arXiv subjects

Chen Pan

Publications and source records attributed to Chen Pan.

At least 19 recordsLinked to original sources

arXiv2026

RADAgent: Automating RADseq Pipelines with Private Open-Source LLMs

Restriction site-associated DNA sequencing (RADseq) is a family of reduced-representation sequencing methods. It uses restriction enzymes to sample subsets of the genome, therefore allowing multiple individuals to be sequenced and genotyped together at low cost. RADseq is widely used for population genomics, evolutionary biology, marker discovery, and the development of SNP panels, but complete analysis still requires expert coordination across multiple tools and processing stages. Traditional RADseq analysis requires expert coordination of heterogeneous bioinformatics tools, intermediate artifacts, quality-control checks, and parameter-dependent decisions across multiple stages. Although individual steps can be scripted, executing the complete workflow still requires human oversight for state tracking, result validation, and failure handling. To automate this expert-driven coordination, we present RADAgent, which introduces a modular agentic method for reliable RADseq workflow execution with an open-weight LLM. RADAgent maps the natural boundaries of the RADseq workflow into 16 independently executable and verifiable agent modules and uses a model-independent runtime for bounded tool execution, on-disk verification, and failure recovery. We evaluate RADAgent on a real population-genomics dataset containing 476 samples from 19 populations. The results demonstrate that RADAgent reliably automates the complete RADseq workflow with an open-weight LLM while keeping genomic data and model execution within the local computing environment. The code is publicly available at https://github.com/wenlu-lab/RADAgent.

q-bio.GN↗

arXiv2026

Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction

Spatio-temporal graphs are widely used in modeling complex dynamic processes such as temporal forecasting, molecular dynamics, and healthcare monitoring. Recently, stringent privacy regulations such as GDPR and CCPA have introduced significant new challenges for existing spatio-temporal graph models, requiring complete unlearning of unauthorized data. Since each node in a spatio-temporal graph diffuses information globally across both spatial and temporal dimensions, existing unlearning methods primarily designed for static graphs and localized data removal cannot efficiently erase a single node without incurring costs nearly equivalent to full model retraining. To address this, we propose CallosumNet, a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure. CallosumNet makes two key technical contributions: (1) it reconstructs subgraphs using biologically-inspired virtual edges; and (2) it restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer. Empirical results on four diverse real-world datasets show that CallosumNet achieves complete unlearning while maintaining accuracy very close to the gold model. The code is publicly available at https://github.com/wenlu-lab/STGraphUnlearning.

cs.LG↗

arXiv2026

Room-temperature ferroelectrically switchable quantum geometry in few-layer WTe2 for complementary in-memory computing

Quantum geometry, describing the inherent geometric structure of electron wavefunctions in momentum space, transcends the traditional charge degree of freedom and provides a novel physical basis for information encoding and processing. The key to such new computing paradigms is the non-volatile electrical programming of quantum geometric states at room temperature, which, however, has not been established. Here, we demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2, which uniquely enables complementary convolutional processing. By employing the intrinsic coupling between ferroelectric polarization and quantum geometry in few-layer WTe2, we show that the second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and electrically switched in a nonvolatile and correlated manner. The switching is robust at room temperature for ~104 cycles and retention of ~105 s. Furthermore, leveraging the opposite switching behaviors of second- and third-order NLAHE at room temperature, we demonstrate complementary in-memory computing and implement a hardware-level complementary convolution kernel. This kernel overcomes the inherent directional specificity of conventional convolutional networks and achieves a texture recognition accuracy of 98%, thereby illustrating a viable pathway towards physics-native computing through exploiting exotic physics in quantum materials.

cond-mat.mtrl-sci↗

arXiv2026

A new proof of uniqueness of ground state for pseudo-relativistic Hartree equation

We present a new proof of the uniqueness of ground states for the pseudo-relativistic Hartree equation in the non-relativistic regime. The proof treats the Euler-Lagrange equation and the mass constraint as a single augmented system, with the Lagrange multiplier as an additional unknown. The key point is the invertibility of the augmented linearization at the non-relativistic ground state.

