pwc-archive/papers-with-abstracts huggingface dataset:
An Extensible and Modular Design and Implementation of Monte Carlo Tree Search for the JVM
Authors: Larkin Liu, Jun Tao Luo
Date: 2021-07-30 00:00:00

Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be powerful for finding the solutions to problems in complex planning. We introduce mctreesearch4j, an MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizing class inheritance and generic typing to standardize custom algorithm definitions. We demonstrate that the design of the MCTS implementation provides ease of adaptation for unique heuristics and customization across varying Markov Decision Process (MDP) domains. In addition, the implementation is reasonably performant and accurate for standard MDP's. In addition, via the implementation of mctreesearch4j, the nuances of different types of MCTS algorithms are discussed.
Integrating LSTMs and GNNs for COVID-19 Forecasting
Authors: Nathan Sesti, Juan Jose Garau-Luis, Edward Crawley, Bruce Cameron
Date: 2021-07-14 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic. Many such models also include Long Short Term Memory (LSTM) networks, a common tool for time series forecasting. In this work, we further investigate the integration of these two methods by implementing GNNs within the gates of an LSTM and exploiting spatial information. In addition, we introduce a skip connection which proves critical to jointly capture the spatial and temporal patterns in the data. We validate our daily COVID-19 new cases forecast model on data of 37 European nations for the last 472 days and show superior performance compared to state-of-the-art graph time series models based on mean absolute scaled error (MASE). This area of research has important applications to policy-making and we analyze its potential for pandemic resource control.
Remote Sensing and Machine Learning for Food Crop Production Data in Africa Post-COVID-19
Authors: Racine Ly, Khadim Dia, Mariam Diallo
Date: 2021-07-14 00:00:00

In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6% and 7%. From the food crop production side, the travel bans and border closures, the late reception and the use of agricultural inputs such as imported seeds, fertilizers, and pesticides could lead to poor food crop production performances. Another layer of disruption introduced by the mobility restriction measures is the scarcity of agricultural workers, mainly seasonal workers. The lockdown measures and border closures limit seasonal workers' availability to get to the farm on time for planting and harvesting activities. Moreover, most of the imported agricultural inputs travel by air, which the pandemic has heavily impacted. Such transportation disruptions can also negatively affect the food crop production system. This chapter assesses food crop production levels in 2020 -- before the harvesting period -- in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique. The remote sensing products are used as input variables and the ANNs as the predictive modeling framework. The input remote sensing products are the Normalized Difference Vegetation Index (NDVI), the daytime Land Surface Temperature (LST), rainfall data, and agricultural lands' Evapotranspiration (ET). The output maps and data are made publicly available on a web-based platform, AAgWa (Africa Agriculture Watch, www.aagwa.org), to facilitate access to such information to policymakers, deciders, and other stakeholders.
Effective and Privacy preserving Tabular Data Synthesizing
Authors: Aditya Kunar
Date: 2021-08-11 00:00:00
Tasks: Attribute, Privacy Preserving

While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limits its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GAN). In this thesis, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types with complex distributions. CTAB-GAN is extensively evaluated with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results in higher accuracy for five machine learning algorithms, by up to 17%. Additionally, to ensure greater security for training tabular GANs against malicious privacy attacks, differential privacy (DP) is studied and used to train CTAB-GAN with strict privacy guarantees. DP-CTAB-GAN is rigorously evaluated using state-of-the-art DP-tabular GANs in terms of data utility and privacy robustness against membership and attribute inference attacks. Our results on three datasets indicate that strict theoretical differential privacy guarantees come only after severely affecting data utility. However, it is shown empirically that these guarantees help provide a stronger defence against privacy attacks. Overall, it is found that DP-CTABGAN is capable of being robust to privacy attacks while maintaining the highest data utility as compared to prior work, by up to 18% in terms of the average precision score.
Dynamic Neural Network Architectural and Topological Adaptation and Related Methods -- A Survey
Authors: Lorenz Kummer
Date: 2021-07-28 00:00:00

Training and inference in deep neural networks (DNNs) has, due to a steady increase in architectural complexity and data set size, lead to the development of strategies for reducing time and space requirements of DNN training and inference, which is of particular importance in scenarios where training takes place in resource constrained computation environments or inference is part of a time critical application. In this survey, we aim to provide a general overview and categorization of state-of-the-art (SOTA) of techniques to reduced DNN training and inference time and space complexities with a particular focus on architectural adaptions.
An Interpretable Approach to Hateful Meme Detection
Authors: Tanvi Deshpande, Nitya Mani
Date: 2021-08-09 00:00:00

Hateful memes are an emerging method of spreading hate on the internet, relying on both images and text to convey a hateful message. We take an interpretable approach to hateful meme detection, using machine learning and simple heuristics to identify the features most important to classifying a meme as hateful. In the process, we build a gradient-boosted decision tree and an LSTM-based model that achieve comparable performance (73.8 validation and 72.7 test auROC) to the gold standard of humans and state-of-the-art transformer models on this challenging task.
Construction Cost Index Forecasting: A Multi-feature Fusion Approach
Authors: Tianxiang Zhan, Yuanpeng He, Fuyuan Xiao
Date: 2021-08-18 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Forecasting

