pwc-archive/papers-with-abstracts huggingface dataset:
DisasterQA: A Benchmark for Assessing the performance of LLMs in Disaster Response
Authors: Rajat Rawat
Date: 2024-10-09 00:00:00
Tasks: Decision Making, Disaster Response

Disasters can result in the deaths of many, making quick response times vital. Large Language Models (LLMs) have emerged as valuable in the field. LLMs can be used to process vast amounts of textual information quickly providing situational context during a disaster. However, the question remains whether LLMs should be used for advice and decision making in a disaster. To evaluate the capabilities of LLMs in disaster response knowledge, we introduce a benchmark: DisasterQA created from six online sources. The benchmark covers a wide range of disaster response topics. We evaluated five LLMs each with four different prompting methods on our benchmark, measuring both accuracy and confidence levels through Logprobs. The results indicate that LLMs require improvement on disaster response knowledge. We hope that this benchmark pushes forth further development of LLMs in disaster response, ultimately enabling these models to work alongside. emergency managers in disasters.
Statistical Test for Auto Feature Engineering by Selective Inference
Authors: Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino, Teruyuki Katsuoka, Ichiro Takeuchi
Date: 2024-10-13 00:00:00
Tasks: Feature Engineering

Auto Feature Engineering (AFE) plays a crucial role in developing practical machine learning pipelines by automating the transformation of raw data into meaningful features that enhance model performance. By generating features in a data-driven manner, AFE enables the discovery of important features that may not be apparent through human experience or intuition. On the other hand, since AFE generates features based on data, there is a risk that these features may be overly adapted to the data, making it essential to assess their reliability appropriately. Unfortunately, because most AFE problems are formulated as combinatorial search problems and solved by heuristic algorithms, it has been challenging to theoretically quantify the reliability of generated features. To address this issue, we propose a new statistical test for generated features by AFE algorithms based on a framework called selective inference. As a proof of concept, we consider a simple class of tree search-based heuristic AFE algorithms, and consider the problem of testing the generated features when they are used in a linear model. The proposed test can quantify the statistical significance of the generated features in the form of $p$-values, enabling theoretically guaranteed control of the risk of false findings.
Deep Learning-driven Mobile Traffic Measurement Collection and Analysis
Authors: Yini Fang
Date: 2024-10-14 00:00:00
Tasks: Deep Learning, Graph Neural Network

Modelling dynamic traffic patterns and especially the continuously changing dependencies between different base stations, which previous studies overlook, is challenging. Traditional algorithms struggle to process large volumes of data and to extract deep insights that help elucidate mobile traffic demands with fine granularity, as well as how these demands will evolve in the future. Therefore, in this thesis we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains and develop solutions for precise city-scale mobile traffic analysis and forecasting. Firstly, we design Spider, a mobile traffic measurement collection and reconstruction framework with a view to reducing the cost of measurement collection and inferring traffic consumption with high accuracy, despite working with sparse information. In particular, we train a reinforcement learning agent to selectively sample subsets of target mobile coverage areas and tackle the large action space problem specific to this setting. We then introduce a lightweight neural network model to reconstruct the traffic consumption based on historical sparse measurements. Our proposed framework outperforms existing solutions on a real-world mobile traffic dataset. Secondly, we design SDGNet, a handover-aware graph neural network model for long-term mobile traffic forecasting. We model the cellular network as a graph, and leverage handover frequency to capture the dependencies between base stations across time. Handover information reflects user mobility such as daily commute, which helps in increasing the accuracy of the forecasts made. We proposed dynamic graph convolution to extract features from both traffic consumption and handover data, showing that our model outperforms other benchmark graph models on a mobile traffic dataset collected by a major network operator.
Novel Development of LLM Driven mCODE Data Model for Improved Clinical Trial Matching to Enable Standardization and Interoperability in Oncology Research
Authors: Aarsh Shekhar, Mincheol Kim
Date: 2024-10-18 00:00:00