math.AP↗

arXiv2026

AERO: Adaptive and Efficient Runtime-Aware OTA Updates for Energy-Harvesting IoT

Energy-harvesting (EH) Internet of Things (IoT) devices operate under intermittent energy availability, which disrupts task execution and makes energy-intensive over-the-air (OTA) updates particularly challenging. Conventional OTA update mechanisms rely on reboots and incur significant overhead, rendering them unsuitable for intermittently powered systems. Recent live OTA update techniques reduce reboot overhead but still lack mechanisms to ensure consistency when updates interact with runtime execution. This paper presents AERO, an Adaptive and Efficient Runtime-Aware OTA update mechanism that integrates update tasks into the device's Directed Acyclic Graph (DAG) and schedules them alongside routine tasks under energy and timing constraints. By identifying update-affected execution regions and dynamically adjusting dependencies, AERO ensures consistent up date integration while adapting to intermittent energy availability. Experiments on representative workloads demonstrate improved update reliability and efficiency compared to existing live update approaches.

cs.AR↗

arXiv2025

Energy-Adaptive Checkpoint-Free Intermittent Inference for Low Power Energy Harvesting Systems

Deep neural network (DNN) inference in energy harvesting (EH) devices poses significant challenges due to resource constraints and frequent power interruptions. These power losses not only increase end-to-end latency, but also compromise inference consistency and accuracy, as existing checkpointing and restore mechanisms are prone to errors. Consequently, the quality of service (QoS) for DNN inference on EH devices is severely impacted. In this paper, we propose an energy-adaptive DNN inference mechanism capable of dynamically transitioning the model into a low-power mode by reducing computational complexity when harvested energy is limited. This approach ensures that end-to-end latency requirements are met. Additionally, to address the limitations of error-prone checkpoint-and-restore mechanisms, we introduce a checkpoint-free intermittent inference framework that ensures consistent, progress-preserving DNN inference during power failures in energy-harvesting systems.

cs.CE↗

arXiv2024

Energy-aware Incremental OTA Update for Flash-based Batteryless IoT Devices

Over-the-air (OTA) firmware updates are essential for updating and maintaining IoT devices, especially those batteryless devices reliant on energy harvesting power sources. Flash memory, favored for its low cost and high density, is extensively used for data storage in many IoT devices. However, due to its high energy demands for update operations, there is often insufficient energy for code updates. This paper proposes an incremental flash-based OTA update approach tailored for energy harvesting IoT devices, tackling the challenges brought by limited memory resources and fluctuating energy availability. Our approach is composed of three techniques: segmentbased update packet design, deferred flash segment writes, and checkpoint-free update resumption. Segment-based update packet design segments firmware updates into smaller packets, each tailored for specific memory segments, thereby minimizing unnecessary flash operations and conserving energy. Deferred flash segment writes accumulate packets in Static Random-Access Memory (SRAM) for collective processing, reducing the frequency of energy-intensive operations. Crucially, our checkpointfree update resumption ensures efficient recovery from power interruptions without significant energy cost on data backup. Through thorough experimental evaluation, we have observed that our approach significantly reduces the total energy consumed during OTA updates, and decreases the average total update time in energy harvesting environments.

cs.DC↗

arXiv2024

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coherence, which is crucial for aligning downstream decisions. Previous works mainly focus on ensuring coherence with some straightforward methods, e.g., aggregation from the forecasts of fine granularity to the coarse ones, and allocation from the coarse granularity to the fine ones. These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy. In this paper, we propose a novel granularity message-passing mechanism (GMP) that leverages temporal hierarchy information to improve forecasting performance and also utilizes an adaptive reconciliation (AR) strategy to maintain coherence without performance loss. Furthermore, we introduce an optimization module to achieve task-based targets while adhering to more real-world constraints. Experiments on real-world datasets demonstrate that our framework (GMP-AR) achieves superior performances on temporal hierarchical forecasting tasks compared to state-of-the-art methods. In addition, our framework has been successfully applied to a real-world task of payment traffic management in Alipay by integrating with the task-based optimization module.

