arXiv subjects

Renlong Jie

Publications and source records attributed to Renlong Jie.

11 recordsLinked to original sources

arXiv2026

Physics-Constrained Co-Optimization and Data-Driven Layer-Resolved Classification of a Hybrid CZT/PIPS Detector for Mixed Radiation Fields

Compact mixed-radiation instruments must preserve a low-mass charged-particle entrance while providing enough high-Z depth for photon sensitivity. We first compare two detector heads within a 40 x 20 x 10 mm^3 design budget. S1 places bare CdZnTe (CZT) and passivated implanted planar silicon (PIPS) branches side by side and estimates three rates. S2 adds 0.50 mm of CZT behind PIPS to estimate X/gamma, low-beta, high-beta, and alpha rates. A hard-constraint search combines photon attenuation and deposition, Hecht charge collection, charged-particle energy loss, solid angle, resolution budgeting, timing, and response-matrix conditioning. Feasible screening points exist inside the initial envelope, but a fresh transport/electronics assessment gives only 25.04-25.07 cps/(uSv/h) under the robust H*(10) convention, and every conservative electronics draw exceeds 2.5% FWHM. We therefore retain the more informative S2 observation structure, relax only the 10 mm package-depth constraint, divide the bare CZT into independently biased layers, and add a low-noise sum channel for spectroscopy plus layer-resolved gradient-boosted classification for mixed-field analysis. The extension campaign contains 2.40 million Geant4 11.4.1 histories over three depths, five transport seeds, and 14 particle/energy cases. The final 40 x 20 x 12.570 mm^3 head uses 7.870 mm of bare CZT in five layers at 180 V per layer. Under the application-scenario U95 electronics profile, its live-time-corrected 662 keV sensitivity is 32.804 cps/(uSv/h), its independent-sum resolution is 2.303% FWHM at the 95th percentile, and the propagated beta/alpha absolute-efficiency lower bounds are 35.679%/39.832%. Additional endpoint transport covers 20 keV-3 MeV photons, 3-7 MeV alpha particles, and 155 keV-3.5 MeV beta spectra. The extended design therefore passes the original detector-performance criteria in the U95 model.

cs.CE↗

arXiv2026

Capability-Aware Early-Stage Research Idea Evaluation

Predicting the outcomes of research ideas at their conceptual stage (i.e. before significant resources are committed) holds great potential for optimizing scientific resource allocation and research planning. While existing methods rely heavily on finished manuscripts or peer reviews, we propose a novel capability-aware framework that predicts paper acceptance and ratings using only author information and research ideas, without requiring full text or experimental results. Our approach integrates author information, (inferred) capability presentation, and research ideas through a three-way transformer architecture with flexible fusion mechanisms. We also introduce a two-stage architecture for learning the capability representation given the author information and idea. Experiments show that our method significantly outperform the single-way models by finetuning bert-base and bert-large, and the capability predicting significantly increase the predictive accuracy of the final model. The proposed method can be applied in both early-stage research outcome prediction and scientific resource allocation.

cs.CL↗

arXiv2025

Beyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences

While scholarly citations are pivotal for assessing academic impact, they often reflect systemic biases beyond research quality. This study examines a critical yet underexplored driver of citation disparities: authors' structural positions within scientific collaboration networks. Through a large-scale analysis of 17,942 papers from three top-tier machine learning conferences (NeurIPS, ICML, ICLR) published between 2005 and 2024, we quantify the influence of author centrality on citations. Methodologically, we advance the field by employing beta regression to model citation percentiles, which appropriately accounts for the bounded nature of citation data. We also propose a novel centrality metric, Harmonic Closeness with Temporal and Collaboration Count Decay (HCTCD), which incorporates temporal decay and collaboration intensity. Our results robustly demonstrate that long-term centrality exerts a significantly stronger effect on citation percentiles than short-term metrics, with closeness centrality and HCTCD emerging as the most potent predictors. Importantly, team-level centrality aggregation, particularly through exponentially weighted summation, explains citation variance more effectively than conventional rank-based approaches, underscoring the primacy of collective network connectivity over individual prominence. Integrating centrality features into machine learning models yields a 2.4% to 4.8% reduction in prediction error (MSE), confirming their value beyond content-based benchmarks. These findings challenge entrenched evaluation paradigms and advocate for network-aware assessment frameworks to mitigate structural inequities in scientific recognition.

