arXiv subjects

Yibo Wu

Publications and source records attributed to Yibo Wu.

At least 19 recordsLinked to original sources

arXiv2026

SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code

Scientific figures are the interface through which research claims are inspected and reused, but final published panels rarely expose the data or plotting code that produced them. Recovering this hidden provenance from pixels is therefore underdetermined. We introduce SciFigure2Code, an AI-reconstructed benchmark that instead evaluates presentation recovery: generating editable Python programs that preserve how a scientific panel is arranged and read. Role-specialized Codex agents generate, execute, visually refine, and audit silver-standard presentation programs that capture geometry, visual hierarchy, encodings, annotations, and typography without claiming to recover original measurements or author source code. This reconstruction-and-audit protocol turns final published panels into auditable reference packages; the resulting resource contains 6,740 reviewed panels and SciFigureBench, a balanced 337-panel test set across 31 chart subtypes, five domains, and three complexity levels. Across 14 zero-shot models in image-only and caption-assisted settings, execution, multi-component layouts, axes, legends, and scientific labels remain weak. Claude Opus 4.7 achieves the highest image-only Overall score, Claude Opus 4.6 leads caption-assisted reconstruction, and two-stage plan-then-code prompting improves Overall for all four tested models. SciFigure2Code provides an auditable testbed for agents that construct editable, visually faithful scientific figure presentations.

cs.CV↗

arXiv2026

DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling

Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different resource usage. However, its wide adoption is hindered by a fundamental conflict with the static, sequential programming model of existing frameworks. Integrating these strategies requires invasive, model-specific code overhauls, representing an intractable engineering cost. This is further amplified by the high sensitivity of strategies to execution contexts (e.g., workload, model architecture, hardware), forcing developers to implement and maintain multiple specialized solutions. To address this, we propose DynaFlow, a framework that enables the transparent and flexible integration of intra-device parallelism by decoupling the logical model definition from the physical execution schedule. DynaFlow introduces a flexible frontend with annotations for graph partitioning and a programmable interface for defining custom intra-device parallelism strategies. Its efficient backend manages complex control/data-flow asynchronously, uses custom memory management to eliminate copy overheads, and preserves compatibility with optimizations like CUDA Graphs and TorchInductor. We demonstrate that DynaFlow can integrate representative parallelism strategies into 6 state-of-the-art ML systems with minimal code changes, achieving up to a 1.29x throughput improvement. DynaFlow is publicly available at https://github.com/uw-syfi/DynaFlow.

cs.DC↗

arXiv2025

SHIFT: Exploring the Boundary of RDMA Network Fault Tolerance

Under gang scheduling for large-scale distributed large language model (LLM) training, a single network anomaly can stall or abort an entire job. Current network fault tolerance mechanisms typically adopt a ``fallback and bypass'' approach within the switching fabric and at the access layer, tolerating in-network and access-layer failures. We explore whether RDMA fault tolerance can be extended to the cross-NIC level by failing over traffic to intra-host backup NICs. For the first time, we prove a fundamental Trilemma: it is impossible to have Cross-NIC RDMA failover that simultaneously preserves Exactly-Once Execution, Receiver-NIC Opacity, and a Zero-Copy datapath. Fortunately, we observe that dominant training frameworks (e.g., NCCL) rely on idempotent bulk transfers that tolerate relaxed memory ordering, as long as notification ordering is preserved. Leveraging this insight, we present SHIFT, a user-space RDMA layer that provides cross-NIC fault tolerance while preserving correct memory semantics. We implement SHIFT in \texttt{rdma-core} and evaluate it with PyTorch distributed training. Results show that SHIFT incurs negligible overhead during normal operation and successfully masks fatal NIC failures and link anomalies, allowing training to continue without costly restarts.

cs.NI↗

arXiv2025

Exiting National Anti-Poverty Campaign, Social Support, and Improved Mental Health

We study the psychological and social impacts of exiting a national anti-poverty campaign, leveraging China's phase-out of its national poverty assistance as a natural experiment. Using a regression discontinuity design, we find that exiting the national campaign improves mental wellbeing. These improvements are accompanied by stronger social and family ties -- such as greater perceived support and communication, while income and material conditions remain largely unchanged. Our findings offer insights into the design of policy exits and underscore the importance of incorporating measures that sustain community- and family-based support systems when implementing or ending assistance programs.

