arXiv subjects

Nan Wu

Publications and source records attributed to Nan Wu.

At least 55 records · Page 3Linked to original sources

arXiv2024

A Benchmark on Directed Graph Representation Learning in Hardware Designs

To keep pace with the rapid advancements in design complexity within modern computing systems, directed graph representation learning (DGRL) has become crucial, particularly for encoding circuit netlists, computational graphs, and developing surrogate models for hardware performance prediction. However, DGRL remains relatively unexplored, especially in the hardware domain, mainly due to the lack of comprehensive and user-friendly benchmarks. This study presents a novel benchmark comprising five hardware design datasets and 13 prediction tasks spanning various levels of circuit abstraction. We evaluate 21 DGRL models, employing diverse graph neural networks and graph transformers (GTs) as backbones, enhanced by positional encodings (PEs) tailored for directed graphs. Our results highlight that bidirected (BI) message passing neural networks (MPNNs) and robust PEs significantly enhance model performance. Notably, the top-performing models include PE-enhanced GTs interleaved with BI-MPNN layers and BI-Graph Isomorphism Network, both surpassing baselines across the 13 tasks. Additionally, our investigation into out-of-distribution (OOD) performance emphasizes the urgent need to improve OOD generalization in DGRL models. This benchmark, implemented with a modular codebase, streamlines the evaluation of DGRL models for both hardware and ML practitioners

cs.LG↗

arXiv2024

DSparsE: Dynamic Sparse Embedding for Knowledge Graph Completion

Addressing the incompleteness problem in knowledge graph remains a significant challenge. Current knowledge graph completion methods have their limitations. For example, ComDensE is prone to overfitting and suffers from the degradation with the increase of network depth while InteractE has the limitations in feature interaction and interpretability. To this end, we propose a new method called dynamic sparse embedding (DSparsE) for knowledge graph completion. The proposed model embeds the input entity-relation pairs by a shallow encoder composed of a dynamic layer and a relation-aware layer. Subsequently, the concatenated output of the dynamic layer and relation-aware layer is passed through a projection layer and a deep decoder with residual connection structure. This model ensures the network robustness and maintains the capability of feature extraction. Furthermore, the conventional dense layers are replaced by randomly initialized sparse connection layers in the proposed method, which can mitigate the model overfitting. Finally, comprehensive experiments are conducted on the datasets of FB15k-237, WN18RR and YAGO3-10. It was demonstrated that the proposed method achieves the state-of-the-art performance in terms of Hits@1 compared to the existing baseline approaches. An ablation study is performed to examine the effects of the dynamic layer and relation-aware layer, where the combined model achieves the best performance.

cs.IR↗

arXiv2024

A First Look at Immersive Telepresence on Apple Vision Pro

Due to the widespread adoption of "work-from-home" policies, videoconferencing applications (e.g., Zoom) have become indispensable for remote communication. However, they often lack immersiveness, leading to the so-called "Zoom fatigue" and degrading communication efficiency. The recent debut of Apple Vision Pro, a mobile headset that supports "spatial persona", aims to offer an immersive telepresence experience. In this paper, we conduct a first-of-its-kind in-depth and empirical study to analyze the performance of immersive telepresence with Apple FaceTime, Cisco Webex, Microsoft Teams, and Zoom on Vision Pro. We find that only FaceTime provides a truly immersive experience with spatial personas, whereas others still operate 2D personas. Our measurement results reveal that (1) FaceTime delivers semantic data to optimize bandwidth consumption, which is even lower than that of 2D persona for other applications, and (2) it employs visibility-aware optimizations to reduce rendering overhead. However, the scalability of FaceTime remains limited, with a simple server-allocation strategy that potentially leads to high network delay for users.

cs.NI↗

arXiv2024

In situ mixer calibration for superconducting quantum circuits

Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly carrier leakage and unwanted sideband signal, can significantly compromise control fidelity. To mitigate these defects, regular and precise mixer calibrations are indispensable, yet they pose a formidable challenge in large-scale quantum control. Here, we introduce an in situ calibration technique and outcome-focused mixer calibration scheme using superconducting qubits. Our method leverages the qubit's response to imperfect signals, allowing for calibration without modifying the wiring configuration. We experimentally validate the efficacy of this technique by benchmarking single-qubit gate fidelity and qubit coherence time.