Each year, the lack of efficient data standardization and interoperability in cancer care contributes to the severe lack of timely and effective diagnosis, while constantly adding to the burden of cost, with cancer costs nationally reaching over $208 billion in 2023 alone. Traditional methods regarding clinical trial enrollment and clinical care in oncology are often manual, time-consuming, and lack a data-driven approach. This paper presents a novel framework to streamline standardization, interoperability, and exchange of cancer domains and enhance the integration of oncology-based EHRs across disparate healthcare systems. This paper utilizes advanced LLMs and Computer Engineering to streamline cancer clinical trials and discovery. By utilizing FHIR's resource-based approach and LLM-generated mCODE profiles, we ensure timely, accurate, and efficient sharing of patient information across disparate healthcare systems. Our methodology involves transforming unstructured patient treatment data, PDFs, free-text information, and progress notes into enriched mCODE profiles, facilitating seamless integration with our novel AI and ML-based clinical trial matching engine. The results of this study show a significant improvement in data standardization, with accuracy rates of our trained LLM peaking at over 92% with datasets consisting of thousands of patient data. Additionally, our LLM demonstrated an accuracy rate of 87% for SNOMED-CT, 90% for LOINC, and 84% for RxNorm codes. This trumps the current status quo, with LLMs such as GPT-4 and Claude's 3.5 peaking at an average of 77%. This paper successfully underscores the potential of our standardization and interoperability framework, paving the way for more efficient and personalized cancer treatment.
Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data
Authors: Mehrad Soltani, Luis Rueda
Date: 2024-10-23 00:00:00
Tasks: Clustering

The task of spatial clustering of transcriptomics data is of paramount importance. It enables the classification of tissue samples into diverse subpopulations of cells, which, in turn, facilitates the analysis of the biological functions of clusters, tissue reconstruction, and cell-cell interactions. Many approaches leverage gene expressions, spatial locations, and histological images to detect spatial domains; however, Graph Neural Networks (GNNs) as state of the art models suffer from a limitation in the assumption of pairwise connections between nodes. In the case of domain detection in spatial transcriptomics, some cells are found to be not directly related. Still, they are grouped as the same domain, which shows the incapability of GNNs for capturing implicit connections among the cells. While graph edges connect only two nodes, hyperedges connect an arbitrary number of nodes along their edges, which lets Hypergraph Neural Networks (HGNNs) capture and utilize richer and more complex structural information than traditional GNNs. We use autoencoders to address the limitation of not having the actual labels, which are well-suited for unsupervised learning. Our model has demonstrated exceptional performance, achieving the highest iLISI score of 1.843 compared to other methods. This score indicates the greatest diversity of cell types identified by our method. Furthermore, our model outperforms other methods in downstream clustering, achieving the highest ARI values of 0.51 and Leiden score of 0.60.
EnergyPlus Room Simulator
Authors: Manuel Weber, Philipp Bogdain, Sophia Viktoria Weißenberger, Diana Marjanovic, Katharina Sammet, Jan Vellmer, Farzan Banihashemi, Peter Mandl
Date: 2024-10-25 00:00:00

Research towards energy optimization in buildings heavily relies on building-related data such as measured indoor climate factors. While data collection is a labor- and cost-intensive task, simulations are a cheap alternative to generate datasets of arbitrary sizes, particularly useful for data-intensive deep learning methods. In this paper, we present the tool EnergyPlus Room Simulator, which enables the simulation of indoor climate in a specific room of a building using the simulation software EnergyPlus. It allows to alter room models and simulate various factors such as temperature, humidity, and CO2 concentration. In contrast to manually working with EnergyPlus, this tool enhances the simulation process by offering a convenient interface, including a user-friendly graphical user interface (GUI) as well as a REST API. The tool is intended to support scientific, building-related tasks such as occupancy detection on a room level by facilitating fast access to simulation data that may, for instance, be used for pre-training machine learning models.
Robust Universum Twin Support Vector Machine for Imbalanced Data
Authors: M. Tanveer, A. Quadir
Date: 2024-10-27 00:00:00

One of the major difficulties in machine learning methods is categorizing datasets that are imbalanced. This problem may lead to biased models, where the training process is dominated by the majority class, resulting in inadequate representation of the minority class. Universum twin support vector machine (UTSVM) produces a biased model towards the majority class, as a result, its performance on the minority class is often poor as it might be mistakenly classified as noise. Moreover, UTSVM is not proficient in handling datasets that contain outliers and noises. Inspired by the concept of incorporating prior information about the data and employing an intuitionistic fuzzy membership scheme, we propose intuitionistic fuzzy UTSVM for imbalanced data (IFUTSVM-ID) by enhancing overall robustness. We use an intuitionistic fuzzy membership scheme to mitigate the impact of noise and outliers. Moreover, to tackle the problem of imbalanced class distribution, data oversampling and undersampling methods are utilized. Prior knowledge about the data is provided by universum data. This leads to better generalization performance. UTSVM is susceptible to overfitting risks due to the omission of the structural risk minimization (SRM) principle in their primal formulations. However, the proposed IFUTSVM-ID model incorporates the SRM principle through the incorporation of regularization terms, effectively addressing the issue of overfitting. We conduct a comprehensive evaluation of the proposed IFUTSVM-ID model on benchmark datasets from KEEL and compare it with existing baseline models. Furthermore, to assess the effectiveness of the proposed IFUTSVM-ID model in diagnosing Alzheimer's disease (AD), we applied them to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results showcase the superiority of the proposed IFUTSVM-ID models compared to the baseline models.
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning
Authors: Jinze Wang, Jiong Jin, Tiehua Zhang, Boon Xian Chai, Adriano Di Pietro, Dimitrios Georgakopoulos
Date: 2024-10-27 00:00:00
Tasks: Fault Diagnosis, Meta-Learning, Robust classification, Transfer Learning