econ.GN↗

arXiv2024

Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions

An unsolved issue in widely used methods such as Support Vector Data Description (SVDD) and Small Sphere and Large Margin SVM (SSLM) for anomaly detection is their nonconvexity, which hampers the analysis of optimal solutions in a manner similar to SVMs and limits their applicability in large-scale scenarios. In this paper, we introduce a novel convex SSLM formulation which has been demonstrated to revert to a convex quadratic programming problem for hyperparameter values of interest. Leveraging the convexity of our method, we derive numerous results that are unattainable with traditional nonconvex approaches. We conduct a thorough analysis of how hyperparameters influence the optimal solution, pointing out scenarios where optimal solutions can be trivially found and identifying instances of ill-posedness. Most notably, we establish connections between our method and traditional approaches, providing a clear determination of when the optimal solution is unique--a task unachievable with traditional nonconvex methods. We also derive the nu-property to elucidate the interactions between hyperparameters and the fractions of support vectors and margin errors in both positive and negative classes.

cs.LG↗

arXiv2024

Location and Map-Assisted Wideband Phase and Time Calibration Between Distributed Antennas

Distributed massive multiple-input multiple-output networks utilize a large number of distributed access points (APs) to serve multiple user equipments (UEs), offering significant potential for both communication and localization. However, these networks require frequent phase and time calibration between distributed antennas due to oscillator phase drifts, crucial for reciprocity-based coherent beamforming and accurate localization. While this calibration is typically performed through bi-directional measurements between antennas, it can be simplified to unidirectional measurement under perfect knowledge of antenna locations. This paper extends a recent phase calibration narrowband line-of-sight (LoS) model to a phase and time calibration wideband orthogonal frequency division multiplexing model, including both LoS and reflection paths and allowing for joint phase and time calibrations. We explore different scenarios, considering whether or not prior knowledge of antenna locations and the map is available. For each case, we introduce a practical maximum likelihood estimator and conduct Cramer-Rao lower bound (CRLB) analyses to benchmark performance. Simulations validate our estimators against the CRLB in these scenarios.

eess.SP↗

arXiv2024

Uplink Cell-Free Massive MIMO OFDM with Phase Noise-Aware Channel Estimation: Separate and Shared Local Oscillators

Cell-free massive multiple-input multiple-output (mMIMO) networks enhance coverage and spectral efficiency (SE) by distributing antennas across access points (APs) with phase coherence between APs. However, the use of cost-efficient local oscillators (LOs) introduces phase noise (PN) that compromises phase coherence, even with centralized processing. Sharing an LO across APs can reduce costs in specific configurations but cause correlated PN between APs, leading to correlated interference that affects centralized combining. This can be improved by exploiting the PN correlation in channel estimation. This paper presents an uplink orthogonal frequency division multiplexing (OFDM) signal model for PN-impaired cell-free mMIMO, addressing gaps in single-carrier signal models. We evaluate mismatches from applying single-carrier methods to OFDM systems, showing how they underestimate the impact of PN and produce over-optimistic achievable SE predictions. Based on our OFDM signal model, we propose two PN-aware channel and common phase error estimators: a distributed estimator for uncorrelated PN with separate LOs and a centralized estimator with shared LOs. We introduce a deep learning-based channel estimator to enhance the performance and reduce the number of iterations of the centralized estimator. The simulation results show that the distributed estimator outperforms mismatched estimators with separate LOs, whereas the centralized estimator enhances distributed estimators with shared LOs.

eess.SP↗

arXiv2024

Propulsion Contribution from Individual Filament in Flagellar Bundle

Flagellated microorganisms overcome the low-Reynolds-number time reversibility by rotating helical flagella. For peritrichous bacteria, such as Escherichia coli, the randomly distributed flagellar filaments align along the same direction to form a bundle, facilitating complex locomotive strategies. To understand the process of flagella bundling, especially the propulsion force, we develop a multi-functional macroscopic experimental system and employ advanced numerical simulations for verification. Flagella arrangements and phase differences between helices are investigated, revealing the variation in propulsion contribution from the individual helix. Numerically, we build a time-dependent model to match the bundling process and study the influence of hydrodynamic interactions. Surprisingly, it is found that the total propulsion generated by a bundle of two filaments is constant at various phase differences between the helices. However, the difference between the propulsion from each helix is significantly affected by the phase difference, and only one of the helices is responsible for the total propulsion at a phase difference equals to pi. Through our experimental and computational results, we provide a new model considering the propulsion contribution of each filament to better understand microbial locomotion mechanisms, especially on the wobbling behavior of the cell. Our work also sheds light on the design and control of artificial microswimmers.

cond-mat.soft↗

arXiv2024

Integrated Communication, Localization, and Sensing in 6G D-MIMO Networks

Future generations of mobile networks call for concurrent sensing and communication functionalities in the same hardware and/or spectrum. Compared to communication, sensing services often suffer from limited coverage, due to the high path loss of the reflected signal and the increased infrastructure requirements. To provide a more uniform quality of service, distributed multiple input multiple output (D-MIMO) systems deploy a large number of distributed nodes and efficiently control them, making distributed integrated sensing and communications (ISAC) possible. In this paper, we investigate ISAC in D-MIMO through the lens of different design architectures and deployments, revealing both conflicts and synergies. In addition, simulation and demonstration results reveal both opportunities and challenges towards the implementation of ISAC in D-MIMO.

