arXiv subjects

Chen Peng

Publications and source records attributed to Chen Peng.

At least 37 records · Page 2Linked to original sources

arXiv2025

Strawberry Robotic Operation Interface: An Open-Source Device for Collecting Dexterous Manipulation Data in Robotic Strawberry Farming

The strawberry farming is labor-intensive, particularly in tasks requiring dexterous manipulation such as picking occluded strawberries. To address this challenge, we present the Strawberry Robotic Operation Interface (SROI), an open-source device designed for collecting dexterous manipulation data in robotic strawberry farming. The SROI features a handheld unit with a modular end effector, a stereo robotic camera, enabling the easy collection of demonstration data in field environments. A data post-processing pipeline is introduced to extract spatial trajectories and gripper states from the collected data. Additionally, we release an open-source dataset of strawberry picking demonstrations to facilitate research in dexterous robotic manipulation. The SROI represents a step toward automating complex strawberry farming tasks, reducing reliance on manual labor.

cs.RO↗

arXiv2025

Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments

Autonomous agricultural vehicles (AAVs), including field robots and autonomous tractors, are becoming essential in modern farming by improving efficiency and reducing labor costs. A critical task in AAV operations is headland turning between crop rows. This task is challenging in orchards with limited headland space, irregular boundaries, operational constraints, and static obstacles. While traditional trajectory planning methods work well in arable farming, they often fail in cluttered orchard environments. This letter presents a novel trajectory planner that enhances the safety and efficiency of AAV headland maneuvers, leveraging advancements in autonomous driving. Our approach includes an efficient front-end algorithm and a high-performance back-end optimization. Applied to vehicles with various implements, it outperforms state-of-the-art methods in both standard and challenging orchard fields. This work bridges agricultural and autonomous driving technologies, facilitating a broader adoption of AAVs in complex orchards.

cs.RO↗

arXiv2025

Scaling and data collapse of two-dimensional random singlet state in magnetic field

Quenched randomness strongly affects properties of magnetic materials. Two-dimensional (2D) random singlet (RS) states emerge in random $J$-$Q$ model by destroying valence bond solid order with spatial randomness. We examine the 2D RS state in magnetic field with quantum Monte Carlo simulations. The magnetization and susceptibilities show power-law scaling with magnetic field at low temperature. Moreover, they show one-parameter scaling behavior with $B/T$, and the scaling functions are remarkably consistent with a phenomenological model of random spin pairs with a singular distribution of interactions. These universal scaling functions can be used to diagnose 2D RS states in experiments.

cond-mat.str-el↗

arXiv2024

Dynamical scaling behavior of the two-dimensional random singlet state in the random $Q$ model

In this work, we study the scaling relation of energy and length scales in the 2D random-singlet (RS) state of the random $Q$ model. To investigate the intrinsic energy scale of the spinon subsystem arising from the model, we develop a constrained subspace update algorithm within the framework of the stochastic series expansion method (SSE) to extract the singlet-triplet gap of the system. The 2D RS state exhibits scaling behavior similar to the infinite randomness fixed point (IRFP), at least within the length scales that we simulate. Furthermore, by rescaling the system size according to the strength of randomness, we observe that the data for the excitation gap and the width of the gap distribution collapse onto a single curve. This implies that the model with different strengths of randomness may correspond to the same fixed point.

cond-mat.str-el↗

arXiv2024

Recall: Empowering Multimodal Embedding for Edge Devices

Human memory is inherently prone to forgetting. To address this, multimodal embedding models have been introduced, which transform diverse real-world data into a unified embedding space. These embeddings can be retrieved efficiently, aiding mobile users in recalling past information. However, as model complexity grows, so do its resource demands, leading to reduced throughput and heavy computational requirements that limit mobile device implementation. In this paper, we introduce RECALL, a novel on-device multimodal embedding system optimized for resource-limited mobile environments. RECALL achieves high-throughput, accurate retrieval by generating coarse-grained embeddings and leveraging query-based filtering for refined retrieval. Experimental results demonstrate that RECALL delivers high-quality embeddings with superior throughput, all while operating unobtrusively with minimal memory and energy consumption.

