pwc-archive/papers-with-abstracts huggingface dataset:
Constructing Custom Thermodynamics Using Deep Learning
Authors: Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eleonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li
Date: 2023-08-08 00:00:00
Tasks: Deep Learning, Physical Intuition, scientific discovery

One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications.
Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers
Authors: Ji Wei Yoon, Adithya Kumar, Pawan Kumar, Kedar Hippalgaonkar, J Senthilnath, Vijila Chellappan
Date: 2023-08-08 00:00:00

The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity > 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.
Asynchronous Evolution of Deep Neural Network Architectures
Authors: Jason Liang, Hormoz Shahrzad, Risto Miikkulainen
Date: 2023-08-08 00:00:00
Tasks: Evolutionary Algorithms, Image Captioning, Neural Architecture Search

Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e.,\ compute clients) are idle much of the time, waiting for the next generation to be created. Evolutionary neural architecture search (ENAS), a class of EAs that optimizes the architecture and hyperparameters of deep neural networks, is particularly vulnerable to this issue. This paper proposes a generic asynchronous evaluation strategy (AES) that is then adapted to work with ENAS. AES increases throughput by maintaining a queue of up to $K$ individuals ready to be sent to the workers for evaluation and proceeding to the next generation as soon as $M<<K$ individuals have been evaluated. A suitable value for $M$ is determined experimentally, balancing diversity and efficiency. To showcase the generality and power of AES, it was first evaluated in eight-line sorting network design (a single-population optimization task with limited evaluation-time variability), achieving an over two-fold speedup. Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed. It was then scaled up to ENAS for image captioning (a multi-population open-ended-optimization task), resulting in an over two-fold speedup. In all problems, a multifold performance improvement was observed, suggesting that AES is a promising method for parallelizing the evolution of complex systems with long and variable evaluation times, such as those in ENAS.
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Authors: Florian J. Kiwit, Marwa Marso, Philipp Ross, Carlos A. Riofrío, Johannes Klepsch, Andre Luckow
Date: 2023-08-08 00:00:00
Tasks: Benchmarking, GPU, Quantum Machine Learning

Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection. The QUantum computing Application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e.g., the novelty and validity of the generated data.
Learning Specialized Activation Functions for Physics-informed Neural Networks
Authors: Honghui Wang, Lu Lu, Shiji Song, Gao Huang
Date: 2023-08-08 00:00:00

Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and activation functions. Specifically, we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties. Existing works usually choose activation functions by inefficient trial-and-error. To avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs, we introduce adaptive activation functions to search for the optimal function when solving different problems. We compare different adaptive activation functions and discuss their limitations in the context of PINNs. Furthermore, we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization, which has a higher requirement for the smoothness and diversity on learned functions. This is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at hand. We further enhance the search space with adaptive slopes. The proposed adaptive activation function can be used to solve different PDE systems in an interpretable way. Its effectiveness is demonstrated on a series of benchmarks. Code is available at https://github.com/LeapLabTHU/AdaAFforPINNs.
Toward Improving Predictive Risk Modelling for New Zealand's Child Welfare System Using Clustering Methods
Authors: Sahar Barmomanesh, Victor Miranda-Soberanis
Date: 2023-08-08 00:00:00
Tasks: Clustering

