OpenRaiser/Intern-Atlas huggingface dataset:
Large Generative Model Assisted 3D Semantic Communication
Venue: arXiv cs.IT 2024
Year: 2024

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario.
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, established through the pre-training, is still underexplored and may help yield more distinctive and informative representations to facilitate time series forecasting. To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space. We first design a tokenization module tailored for cross-modality alignment, which explicitly concatenates patches of decomposed time series components to create embeddings that effectively encode the temporal dynamics. Next, we leverage the pre-trained word token embeddings to derive semantic anchors and align selected anchors with time series embeddings by maximizing the cosine similarity in the joint space. This way, $S^2$IP-LLM can retrieve relevant semantic anchors as prompts to provide strong indicators (context) for time series that exhibit different temporal dynamics. With thorough empirical studies on multiple benchmark datasets, we demonstrate that the proposed $S^2$IP-LLM can achieve superior forecasting performance over state-of-the-art baselines. Furthermore, our ablation studies and visualizations verify the necessity of prompt learning informed by semantic space.
PR-NET: Leveraging Pathway Refined Network Structures for Prostate Cancer Patient Condition Prediction
Venue: arXiv cs.LG 2024
Year: 2024

The diagnosis and monitoring of Castrate Resistant Prostate Cancer (CRPC) are crucial for cancer patients, but the current models (such as P-NET) have limitations in terms of parameter count, generalization, and cost. To address the issue, we develop a more accurate and efficient Prostate Cancer patient condition prediction model, named PR-NET. By compressing and optimizing the network structure of P-NET, the model complexity is reduced while maintaining high accuracy and interpretability. The PR-NET demonstrated superior performance in predicting prostate cancer patient outcomes, outshining P-NET and six other traditional models with a significant margin. In our rigorous evaluation, PR-NET not only achieved impressive average AUC and Recall scores of 0.94 and 0.83, respectively, on known data but also maintained robust generalizability on five unknown datasets with a higher average AUC of 0.73 and Recall of 0.72, compared to P-NET's 0.68 and 0.5. PR-NET's efficiency was evidenced by its shorter average training and inference times, and its gene-level analysis revealed 46 key genes, demonstrating its enhanced predictive power and efficiency in identifying critical biomarkers for prostate cancer. Future research can further expand its application domains and optimize the model's performance and reliability.
Channel Estimation for Stacked Intelligent Metasurface-Assisted Wireless Networks
Venue: arXiv cs.IT 2024
Year: 2024

Emerging technologies, such as holographic multiple-input multiple-output (HMIMO) and stacked intelligent metasurface (SIM), are driving the development of wireless communication systems. Specifically, the SIM is physically constructed by stacking multiple layers of metasurfaces and has an architecture similar to an artificial neural network (ANN), which can flexibly manipulate the electromagnetic waves that propagate through it at the speed of light. This architecture enables the SIM to achieve HMIMO precoding and combining in the wave domain, thus significantly reducing the hardware cost and energy consumption. In this letter, we investigate the channel estimation problem in SIM-assisted multi-user HMIMO communication systems. Since the number of antennas at the base station (BS) is much smaller than the number of meta-atoms per layer of the SIM, it is challenging to acquire the channel state information (CSI) in SIM-assisted multi-user systems. To address this issue, we collect multiple copies of the uplink pilot signals that propagate through the SIM. Furthermore, we leverage the array geometry to identify the subspace that spans arbitrary spatial correlation matrices. Based on partial CSI about the channel statistics, a pair of subspace-based channel estimators are proposed. Additionally, we compute the mean square error (MSE) of the proposed channel estimators and optimize the phase shifts of the SIM to minimize the MSE. Numerical results are illustrated to analyze the effectiveness of the proposed channel estimation schemes.
Unleashing the Power of T1-cells in SFQ Arithmetic Circuits
Venue: arXiv cs.ET 2024
Year: 2024

Rapid single-flux quantum (RSFQ), a leading cryogenic superconductive electronics (SCE) technology, offers extremely low power dissipation and high speed. However, implementing RSFQ systems at VLSI complexity faces challenges, such as substantial area overhead from gate-level pipelining and path balancing, exacerbated by RSFQ's limited layout density. T1 flip-flop (T1-FF) is an RSFQ logic cell operating as a pulse counter. Using T1-FF the full adder function can be realized with only 40% of the area required by the conventional realization. This cell however imposes complex constraints on input signal timing, complicating its use. Multiphase clocking has been recently proposed to alleviate gate-level pipelining overhead. The fanin signals can be efficiently controlled using multiphase clocking. We present the novel two-stage SFQ technology mapping methodology supporting the T1-FF. Compatible parts of the SFQ network are first replaced by the efficient T1-FFs. Multiphase retiming is next applied to assign clock phases to each logic gate and insert DFFs to satisfy the input timing. Using our flow, the area of the SFQ networks is reduced, on average, by 6% with up to 25% reduction in optimizing the 128-bit adder.
Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry
Venue: arXiv cs.CV 2024
Year: 2024

Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. However, current labor-intensive methods result in a backlog of unsorted images, highlighting the need for automated classification. Few studies have simultaneously utilized accurate annotations along with updated algorithms and datasets. This study aimed to create a dataset representing common coral conditions and associated stressors in the Indo-Pacific. Concurrently, it assessed existing classification algorithms and proposed a new multi-label method for automatically detecting coral conditions and extracting ecological information. A dataset containing over 20,000 high-resolution coral images of different health conditions and stressors was constructed based on the field survey. Seven representative deep learning architectures were tested on this dataset, and their performance was quantitatively evaluated using the F1 metric and the match ratio. Based on this evaluation, a new method utilizing the ensemble learning approach was proposed. The proposed method accurately classified coral conditions as healthy, compromised, dead, and rubble; it also identified corresponding stressors, including competition, disease, predation, and physical issues. This method can help develop the coral image archive, guide conservation activities, and provide references for decision-making for reef managers and conservationists. The proposed ensemble learning approach outperforms others on the dataset, showing State-Of-The-Art (SOTA) performance. Future research should improve its generalizability and accuracy to support global coral conservation efforts.
Thread Detection and Response Generation using Transformers with Prompt Optimisation
Venue: arXiv cs.CL 2024
Year: 2024

Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models.
General surgery vision transformer: A video pre-trained foundation model for general surgery
Venue: arXiv cs.CV 2024
Year: 2024

The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery. Toward this, (i) we open-source the largest dataset of general surgery videos to-date, consisting of 680 hours of surgical videos, including data from robotic and laparoscopic techniques across 28 procedures; (ii) we propose a technique for video pre-training a general surgery vision transformer (GSViT) on surgical videos based on forward video prediction that can run in real-time for surgical applications, toward which we open-source the code and weights of GSViT; (iii) we also release code and weights for procedure-specific fine-tuned versions of GSViT across 10 procedures; (iv) we demonstrate the performance of GSViT on the Cholec80 phase annotation task, displaying improved performance over state-of-the-art single frame predictors.
Enhanced Auto Language Prediction with Dictionary Capsule -- A Novel Approach
Venue: arXiv cs.CL 2024
Year: 2024

The paper presents a novel Auto Language Prediction Dictionary Capsule (ALPDC) framework for language prediction and machine translation. The model uses a combination of neural networks and symbolic representations to predict the language of a given input text and then translate it to a target language using pre-built dictionaries. This research work also aims to translate the text of various languages to its literal meaning in English. The proposed model achieves state-of-the-art results on several benchmark datasets and significantly improves translation accuracy compared to existing methods. The results show the potential of the proposed method for practical use in multilingual communication and natural language processing tasks.
Integrating Static Code Analysis Toolchains
Venue: arXiv cs.SE 2024
Year: 2024

This paper proposes an approach for a tool-agnostic and heterogeneous static code analysis toolchain in combination with an exchange format. This approach enhances both traceability and comparability of analysis results. State of the art toolchains support features for either test execution and build automation or traceability between tests, requirements and design information. Our approach combines all those features and extends traceability to the source code level, incorporating static code analysis. As part of our approach we introduce the "ASSUME Static Code Analysis tool exchange format" that facilitates the comparability of different static code analysis results. We demonstrate how this approach enhances the usability and efficiency of static code analysis in a development process. On the one hand, our approach enables the exchange of results and evaluations between static code analysis tools. On the other hand, it enables a complete traceability between requirements, designs, implementation, and the results of static code analysis. Within our approach we also propose an OSLC specification for static code analysis tools and an OSLC communication framework.
Evaluation and improvement of ETSI ITS Contention-Based Forwarding (CBF) of warning messages in highway scenarios
Venue: arXiv cs.NI 2024
Year: 2024

This paper evaluates the performance of the ETSI Contention-Based Forwarding (CBF) GeoNetworking protocol for distributing warning messages in highway scenarios, including its interaction with the Decentralized Congestion Control (DCC) mechanism. Several shortcomings of the standard ETSI CBF algorithm are identified, and we propose different solutions to these problems, which are able to reduce the number of transmissions by an order of magnitude, while reducing the message end-to-end delay and providing a reliability close to 100% in a large area of interest.
Hard-label based Small Query Black-box Adversarial Attack
Venue: arXiv cs.LG 2024
Year: 2024

We consider the hard label based black box adversarial attack setting which solely observes predicted classes from the target model. Most of the attack methods in this setting suffer from impractical number of queries required to achieve a successful attack. One approach to tackle this drawback is utilising the adversarial transferability between white box surrogate models and black box target model. However, the majority of the methods adopting this approach are soft label based to take the full advantage of zeroth order optimisation. Unlike mainstream methods, we propose a new practical setting of hard label based attack with an optimisation process guided by a pretrained surrogate model. Experiments show the proposed method significantly improves the query efficiency of the hard label based black-box attack across various target model architectures. We find the proposed method achieves approximately 5 times higher attack success rate compared to the benchmarks, especially at the small query budgets as 100 and 250.
Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills
Venue: arXiv cs.HC 2024
Year: 2024