cs.LG↗

arXiv2024

EasyTPP: Towards Open Benchmarking Temporal Point Processes

Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many powerful models in recent years, there hasn't been a central benchmark for these models and future research endeavors. This lack of standardization impedes researchers and practitioners from comparing methods and reproducing results, potentially slowing down progress in this field. In this paper, we present EasyTPP, the first central repository of research assets (e.g., data, models, evaluation programs, documentations) in the area of event sequence modeling. Our EasyTPP makes several unique contributions to this area: a unified interface of using existing datasets and adding new datasets; a wide range of evaluation programs that are easy to use and extend as well as facilitate reproducible research; implementations of popular neural TPPs, together with a rich library of modules by composing which one could quickly build complex models. All the data and implementation can be found at https://github.com/ant-research/EasyTemporalPointProcess. We will actively maintain this benchmark and welcome contributions from other researchers and practitioners. Our benchmark will help promote reproducible research in this field, thus accelerating research progress as well as making more significant real-world impacts.

cs.LG↗

arXiv2023

Moire synaptic transistor for homogeneous-architecture reservoir computing

Reservoir computing has been considered as a promising intelligent computing paradigm for effectively processing complex temporal information. Exploiting tunable and reproducible dynamics in the single electronic device have been desired to implement the reservoir and the readout layer of reservoir computing system. Two-dimensional moire material, with an artificial lattice constant many times larger than the atomic length scale, is one type of most studied artificial quantum materials in community of material science and condensed-matter physics over the past years. These materials are featured with gate-tunable periodic potential and electronic correlation, thus varying the electric field allows the electrons in the moire potential per unit cell to exhibit distinct and reproducible dynamics, showing great promise in robust reservoir computing. Here, we report that a moire synaptic transistor can be used to implement the reservoir computing system with a homogeneous reservoir-readout architecture. The synaptic transistor is fabricated based on a h-BN/bilayer graphene/h-BN moire heterostructure, exhibiting ferroelectricity-like hysteretic gate voltage dependence of resistance. Varying the magnitude of the gate voltage enables the moire transistor to be switched between long-term memory and short-term memory with nonlinear dynamics. By employing the short- and long-term memory as the reservoir nodes and weights of the readout layer, respectively, we construct a full-moire physical neural network and demonstrate that the classification accuracy of 90.8% can be achieved for the MNIST handwritten digit database. Our work would pave the way towards the development of neuromorphic computing based on the moire materials.

cond-mat.mes-hall↗

arXiv2023

Deep Optimal Timing Strategies for Time Series

Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data. This solution has plenty of valuable applications to reduce the operation costs. In this paper, we propose a mechanism that combines a probabilistic time series forecasting task and an optimal timing decision task as a first systematic attempt to tackle these practical problems with both solid theoretical foundation and real-world flexibility. Specifically, it generates the future paths of the underlying time series via probabilistic forecasting algorithms, which does not need a sophisticated mathematical dynamic model relying on strong prior knowledge as most other common practices. In order to find the optimal execution time, we formulate the decision task as an optimal stopping problem, and employ a recurrent neural network structure (RNN) to approximate the optimal times. Github repository: \url{github.com/ChenPopper/optimal_timing_TSF}.