cs.DL↗

arXiv2025

Learning to Collaborate: A Capability Vectors-based Architecture for Adaptive Human-AI Decision Making

Effective human-AI collaboration hinges on the ability to dynamically integrate the complementary strengths of human experts and AI models across diverse decision contexts. Context-aware weighted combination of human and AI outputs is a promising technique, which involves the optimization of combination weights based on capabilities of decision agents on a given task. However, existing approaches treat humans and AI as isolated entities, lacking a unified representation to model the heterogeneous capabilities of multiple decision agents. To address this gap, we propose a novel capability-aware architecture that models both human and AI decision-makers using learnable capability vectors. These vectors encode task-relevant competencies in a shared latent space and are used by a transformer-based weight generation module to produce instance-specific aggregation weights. Our framework supports flexible integration of confidence scores or one-hot decisions from a variable number of agents. We further introduce a learning-free baseline using optimized global weights for human-AI collaboration. Extensive experiments on image classification and hate speech detection tasks demonstrate that our approach outperforms state-of-the-art methods under various collaboration settings with both simulated and real human labels. The results highlight the robustness, scalability, and superior accuracy of our method, underscoring its potential for real-world applications.

cs.HC↗

arXiv2024

Prompt-Based Length Controlled Generation with Multiple Control Types

Large language models (LLMs) have attracted great attention given their strong performance on a wide range of NLP tasks. In practice, users often expect generated texts to fall within a specific length range, making length controlled generation an important topic, especially for GPT-style models. Existing length control methods mostly focus on a simple control type of "equal to" a target length. Different from them, we propose a prompt-based method to achieve length controlled generation under different control types with high accuracy. In particular, we adopt reinforcement learning (RL) and sample filtering with the reward signal given by rule-based reward models, which enhances the length control ability of models by rewarding outputs that follow certain control instructions. In addition, we introduce a standard prompt extractor to parse arbitrary users' input into standard control instructions. Experiments show that our method significantly improves the accuracy of prompt-based length control on popular summarization datasets like CNNDM and NYT under multiple control types. Moreover, both the standard prompt extractor and RL-tuned model show strong generalization to unseen control prompt templates.

cs.CL↗

arXiv2023

Unsupervised Extractive Summarization with Learnable Length Control Strategies

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and PACSUM rely on graph-based ranking on sentence centrality. However, this scorer can not be directly applied in end-to-end training, and the positional-related prior assumption is often needed for achieving good summaries. In addition, less attention is paid to length-controllable extractor, where users can decide to summarize texts under particular length constraint. This paper introduces an unsupervised extractive summarization model based on a siamese network, for which we develop a trainable bidirectional prediction objective between the selected summary and the original document. Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption. In addition, we introduce a differentiable length control module by approximating 0-1 knapsack solver for end-to-end length-controllable extracting. Experiments show that our unsupervised method largely outperforms the centrality-based baseline using a same sentence encoder. In terms of length control ability, via our trainable knapsack module, the performance consistently outperforms the strong baseline without utilizing end-to-end training. Human evaluation further evidences that our method performs the best among baselines in terms of relevance and consistency.