Reading, understanding and explaining code have traditionally been important skills for novices learning programming. As large language models (LLMs) become prevalent, these foundational skills are more important than ever given the increasing need to understand and evaluate model-generated code. Brand new skills are also needed, such as the ability to formulate clear prompts that can elicit intended code from an LLM. Thus, there is great interest in integrating pedagogical approaches for the development of both traditional coding competencies and the novel skills required to interact with LLMs. One effective way to develop and assess code comprehension ability is with ``Explain in plain English'' (EiPE) questions, where students succinctly explain the purpose of a fragment of code. However, grading EiPE questions has always been difficult given the subjective nature of evaluating written explanations and this has stifled their uptake. In this paper, we explore a natural synergy between EiPE questions and code-generating LLMs to overcome this limitation. We propose using an LLM to generate code based on students' responses to EiPE questions -- not only enabling EiPE responses to be assessed automatically, but helping students develop essential code comprehension and prompt crafting skills in parallel. We investigate this idea in an introductory programming course and report student success in creating effective prompts for solving EiPE questions. We also examine student perceptions of this activity and how it influences their views on the use of LLMs for aiding and assessing learning.
Steering a Fleet: Adaptation for Large-Scale, Workflow-Based Experiments
Venue: arXiv cs.DC 2024
Year: 2024

Experimental science is increasingly driven by instruments that produce vast volumes of data and thus a need to manage, compute, describe, and index this data. High performance and distributed computing provide the means of addressing the computing needs; however, in practice, the variety of actions required and the distributed set of resources involved, requires sophisticated "flows" defining the steps to be performed on data. As each scan or measurement is performed by an instrument, a new instance of the flow is initiated resulting in a "fleet" of concurrently running flows, with the overall goal to process all the data collected during a potentially long-running experiment. During the course of the experiment, each flow may need to adapt its execution due to changes in the environment, such as computational or storage resource availability, or based on the progress of the fleet as a whole such as completion or discovery of an intermediate result leading to a change in subsequent flow's behavior. We introduce a cloud-based decision engine, Braid, which flows consult during execution to query their run-time environment and coordinate with other flows within their fleet. Braid accepts streams of measurements taken from the run-time environment or from within flow runs which can then be statistically aggregated and compared to other streams to determine a strategy to guide flow execution. For example, queue lengths in execution environments can be used to direct a flow to run computations in one environment or another, or experiment progress as measured by individual flows can be aggregated to determine the progress and subsequent direction of the flows within a fleet. We describe Braid, its interface, implementation and performance characteristics. We further show through examples and experience modifying an existing scientific flow how Braid is used to make adaptable flows.
Generalization of Graph Neural Networks through the Lens of Homomorphism
Venue: arXiv cs.LG 2024
Year: 2024

Despite the celebrated popularity of Graph Neural Networks (GNNs) across numerous applications, the ability of GNNs to generalize remains less explored. In this work, we propose to study the generalization of GNNs through a novel perspective - analyzing the entropy of graph homomorphism. By linking graph homomorphism with information-theoretic measures, we derive generalization bounds for both graph and node classifications. These bounds are capable of capturing subtleties inherent in various graph structures, including but not limited to paths, cycles and cliques. This enables a data-dependent generalization analysis with robust theoretical guarantees. To shed light on the generality of of our proposed bounds, we present a unifying framework that can characterize a broad spectrum of GNN models through the lens of graph homomorphism. We validate the practical applicability of our theoretical findings by showing the alignment between the proposed bounds and the empirically observed generalization gaps over both real-world and synthetic datasets.
Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
Venue: arXiv cs.CL 2024
Year: 2024

We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and \textbf{L}arge \textbf{L}anguage \textbf{M}odel annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at \url{https://github.com/zhhvvv/CNER-UAV}.
Automatic design optimization of preference-based subjective evaluation with online learning in crowdsourcing environment
Venue: arXiv cs.HC 2024
Year: 2024

A preference-based subjective evaluation is a key method for evaluating generative media reliably. However, its huge combinations of pairs prohibit it from being applied to large-scale evaluation using crowdsourcing. To address this issue, we propose an automatic optimization method for preference-based subjective evaluation in terms of pair combination selections and allocation of evaluation volumes with online learning in a crowdsourcing environment. We use a preference-based online learning method based on a sorting algorithm to identify the total order of evaluation targets with minimum sample volumes. Our online learning algorithm supports parallel and asynchronous execution under fixed-budget conditions required for crowdsourcing. Our experiment on preference-based subjective evaluation of synthetic speech shows that our method successfully optimizes the test by reducing pair combinations from 351 to 83 and allocating optimal evaluation volumes for each pair ranging from 30 to 663 without compromising evaluation accuracies and wasting budget allocations.