The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning in automating fault identification, the scarcity of labeled training data, particularly for equipment failure instances, poses a significant challenge. This limitation hampers the development of robust classification models. Existing methods like model-agnostic meta-learning (MAML) do not adequately address variable working conditions, affecting knowledge transfer. To address these challenges, a Related Task Aware Curriculum Meta-learning (RT-ACM) enhanced fault diagnosis framework is proposed in this paper, inspired by human cognitive learning processes. RT-ACM improves training by considering the relevance of auxiliary sensor working conditions, adhering to the principle of ``paying more attention to more relevant knowledge", and focusing on ``easier first, harder later" curriculum sampling. This approach aids the meta-learner in achieving a superior convergence state. Extensive experiments on two real-world datasets demonstrate the superiority of RT-ACM framework.
Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
Authors: Yunhui Liang, Jianwen Gan, Yan Chen, Peng Zhou, Liang Du
Date: 2024-10-27 00:00:00
Tasks: feature selection, Re-Ranking

High-dimensional data is commonly encountered in numerous data analysis tasks. Feature selection techniques aim to identify the most representative features from the original high-dimensional data. Due to the absence of class label information, it is significantly more challenging to select appropriate features in unsupervised learning scenarios compared to supervised ones. Traditional unsupervised feature selection methods typically score the features of samples based on certain criteria, treating samples indiscriminately. However, these approaches fail to fully capture the internal structure of the data. The importance of different samples should vary, and there is a dual relationship between the weight of samples and features that will influence each other. Therefore, an unsupervised feature selection algorithm based on dual manifold re-ranking (DMRR) is proposed in this paper. Different similarity matrices are constructed to depict the manifold structures among samples, between samples and features, and among features themselves. Then, manifold re-ranking is performed by combining the initial scores of samples and features. By comparing DMRR with three original unsupervised feature selection algorithms and two unsupervised feature selection post-processing algorithms, experimental results confirm that the importance information of different samples and the dual relationship between sample and feature are beneficial for achieving better feature selection.
Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity
Authors: Lei Wang, Liang Du, Peng Zhou
Date: 2024-10-27 00:00:00
Tasks: Diversity

Multiple kernel learning (MKL) aims to find an optimal, consistent kernel function. In the hierarchical multiple kernel clustering (HMKC) algorithm, sample features are extracted layer by layer from a high-dimensional space to maximize the retention of effective information. However, information interaction between layers is often ignored. In this model, only corresponding nodes in adjacent layers exchange information; other nodes remain isolated, and if full connectivity is adopted, the diversity of the final consistency matrix is reduced. Therefore, this paper proposes a hierarchical multiple kernel K-Means (SCHMKKM) algorithm based on sparse connectivity, which controls the assignment matrix to achieve sparse connections through a sparsity rate, thereby locally fusing the features obtained by distilling information between layers. Finally, we conduct cluster analysis on multiple datasets and compare it with the fully connected hierarchical multiple kernel K-Means (FCHMKKM) algorithm in experiments. It is shown that more discriminative information fusion is beneficial for learning a better consistent partition matrix, and the fusion strategy based on sparse connection outperforms the full connection strategy.
TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation
Authors: Juntong Shi, Minkai Xu, Harper Hua, Hengrui Zhang, Stefano Ermon, Jure Leskovec
Date: 2024-10-27 00:00:00
Tasks: Imputation, Tabular Data Generation