The construction cost index is an important indicator of the construction industry. Predicting CCI has important practical significance. This paper combines information fusion with machine learning, and proposes a multi-feature fusion (MFF) module for time series forecasting. The main contribution of MFF is to improve the prediction accuracy of CCI, and propose a feature fusion framework for time series. Compared with the convolution module, the MFF module is a module that extracts certain features. Experiments have proved that the combination of MFF module and multi-layer perceptron has a relatively good prediction effect. The MFF neural network model has high prediction accuracy and prediction efficiency, which is a study of continuous attention.
Improving the trustworthiness of image classification models by utilizing bounding-box annotations
Authors: Dharma KC, Chicheng Zhang
Date: 2021-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Classification, image-classification, Image Classification

We study utilizing auxiliary information in training data to improve the trustworthiness of machine learning models. Specifically, in the context of image classification, we propose to optimize a training objective that incorporates bounding box information, which is available in many image classification datasets. Preliminary experimental results show that the proposed algorithm achieves better performance in accuracy, robustness, and interpretability compared with baselines.
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
Authors: Shanshan Wang, Taohui Xiao, Qiegen Liu, Hairong Zheng
Date: 2020-12-15 00:00:00
Tasks: Image Reconstruction

Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed". Reducing the scanned measurements can accelerate MR imaging with the aid of powerful reconstruction methods, which have evolved from linear analytic models to nonlinear iterative ones. The emerging trend in this area is replacing human-defined signal models with that learned from data. Specifically, from 2016, deep learning has been incorporated into the fast MR imaging task, which draws valuable prior knowledge from big datasets to facilitate accurate MR image reconstruction from limited measurements. This survey aims to review deep learning based MR image reconstruction works from 2016- June 2020 and will discuss merits, limitations and challenges associated with such methods. Last but not least, this paper will provide a starting point for researchers interested in contributing to this field by pointing out good tutorial resources, state-of-the-art open-source codes and meaningful data sources.
Duplicate Bug Report Detection With a Combination of Information Retrieval and Topic Modeling
Authors: Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen, David Lo, Chengnian Sun
Date: 2013-04-08 00:00:00
Proceeding: 27th IEEE/ACM International Conference on Automated Software Engineering 2013 4
Tasks: Descriptive, Information Retrieval, Retrieval

Detecting duplicate bug reports helps reduce triaging efforts and save time for developers in fixing the same issues. Among several automated detection approaches, text-based information retrieval (IR) approaches have been shown to outperform others in term of both accuracy and time efficiency. However, those IR-based approaches do not detect well the duplicate reports on the same technical issues written in different descriptive terms. This paper introduces DBTM, a duplicate bug report detection approach that takes advantage of both IR-based features and topic-based features. DBTM models a bug report as a textual document describing certain technical issue(s), and models duplicate bug reports as the ones about the same technical issue(s). Trained with historical data including identified duplicate reports, it is able to learn the sets of different terms describing the same technical issues and to detect other not-yet-identified duplicate ones. Our empirical evaluation on real-world systems shows that DBTM improves the state-of-the-art approaches by up to 20% in accuracy.
MedSRGAN: medical images super-resolution using generative adversarial networks
Authors: Yuchong Gu, Zitao Zen, Haibin Chen, Jun Wei, Yaqin Zhang, Binghui Chen, Yingqin Li, Yujuan Qin, Qing Xie, Zhuoren Jiang, Yao Lu
Date: 2020-05-13 00:00:00
Proceeding: Springer 2020 5
Tasks: Super-Resolution

Super-resolution (SR) in medical imaging is an emerging application in medical
imaging due to the needs of high quality images acquired with limited radiation
dose, such as low dose Computer Tomography (CT), low field magnetic resonance
imaging (MRI). However, because of its complexity and higher visual requirements
of medical images, SR is still a challenging task in medical imaging. In this study,
we developed a deep learning based method called Medical Images SR using
Generative Adversarial Networks (MedSRGAN) for SR in medical imaging. A
novel convolutional neural network, Residual Whole Map Attention Network
(RWMAN) was developed as the generator network for our MedSRGAN in
extracting the useful information through different channels, as well as paying
more attention on meaningful regions. In addition, a weighted sum of content loss,
adversarial loss, and adversarial feature loss were fused to form a multi-task loss
function during the MedSRGAN training. 242 thoracic CT scans and 110 brain
MRI scans were collected for training and evaluation of MedSRGAN. The results
showed that MedSRGAN not only preserves more texture details but also generates
more realistic patterns on reconstructed SR images. A mean opinion score (MOS)
test on CT slices scored by five experienced radiologists demonstrates the efficiency of our methods.
Build a Deep Neural Network model using CPUs Builds a feed-forward multilayer artificial neural network on an H2OFrame
Authors: Anisha Arora, Arno Candel, Jessica Lanford, Erin LeDell, Viraj Parmar
Date: 2015-09-01 00:00:00
Proceeding: H2O.ai, Inc. 2015 9
Tasks: Fraud Detection

