pwc-archive/papers-with-abstracts huggingface dataset:
Explainable AI for computational pathology identifies model limitations and tissue biomarkers
Authors: Jakub R. Kaczmarzyk, Joel H. Saltz, Peter K. Koo
Date: 2024-09-04 00:00:00
Tasks: Bias Detection, counterfactual, Decision Making, Multiple Instance Learning, whole slide images

Introduction: Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder clinical adoption. Explainable AI methods are essential to enhance model transparency and reliability. Methods: We developed HIPPO, an explainable AI framework that systematically modifies tissue regions in whole slide images to generate image counterfactuals, enabling quantitative hypothesis testing, bias detection, and model evaluation beyond traditional performance metrics. HIPPO was applied to a variety of clinically important tasks, including breast metastasis detection in axillary lymph nodes, prognostication in breast cancer and melanoma, and IDH mutation classification in gliomas. In computational experiments, HIPPO was compared against traditional metrics and attention-based approaches to assess its ability to identify key tissue elements driving model predictions. Results: In metastasis detection, HIPPO uncovered critical model limitations that were undetectable by standard performance metrics or attention-based methods. For prognostic prediction, HIPPO outperformed attention by providing more nuanced insights into tissue elements influencing outcomes. In a proof-of-concept study, HIPPO facilitated hypothesis generation for identifying melanoma patients who may benefit from immunotherapy. In IDH mutation classification, HIPPO more robustly identified the pathology regions responsible for false negatives compared to attention, suggesting its potential to outperform attention in explaining model decisions. Conclusions: HIPPO expands the explainable AI toolkit for computational pathology by enabling deeper insights into model behavior. This framework supports the trustworthy development, deployment, and regulation of weakly-supervised models in clinical and research settings, promoting their broader adoption in digital pathology.
Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
Authors: Quoc-Bao-Huy Tran, Aagha Abdul Waheed, Sun-Tae Chung
Date: 2024-09-04 00:00:00
Proceeding: MDPI Applied Scienses 2024 9
Tasks: Natural Language Queries, Semantic Parsing, text-to-Cypher

Graph databases have become essential for managing and analyzing complex data relationships, with Neo4j emerging as a leading player in this domain. Neo4j, a high-performance NoSQL graph database, excels in efficiently handling connected data, offering powerful querying capabilities through its Cypher query language. However, due to Cypher’s complexities, making it more accessible for nonexpert users requires translating natural language queries into Cypher. Thus, in this paper, we propose a text-to-Cypher model to effectively translate natural language queries into Cypher. In our proposed model, we combine several methods to enable nonexpert users to interact with graph databases using the English language. Our approach includes three modules: key-value extraction, relation–properties prediction, and Cypher query generation. For key-value extraction and relation–properties prediction, we leverage BERT and GraphSAGE to extract features from natural language. Finally, we use a Transformer model to generate the Cypher query from these features. Additionally, due to the lack of text-to-Cypher datasets, we introduced a new dataset that contains English questions querying information within a graph database, paired with corresponding Cypher query ground truths. This dataset aids future model learning, validation, and comparison on text-to-Cypher task. Through experiments and evaluations, we demonstrate that our model achieves high accuracy and efficiency when comparing with some well-known seq2seq model such as T5 and GPT2, with an 87.1% exact match score on the dataset.
Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts
Authors: Guandong Feng, Guoliang Zhu, Shengze Shi, Yue Sun, Zhongyi Fan, Sulin Gao, Jun Hu
Date: 2023-10-28 00:00:00
Proceeding: CCKS 2023 10
Tasks: Knowledge Base Question Answering, Knowledge Graphs, Language Modeling, Language Modelling, Large Language Model, Natural Language Queries, Question Answering, Semantic Parsing, text-to-Cypher

