we show that this approach is susceptible to a {\em frame delay attack} consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about $50,000\,\text{m}^2$. In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a $\mathsf{LoRaTS}$ gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa's chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa's default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. Extensive experiments show the effectiveness of our approach.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Black phosphorus van der Waals heterostructures light emitting diodes for mid-infrared silicon photonics

Light-emitting diodes (LEDs) based on III-V/II-VI materials have delivered a compelling performance in the mid-infrared (mid-IR) region, which enabled wide-ranging applications, including environmental monitoring, defense and medical diagnostics. Continued efforts are underway to realize on-chip sensors via heterogeneous integration of mid-IR emitters on a silicon photonic chip. But the uptake of such approach is limited by the high costs and interfacial strains, associated with the process of heterogeneous integrations. Here, the black phosphorus (BP)-based van der Waals (vdW) heterostructures are exploited as room temperature LEDs. The demonstrated devices can emit linearly polarized light, and their spectra cover the technologically important mid-IR atmospheric window (3-4 um). Additionally, the BP LEDs exhibit fast modulation speed as well as exceptional stability, and its peak extrinsic quantum efficiency (QE~0.9%) is comparable to the III-V/II-VI mid-IR LEDs. By leveraging the integrability of vdW heterostructures, we further demonstrate a silicon photonic waveguide-integrated BP LED. The reported hybrid platform holds great promise for mid-IR silicon photonics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Programmable Phase-change Metasurfaces on Waveguides for Multimode Photonic Convolutional Neural Network

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics, for machine learning algorithms such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on metasurface made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge-Sb-Te during phase transition to control the waveguide spatial modes with a very high precision of up 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate an optical convolutional neural network that can perform image processing and classification tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is very promising toward a large-scale photonic processor for high-throughput optical neural networks.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Nonvolatile electrically reconfigurable integrated photonic switch

Reconfigurability of photonic integrated circuits (PICs) has become increasingly important due to the growing demands for electronic-photonic systems on a chip driven by emerging applications, including neuromorphic computing, quantum information, and microwave photonics. Success in these fields usually requires highly scalable photonic switching units as essential building blocks. Current photonic switches, however, mainly rely on materials with weak, volatile thermo-optic or electro-optic modulation effects, resulting in a large footprint and high energy consumption. As a promising alternative, chalcogenide phase-change materials (PCMs) exhibit strong modulation in a static, self-holding fashion. Here, we demonstrate nonvolatile electrically reconfigurable photonic switches using PCM-clad silicon waveguides and microring resonators that are intrinsically compact and energy-efficient. With phase transitions actuated by in-situ silicon PIN heaters, near-zero additional loss and reversible switching with high endurance are obtained in a complementary metal-oxide-semiconductor (CMOS)-compatible process. Our work can potentially enable very large-scale general-purpose programmable integrated photonic processors.

0 citations0 reviewsNo code linkedOpen access