cs.IR↗

arXiv2024

Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approach

Breast cancer presents a significant healthcare challenge globally, demanding precise diagnostics and effective treatment strategies, where histopathological examination of Hematoxylin and Eosin (H&E) stained tissue sections plays a central role. Despite its importance, evaluating specific biomarkers like Human Epidermal Growth Factor Receptor 2 (HER2) for personalized treatment remains constrained by the resource-intensive nature of Immunohistochemistry (IHC). Recent strides in deep learning, particularly in image-to-image translation, offer promise in synthesizing IHC-HER2 slides from H\&E stained slides. However, existing methodologies encounter challenges, including managing multiple magnifications in pathology images and insufficient focus on crucial information during translation. To address these issues, we propose a novel model integrating attention mechanisms and multi-magnification information processing. Our model employs a multi-magnification processing strategy to extract and utilize information from various magnifications within pathology images, facilitating robust image translation. Additionally, an attention module within the generative network prioritizes critical information for image distribution translation while minimizing less pertinent details. Rigorous testing on a publicly available breast cancer dataset demonstrates superior performance compared to existing methods, establishing our model as a state-of-the-art solution in advancing pathology image translation from H&E to IHC staining.

eess.IV↗

arXiv2024

Optimization-Based Motion Planning for Autonomous Agricultural Vehicles Turning in Constrained Headlands

Headland maneuvering is a crucial aspect of unmanned field operations for autonomous agricultural vehicles (AAVs). While motion planning for headland turning in open fields has been extensively studied and integrated into commercial auto-guidance systems, the existing methods primarily address scenarios with ample headland space and thus may not work in more constrained headland geometries. Commercial orchards often contain narrow and irregularly shaped headlands, which may include static obstacles,rendering the task of planning a smooth and collision-free turning trajectory difficult. To address this challenge, we propose an optimization-based motion planning algorithm for headland turning under geometrical constraints imposed by field geometry and obstacles.

cs.RO↗

arXiv2024

Universal entanglement correction induced by relevant deformations at the quantum critical point

Local relevant deformations are important tool to study universal properties of quantum critical points. We investigate the effect of small relevant deformations on the bi-partite entanglement entropy at the quantum critical points. Within the quantum critical region, a universal power-law correction in the entanglement entropy induced by the relevant operator is found in both one- and two-dimensional critical lattice models. The exponent of the power-law correction term is determined by the scaling dimension of the relevant operator. Based on numerical simulations and scaling theory argument, it is conjectured that such a universal power-law correction in the entanglement entropy is universal for Lorentz invariant quantum critical points. Without Lorentz invariance, it is found the exponent in the power-law correction term does not fit in with the scaling argument in models with a dynamical exponent z=2 in two dimension. This may be because the relevant operator added in the lattice model corresponds to complicated operators in the corresponding conformal field theory. Our study provides a different perspective to extract universal information of quantum critical points. We expect it would be useful to detect unique properties of topological quantum phase transitions.

cond-mat.str-el↗

arXiv2024

MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with its Application for Colonic Polyp Image Segmentation

Biomedical image segmentation is a very important part in disease diagnosis. The term "colonic polyps" refers to polypoid lesions that occur on the surface of the colonic mucosa within the intestinal lumen. In clinical practice, early detection of polyps is conducted through colonoscopy examinations and biomedical image processing. Therefore, the accurate polyp image segmentation is of great significance in colonoscopy examinations. Convolutional Neural Network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer utilizes a self-attention mechanism, which essentially assigns different importance weights to each piece of information, thus achieving high computational efficiency during segmentation. However, a potential drawback is the risk of information loss. In the study reported in this paper, based on the well-known hybridization principle, we proposed a method to combine CNN and Transformer to retain the strengths of both, and we applied this method to build a system called MugenNet for colonic polyp image segmentation. We conducted a comprehensive experiment to compare MugenNet with other CNN models on five publicly available datasets. The ablation experiment on MugentNet was conducted as well. The experimental results show that MugenNet achieves significantly higher processing speed and accuracy compared with CNN alone. The generalized implication with our work is a method to optimally combine two complimentary methods of machine learning.

eess.IV↗

arXiv2023

Interference-Constrained Scheduling of a Cognitive Multi-hop Underwater Acoustic Network

