pwc-archive/papers-with-abstracts huggingface dataset:
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models
Authors: J. Moran A., P. G. Morato, P. Rigo
Date: 2023-02-23 00:00:00
Tasks: Active Learning

Existing active strategies for training surrogate models yield accurate structural reliability estimates by aiming at design space regions in the vicinity of a specified limit state function. In many practical engineering applications, various damage conditions, e.g. repair, failure, should be probabilistically characterized, thus demanding the estimation of multiple performance functions. In this work, we investigate the capability of active learning approaches for efficiently selecting training samples under a limited computational budget while still preserving the accuracy associated with multiple surrogated limit states. Specifically, PC-Kriging-based surrogate models are actively trained considering a variance correction derived from leave-one-out cross-validation error information, whereas the sequential learning scheme relies on U-function-derived metrics. The proposed active learning approaches are tested in a highly nonlinear structural reliability setting, whereas in a more practical application, failure and repair events are stochastically predicted in the aftermath of a ship collision against an offshore wind substructure. The results show that a balanced computational budget administration can be effectively achieved by successively targeting the specified multiple limit state functions within a unified active learning scheme.
Deep learning model for Mongolian Citizens Feedback Analysis using Word Vector Embeddings
Authors: Zolzaya Dashdorj, Tsetsentsengel Munkhbayar, Stanislav Grigorev
Date: 2023-02-23 00:00:00
Tasks: Deep Learning, Sentence, Word Embeddings

A large amount of feedback was collected over the years. Many feedback analysis models have been developed focusing on the English language. Recognizing the concept of feedback is challenging and crucial in languages which do not have applicable corpus and tools employed in Natural Language Processing (i.e., vocabulary corpus, sentence structure rules, etc). However, in this paper, we study a feedback classification in Mongolian language using two different word embeddings for deep learning. We compare the results of proposed approaches. We use feedback data in Cyrillic collected from 2012-2018. The result indicates that word embeddings using their own dataset improve the deep learning based proposed model with the best accuracy of 80.1% and 82.7% for two classification tasks.
SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention
Authors: Konstantinos Kogkalidis, Michael Moortgat, Richard Moot
Date: 2023-02-23 00:00:00
Tasks: Graph Attention

This paper describes SPINDLE - an open source Python module implementing an efficient and accurate parser for written Dutch that transforms raw text input to programs for meaning composition, expressed as {\lambda} terms. The parser integrates a number of breakthrough advances made in recent years. Its output consists of hi-res derivations of a multimodal type-logical grammar, capturing two orthogonal axes of syntax, namely deep function-argument structures and dependency relations. These are produced by three interdependent systems: a static type-checker asserting the well-formedness of grammatical analyses, a state-of-the-art, structurally-aware supertagger based on heterogeneous graph convolutions, and a massively parallel proof search component based on Sinkhorn iterations. Packed in the software are also handy utilities and extras for proof visualization and inference, intended to facilitate end-user utilization.
Natural Language Processing in the Legal Domain
Authors: Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Bommarito II
Date: 2023-02-23 00:00:00

In this paper, we summarize the current state of the field of NLP & Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a nearly complete corpus of more than six hundred NLP & Law related papers published over the past decade. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and languages covered over the course of the past decade. We observe an increase in the sophistication of the methods which researchers deployed in this applied context. Slowly but surely, Legal NLP is beginning to match not only the methodological sophistication of general NLP but also the professional standards of data availability and code reproducibility observed within the broader scientific community. We believe all of these trends bode well for the future of the field, but many questions in both the academic and commercial sphere still remain open.
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Authors: Andrea Coletta, Svitlana Vyetrenko, Tucker Balch
Date: 2023-02-23 00:00:00
Tasks: Clustering, Decision Making, Imitation Learning

Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with the environment and other agents. While multiple learning techniques have been proposed in the literature, there is one particular setting that has not been explored yet: multi agent systems where agent identities remain anonymous. For instance, in financial markets labeled data that identifies market participant strategies is typically proprietary, and only the anonymous state-action pairs that result from the interaction of multiple market participants are publicly available. As a result, sequences of agent actions are not observable, restricting the applicability of existing work. In this paper, we propose a Policy Clustering algorithm, called K-SHAP, that learns to group anonymous state-action pairs according to the agent policies. We frame the problem as an Imitation Learning (IL) task, and we learn a world-policy able to mimic all the agent behaviors upon different environmental states. We leverage the world-policy to explain each anonymous observation through an additive feature attribution method called SHAP (SHapley Additive exPlanations). Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly. We evaluate our approach on simulated synthetic market data and a real-world financial dataset. We show that our proposal significantly and consistently outperforms the existing methods, identifying different agent strategies.
Unsupervised Noise adaptation using Data Simulation
Authors: Chen Chen, Yuchen Hu, Heqing Zou, Linhui Sun, Eng Siong Chng
Date: 2023-02-23 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network, Speech Enhancement

Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm. However, such a trained-well transformation is vulnerable to unseen noises that are not included in training set. In this work, we focus on the unsupervised noise adaptation problem in speech enhancement, where the ground truth of target domain data is completely unavailable. Specifically, we propose a generative adversarial network based method to efficiently learn a converse clean-to-noisy transformation using a few minutes of unpaired target domain data. Then this transformation is utilized to generate sufficient simulated data for domain adaptation of the enhancement model. Experimental results show that our method effectively mitigates the domain mismatch between training and test sets, and surpasses the best baseline by a large margin.
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
Authors: Zhichen Lai, Dalin Zhang, Huan Li, Christian S. Jensen, Hua Lu, Yan Zhao
Date: 2023-02-23 00:00:00
Tasks: Computational Efficiency, Correlated Time Series Forecasting, Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting, Traffic Prediction, Univariate Time Series Forecasting

Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensive, they struggle to improve accuracy. Pursuing a different direction, this study aims instead to enable much more efficient, lightweight models that preserve accuracy while being able to be deployed on resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operator modules, called L-TCN and GL-Former, that offer improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Experiments with single-step and multi-step forecasting benchmark datasets show that LightCTS is capable of nearly state-of-the-art accuracy at much reduced computational and storage overheads.
The Generalizability of Explanations
Authors: Hanxiao Tan
Date: 2023-02-23 00:00:00

Due to the absence of ground truth, objective evaluation of explainability methods is an essential research direction. So far, the vast majority of evaluations can be summarized into three categories, namely human evaluation, sensitivity testing, and salinity check. This work proposes a novel evaluation methodology from the perspective of generalizability. We employ an Autoencoder to learn the distributions of the generated explanations and observe their learnability as well as the plausibility of the learned distributional features. We first briefly demonstrate the evaluation idea of the proposed approach at LIME, and then quantitatively evaluate multiple popular explainability methods. We also find that smoothing the explanations with SmoothGrad can significantly enhance the generalizability of explanations.
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
Authors: Zhengbao He, Tao Li, Sizhe Chen, Xiaolin Huang
Date: 2023-02-23 00:00:00
Tasks: Self-Learning

Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting". Self-fitting, i.e., the network learns the self-information embedded in single-step perturbations, naturally leads to the occurrence of CO. When self-fitting occurs, the network experiences an obvious "channel differentiation" phenomenon that some convolution channels accounting for recognizing self-information become dominant, while others for data-information are suppressed. In this way, the network can only recognize images with sufficient self-information and loses generalization ability to other types of data. Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training. Our findings reveal a self-learning mechanism in adversarial training and open up new perspectives for suppressing different kinds of information to mitigate CO.
Sentence Simplification via Large Language Models
Authors: Yutao Feng, Jipeng Qiang, Yun Li, Yunhao Yuan, Yi Zhu
Date: 2023-02-23 00:00:00
Tasks: Few-Shot Learning, Sentence

Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning. Large Language models (LLMs) have demonstrated the ability to perform a variety of natural language processing tasks. However, it is not yet known whether LLMs can be served as a high-quality sentence simplification system. In this work, we empirically analyze the zero-/few-shot learning ability of LLMs by evaluating them on a number of benchmark test sets. Experimental results show LLMs outperform state-of-the-art sentence simplification methods, and are judged to be on a par with human annotators.
Evaluating the Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation
Authors: Ling Li, Bandara Dissanayake, Tatsuya Omotezako, Yunjie Zhong, Qing Zhang, Rizhao Cai, Qian Zheng, Dennis Sng, Weisi Lin, YuFei Wang, Alex C Kot
Date: 2023-02-23 00:00:00

