pwc-archive/papers-with-abstracts huggingface dataset:
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery
Authors: Hang Fu, Genyun Sun, Aizhu Zhang, Xiuping Jia
Date: 2022-01-31 00:00:00
Proceeding: IEEE 2022 1
Tasks: Dimensionality Reduction

As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral–spatial HSI feature extraction and classification. Considering the overall spectra and adjacent band correlations of objects, the PCA and SPCA methods are utilized first for spectral dimension reduction, respectively. Then, multiscale 2-D-SSA is applied onto the SPCA dimension-reduced images to extract abundant spatial features at different scales, where PCA is applied again for dimensionality reduction. The obtained multiscale spatial features are then fused with the global spectral features derived from PCA to form multiscale spectral–spatial features (MSF-PCs). The performance of the extracted MSF-PCsis evaluated using the support vector machine (SVM) classifier. Experiments on four benchmark HSI data sets have shown that the proposed method outperforms other stateof-the-art feature extraction methods, including several deep learning approaches, when only a small number of training samples are available.
Parameter Identification of DC Motor based on Compound Least Square Method
Authors: Li, MD (Li, Meidong) [1] ; Ma, YJ (Ma, Yanjiao)
Date: 2021-03-02 00:00:00
Proceeding: IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) 2021 3

For forgetting factor least square identification results are prone to volatility of faults, by introducing selection control, this paper proposes a composite method of least square motor parameter identification, when the motor parameters change, using the composite method of least squares, the real-time identification of dc motor parameters could be faster, more accurate and more stable. The simulation results show that the hybrid least square parameter identifier overcomes the fluctuation phenomenon of the identification results of the forgetting factor least square parameter identifier, and its identification speed is faster, the accuracy is higher, the stability is better, and the motor parameters can be identified online well at different speeds of the dc motor.
Approach to Predicting News -- A Precise Multi-LSTM Network With BERT
Authors: Chia-Lin Chen, Pei-Yu Huang, Yi-Ting Huang, Chun Lin
Date: 2022-04-26 00:00:00
Tasks: Document Embedding, Sentence, Sentence Embeddings, Word Embeddings

Varieties of Democracy (V-Dem) is a new approach to conceptualizing and measuring democracy and politics. It has information for 200 countries and is one of the biggest databases for political science. According to the V-Dem annual democracy report 2019, Taiwan is one of the two countries that got disseminated false information from foreign governments the most. It also shows that the "made-up news" has caused a great deal of confusion in Taiwanese society and has serious impacts on global stability. Although there are several applications helping distinguish the false information, we found out that the pre-processing of categorizing the news is still done by human labor. However, human labor may cause mistakes and cannot work for a long time. The growing demands for automatic machines in the near decades show that while the machine can do as good as humans or even better, using machines can reduce humans' burden and cut down costs. Therefore, in this work, we build a predictive model to classify the category of news. The corpora we used contains 28358 news and 200 news scraped from the online newspaper Liberty Times Net (LTN) website and includes 8 categories: Technology, Entertainment, Fashion, Politics, Sports, International, Finance, and Health. At first, we use Bidirectional Encoder Representations from Transformers (BERT) for word embeddings which transform each Chinese character into a (1,768) vector. Then, we use a Long Short-Term Memory (LSTM) layer to transform word embeddings into sentence embeddings and add another LSTM layer to transform them into document embeddings. Each document embedding is an input for the final predicting model, which contains two Dense layers and one Activation layer. And each document embedding is transformed into 1 vector with 8 real numbers, then the highest one will correspond to the 8 news categories with up to 99% accuracy.
PyGOD: A Python Library for Graph Outlier Detection
Authors: Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu
Date: 2022-04-26 00:00:00
Tasks: Graph Outlier Detection

PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API designed for use by both researchers and practitioners. PyGOD provides modularized components of the different detectors implemented so that users can easily customize each detector for their purposes. To ease the construction of detection workflows, PyGOD offers numerous commonly used utility functions. To scale computation to large graphs, PyGOD supports functionalities for deep models such as sampling and mini-batch processing. PyGOD uses best practices in fostering code reliability and maintainability, including unit testing, continuous integration, and code coverage. To facilitate accessibility, PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI).
Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System
Authors: Tobias Schlosser, Frederik Beuth, Trixy Meyer, Arunodhayan Sampath Kumar, Gabriel Stolze, Olga Furashova, Katrin Engelmann, Danny Kowerko
Date: 2022-04-25 00:00:00

In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema (DME), as well as the retinal vein occlusion (RVO). However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. For the disease AMD, we found out a significant deterioration of the visual acuity over time. Within our proposed multistage system, we subsequently classify the VA progression into the three groups of therapy "winners", "stabilizers", and "losers" (WSL classification scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98 %, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modeling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM / no therapy. We achieve a final prediction accuracy of 69 % in macro average F1-score, while being in the same range as the ophthalmologists with 57.8 and 50 +- 10.7 % F1-score.
Cross Pairwise Ranking for Unbiased Item Recommendation
Authors: Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang
Date: 2022-04-26 00:00:00
Tasks: Recommendation Systems

Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The loss functions, such as the mostly used pointwise Binary Cross-Entropy and pairwise Bayesian Personalized Ranking, are not designed to consider the biases in observed data. As a result, the model optimized on the loss would inherit the data biases, or even worse, amplify the biases. For example, a few popular items take up more and more exposure opportunities, severely hurting the recommendation quality on niche items -- known as the notorious Mathew effect. In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism. Distinct from inverse propensity scoring (IPS), we change the loss term of a sample -- we innovatively sample multiple observed interactions once and form the loss as the combination of their predictions. We prove in theory that this way offsets the influence of user/item propensity on the learning, removing the influence of data biases caused by the exposure mechanism. Advantageous to IPS, our proposed CPR ensures unbiased learning for each training instance without the need of setting the propensity scores. Experimental results demonstrate the superiority of CPR over state-of-the-art debiasing solutions in both model generalization and training efficiency. The codes are available at https://github.com/Qcactus/CPR.
Uncorrelated Modes of the Nonlinear Power Spectrum
Authors: A. J. S. Hamilton
Date: 1999-05-15 00:00:00

Nonlinear evolution causes the galaxy power spectrum to become broadly correlated over different wavenumbers. It is shown that prewhitening the power spectrum - transforming the power spectrum in such a way that the noise covariance becomes proportional to the unit matrix - greatly narrows the covariance of power. The eigenfunctions of the covariance of the prewhitened nonlinear power spectrum provide a set of almost uncorrelated nonlinear modes somewhat analogous to the Fourier modes of the power spectrum itself in the linear, Gaussian regime. These almost uncorrelated modes make it possible to construct a near minimum variance estimator and Fisher matrix of the prewhitened nonlinear power spectrum analogous to the Feldman-Kaiser-Peacock estimator of the linear power spectrum. The paper concludes with summary recipes, in gourmet, fine, and fastfood versions, of how to measure the prewhitened nonlinear power spectrum from a galaxy survey in the FKP approximation. An Appendix presents FFTLog, a code for taking the fast Fourier or Hankel transform of a periodic sequence of logarithmically spaced points, which proves useful in some of the manipulations.
Digital Twins for Dynamic Management of Blockchain Systems
Authors: Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
Date: 2022-04-26 00:00:00
Tasks: Management

Blockchain systems are challenged by the so-called Trilemma tradeoff: decentralization, scalability and security. Infrastructure and node configuration, choice of the Consensus Protocol and complexity of the application transactions are cited amongst the factors that affect the tradeoffs balance. Given that Blockchains are complex, dynamic dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This paper introduces the utilisation of Digital Twins for this purpose. The novel contribution of the paper is design of a framework and conceptual architecture of a Digital Twin that can assist in maintaining the Trilemma tradeoffs of time critical systems. The proposed Digital Twin is illustrated via an innovative approach to dynamic selection of Consensus Protocols. Simulations results show that the proposed framework can effectively support the dynamic adaptation and management of the Blockchain
Coarse-to-fine Q-attention with Tree Expansion
Authors: Stephen James, Pieter Abbeel
Date: 2022-04-26 00:00:00
Tasks: Robot Manipulation

Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at each layer in the hierarchy. Although effective, Q-attention suffers from "coarse ambiguity" - when voxelization is significantly coarse, it is not feasible to distinguish similar-looking objects without first inspecting at a finer resolution. To combat this, we propose to envision Q-attention as a tree that can be expanded and used to accumulate value estimates across the top-k voxels at each Q-attention depth. When our extension, Q-attention with Tree Expansion (QTE), replaces standard Q-attention in the Attention-driven Robot Manipulation (ARM) system, we are able to accomplish a larger set of tasks; especially on those that suffer from "coarse ambiguity". In addition to evaluating our approach across 12 RLBench tasks, we also show that the improved performance is visible in a real-world task involving small objects.
Learning Value Functions from Undirected State-only Experience
Authors: Matthew Chang, Arjun Gupta, Saurabh Gupta
Date: 2022-04-26 00:00:00
Proceeding: ICLR 2022 4
Tasks: Future prediction, Imitation Learning, Offline RL, Q-Learning, Visual Navigation

This paper tackles the problem of learning value functions from undirected state-only experience (state transitions without action labels i.e. (s,s',r) tuples). We first theoretically characterize the applicability of Q-learning in this setting. We show that tabular Q-learning in discrete Markov decision processes (MDPs) learns the same value function under any arbitrary refinement of the action space. This theoretical result motivates the design of Latent Action Q-learning or LAQ, an offline RL method that can learn effective value functions from state-only experience. Latent Action Q-learning (LAQ) learns value functions using Q-learning on discrete latent actions obtained through a latent-variable future prediction model. We show that LAQ can recover value functions that have high correlation with value functions learned using ground truth actions. Value functions learned using LAQ lead to sample efficient acquisition of goal-directed behavior, can be used with domain-specific low-level controllers, and facilitate transfer across embodiments. Our experiments in 5 environments ranging from 2D grid world to 3D visual navigation in realistic environments demonstrate the benefits of LAQ over simpler alternatives, imitation learning oracles, and competing methods.
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?
Authors: Tiago Gonçalves, Isabel Rio-Torto, Luís F. Teixeira, Jaime S. Cardoso
Date: 2022-04-26 00:00:00
Tasks: image-classification, Image Classification, Medical Image Classification