Knowledge Base Question Answering (KBQA) is a significant task in natural language processing, aiming to retrieve answers from structured knowledge bases in response to natural language questions. NL2Cypher is crucial for accurately querying answers from knowledge bases, but there is limited research in this area or the results are unsatisfactory. Our work explores the convergence of advanced natural language processing techniques with knowledge base question answering (KBQA), focusing on the automated generation of Cypher queries from natural language queries. By leveraging the capabilities of large language model (LLM), our approach bridges the gap between textual questions and structured knowledge representations. The proposed methodology showcases promising results in accurately formulating Cypher queries. We achieved substantial performance in the CCKS2023 Foreign Military Unmanned Systems Knowledge Graph Reasoning Question-Answering Evaluation Task. Our method achieved an F1 score of 0.94269 on the final testing dataset.
Contextual unsupervised deep clustering in digital pathology
Authors: Mariia Sidulova, Seyed Kahaki, Ian Hagemann, Alexej Gossmann
Date: 2024-07-24 00:00:00
Proceeding: Conference on Health, Inference, and Learning 2024 7
Tasks: Clustering, Deep Clustering, whole slide images

Clustering can be used in medical imaging research to identify different domains within a specific dataset, aiding in a better understanding of subgroups or strata that may not have been annotated. Moreover, in digital pathology, clustering can be used to effectively sample image patches from whole slide images (WSI). In this work, we conduct a comparative analysis of three deep clustering algorithms -- a simple two-step approach applying K-means onto a learned feature space, an end-to-end deep clustering method (DEC), and a Graph Convolutional Network (GCN) based method -- in application to a digital pathology dataset of endometrial biopsy WSIs. For consistency, all methods use the same Autoencoder (AE) architecture backbone that extracts features from image patches. The GCN-based model, specifically, stands out as a deep clustering algorithm that considers spatial contextual information in predicting clusters. Our study highlights the computation of graphs for WSIs and emphasizes the impact of these graphs on the formation of clusters. The main finding of our research indicates that GCN-based deep clustering demonstrates heightened spatial awareness compared to the other methods, resulting in higher cluster agreement with previous clinical annotations of WSIs.
Design of CANSAT for Air Quality Monitoring for an altitude of 900 meters
Authors: Soma Kunal Raj, Yalamanchili Surya Teja
Date: 2024-09-05 00:00:00

This paper presents the design and development of NAMBI-VJ, a CANSAT specifically designed for air quality monitoring and stabilization. The CANSAT's cylindrical structure, measuring 310mm in height and 125mm in diameter, is equipped with a mechanical gyroscope for stabilization and a spill-hole parachute for controlled descent. The primary objective of this research is to create a compact, lightweight satellite capable of monitoring air quality parameters such as particulate matter (PM), carbon dioxide (CO2), longitude, and latitude. To achieve this, the CANSAT utilizes Zigbee communication to transmit data to a ground station. Experimental testing involved dropping the CANSAT from an altitude of 900 meters using a drone. The results demonstrate the CANSAT's ability to successfully gather and transmit air quality data, highlighting its potential for environmental monitoring applications.
Text classification optimization algorithm based on graph neural network
Authors: Erdi Gao, Haowei Yang, Dan Sun, Haohao Xia, Yuhan Ma, Yuanjing Zhu
Date: 2024-08-09 00:00:00
Tasks: Classification, graph construction, Graph Neural Network, text-classification, Text Classification

In the field of natural language processing, text classification, as a basic task, has important research value and application prospects. Traditional text classification methods usually rely on feature representations such as the bag of words model or TF-IDF, which overlook the semantic connections between words and make it challenging to grasp the deep structural details of the text. Recently, GNNs have proven to be a valuable asset for text classification tasks, thanks to their capability to handle non-Euclidean data efficiently. However, the existing text classification methods based on GNN still face challenges such as complex graph structure construction and high cost of model training. This paper introduces a text classification optimization algorithm utilizing graph neural networks. By introducing adaptive graph construction strategy and efficient graph convolution operation, the accuracy and efficiency of text classification are effectively improved. The experimental results demonstrate that the proposed method surpasses traditional approaches and existing GNN models across multiple public datasets, highlighting its superior performance and feasibility for text classification tasks.
CSAC Drift Modeling Considering GPS Signal Quality in the Case of GPS Signal Unavailability
Authors: Seunghyeon Park, Joon Hyo Rhee
Date: 2024-09-01 00:00:00