The combination of clinical judgement and predictive risk models crucially assist social workers to segregate children at risk of maltreatment and decide when authorities should intervene. Predictive risk modelling to address this matter has been initiated by several governmental welfare authorities worldwide involving administrative data and machine learning algorithms. While previous studies have investigated risk factors relating to child maltreatment, several gaps remain as to understanding how such risk factors interact and whether predictive risk models perform differently for children with different features. By integrating Principal Component Analysis and K-Means clustering, this paper presents initial findings of our work on the identification of such features as well as their potential effect on current risk modelling frameworks. This approach allows examining existent, unidentified yet, clusters of New Zealand (NZ) children reported with care and protection concerns, as well as to analyse their inner structure, and evaluate the performance of prediction models trained cluster wise. We aim to discover the extent of clustering degree required as an early step in the development of predictive risk models for child maltreatment and so enhance the accuracy of such models intended for use by child protection authorities. The results from testing LASSO logistic regression models trained on identified clusters revealed no significant difference in their performance. The models, however, performed slightly better for two clusters including younger children. our results suggest that separate models might need to be developed for children of certain age to gain additional control over the error rates and to improve model accuracy. While results are promising, more evidence is needed to draw definitive conclusions, and further investigation is necessary.
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization
Authors: Danny D'Agostino
Date: 2023-08-08 00:00:00
Tasks: Anomaly Detection, Bayesian Optimization, Dimensionality Reduction, global-optimization

Our work presents a novel approach to shape optimization, with the twofold objective to improve the efficiency of global optimization algorithms while promoting the generation of high-quality designs during the optimization process free of geometrical anomalies. This is accomplished by reducing the number of the original design variables defining a new reduced subspace where the geometrical variance is maximized and modeling the underlying generative process of the data via probabilistic linear latent variable models such as factor analysis and probabilistic principal component analysis. We show that the data follows approximately a Gaussian distribution when the shape modification method is linear and the design variables are sampled uniformly at random, due to the direct application of the central limit theorem. The degree of anomalousness is measured in terms of Mahalanobis distance, and the paper demonstrates that abnormal designs tend to exhibit a high value of this metric. This enables the definition of a new optimization model where anomalous geometries are penalized and consequently avoided during the optimization loop. The procedure is demonstrated for hull shape optimization of the DTMB 5415 model, extensively used as an international benchmark for shape optimization problems. The global optimization routine is carried out using Bayesian optimization and the DIRECT algorithm. From the numerical results, the new framework improves the convergence of global optimization algorithms, while only designs with high-quality geometrical features are generated through the optimization routine thereby avoiding the wastage of precious computationally expensive simulations.
A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset
Authors: Mamata Das, Selvakumar K., P. J. A. Alphonse
Date: 2023-08-08 00:00:00
Tasks: Classification, Information Retrieval, Retrieval, Sentiment Analysis, text-classification, Text Classification

Text Classification is the process of categorizing text into the relevant categories and its algorithms are at the core of many Natural Language Processing (NLP). Term Frequency-Inverse Document Frequency (TF-IDF) and NLP are the most highly used information retrieval methods in text classification. We have investigated and analyzed the feature weighting method for text classification on unstructured data. The proposed model considered two features N-Grams and TF-IDF on the IMDB movie reviews and Amazon Alexa reviews dataset for sentiment analysis. Then we have used the state-of-the-art classifier to validate the method i.e., Support Vector Machine (SVM), Logistic Regression, Multinomial Naive Bayes (Multinomial NB), Random Forest, Decision Tree, and k-nearest neighbors (KNN). From those two feature extractions, a significant increase in feature extraction with TF-IDF features rather than based on N-Gram. TF-IDF got the maximum accuracy (93.81%), precision (94.20%), recall (93.81%), and F1-score (91.99%) value in Random Forest classifier.
Continual Pre-Training of Large Language Models: How to (re)warm your model?
Authors: Kshitij Gupta, Benjamin Thérien, Adam Ibrahim, Mats L. Richter, Quentin Anthony, Eugene Belilovsky, Irina Rish, Timothée Lesort
Date: 2023-08-08 00:00:00
Tasks: Language Modelling

