pwc-archive/papers-with-abstracts huggingface dataset:
CLAIMED: A CLAssification-Incorporated Minimum Energy Design to explore a multivariate response surface with feasibility constraints
Authors: Mert Y. Sengul, Yao Song, Linglin He, Adri C. T. van Duin, Ying Hung, Tirthankar Dasgupta
Date: 2020-06-09 00:00:00
Tasks: Experimental Design, General Classification

Motivated by the problem of optimization of force-field systems in physics using large-scale computer simulations, we consider exploration of a deterministic complex multivariate response surface. The objective is to find input combinations that generate output close to some desired or "target" vector. In spite of reducing the problem to exploration of the input space with respect to a one-dimensional loss function, the search is nontrivial and challenging due to infeasible input combinations, high dimensionalities of the input and output space and multiple "desirable" regions in the input space and the difficulty of emulating the objective function well with a surrogate model. We propose an approach that is based on combining machine learning techniques with smart experimental design ideas to locate multiple good regions in the input space.
Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
Authors: Wei Wu, Yu Shi, Xukun Li, Yukun Zhou, Peng Du, Shuangzhi Lv, Tingbo Liang, Jifang Sheng
Date: 2020-06-09 00:00:00
Tasks: Computed Tomography (CT), Prognosis

Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID-19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID-19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firstly utilized to generate the initial dirty version of labeled masks both for intact lung and infected regions. Then, the samples were carefully adjusted and improved by two professional radiologists to generate the final training set and test benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the segment of infected regions, a deep learning based classifier was followed to remove unrelated blur-edged regions that were wrongly segmented out such as air tube and blood vessel tissue etc. For the segmented masks of intact lung and infected regions, the best method could achieve 0.972 and 0.757 measure in mean Dice similarity coefficient on our test benchmark. As the overall proportion of infected region of lung, the final result showed 0.961 (Pearson's correlation coefficient) and 11.7% (mean absolute percent error). The instant proportion of infected regions of lung could be used as a visual evidence to assist clinical physician to determine the severity of the case. Furthermore, a quantified report of infected regions can help predict the prognosis for COVID-19 cases which were scanned periodically within the treatment cycle.
Can Synthetic Data Improve Object Detection Results for Remote Sensing Images?
Authors: Weixing Liu, Jun Liu, Bin Luo
Date: 2020-06-09 00:00:00
Tasks: object-detection, Object Detection

Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose the use of realistic synthetic data with a wide distribution to improve the performance of remote sensing image aircraft detection. Specifically, to increase the variability of synthetic data, we randomly set the parameters during rendering, such as the size of the instance and the class of background images. In order to make the synthetic images more realistic, we then refine the synthetic images at the pixel level using CycleGAN with real unlabeled images. We also fine-tune the model with a small amount of real data, to obtain a higher accuracy. Experiments on NWPU VHR-10, UCAS-AOD and DIOR datasets demonstrate that the proposed method can be applied for augmenting insufficient real data.
HausaMT v1.0: Towards English-Hausa Neural Machine Translation
Authors: Adewale Akinfaderin
Date: 2020-06-09 00:00:00
Tasks: Decoder, Diversity, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) for low-resource languages suffers from low performance because of the lack of large amounts of parallel data and language diversity. To contribute to ameliorating this problem, we built a baseline model for English-Hausa machine translation, which is considered a task for low-resource language. The Hausa language is the second largest Afro-Asiatic language in the world after Arabic and it is the third largest language for trading across a larger swath of West Africa countries, after English and French. In this paper, we curated different datasets containing Hausa-English parallel corpus for our translation. We trained baseline models and evaluated the performance of our models using the Recurrent and Transformer encoder-decoder architecture with two tokenization approaches: standard word-level tokenization and Byte Pair Encoding (BPE) subword tokenization.
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent
Authors: Zhenyu Liao, Romain Couillet, Michael W. Mahoney
Date: 2020-06-09 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: regression

This article characterizes the exact asymptotics of random Fourier feature (RFF) regression, in the realistic setting where the number of data samples $n$, their dimension $p$, and the dimension of feature space $N$ are all large and comparable. In this regime, the random RFF Gram matrix no longer converges to the well-known limiting Gaussian kernel matrix (as it does when $N \to \infty$ alone), but it still has a tractable behavior that is captured by our analysis. This analysis also provides accurate estimates of training and test regression errors for large $n,p,N$. Based on these estimates, a precise characterization of two qualitatively different phases of learning, including the phase transition between them, is provided; and the corresponding double descent test error curve is derived from this phase transition behavior. These results do not depend on strong assumptions on the data distribution, and they perfectly match empirical results on real-world data sets.
RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
Authors: Etienne Dubeau, Mathieu Garon, Benoit Debaque, Raoul de Charette, Jean-François Lalonde
Date: 2020-06-09 00:00:00
Tasks: 3D Object Tracking, Camera Calibration, Object, Object Tracking

