Search Results for author:
Found 38 papers, 13 papers with code
STAPI: An Automatic Scraper for Extracting Iterative Title-Text Structure from Web Documents
Therefore, we propose the first title-text dataset on web documents that incorporates a wide variety of domains to facilitate downstream training.
A Foundational Generative Model for Breast Ultrasound Image Analysis
Additionally, we characterized the scaling effect of using generated data which was as effective as the collected real-world data for training diagnostic models.
Distributed satellite information networks: Architecture, enabling technologies, and trends
In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services.
From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
Since the first instances of online education, where courses were uploaded to accessible and shared online platforms, this form of scaling the dissemination of human knowledge to reach a broader audience has sparked extensive discussion and widespread adoption.
Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses
In the prospective external evaluation, our diagnostic model outperforms the average performance of nine radiologists by 33. 5% in specificity with the same sensitivity, improving their performance by providing predictions with an interpretable decision-making process.
A Survey on Employing Large Language Models for Text-to-SQL Tasks
With the development of Large Language Models (LLMs), a range of LLM-based Text2SQL methods have emerged.
MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction
In the absence of ground-truth data, a synthetic data generation procedure is proposed to emulate the causal mechanism between traffic speed, crashes, and covariates.
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
Sustainable Wireless Networks via Reconfigurable Intelligent Surfaces (RISs): Overview of the ETSI ISG RIS
Reconfigurable Intelligent Surfaces (RISs) are a novel form of ultra-low power devices that are capable to increase the communication data rates as well as the cell coverage in a cost- and energy-efficient way.
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset Challenge
Firstly, we improve semantic segmentation models, using backbone pretrained with Depth Anything to improve UperNet model and SETRMLA model, and adding language guidance based on both weather and category information to InternImage model.
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Our critical survey based on the newly categorized research questions shows that (1) no prior work demonstrates successful self-correction with feedback from prompted LLMs, except for studies in tasks that are exceptionally suited for self-correction, (2) self-correction works well in tasks that can use reliable external feedback, and (3) large-scale fine-tuning enables self-correction.
Large Language Models for Explainable Decisions in Dynamic Digital Twins
Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system.
Pruning as a Domain-specific LLM Extractor
Moreover, by efficiently approximating weight importance with the refined training loss on a domain-specific calibration dataset, we obtain a pruned model emphasizing generality and specificity.
Evaluating LLMs at Detecting Errors in LLM Responses
This work introduces ReaLMistake, the first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs.
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents
To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource.
Optical Character Recognition
Optical Character Recognition (OCR)
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
Point Cloud Semantic Segmentation with Sparse and Inhomogeneous Annotations
Utilizing uniformly distributed sparse annotations, weakly supervised learning alleviates the heavy reliance on fine-grained annotations in point cloud semantic segmentation tasks.
Fair Abstractive Summarization of Diverse Perspectives
However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization.
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks.
A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics
Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime.
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem.
Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning
This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes.
Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering
Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies.
Coordinating Cross-modal Distillation for Molecular Property Prediction
The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP.
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022).
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification
In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading.
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins
DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates.
DT-SV: A Transformer-based Time-domain Approach for Speaker Verification
Therefore, we propose an approach to derive utterance-level speaker embeddings via a Transformer architecture that uses a novel loss function named diffluence loss to integrate the feature information of different Transformer layers.
Knowledge Equivalence in Digital Twins of Intelligent Systems
Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous.
Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement Learning
We plan to apply and adjust some well-known reinforcement learning (RL) algorithms to train an automatic agent to play the 1985 Nintendo game Super Mario Bros under a speedrun rule.
For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning
With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable.
Leveraging History for Faster Sampling of Online Social Networks
Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i. e., no access to the full graph topology).
Real Time 3D Indoor Human Image Capturing Based on FMCW Radar
Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy.
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records
Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.
Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home
The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built.
Cryptography and Security
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas.
Cryptography and Security
Fine-Grained Change Detection of Misaligned Scenes With Varied Illuminations
To guarantee detection sensitivity and accuracy of minute changes, in an observation, we capture a group of images under multiple illuminations, which need only to be roughly aligned to the last time lighting conditions.