This paper investigates optimal scheduling for a cognitive multi-hop underwater acoustic network with a primary user interference constraint. The network consists of primary and secondary users, with multi-hop transmission adopted for both user types to provide reliable communications. Critical characteristics of underwater acoustic channels, including significant propagation delay, distance-and-frequency dependent attenuation, half-duplex modem, and inter-hop interference, are taken into account in the design and analysis. In particular, time-slot allocation is found to be more effective than frequency-slot allocation due to the underwater channel model. The goal of the network scheduling problem is to maximize the end-to-end throughput of the overall system while limiting the throughput loss of primary users. Both centralized and decentralized approaches are considered. Partially Observable Markov Decision Processes (POMDP) framework is applied to formulate the optimization problem, and an optimal dynamic programming algorithm is derived. However, the optimal dynamic programming solution is computationally intractable. Key properties are shown for the objective function, enabling the design of approximate schemes with significant complexity reduction. Numerical results show that the proposed schemes significantly increase system throughput while maintaining the primary throughput loss constraint. Under certain traffic conditions, the throughput gain over frequency-slot allocation schemes can be as high as 50%.

eess.SY↗

arXiv2023

MSVQ: Self-Supervised Learning with Multiple Sample Views and Queues

Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer from the problem of false negative samples. Inspired by the mean shift for self-supervised learning, we propose a new simple framework, namely Multiple Sample Views and Queues (MSVQ). We jointly construct three soft labels on-the-fly by utilizing two complementary and symmetric approaches: multiple augmented positive views and two momentum encoders that generate various semantic features for negative samples. Two teacher networks perform similarity relationship calculations with negative samples and then transfer this knowledge to the student network. Let the student network mimic the similarity relationships between the samples, thus giving the student network a more flexible ability to identify false negative samples in the dataset. The classification results on four benchmark image datasets demonstrate the high effectiveness and efficiency of our approach compared to some classical methods. Source code and pretrained models are available \href{https://github.com/pc-cp/MSVQ}{here}.

cs.CV↗

arXiv2023

MNN: Mixed Nearest-Neighbors for Self-Supervised Learning

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the problem of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is an objective but often overlooked challenge due to the query of neighbor samples without supervision information. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the influence of neighbor samples on the semantics of positive samples through an intuitive weighting approach and image mixture operations. The results demonstrate that MNN exhibits exceptional generalization performance and training efficiency on four benchmark datasets.

cs.CV↗

arXiv2023

Is Silent eHMI Enough? A Passenger-Centric Study on Effective eHMI for Autonomous Personal Mobility Vehicles in the Field

Autonomous Personal Mobility Vehicle (APMV) is a miniaturized autonomous vehicle designed to provide short-distance mobility to everyone in pedestrian-rich environments. By the characteristic of the open design, passengers on the APMV are exposed to the communication between the eHMI deployed on APMVs and pedestrians. Therefore, to ensure an optimal passenger experience, eHMI designs for APMVs must consider the potential impact of APMV-pedestrian communications on passengers' subjective feelings. To this end, this study discussed three external human-machine interface (eHMI) designs, i.e., 1) graphical user interface (GUI)-based eHMI with text message (eHMI-T), 2) multimodal user interface (MUI)-based eHMI with neutral voice (eHMI-NV), and 3) MUI-based eHMI with affective voice (eHMI-AV), from the perspective of APMV passengers in the communication between APMV and pedestrians. In the riding field experiment (N=24), we found that eHMI-T may be less suitable for APMVs. This conclusion was drawn based on passengers' feedback, as they expressed an awkward feeling during the "silent time" when the eHMI-T provided information only to pedestrians but not to passengers. Additionally, these two MUI-based eHMIs with voice cues had their own advantages, i.e., eHMI-NV has an advantage in pragmatic quality, while eHMI-AV has an advantage in hedonic quality. The study also highlights the necessity of considering passengers' personalities when desig

cs.HC↗

arXiv2023

Integrative Modeling and Analysis of the Interplay Between Epidemic and News Propagation Processes

The COVID-19 pandemic has witnessed the role of online social networks (OSNs) in the spread of infectious diseases. The rise in severity of the epidemic augments the need for proper guidelines, but also promotes the propagation of fake news-items. The popularity of a news-item can reshape the public health behaviors and affect the epidemic processes. There is a clear inter-dependency between the epidemic process and the spreading of news-items. This work creates an integrative framework to understand the interplay. We first develop a population-dependent `saturated branching process' to continually track the propagation of trending news-items on OSNs. A two-time scale dynamical system is obtained by integrating the news-propagation model with SIRS epidemic model, to analyze the holistic system. It is observed that a pattern of periodic infections emerges under a linear behavioral influence, which explains the waves of infection and reinfection that we have experienced in the pandemic. We use numerical experiments to corroborate the results and use Twitter and COVID-19 data-sets to recreate the historical infection curve using the integrative model.