The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning (including Transformers) for several medical applications. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.
Quantum-classical convolutional neural networks in radiological image classification
Authors: Andrea Matic, Maureen Monnet, Jeanette Miriam Lorenz, Balthasar Schachtner, Thomas Messerer
Date: 2022-04-26 00:00:00
Tasks: BIG-bench Machine Learning, Classification, image-classification, Image Classification, Quantum Machine Learning

Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts -- which might be particularly beneficial in situations with little training data available. Such situations naturally arise in medical classification tasks. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed. They are applied to two- and three-dimensional medical imaging data, e.g. featuring different, potentially malign, lesions in computed tomography scans. The performance of these QCCNNs is already similar to the one of their classical counterparts -- therefore encouraging further studies towards the direction of applying these algorithms within medical imaging tasks.
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings
Authors: Danushka Bollegala
Date: 2022-04-26 00:00:00
Tasks: Word Embeddings

Given multiple source word embeddings learnt using diverse algorithms and lexical resources, meta word embedding learning methods attempt to learn more accurate and wide-coverage word embeddings. Prior work on meta-embedding has repeatedly discovered that simple vector concatenation of the source embeddings to be a competitive baseline. However, it remains unclear as to why and when simple vector concatenation can produce accurate meta-embeddings. We show that weighted concatenation can be seen as a spectrum matching operation between each source embedding and the meta-embedding, minimising the pairwise inner-product loss. Following this theoretical analysis, we propose two \emph{unsupervised} methods to learn the optimal concatenation weights for creating meta-embeddings from a given set of source embeddings. Experimental results on multiple benchmark datasets show that the proposed weighted concatenated meta-embedding methods outperform previously proposed meta-embedding learning methods.
Multi-task Learning for Concurrent Prediction of Thermal Comfort, Sensation, and Preference
Authors: Betty Lala, Hamada Rizk, Srikant Manas Kala, Aya Hagishima
Date: 2022-04-26 00:00:00
Tasks: Multi-Task Learning, Prediction

Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the current focus is on data-driven TC prediction solutions that leverage state-of-the-art machine learning (ML) algorithms. However, an indoor occupant's perception of indoor thermal comfort (TC) is subjective and multi-dimensional. Different aspects of TC are represented by various standard metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and thermal preference (TPV). The current ML-based TC prediction solutions adopt the Single-task Learning approach, i.e., one prediction model per metric. Consequently, solutions often focus on only one TC metric. Moreover, when several metrics are considered, multiple TC models for a single indoor space lead to conflicting predictions, making real-world deployment infeasible. This work addresses these problems. With the vision toward energy conservation and real-world application, naturally ventilated primary school classrooms are considered. First, month-long field experiments are conducted in 5 schools and 14 classrooms, including 512 unique student participants. Further, "DeepComfort," a Multi-task Learning inspired deep-learning model is proposed. DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV, simultaneously, through a single model. It demonstrates high F1-scores, Accuracy (>90%), and generalization capability when validated on the ASHRAE-II database and the dataset created in this study. DeepComfort is also shown to outperform 6 popular metric-specific single-task machine learning algorithms. To the best of our knowledge, this work is the first application of Multi-task Learning to thermal comfort prediction in classrooms.
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks
Authors: Jens Henriksson, Christian Berger, Markus Borg, Lars Tornberg, Sankar Raman Sathyamoorthy, Cristofer Englund
Date: 2022-04-26 00:00:00
Tasks: Outlier Detection, Out-of-Distribution Detection

Several areas have been improved with Deep Learning during the past years. Implementing Deep Neural Networks (DNN) for non-safety related applications have shown remarkable achievements over the past years; however, for using DNNs in safety critical applications, we are missing approaches for verifying the robustness of such models. A common challenge for DNNs occurs when exposed to out-of-distribution samples that are outside of the scope of a DNN, but which result in high confidence outputs despite no prior knowledge of such input. In this paper, we analyze three methods that separate between in- and out-of-distribution data, called supervisors, on four well-known DNN architectures. We find that the outlier detection performance improves with the quality of the model. We also analyse the performance of the particular supervisors during the training procedure by applying the supervisor at a predefined interval to investigate its performance as the training proceeds. We observe that understanding the relationship between training results and supervisor performance is crucial to improve the model's robustness and to indicate, what input samples require further measures to improve the robustness of a DNN. In addition, our work paves the road towards an instrument for safety argumentation for safety critical applications. This paper is an extended version of our previous work presented at 2019 SEAA (cf. [1]); here, we elaborate on the used metrics, add an additional supervisor and test them on two additional datasets.
Designing Perceptual Puzzles by Differentiating Probabilistic Programs
Authors: Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum, Jonathan Ragan-Kelley
Date: 2022-04-26 00:00:00
Tasks: Bayesian Inference, Color Constancy, Probabilistic Programming

We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.