quant-ph↗

arXiv2024

The Power of Combining Data and Knowledge: GPT-4o is an Effective Interpreter of Machine Learning Models in Predicting Lymph Node Metastasis of Lung Cancer

Lymph node metastasis (LNM) is a crucial factor in determining the initial treatment for patients with lung cancer, yet accurate preoperative diagnosis of LNM remains challenging. Recently, large language models (LLMs) have garnered significant attention due to their remarkable text generation capabilities. Leveraging the extensive medical knowledge learned from vast corpora, LLMs can estimate probabilities for clinical problems, though their performance has historically been inferior to data-driven machine learning models. In this paper, we propose a novel ensemble method that combines the medical knowledge acquired by LLMs with the latent patterns identified by machine learning models to enhance LNM prediction performance. Initially, we developed machine learning models using patient data. We then designed a prompt template to integrate the patient data with the predicted probability from the machine learning model. Subsequently, we instructed GPT-4o, the most advanced LLM developed by OpenAI, to estimate the likelihood of LNM based on patient data and then adjust the estimate using the machine learning output. Finally, we collected three outputs from the GPT-4o using the same prompt and ensembled these results as the final prediction. Using the proposed method, our models achieved an AUC value of 0.778 and an AP value of 0.426 for LNM prediction, significantly improving predictive performance compared to baseline machine learning models. The experimental results indicate that GPT-4o can effectively leverage its medical knowledge and the probabilities predicted by machine learning models to achieve more accurate LNM predictions. These findings demonstrate that LLMs can perform well in clinical risk prediction tasks, offering a new paradigm for integrating medical knowledge and patient data in clinical predictions.

cs.CL↗

arXiv2024

Predicting Lung Cancer Patient Prognosis with Large Language Models

Prognosis prediction is crucial for determining optimal treatment plans for lung cancer patients. Traditionally, such predictions relied on models developed from retrospective patient data. Recently, large language models (LLMs) have gained attention for their ability to process and generate text based on extensive learned knowledge. In this study, we evaluate the potential of GPT-4o mini and GPT-3.5 in predicting the prognosis of lung cancer patients. We collected two prognosis datasets, i.e., survival and post-operative complication datasets, and designed multiple tasks to assess the models' performance comprehensively. Logistic regression models were also developed as baselines for comparison. The experimental results demonstrate that LLMs can achieve competitive, and in some tasks superior, performance in lung cancer prognosis prediction compared to data-driven logistic regression models despite not using additional patient data. These findings suggest that LLMs can be effective tools for prognosis prediction in lung cancer, particularly when patient data is limited or unavailable.

cs.CL↗

arXiv2024

When Locally Linear Embedding Hits Boundary

Based on the Riemannian manifold model, we study the asymptotic behavior of a widely applied unsupervised learning algorithm, locally linear embedding (LLE), when the point cloud is sampled from a compact, smooth manifold with boundary. We show several peculiar behaviors of LLE near the boundary that are different from those diffusion-based algorithms. In particular, we show that LLE pointwisely converges to a mixed-type differential operator with degeneracy and we calculate the convergence rate. The impact of the hyperbolic part of the operator is discussed and we propose a clipped LLE algorithm which is a potential approach to recover the Dirichlet Laplace-Beltrami operator.

math.ST↗

arXiv2024

Systematic Literature Review of AI-enabled Spectrum Management in 6G and Future Networks

Artificial Intelligence (AI) has advanced significantly in various domains like healthcare, finance, and cybersecurity, with successes such as DeepMind's medical imaging and Tesla's autonomous vehicles. As telecommunications transition from 5G to 6G, integrating AI is crucial for complex demands like data processing, network optimization, and security. Despite ongoing research, there's a gap in consolidating AI-enabled Spectrum Management (AISM) advancements. Traditional spectrum management methods are inadequate for 6G due to its dynamic and complex demands, making AI essential for spectrum optimization, security, and network efficiency. This study aims to address this gap by: (i) Conducting a systematic review of AISM methodologies, focusing on learning models, data handling techniques, and performance metrics. (ii) Examining security and privacy concerns related to AI and traditional network threats within AISM contexts. Using the Systematic Literature Review (SLR) methodology, we meticulously analyzed 110 primary studies to: (a) Identify AI's utility in spectrum management. (b) Develop a taxonomy of AI approaches. (c) Classify datasets and performance metrics used. (d) Detail security and privacy threats and countermeasures. Our findings reveal challenges such as under-explored AI usage in critical AISM systems, computational resource demands, transparency issues, the need for real-world datasets, imbalances in security and privacy research, and the absence of testbeds, benchmarks, and security analysis tools. Addressing these challenges is vital for maximizing AI's potential in advancing 6G technology.