Simulating the effects of skincare products on face is a potential new way to communicate the efficacy of skincare products in skin diagnostics and product recommendations. Furthermore, such simulations enable one to anticipate his/her skin conditions and better manage skin health. However, there is a lack of effective simulations today. In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products. Our simulation pipeline consists of 2 steps: training data establishment and facial pore simulation. To establish training data, we collect face images with various pore quality indexes from short-term (8-weeks) clinical studies. People often experience significant skin fluctuations (due to natural rhythms, external stressors, etc.,), which introduces large perturbations in clinical data. To address this problem, we propose a sliding window mechanism to clean data and select representative index(es) to represent facial pore changes. Facial pore simulation stage consists of 3 modules: UNet-based segmentation module to localize facial pores; regression module to predict time-dependent warping hyperparameters; and deformation module, taking warping hyperparameters and pore segmentation labels as inputs, to precisely deform pores accordingly. The proposed simulation is able to render realistic facial pore changes. And this work will pave the way for future research in facial skin simulation and skincare product developments.
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Authors: Yunyang Zhang, Zhiqiang Gong, Xiaoyu Zhao, Wen Yao
Date: 2023-02-23 00:00:00
Tasks: Pseudo Label

Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering. General reconstruction methods for recovering the field, especially the deep learning with more parameters and better representational ability, usually require large amounts of labeled data which is unaffordable. To solve the problem, this paper proposes Uncertainty Guided Ensemble Self-Training (UGE-ST), using plentiful unlabeled data to improve reconstruction performance. A novel self-training framework with the ensemble teacher and pretraining student designed to improve the accuracy of the pseudo-label and remedy the impact of noise is first proposed. On the other hand, uncertainty-guided learning is proposed to encourage the model to focus on the highly confident regions of pseudo-labels and mitigate the effects of wrong pseudo-labeling in self-training, improving the performance of the reconstruction model. Experiments include the pressure velocity field reconstruction of airfoil and the temperature field reconstruction of aircraft system indicate that our UGE-ST can save up to 90% of the data with the same accuracy as supervised learning.
One Fits All:Power General Time Series Analysis by Pretrained LM
Authors: Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin
Date: 2023-02-23 00:00:00
Tasks: Anomaly Detection, Few-Shot Learning, Time Series, Time Series Analysis, Time Series Forecasting

Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in each time series analysis task such as classification, anomaly detection, forecasting, and few-shot learning. The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training. In this work, we address this challenge by leveraging language or CV models, pre-trained from billions of tokens, for time series analysis. Specifically, we refrain from altering the self-attention and feedforward layers of the residual blocks in the pre-trained language or image model. This model, known as the Frozen Pretrained Transformer (FPT), is evaluated through fine-tuning on all major types of tasks involving time series. Our results demonstrate that pre-trained models on natural language or images can lead to a comparable or state-of-the-art performance in all main time series analysis tasks, as illustrated in Figure 1. We also found both theoretically and empirically that the self-attention module behaviors similarly to principle component analysis (PCA), an observation that helps explains how transformer bridges the domain gap and a crucial step towards understanding the universality of a pre-trained transformer.The code is publicly available at https://github.com/DAMO-DI-ML/One_Fits_All.
Improving safety in physical human-robot collaboration via deep metric learning
Authors: Maryam Rezayati, Grammatiki Zanni, Ying Zaoshi, Davide Scaramuzza, Hans Wernher van de Venn
Date: 2023-02-23 00:00:00
Tasks: Metric Learning