cs.AI↗

arXiv2023

Parallel in-memory wireless computing

Parallel wireless digital communication with ultralow power consumption is critical for emerging edge technologies such as 5G and Internet of Things. However, the physical separation between digital computing units and analogue transmission units in traditional wireless technology leads to high power consumption. Here we report a parallel in-memory wireless computing scheme. The approach combines in-memory computing with wireless communication using memristive crossbar arrays. We show that the system can be used for the radio transmission of a binary stream of 480 bits with a bit error rate of 0. The in-memory wireless computing uses two orders of magnitude less power than conventional technology (based on digital-to-analogue and analogue-to-digital converters). We also show that the approach can be applied to acoustic and optical wireless communications

cs.AR↗

arXiv2023

Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction

Automatic bill payment is an important part of business operations in fintech companies. The practice of deduction was mainly based on the total amount or heuristic search by dividing the bill into smaller parts to deduct as much as possible. This article proposes an end-to-end approach of automatically learning the optimal deduction paths (deduction amount in order), which reduces the cost of manual path design and maximizes the amount of successful deduction. Specifically, in view of the large search space of the paths and the extreme sparsity of historical successful deduction records, we propose a deep hierarchical reinforcement learning approach which abstracts the action into a two-level hierarchical space: an upper agent that determines the number of steps of deductions each day and a lower agent that decides the amount of deduction at each step. In such a way, the action space is structured via prior knowledge and the exploration space is reduced. Moreover, the inherited information incompleteness of the business makes the environment just partially observable. To be precise, the deducted amounts indicate merely the lower bounds of the available account balance. To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business. In the world's largest electronic payment business, we have verified the effectiveness of this scheme offline and deployed it online to serve millions of users.

cs.LG↗

arXiv2023

A positive feedback method based on F-measure value for Salient Object Detection

The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to the development of SOD. Their primary research objective is to develop novel algorithms that can outperform state-of-the-art models, a task that is extremely difficult and time-consuming. In contrast, this paper proposes a positive feedback method based on F-measure value for SOD, aiming to improve the accuracy of saliency prediction using existing methods. Specifically, our proposed method takes an image to be detected and inputs it into several existing models to obtain their respective prediction maps. These prediction maps are then fed into our positive feedback method to generate the final prediction result, without the need for careful decoder design or model training. Moreover, our method is adaptive and can be implemented based on existing models without any restrictions. Experimental results on five publicly available datasets show that our proposed positive feedback method outperforms the latest 12 methods in five evaluation metrics for saliency map prediction. Additionally, we conducted a robustness experiment, which shows that when at least one good prediction result exists in the selected existing model, our proposed approach can ensure that the prediction result is not worse. Our approach achieves a prediction speed of 20 frames per second (FPS) when evaluated on a low configuration host and after removing the prediction time overhead of inserted models. These results highlight the effectiveness, efficiency, and robustness of our proposed approach for salient object detection.

cs.CV↗

arXiv2023

Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.

cs.CV↗

arXiv2023

Machine learning guided discovery of superconducting calcium borocarbides

Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary Ca-B-C compounds. Three new layered borocarbides (stable CaBC5 and metastable Ca2BC11 and CaB3C3) are predicted to be phonon-mediated superconductors at ambient pressure. The hexagonal CaB3C3 possesses the highest Tc of 26.05 K among the three compounds. The σ-bonging bands around the Fermi level account for the large electron-phonon coupling (λ = 0.980) of hexagonal CaB3C3. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-Tc superconductors.

cond-mat.mtrl-sci↗

arXiv2023

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraints

cs.LG↗

arXiv2022

Cascadable in-memory computing based on symmetric writing and read out

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and read-out operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a new symmetric write and read-out mechanism can be realized in perpendicular-anisotropy spin-orbit (PASO) quantum materials based on Fe3GeTe2 and WTe2. We demonstrate that field-free and deterministic reversal of the perpendicular magnetization can be achieved by employing unconventional charge to z-spin conversion. The resulting magnetic state can be readily probed with its intrinsic inverse process, i.e., z-spin to charge conversion. Using the PASO quantum material as a fundamental building block, we implement the functionally complete set of logic-in-memory operations and a more complex nonvolatile half-adder logic function. Our work highlights the potential of PASO quantum materials for the development of scalable energy-efficient and ultrafast spintronic computing.

cond-mat.mes-hall↗