cs.NI↗

arXiv2024

Probabilistic Neural Transfer Function Estimation with Bayesian System Identification

Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually requires a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. Furthermore, our approach enables to identify response properties with credible intervals and perform statistical test for the learned neural features, which avoid the idiosyncrasy of a single model. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models, particularly in the limited-data regime.

q-bio.NC↗

arXiv2024

Sensing Aided Covert Communications: Turning Interference into Allies

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

cs.IT↗

arXiv2023

Zero-shot information extraction from radiological reports using ChatGPT

Electronic health records contain an enormous amount of valuable information, but many are recorded in free text. Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis. However, the traditional information extraction components, such as named entity recognition and relation extraction, require annotated data to optimize the model parameters, which has become one of the major bottlenecks in building information extraction systems. With the large language models achieving good performances on various downstream NLP tasks without parameter tuning, it becomes possible to use large language models for zero-shot information extraction. In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract useful information from the radiological reports. We first design the prompt template for the interested information in the CT reports. Then, we generate the prompts by combining the prompt template with the CT reports as the inputs of ChatGPT to obtain the responses. A post-processing module is developed to transform the responses into structured extraction results. We conducted the experiments with 847 CT reports collected from Peking University Cancer Hospital. The experimental results indicate that ChatGPT can achieve competitive performances for some extraction tasks compared with the baseline information extraction system, but some limitations need to be further improved.

cs.CL↗

arXiv2023

Improving Zero-Shot Action Recognition using Human Instruction with Text Description

Zero-shot action recognition, which recognizes actions in videos without having received any training examples, is gaining wide attention considering it can save labor costs and training time. Nevertheless, the performance of zero-shot learning is still unsatisfactory, which limits its practical application. To solve this problem, this study proposes a framework to improve zero-shot action recognition using human instructions with text descriptions. The proposed framework manually describes video contents, which incurs some labor costs; in many situations, the labor costs are worth it. We manually annotate text features for each action, which can be a word, phrase, or sentence. Then by computing the matching degrees between the video and all text features, we can predict the class of the video. Furthermore, the proposed model can also be combined with other models to improve its accuracy. In addition, our model can be continuously optimized to improve the accuracy by repeating human instructions. The results with UCF101 and HMDB51 showed that our model achieved the best accuracy and improved the accuracies of other models.

cs.CV↗

arXiv2023

Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks

Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapath synthesis, malicious logic identification, etc. Mostly, conventional reasoning approaches leverage structural hashing and functional propagation, suffering from limited scalability and inefficient usage of modern computing power. In response, we propose a novel symbolic reasoning framework exploiting graph neural networks (GNNs) and GPU acceleration to reason high-level functional blocks from gate-level netlists, namely Gamora, which offers high reasoning performance w.r.t exact reasoning algorithms, strong scalability to BNs with over 33 million nodes, and generalization capability from simple to complex designs. To further demonstrate the capability of Gamora, we also evaluate its reasoning performance after various technology mapping options, since technology-dependent optimizations are known to make functional reasoning much more challenging. Experimental results show that (1) Gamora reaches almost 100% and over 97% reasoning accuracy for carry-save-array (CSA) and Booth-encoded multipliers, respectively, with up to six orders of magnitude speedups compared to the state-of-the-art implementation in the ABC framework; (2) Gamora maintains high reasoning accuracy (>92%) in finding functional modules after complex technology mapping, upon which we comprehensively analyze the impacts on Gamora reasoning from technology mapping.

cs.AR↗

arXiv2023

Unsupervised machine learning for identifying phase transition using two-times clustering