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes available. A much cheaper and more efficient solution would be to enable the continual pre-training of these models, i.e. updating pre-trained models with new data instead of re-training them from scratch. However, the distribution shift induced by novel data typically results in degraded performance on past data. Taking a step towards efficient continual pre-training, in this work, we examine the effect of different warm-up strategies. Our hypothesis is that the learning rate must be re-increased to improve compute efficiency when training on a new dataset. We study the warmup phase of models pre-trained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens), following a linear warmup and cosine decay schedule. We conduct all experiments on the Pythia 410M language model architecture and evaluate performance through validation perplexity. We experiment with different pre-training checkpoints, various maximum learning rates, and various warmup lengths. Our results show that while rewarming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch$\unicode{x2013}$even for a large downstream dataset.
Generalization bound for estimating causal effects from observational network data
Authors: Ruichu Cai, Zeqin Yang, Weilin Chen, Yuguang Yan, Zhifeng Hao
Date: 2023-08-08 00:00:00
Tasks: Causal Inference, Representation Learning

Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm.
Fourier neural operator for real-time simulation of 3D dynamic urban microclimate
Authors: Wenhui Peng, Shaoxiang Qin, Senwen Yang, Jianchun Wang, Xue Liu, Liangzhu Leon Wang
Date: 2023-08-08 00:00:00

Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency. They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible.
Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models
Authors: Anjian Li, Amlan Sinha, Ryne Beeson
Date: 2023-08-07 00:00:00
Tasks: Clustering

Preliminary trajectory design is a global search problem that seeks multiple qualitatively different solutions to a trajectory optimization problem. Due to its high dimensionality and non-convexity, and the frequent adjustment of problem parameters, the global search becomes computationally demanding. In this paper, we exploit the clustering structure in the solutions and propose an amortized global search (AmorGS) framework. We use deep generative models to predict trajectory solutions that share similar structures with previously solved problems, which accelerates the global search for unseen parameter values. Our method is evaluated using De Jong's 5th function and a low-thrust circular restricted three-body problem.
Fixed Inter-Neuron Covariability Induces Adversarial Robustness
Authors: Muhammad Ahmed Shah, Bhiksha Raj
Date: 2023-08-07 00:00:00
Tasks: Adversarial Robustness

The vulnerability to adversarial perturbations is a major flaw of Deep Neural Networks (DNNs) that raises question about their reliability when in real-world scenarios. On the other hand, human perception, which DNNs are supposed to emulate, is highly robust to such perturbations, indicating that there may be certain features of the human perception that make it robust but are not represented in the current class of DNNs. One such feature is that the activity of biological neurons is correlated and the structure of this correlation tends to be rather rigid over long spans of times, even if it hampers performance and learning. We hypothesize that integrating such constraints on the activations of a DNN would improve its adversarial robustness, and, to test this hypothesis, we have developed the Self-Consistent Activation (SCA) layer, which comprises of neurons whose activations are consistent with each other, as they conform to a fixed, but learned, covariability pattern. When evaluated on image and sound recognition tasks, the models with a SCA layer achieved high accuracy, and exhibited significantly greater robustness than multi-layer perceptron models to state-of-the-art Auto-PGD adversarial attacks \textit{without being trained on adversarially perturbed data
PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning
Authors: Yang Li, Shitu Zhang, Yuanzheng Li, Jiting Cao, Shuyue Jia
Date: 2023-08-07 00:00:00
Tasks: Data Augmentation, Deep Learning, Transfer Learning

Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers significant advantages over shallow learning methods and other deep learning-based approaches.
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Authors: Yulan Gao, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Han Yu
Date: 2023-08-07 00:00:00
Tasks: Federated Learning

Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental challenges of data heterogeneity and the large scale of data owners involved. In this paper, we investigate the prospect of Transformer-based FL models for achieving generalization and personalization in this setting. We conduct extensive comparative experiments involving FL with Transformers, ResNet, and personalized ResNet-based FL approaches under various scenarios. These experiments consider varying numbers of data owners to demonstrate Transformers' advantages over deep neural networks in large-scale heterogeneous FL tasks. In addition, we analyze the superior performance of Transformers by comparing the Centered Kernel Alignment (CKA) representation similarity across different layers and FL models to gain insight into the reasons behind their promising capabilities.