cs.AI↗

arXiv2023

Prompt-Based Length Controlled Generation with Reinforcement Learning

Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks. Length controlled generation of LLMs emerges as an important topic, which enables users to fully leverage the capability of LLMs in more real-world scenarios like generating a proper answer or essay of a desired length. In addition, the autoregressive generation in LLMs is extremely time-consuming, while the ability of controlling this generated length can reduce the inference cost by limiting the length. Therefore, we propose a prompt-based length control method to achieve high-accuracy length controlled generation. In particular, we adopt reinforcement learning with the reward signal given by either trainable or rule-based reward models, which further enhances the length-control ability of LLMs by rewarding outputs that follows pre-defined control instruction. To enable rule-based inference, we also introduce standard prompt extractor to collect the standard control information from users' input. Experiments show that our method significantly improves the accuracy of prompt-based length control for summarization task on popular datasets like CNNDM and NYT. Both the standard prompt extractor and the RL-tuned model have show strong generalization ability to unseen control prompt templates.

cs.CL↗

arXiv2023

Enhancing Coherence of Extractive Summarization with Multitask Learning

This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discriminator module. The coherent discriminator is trained online on the sentence vectors of the augmented textual input, thus improving its general ability of judging whether the input sentences are coherent. Meanwhile, we maximize the coherent scores from the coherent discriminator by updating the parameters of the summarizer. To make the extractive sentences trainable in a differentiable manner, we introduce two strategies, including pre-trained converting model (model-based) and converting matrix (MAT-based) that merge sentence representations. Experiments show that our proposed method significantly improves the proportion of consecutive sentences in the extracted summaries based on their positions in the original article (i.e., automatic sentence-level coherence metric), while the goodness in terms of other automatic metrics (i.e., Rouge scores and BertScores) are preserved. Human evaluation also evidences the improvement of coherence and consistency of the extracted summaries given by our method.

cs.CL↗

arXiv2021

Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures

This study aims at making the architecture search process more adaptive for one-shot or online training. It is extended from the existing study on differentiable neural architecture search, and we made the backbone architecture transformable rather than fixed during the training process. As is known, differentiable neural architecture search (DARTS) requires a pre-defined over-parameterized backbone architecture, while its size is to be determined manually. Also, in DARTS backbone, Hadamard product of two elements is not introduced, which exists in both LSTM and GRU cells for recurrent nets. This study introduces a growing mechanism for differentiable neural architecture search based on network morphism. It enables growing of the cell structures from small size towards large size ones with one-shot training. Two modes can be applied in integrating the growing and original pruning process. We also implement a recently proposed two-input backbone architecture for recurrent neural networks. Initial experimental results indicate that our approach and the two-input backbone structure can be quite effective compared with other baseline architectures including LSTM, in a variety of learning tasks including multi-variate time series forecasting and language modeling. On the other hand, we find that dynamic network transformation is promising in improving the efficiency of differentiable architecture search.

cs.AI↗

arXiv2021

Adaptive Hierarchical Hyper-gradient Descent

In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures. Meanwhile, we show the relationship between regularizing over-parameterized learning rates and building combinations of adaptive learning rates at different levels. The experiments on several network architectures, including feed-forward networks, LeNet-5 and ResNet-18/34, show that the proposed multi-level adaptive approach can outperform baseline adaptive methods in a variety of circumstances.

cs.LG↗

arXiv2020

Regularized Flexible Activation Function Combinations for Deep Neural Networks

Activation in deep neural networks is fundamental to achieving non-linear mappings. Traditional studies mainly focus on finding fixed activations for a particular set of learning tasks or model architectures. The research on flexible activation is quite limited in both designing philosophy and application scenarios. In this study, three principles of choosing flexible activation components are proposed and a general combined form of flexible activation functions is implemented. Based on this, a novel family of flexible activation functions that can replace sigmoid or tanh in LSTM cells are implemented, as well as a new family by combining ReLU and ELUs. Also, two new regularisation terms based on assumptions as prior knowledge are introduced. It has been shown that LSTM models with proposed flexible activations P-Sig-Ramp provide significant improvements in time series forecasting, while the proposed P-E2-ReLU achieves better and more stable performance on lossy image compression tasks with convolutional auto-encoders. In addition, the proposed regularization terms improve the convergence, performance and stability of the models with flexible activation functions.

cs.NE↗