cs.IT↗

arXiv2024

Time vs. Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of PAs, frequency domain (FD) DPD has a complexity scaling with the number of user equipments (UEs). In this work, we provide a comprehensive analysis of different state-of-the-art TD and FD-DPD schemes in terms of complexity and linearization performance in both rich scattering and line-of-sight (LOS) channels and with antenna crosstalk. We propose a novel low-complexity FD convolutional neural network (CNN) DPD. We also propose a learning algorithm for any FD-DPDs with differentiable structure. The analysis shows that FD-DPD, particularly the proposed FD CNN, is preferable in LOS scenarios with few users, due to the favorable trade-off between complexity and linearization performance. On the other hand, in scenarios with more users or isotropic scattering channels, significant intermodulation distortions among UEs degrade FD-DPD performance, making TD-DPD more suitable. The proposed learning algorithm allows FD-DPDs to outperform TD-DPD optimized by indirect learning architecture under antenna crosstalk.

eess.SP↗

arXiv2023

Impact of Phase Noise on Uplink Cell-Free Massive MIMO OFDM

Cell-Free massive MIMO networks provide huge power gains and resolve inter-cell interference by coherent processing over a massive number of distributed instead of co-located antennas in access points (APs). Cost-efficient hardware is preferred but imperfect local oscillators in both APs and users introduce multiplicative phase noise (PN), which affects the phase coherence between APs and users even with centralized processing. In this paper, we first formulate the system model of a PN-impaired uplink Cell-Free massive MIMO orthogonal frequency division multiplexing network, and then propose a PN-aware linear minimum mean square error channel estimator and derive a PN-impaired uplink spectral efficiency expression. Numerical results are used to quantify the spectral efficiency gain of the proposed channel estimator over alternative schemes for different receiving combiners.

eess.SP↗

arXiv2023

Blind Channel Equalization Using Vector-Quantized Variational Autoencoders

State-of-the-art high-spectral-efficiency communication systems employ high-order modulation formats coupled with high symbol rates to accommodate the ever-growing demand for data rate-hungry applications. However, such systems are more vulnerable to linear and nonlinear transmission impairments, and it is important to mitigate the performance loss via digital signal processing. In this paper, we propose a novel machine learning approach for blind channel equalization and estimation using the vector quantized (VQ) \ac{VAE} framework. The proposed approach generalizes the applicability of the conventional \ac{VAE}-based equalizer to nonlinear systems employing high-order modulation formats by introducing a codebook component and an associated novel loss function. We evaluate the performance of the proposed method over a linear additive white Gaussian noise channel with intersymbol interference and two nonlinear scenarios. Simulation results show that the proposed method can achieve similar performance as a data aided equalizer using the \acf{MMSE} criterion, and outperforms the blind\ac{CMA} and the \ac{VAE}-based channel equalizer. Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.

eess.SP↗

arXiv2022

Spatial Signal Design for Positioning via End-to-End Learning

This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user-equipment(UE) position with multiple base-stations (BSs) and demonstrate that end-to-end learning can match model-based design, both for angle of departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.

eess.SP↗

arXiv2022

MCRB-based Performance Analysis of 6G Localization under Hardware Impairments

Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems. User localization is achieved based on delay and/or angle estimation using uplink or downlink pilot signals. However, hardware impairments (HWIs) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies where HWIs are less severe, modeling and analysis efforts are needed for 6G to evaluate the localization degradation due to HWIs. In this work, we model various types of impairments and conduct a misspecified Cramér-Rao bound analysis to evaluate the HWI-induced performance loss. Simulation results with different types of HWIs show that each HWI leads to a different level of degradation in angle and delay estimation performance.

eess.SP↗

arXiv2022

Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).

eess.SP↗

arXiv2021

Iterated Posterior Linearization PMB Filter for 5G SLAM

5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.

eess.SP↗

arXiv2021

Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning

We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between downsampled messages at the receiver side. The algorithm does not require any knowledge about the underlying hardware or channel. For a generalized memory polynomial power amplifier and additive white Gaussian noise channel, we show that the proposed algorithm achieves performance improvements in terms of symbol error rate compared with an indirect learning architecture even when the latter is coupled with a full sampling rate ADC in the feedback path. Furthermore, it maintains a satisfactory adjacent channel power ratio.

eess.SP↗

arXiv2021

End-to-End Learning for Integrated Sensing and Communication

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.

eess.SP↗