Augmented reality devices require multiple sensors to perform various tasks such as localization and tracking. Currently, popular cameras are mostly frame-based (e.g. RGB and Depth) which impose a high data bandwidth and power usage. With the necessity for low power and more responsive augmented reality systems, using solely frame-based sensors imposes limits to the various algorithms that needs high frequency data from the environement. As such, event-based sensors have become increasingly popular due to their low power, bandwidth and latency, as well as their very high frequency data acquisition capabilities. In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom. This application requires handling very high object speed to convey compelling AR experiences. To this end, we propose a new system which combines a recent RGB-D sensor (Kinect Azure) with an event camera (DAVIS346). We develop a deep learning approach, which combines an existing RGB-D network along with a novel event-based network in a cascade fashion, and demonstrate that our approach significantly improves the robustness of a state-of-the-art frame-based 6-DOF object tracker using our RGB-D-E pipeline.
Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets
Authors: Qianyu Liu, Chiew Foong Kwong, Sun Wei, Sijia Zhou, Lincan Li
Date: 2020-06-09 00:00:00
Tasks: Clustering, Management, Q-Learning

5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macro system. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimised triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature. Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimised FLHA can effectively generate MF and fuzzy rules for the FLHA. By comparing with conventional triggering mechanisms, the proposed approach can decrease the HO, ping-pong HO, and HO failure ratios by approximately 91%, 49%, and 97.5% while improving network throughput and latency by 8% and 35%, respectively.
Few-Shot Generative Conversational Query Rewriting
Authors: Shi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong, Paul Bennett, Jianfeng Gao, Zhiyuan Liu
Date: 2020-06-09 00:00:00
Tasks: Information Retrieval, Retrieval, Self-Supervised Learning, Zero-Shot Learning

Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing information retrieval systems. This paper presents a few-shot generative approach to conversational query rewriting. We develop two methods, based on rules and self-supervised learning, to generate weak supervision data using large amounts of ad hoc search sessions, and to fine-tune GPT-2 to rewrite conversational queries. On the TREC Conversational Assistance Track, our weakly supervised GPT-2 rewriter improves the state-of-the-art ranking accuracy by 12%, only using very limited amounts of manual query rewrites. In the zero-shot learning setting, the rewriter still gives a comparable result to previous state-of-the-art systems. Our analyses reveal that GPT-2 effectively picks up the task syntax and learns to capture context dependencies, even for hard cases that involve group references and long-turn dependencies.
Universal Vector Neural Machine Translation With Effective Attention
Authors: Satish Mylapore, Ryan Quincy Paul, Joshua Yi, Robert D. Slater
Date: 2020-06-09 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever introduced a sequence to sequence based encoder-decoder model which became the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sentences and improving overall accuracy. In this paper, we propose a singular model for Neural Machine Translation based on encoder-decoder models. Most translation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on the source and a provided target. Secondly, we introduce an attention model by adding an overall learning vector to the multiplicative model. With these two changes, by using the novel universal model the number of models needed for multiple language translation applications are reduced.
In Proximity of ReLU DNN, PWA Function, and Explicit MPC
Authors: Saman Fahandezh-Saadi, Masayoshi Tomizuka
Date: 2020-06-09 00:00:00
Tasks: Model Predictive Control

Rectifier (ReLU) deep neural networks (DNN) and their connection with piecewise affine (PWA) functions is analyzed. The paper is an effort to find and study the possibility of representing explicit state feedback policy of model predictive control (MPC) as a ReLU DNN, and vice versa. The complexity and architecture of DNN has been examined through some theorems and discussions. An approximate method has been developed for identification of input-space in ReLU net which results a PWA function over polyhedral regions. Also, inverse multiparametric linear or quadratic programs (mp-LP or mp-QP) has been studied which deals with reconstruction of constraints and cost function given a PWA function.
Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort
Authors: Eduardo Jose Mortani Barbosa Jr., Bogdan Georgescu, Shikha Chaganti, Gorka Bastarrika Aleman, Jordi Broncano Cabrero, Guillaume Chabin, Thomas Flohr, Philippe Grenier, Sasa Grbic, Nakul Gupta, François Mellot, Savvas Nicolaou, Thomas Re, Pina Sanelli, Alexander W. Sauter, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu
Date: 2020-06-09 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, General Classification, Specificity

Objectives: To investigate machine-learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, ILD and normal CTs. Methods: Our retrospective multi-institutional study obtained 2096 chest CTs from 16 institutions (including 1077 COVID-19 patients). Training/testing cohorts included 927/100 COVID-19, 388/33 ILD, 189/33 other pneumonias, and 559/34 normal (no pathologies) CTs. A metric-based approach for classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning-based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. Results: Most discriminative features of COVID-19 are percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC=0.83, sensitivity=0.74, and specificity=0.79 of versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. Conclusions: Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and no pathologies CTs, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance, and therefore may be useful to facilitate diagnosis of COVID-19.
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
Authors: Omar Abdel Wahab, Jamal Bentahar, Robin Cohen, Hadi Otrok, Azzam Mourad
Date: 2020-06-09 00:00:00