Synthesizing high-quality tabular data is an important topic in many data science tasks, ranging from dataset augmentation to privacy protection. However, developing expressive generative models for tabular data is challenging due to its inherent heterogeneous data types, complex inter-correlations, and intricate column-wise distributions. In this paper, we introduce TabDiff, a joint diffusion framework that models all multi-modal distributions of tabular data in one model. Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data, where we propose feature-wise learnable diffusion processes to counter the high disparity of different feature distributions. TabDiff is parameterized by a transformer handling different input types, and the entire framework can be efficiently optimized in an end-to-end fashion. We further introduce a multi-modal stochastic sampler to automatically correct the accumulated decoding error during sampling, and propose classifier-free guidance for conditional missing column value imputation. Comprehensive experiments on seven datasets demonstrate that TabDiff achieves superior average performance over existing competitive baselines across all eight metrics, with up to $22.5\%$ improvement over the state-of-the-art model on pair-wise column correlation estimations. Code is available at https://github.com/MinkaiXu/TabDiff.
Plastic Learning with Deep Fourier Features
Authors: Alex Lewandowski, Dale Schuurmans, Marlos C. Machado
Date: 2024-10-27 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning

Deep neural networks can struggle to learn continually in the face of non-stationarity. This phenomenon is known as loss of plasticity. In this paper, we identify underlying principles that lead to plastic algorithms. In particular, we provide theoretical results showing that linear function approximation, as well as a special case of deep linear networks, do not suffer from loss of plasticity. We then propose deep Fourier features, which are the concatenation of a sine and cosine in every layer, and we show that this combination provides a dynamic balance between the trainability obtained through linearity and the effectiveness obtained through the nonlinearity of neural networks. Deep networks composed entirely of deep Fourier features are highly trainable and sustain their trainability over the course of learning. Our empirical results show that continual learning performance can be drastically improved by replacing ReLU activations with deep Fourier features. These results hold for different continual learning scenarios (e.g., label noise, class incremental learning, pixel permutations) on all major supervised learning datasets used for continual learning research, such as CIFAR10, CIFAR100, and tiny-ImageNet.
Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis
Authors: William Murphy, Nikolaus Holzer, Feitong Qiao, Leyi Cui, Raven Rothkopf, Nathan Koenig, Mark Santolucito
Date: 2024-09-18 00:00:00
Tasks: Code Generation, Program Synthesis

In the past few years, Large Language Models (LLMs) have exploded in usefulness and popularity for code generation tasks. However, LLMs still struggle with accuracy and are unsuitable for high-risk applications without additional oversight and verification. In particular, they perform poorly at generating code for highly complex systems, especially with unusual or out-of-sample logic. For such systems, verifying the code generated by the LLM may take longer than writing it by hand. We introduce a solution that divides the code generation into two parts; one to be handled by an LLM and one to be handled by formal methods-based program synthesis. We develop a benchmark to test our solution and show that our method allows the pipeline to solve problems previously intractable for LLM code generation.
Physical Simulation for Multi-agent Multi-machine Tending
Authors: Abdalwhab Abdalwhab, Giovanni Beltrame, David St-Onge
Date: 2024-10-11 00:00:00
Tasks: Reinforcement Learning (RL)

The manufacturing sector was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize. Simultaneously, reinforcement learning (RL) offers a promising solution where robots can learn through interaction with the environment. In this work, we leveraged a simplistic robotic system to work with RL with "real" data without having to deploy large expensive robots in a manufacturing setting. A real-world tabletop arena was designed with robots that mimic the agents' behavior in the simulation. Despite the difference in dynamics and machine size, the robots were able to depict the same behavior as in the simulation. In addition, those experiments provided an initial understanding of the real deployment challenges.
Learning Robust Representations for Communications over Interference-limited Channels
Authors: Shubham Paul, Sudharsan Senthil, Preethi Seshadri, Nambi Seshadri, R David Koilpillai
Date: 2024-10-13 00:00:00

In the context of cellular networks, users located at the periphery of cells are particularly vulnerable to substantial interference from neighbouring cells, which can be represented as a two-user interference channel. This study introduces two highly effective methodologies, namely TwinNet and SiameseNet, using autoencoders, tailored for the design of encoders and decoders for block transmission and detection in interference-limited environments. The findings unambiguously illustrate that the developed models are capable of leveraging the interference structure to outperform traditional methods reliant on complete orthogonality. While it is recognized that systems employing coordinated transmissions and independent detection can offer greater capacity, the specific gains of data-driven models have not been thoroughly quantified or elucidated. This paper conducts an analysis to demonstrate the quantifiable advantages of such models in particular scenarios. Additionally, a comprehensive examination of the characteristics of codewords generated by these models is provided to offer a more intuitive comprehension of how these models achieve superior performance.