In recent years, developing unsupervised machine learning for identifying phase transition is a research direction. In this paper, we introduce a two-times clustering method that can help select perfect configurations from a set of degenerate samples and assign the configuration with labels in a manner of unsupervised machine learning. These perfect configurations can then be used to train a neural network to classify phases. The derivatives of the predicted classification in the phase diagram, show peaks at the phase transition points. The effectiveness of our method is tested for the Ising, Potts, and Blume-Capel models. By using the ordered configuration from two-times clustering, our method can provide a useful way to obtain phase diagrams.

cond-mat.dis-nn↗

arXiv2023

Snake net and balloon force with a neural network for detecting multiple phases

Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys.Rev.Lett. 120, 176401(2018)]. It guesses the phase boundary as an initial snake and then drives the snake to convergence with forces estimated by the artificial neural network. In this paper, we extend this unsupervised learning method with one contour to a snake net with multiple contours for the purpose of obtaining several phase boundaries in a phase diagram. For the classical Blume-Capel model, the phase diagram containing three and four phases is obtained. Moreover, to overcome the limitations of the initial position and speed up the movement of the snake, the balloon force decaying with the iteration steps is introduced and applied to the snake net structure. Our method is helpful in determining the phase diagram with multiple phases, using just snapshots of configurations from cold atoms or other experiments without knowledge of the phases.

cond-mat.stat-mech↗

arXiv2023

Air-Ground Integrated Sensing and Communications: Opportunities and Challenges

The air-ground integrated sensing and communications (AG-ISAC) network, which consists of unmanned aerial vehicles (UAVs) and ground terrestrial networks, offers unique capabilities and demands special design techniques. In this article, we provide a review on AG-ISAC, by introducing UAVs as ``relay'' nodes for both communications and sensing to resolve the power and computation constraints on UAVs. We first introduce an AG-ISAC framework, including the system architecture and protocol. Four potential use cases are then discussed, with the analysis on the characteristics and merits of AG-ISAC networks. The research on several critical techniques for AG-ISAC is then discussed. Finally, we present our vision of the challenges and future research directions for AG-ISAC, to facilitate the advancement of the technology.

eess.SP↗

arXiv2023

Will Metaverse be NextG Internet? Vision, Hype, and Reality

Metaverse, with the combination of the prefix "meta" (meaning transcending) and the word "universe", has been deemed as the next-generation (NextG) Internet. It aims to create a shared virtual space that connects all virtual worlds via the Internet, where users, represented as digital avatars, can communicate and collaborate as if they are in the physical world. Nevertheless, there is still no unified definition of the Metaverse. This article first presents our vision of what the key requirements of Metaverse should be and reviews what has been heavily advocated by the industry and the positions of various high-tech companies. It then briefly introduces existing social virtual reality (VR) platforms that can be viewed as early prototypes of Metaverse and conducts a reality check by diving into the network operation and performance of two representative platforms, Workrooms from Meta and AltspaceVR from Microsoft. Finally, it concludes by discussing several opportunities and future directions for further innovation.

cs.NI↗

arXiv2023

Privacy-Preserving Record Linkage for Cardinality Counting

Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem. Example applications include health applications such as rare disease patients counting for adequate awareness and funding, and counting the number of cases of a new disease for outbreak detection, marketing applications such as counting the visibility reached for a new product, and cybersecurity applications such as tracking the number of unique views of social media posts. The data needed for the counting is however often personal and sensitive, and need to be processed using privacy-preserving techniques. The quality of data in different databases, for example typos, errors and variations, poses additional challenges for accurate cardinality estimation. While privacy-preserving cardinality counting has gained much attention in the recent times and a few privacy-preserving algorithms have been developed for cardinality estimation, no work has so far been done on privacy-preserving cardinality counting using record linkage techniques with fuzzy matching and provable privacy guarantees. We propose a novel privacy-preserving record linkage algorithm using unsupervised clustering techniques to link and count the cardinality of individuals in multiple datasets without compromising their privacy or identity. In addition, existing Elbow methods to find the optimal number of clusters as the cardinality are far from accurate as they do not take into account the purity and completeness of generated clusters. We propose a novel method to find the optimal number of clusters in unsupervised learning. Our experimental results on real and synthetic datasets are highly promising in terms of significantly smaller error rate of less than 0.1 with a privacy budget ε = 1.0 compared to the state-of-the-art fuzzy matching and clustering method.

cs.CR↗