The Global Positioning System (GPS), one of the Global Navigation Satellite Systems (GNSS), provides accurate position, navigation and time (PNT) information to various applications. One of the application that is highly receiving attention is satellite vehicles, especially Low Earth Orbit (LEO) satellites. Due to their limited ways to get PNT information and low performance of their onboard clocks, GPS system time (GPST) provided by GPS is a good reference clock to synchronize. However, GPS is well-known for its vulnerability to intentional or unintentional interference. This study aims to maintain the onboard clock with less error relative to the GPST even when the GPS signal is disrupted. In this study, we analyzed two major factors that affects the quality of the GPS measurements: the number of the visible satellites and the geometry of the satellites. Then, we proposed a weighted model for a Chip-Scale Atomic Clock (CSAC) that mitigates the clock error relative to the GPST while considering the two factors. Based on this model, a stand-alone CSAC could maintain its error less than 4 microseconds, even in a situation where no GPS signals are received for 12 hours.
Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting
Authors: Tao Ni, Zehua Sun, Mingda Han, Guohao Lan, Yaxiong Xie, Zhenjiang Li, Tao Gu, Weitao Xu
Date: 2024-08-26 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption by 98.7% and harvesting up to 4.5mW of power from RF energy.
Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes
Authors: Vikram Sudarshan, Warren D. Seider
Date: 2024-08-31 00:00:00
Tasks: Computational Efficiency

Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics, such as: $\textit{RMSE}$, model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.
Learning-Based Finite Element Methods Modeling for Complex Mechanical Systems
Authors: Jiasheng Shi, Fu Lin, Weixiong Rao
Date: 2024-08-30 00:00:00

Complex mechanic systems simulation is important in many real-world applications. The de-facto numeric solver using Finite Element Method (FEM) suffers from computationally intensive overhead. Though with many progress on the reduction of computational time and acceptable accuracy, the recent CNN or GNN-based simulation models still struggle to effectively represent complex mechanic simulation caused by the long-range spatial dependency of distance mesh nodes and independently learning local and global representation. In this paper, we propose a novel two-level mesh graph network. The key of the network is to interweave the developed Graph Block and Attention Block to better learn mechanic interactions even for long-rang spatial dependency. Evaluation on three synthetic and one real datasets demonstrates the superiority of our work. For example, on the Beam dataset, our work leads to 54.3\% lower prediction errors and 9.87\% fewer learnable network parameters.
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment
Authors: Yihe Wang, Nadia Mammone, Darina Petrovsky, Alexandros T. Tzallas, Francesco C. Morabito, Xiang Zhang
Date: 2024-08-17 00:00:00
Tasks: Anomaly Detection, EEG, Electroencephalogram (EEG), Time Series Analysis

Electroencephalogram (EEG) has emerged as a cost-effective and efficient method for supporting neurologists in assessing Alzheimer's disease (AD). Existing approaches predominantly utilize handcrafted features or Convolutional Neural Network (CNN)-based methods. However, the potential of the transformer architecture, which has shown promising results in various time series analysis tasks, remains underexplored in interpreting EEG for AD assessment. Furthermore, most studies are evaluated on the subject-dependent setup but often overlook the significance of the subject-independent setup. To address these gaps, we present ADformer, a novel multi-granularity transformer designed to capture temporal and spatial features to learn effective EEG representations. We employ multi-granularity data embedding across both dimensions and utilize self-attention to learn local features within each granularity and global features among different granularities. We conduct experiments across 5 datasets with a total of 525 subjects in setups including subject-dependent, subject-independent, and leave-subjects-out. Our results show that ADformer outperforms existing methods in most evaluations, achieving F1 scores of 75.19% and 93.58% on two large datasets with 65 subjects and 126 subjects, respectively, in distinguishing AD and healthy control (HC) subjects under the challenging subject-independent setup.
FIDAVL: Fake Image Detection and Attribution using Vision-Language Model
Authors: Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene, Abdelmalik Taleb-Ahmed, Abdenour Hadid
Date: 2024-08-22 00:00:00
Tasks: Attribute, Fake Image Detection, Language Modeling, Language Modelling, Zero-Shot Learning