math.DS↗

arXiv2023

XFL: A High Performace, Lightweighted Federated Learning Framework

This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryption, differential privacy, secure multi-party computation and other security technologies ensuring no leakage of data. XFL provides an abundant algorithms library, integrating a large number of pre-built, secure and outstanding federated learning algorithms, covering both the horizontally and vertically federated learning scenarios. Numerical experiments have shown the prominent performace of these algorithms. XFL builds a concise configuration interfaces with presettings for all federation algorithms, and supports the rapid deployment via docker containers.Therefore, we believe XFL is the most user-friendly and easy-to-develop federated learning framework. XFL is open-sourced, and both the code and documents are available at https://github.com/paritybit-ai/XFL.

cs.LG↗

arXiv2023

Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations

Machine learning systems have been extensively used as auxiliary tools in domains that require critical decision-making, such as healthcare and criminal justice. The explainability of decisions is crucial for users to develop trust on these systems. In recent years, the globally-consistent rule-based summary-explanation and its max-support (MS) problem have been proposed, which can provide explanations for particular decisions along with useful statistics of the dataset. However, globally-consistent summary-explanations with limited complexity typically have small supports, if there are any. In this paper, we propose a relaxed version of summary-explanation, i.e., the $q$-consistent summary-explanation, which aims to achieve greater support at the cost of slightly lower consistency. The challenge is that the max-support problem of $q$-consistent summary-explanation (MSqC) is much more complex than the original MS problem, resulting in over-extended solution time using standard branch-and-bound solvers. To improve the solution time efficiency, this paper proposes the weighted column sampling~(WCS) method based on solving smaller problems by sampling variables according to their simplified increase support (SIS) values. Experiments verify that solving MSqC with the proposed SIS-based WCS method is not only more scalable in efficiency, but also yields solutions with greater support and better global extrapolation effectiveness.

cs.AI↗

arXiv2022

Persistent Corner Spin Mode at the Quantum Critical Point of a Plaquette Heisenberg Model

Gapless edge states are the hallmark of a large class of topological states of matter. Recently, intensive research has been devoted to understanding the physical properties of the edge states at the quantum phase transitions of the bulk topological states. A higher-order symmetry-protected topological state is realized in a plaquette Heisenberg model on the square lattice. In its disordered phase, the lattice with an open boundary hosts either dangling corner states with spin-$1/2$ degeneracy characterizing the topological phase, or nondangling corner states without degeneracy, which depends on the bond configuration near the corners. In this work, we study the critical behavior of these corner states at the quantum critical point (QCP), and find that the spin-$1/2$ corner state induces a new universality class of the corner critical behavior, which is distinct from the ordinary transition of the nondangling corners. In particular, we find that the dangling spin-$1/2$ corner state persists at the QCP despite its coupling to the critical spin fluctuations in the bulk. This shows the robustness of the corner state of the higher-order topological state.

cond-mat.str-el↗

arXiv2022

Thermal load models for the static design of steel-concrete composite girders

Although the recommended temperature gradient models of composite girders are considered in current specifications for classifying temperature effects in various countries, they are not appropriate enough for static design. Moreover, existing national standards cannot explain the mechanism of the thermal effect on the bridge. To further investigate thermal load models of composite girders, this work proposed a decomposing method for vertical nonlinear temperature gradients based on thermal effects and a calculating method for the thermal stress of composite girders. Equivalent temperature (equivalent uniform temperature, equivalent linear temperature, and equivalent nonlinear temperature), temperature difference, and cyclic equivalent uniform temperature are analyzed to reflect the characteristics of thermal effect in composite girders. The stand values of temperature differences and equivalent temperature with a 50-year return period were investigated via probabilistic statistical analysis. Additionally, two vertical thermal load models (VTLM 1 and VTLM 2) were set up to facilitate the design and applied to stress analysis. The result demonstrates that the proposed thermal load model is more suitable than the Chinese Specification for calculating the thermal effects of composite girders.

physics.app-ph↗