Direct physical interaction with robots is becoming increasingly important in flexible production scenarios, but robots without protective fences also pose a greater risk to the operator. In order to keep the risk potential low, relatively simple measures are prescribed for operation, such as stopping the robot if there is physical contact or if a safety distance is violated. Although human injuries can be largely avoided in this way, all such solutions have in common that real cooperation between humans and robots is hardly possible and therefore the advantages of working with such systems cannot develop its full potential. In human-robot collaboration scenarios, more sophisticated solutions are required that make it possible to adapt the robot's behavior to the operator and/or the current situation. Most importantly, during free robot movement, physical contact must be allowed for meaningful interaction and not recognized as a collision. However, here lies a key challenge for future systems: detecting human contact by using robot proprioception and machine learning algorithms. This work uses the Deep Metric Learning (DML) approach to distinguish between non-contact robot movement, intentional contact aimed at physical human-robot interaction, and collision situations. The achieved results are promising and show show that DML achieves 98.6\% accuracy, which is 4\% higher than the existing standards (i.e. a deep learning network trained without DML). It also indicates a promising generalization capability for easy portability to other robots (target robots) by detecting contact (distinguishing between contactless and intentional or accidental contact) without having to retrain the model with target robot data.
PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles
Authors: Andrea Piazzoni, Jim Cherian, Justin Dauwels, Lap-Pui Chau
Date: 2023-02-23 00:00:00
Tasks: Autonomous Vehicles

Even though virtual testing of Autonomous Vehicles (AVs) has been well recognized as essential for safety assessment, AV simulators are still undergoing active development. One particularly challenging question is to effectively include the Sensing and Perception (S&P) subsystem into the simulation loop. In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves. We propose a generalized data-driven procedure towards parametric modeling and evaluate it using Apollo, an open-source driving software, and nuScenes, a public AV dataset. Additionally, we implement PEMs in SVL, an open-source vehicle simulator. Furthermore, we demonstrate the usefulness of PEM-based virtual tests, by evaluating camera, LiDAR, and camera-LiDAR setups. Our virtual tests highlight limitations in the current evaluation metrics, and the proposed approach can help study the impact of perception errors on AV safety.
Diverse Policy Optimization for Structured Action Space
Authors: Wenhao Li, Baoxiang Wang, Shanchao Yang, Hongyuan Zha
Date: 2023-02-23 00:00:00
Tasks: Diversity, Reinforcement Learning (RL)

Enhancing the diversity of policies is beneficial for robustness, exploration, and transfer in reinforcement learning (RL). In this paper, we aim to seek diverse policies in an under-explored setting, namely RL tasks with structured action spaces with the two properties of composability and local dependencies. The complex action structure, non-uniform reward landscape, and subtle hyperparameter tuning due to the properties of structured actions prevent existing approaches from scaling well. We propose a simple and effective RL method, Diverse Policy Optimization (DPO), to model the policies in structured action space as the energy-based models (EBM) by following the probabilistic RL framework. A recently proposed novel and powerful generative model, GFlowNet, is introduced as the efficient, diverse EBM-based policy sampler. DPO follows a joint optimization framework: the outer layer uses the diverse policies sampled by the GFlowNet to update the EBM-based policies, which supports the GFlowNet training in the inner layer. Experiments on ATSC and Battle benchmarks demonstrate that DPO can efficiently discover surprisingly diverse policies in challenging scenarios and substantially outperform existing state-of-the-art methods.
Multi-Valued Neural Networks I A Multi-Valued Associative Memory
Authors: Dmitry Maximov, Vladimir I. Goncharenko, Yury S. Legovich
Date: 2023-02-23 00:00:00

A new concept of a multi-valued associative memory is introduced, generalizing a similar one in fuzzy neural networks. We expand the results on fuzzy associative memory with thresholds, to the case of a multi-valued one: we introduce the novel concept of such a network without numbers, investigate its properties, and give a learning algorithm in the multi-valued case. We discovered conditions under which it is possible to store given pairs of network variable patterns in such a multi-valued associative memory. In the multi-valued neural network, all variables are not numbers, but elements or subsets of a lattice, i.e., they are all only partially-ordered. Lattice operations are used to build the network output by inputs. In this paper, the lattice is assumed to be Brouwer and determines the implication used, together with other lattice operations, to determine the neural network output. We gave the example of the network use to classify aircraft/spacecraft trajectories.