We introduce FIDAVL: Fake Image Detection and Attribution using a Vision-Language Model. FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between vision and language processing. Leveraging the benefits of zero-shot learning, FIDAVL exploits the complementarity between vision and language along with soft prompt-tuning strategy to detect fake images and accurately attribute them to their originating source models. We conducted extensive experiments on a comprehensive dataset comprising synthetic images generated by various state-of-the-art models. Our results demonstrate that FIDAVL achieves an encouraging average detection accuracy of 95.42% and F1-score of 95.47% while also obtaining noteworthy performance metrics, with an average F1-score of 92.64% and ROUGE-L score of 96.50% for attributing synthetic images to their respective source generation models. The source code of this work will be publicly released at https://github.com/Mamadou-Keita/FIDAVL.
Post-mastoidectomy Surface Multi-View Synthesis from a Single Microscopy Image
Authors: Yike Zhang, Jack Noble
Date: 2024-08-31 00:00:00

Cochlear Implant (CI) procedures involve performing an invasive mastoidectomy to insert an electrode array into the cochlea. In this paper, we introduce a novel pipeline that is capable of generating synthetic multi-view videos from a single CI microscope image. In our approach, we use a patient's pre-operative CT scan to predict the post-mastoidectomy surface using a method designed for this purpose. We manually align the surface with a selected microscope frame to obtain an accurate initial pose of the reconstructed CT mesh relative to the microscope. We then perform UV projection to transfer the colors from the frame to surface textures. Novel views of the textured surface can be used to generate a large dataset of synthetic frames with ground truth poses. We evaluated the quality of synthetic views rendered using Pytorch3D and PyVista. We found both rendering engines lead to similarly high-quality synthetic novel-view frames compared to ground truth with a structural similarity index for both methods averaging about 0.86. A large dataset of novel views with known poses is critical for ongoing training of a method to automatically estimate microscope pose for 2D to 3D registration with the pre-operative CT to facilitate augmented reality surgery. This dataset will empower various downstream tasks, such as integrating Augmented Reality (AR) in the OR, tracking surgical tools, and supporting other video analysis studies.
CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis
Authors: Gautham Ramachandran, Rick Yang
Date: 2024-08-23 00:00:00
Tasks: Code Generation, Real-Time Strategy Games

Current approaches to automated code generation often rely on monolithic models that lack real-time adaptability and scalability. This limitation is particularly evident in complex programming tasks that require dynamic adjustment and efficiency. The integration of neuroscience principles into Natural Language Processing (NLP) has the potential to revolutionize automated code generation. This paper presents CortexCompile, a novel modular system inspired by the specialized functions of the human brain's cortical regions. By emulating the distinct roles of the Prefrontal Cortex, Parietal Cortex, Temporal Lobe, and Motor Cortex, CortexCompile achieves significant advancements in scalability, efficiency, and adaptability compared to traditional monolithic models like GPT-4o. The system's architecture features a Task Orchestration Agent that manages dynamic task delegation and parallel processing, facilitating the generation of highly accurate and optimized code across increasingly complex programming tasks. Experimental evaluations demonstrate that CortexCompile consistently outperforms GPT-4o in development time, accuracy, and user satisfaction, particularly in tasks involving real-time strategy games and first-person shooters. These findings underscore the viability of neuroscience-inspired architectures in addressing the limitations of current NLP models, paving the way for more efficient and human-like AI systems.
A Note On Deterministic Submodular Maximization With Bounded Curvature
Authors: Wenxin Li
Date: 2024-08-25 00:00:00

We show that the recent breakthrough result of [Buchbinder and Feldman, FOCS'24] could further lead to a deterministic $(1-\kappa_{f}/e-\varepsilon)$-approximate algorithm for maximizing a submodular function with curvature $\kappa_{f}$ under matroid constraint.
Sample-Efficient Diffusion for Text-To-Speech Synthesis
Authors: Justin Lovelace, Soham Ray, Kwangyoun Kim, Kilian Q. Weinberger, Felix Wu
Date: 2024-09-01 00:00:00
Tasks: Language Modeling, Language Modelling, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This work introduces Sample-Efficient Speech Diffusion (SESD), an algorithm for effective speech synthesis in modest data regimes through latent diffusion. It is based on a novel diffusion architecture, that we call U-Audio Transformer (U-AT), that efficiently scales to long sequences and operates in the latent space of a pre-trained audio autoencoder. Conditioned on character-aware language model representations, SESD achieves impressive results despite training on less than 1k hours of speech - far less than current state-of-the-art systems. In fact, it synthesizes more intelligible speech than the state-of-the-art auto-regressive model, VALL-E, while using less than 2% the training data.