In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in which an agent requests recommendations from multiple parties to build trust toward another agent. At the collection level, we propose to allow agents to self-assess the accuracy of their recommendations and autonomously decide on whether they would participate in the recommendation process or not. At the processing level, we propose a recommendations aggregation technique that is resilient to collusion attacks, followed by a credibility update mechanism for the participating agents. The originality of our work stems from its consideration of dishonest opinions at both the collection and processing levels, which allows for better and more persistent protection against dishonest recommenders. Experiments conducted on the Epinions dataset show that our solution yields better performance in protecting the recommendation process against Sybil attacks, in comparison with a competing model that derives the optimal network of advisors based on the agents' trust values.
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Authors: Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei
Date: 2020-06-09 00:00:00
Proceeding: ICML 2020 1
Tasks: Domain Adaptation, Pseudo Label, Unsupervised Domain Adaptation

We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. Instead, we present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels. Theoretical analysis reveals the existence of a domain-discriminator shortcut in misaligned classes, which is addressed by the proposed implicit alignment approach to facilitate domain-adversarial learning. Empirical results and ablation studies confirm the effectiveness of the proposed approach, especially in the presence of within-domain class imbalance and between-domain class distribution shift.
Deep Stock Predictions
Authors: Akash Doshi, Alexander Issa, Puneet Sachdeva, Sina Rafati, Somnath Rakshit
Date: 2020-06-08 00:00:00
Tasks: Portfolio Optimization, Prediction, Stock Price Prediction, Time Series, Time Series Analysis, Time Series Prediction

Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider the design of a trading strategy that performs portfolio optimization using the LSTM stock price prediction for four different companies. We then customize the loss function used to train the LSTM to increase the profit earned. Moreover, we propose a data driven approach for optimal selection of window length and multi-step prediction length, and consider the addition of analyst calls as technical indicators to a multi-stack Bidirectional LSTM strengthened by the addition of Attention units. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA, while the addition of analyst call does improve the performance for certain datasets.
Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification
Authors: Zhizheng Zhang, Cuiling Lan, Wen-Jun Zeng, Zhibo Chen, Shih-Fu Chang
Date: 2020-06-08 00:00:00
Tasks: Classification, General Classification, Metric Learning, Multi-class Classification, Person Re-Identification, Triplet

Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulling closer features of the same identity and pushing far apart features of different identities. In recent years, triplet loss achieves superior performance and is predominant in ReID. However, triplet loss considers only three instances of two classes in per-query optimization (with an anchor sample as query) and it is actually equivalent to a two-class classification. There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID. In this paper, we introduce a multi-class classification loss, i.e., N-tuple loss, to jointly consider multiple (N) instances for per-query optimization. This in fact aligns better with the ReID test/inference process, which conducts the ranking/comparisons among multiple instances. Furthermore, for more efficient multi-class classification, we propose a new meta prototypical N-tuple loss. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets.
Object Segmentation Without Labels with Large-Scale Generative Models
Authors: Andrey Voynov, Stanislav Morozov, Artem Babenko
Date: 2020-06-08 00:00:00
Tasks: image-classification, Image Classification, Object, Saliency Detection, Segmentation, Self-Supervised Learning, Semantic Segmentation, Unsupervised Object Segmentation

The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks. Furthermore, recent works employed these representations in a fully unsupervised setup for image classification, reducing the need for human labels on the fine-tuning stage as well. This work demonstrates that large-scale unsupervised models can also perform a more challenging object segmentation task, requiring neither pixel-level nor image-level labeling. Namely, we show that recent unsupervised GANs allow to differentiate between foreground/background pixels, providing high-quality saliency masks. By extensive comparison on standard benchmarks, we outperform existing unsupervised alternatives for object segmentation, achieving new state-of-the-art.
5* Knowledge Graph Embeddings with Projective Transformations
Authors: Mojtaba Nayyeri, Sahar Vahdati, Can Aykul, Jens Lehmann
Date: 2020-06-08 00:00:00
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction, Translation

Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of subgraphs is also transformed. Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs. However, multi-relational knowledge graphs often include multiple sub-graph structures in a neighborhood (e.g. combinations of path and loop structures), which current embedding models do not capture well. To tackle this problem, we propose a novel KGE model (5*E) in projective geometry, which supports multiple simultaneous transformations - specifically inversion, reflection, translation, rotation, and homothety. The model has several favorable theoretical properties and subsumes the existing approaches. It outperforms them on the most widely used link prediction benchmarks