H2O is fast, scalable, open-source machine learning and deep learning for
smarter applications. With H2O, enterprises like PayPal, Nielsen Catalina,
Cisco, and others can use all their data without sampling to get accurate
predictions faster. Advanced algorithms such as deep learning, boosting, and
bagging ensembles are built-in to help application designers create smarter
applications through elegant APIs. Some of our initial customers have built
powerful domain-specic predictive engines for recommendations, customer
churn, propensity to buy, dynamic pricing, and fraud detection for the insurance,
healthcare, telecommunications, ad tech, retail, and payment systems industries.
Using in-memory compression, H2O handles billions of data rows in-memory,
even with a small cluster. To make it easier for non-engineers to create complete
analytic work
ows, H2O's platform includes interfaces for R, Python, Scala,
Java, JSON, and CoeeScript/JavaScript, as well as a built-in web interface,
Flow. H2O is designed to run in standalone mode, on Hadoop, or within a
Spark Cluster, and typically deploys within minutes.
H2O includes many common machine learning algorithms, such as generalized
linear modeling (linear regression, logistic regression, etc.), Nave Bayes, principal
components analysis, k-means clustering, and others. H2O also implements
best-in-class algorithms at scale, such as distributed random forest, gradient
boosting and deep learning. Customers can build thousands of models and
compare the results to get the best predictions.
H2O is nurturing a grassroots movement of physicists, mathematicians, and
computer scientists to herald the new wave of discovery with data science by
collaborating closely with academic researchers and industrial data scientists.
Stanford university giants Stephen Boyd, Trevor Hastie, Rob Tibshirani advise
the H2O team on building scalable machine learning algorithms. With hundreds
of meetups over the past three years, H2O has become a word-of-mouth
phenomenon, growing amongst the data community by a hundred-fold, and
is now used by 30,000+ users and is deployed using R, Python, Hadoop, and
Spark in 2000+ corporations.
Model-Free Learning of Optimal Deterministic Resource Allocations in Wireless Systems via Action-Space Exploration
Authors: Hassaan Hashmi, Dionysios S. Kalogerias
Date: 2021-08-23 00:00:00

Wireless systems resource allocation refers to perpetual and challenging nonconvex constrained optimization tasks, which are especially timely in modern communications and networking setups involving multiple users with heterogeneous objectives and imprecise or even unknown models and/or channel statistics. In this paper, we propose a technically grounded and scalable primal-dual deterministic policy gradient method for efficiently learning optimal parameterized resource allocation policies. Our method not only efficiently exploits gradient availability of popular universal policy representations, such as deep neural networks, but is also truly model-free, as it relies on consistent zeroth-order gradient approximations of the associated random network services constructed via low-dimensional perturbations in action space, thus fully bypassing any dependence on critics. Both theory and numerical simulations confirm the efficacy and applicability of the proposed approach, as well as its superiority over the current state of the art in terms of both achieving near-optimal performance and scalability.
A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections
Authors: Kianoush Falahkheirkhah, Tao Guo, Michael Hwang, Pheroze Tamboli, Christopher G Wood, Jose A Karam, Kanishka Sircar, Rohit Bhargava
Date: 2021-08-24 00:00:00
Tasks: Decision Making, Diagnostic, Generative Adversarial Network

In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (more than 24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (less than 1 hour) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images ,virtual FFPE, from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.
Improving 3D Object Detection with Channel-wise Transformer
Authors: Hualian Sheng, Sijia Cai, YuAn Liu, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Min-Jian Zhao
Date: 2021-08-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Region Proposal

Though 3D object detection from point clouds has achieved rapid progress in recent years, the lack of flexible and high-performance proposal refinement remains a great hurdle for existing state-of-the-art two-stage detectors. Previous works on refining 3D proposals have relied on human-designed components such as keypoints sampling, set abstraction and multi-scale feature fusion to produce powerful 3D object representations. Such methods, however, have limited ability to capture rich contextual dependencies among points. In this paper, we leverage the high-quality region proposal network and a Channel-wise Transformer architecture to constitute our two-stage 3D object detection framework (CT3D) with minimal hand-crafted design. The proposed CT3D simultaneously performs proposal-aware embedding and channel-wise context aggregation for the point features within each proposal. Specifically, CT3D uses proposal's keypoints for spatial contextual modelling and learns attention propagation in the encoding module, mapping the proposal to point embeddings. Next, a new channel-wise decoding module enriches the query-key interaction via channel-wise re-weighting to effectively merge multi-level contexts, which contributes to more accurate object predictions. Extensive experiments demonstrate that our CT3D method has superior performance and excellent scalability. Remarkably, CT3D achieves the AP of 81.77% in the moderate car category on the KITTI test 3D detection benchmark, outperforms state-of-the-art 3D detectors.
Efficacy of BERT embeddings on predicting disaster from Twitter data
Authors: Ashis Kumar Chanda
Date: 2021-08-08 00:00:00
Tasks: Word Embeddings

Social media like Twitter provide a common platform to share and communicate personal experiences with other people. People often post their life experiences, local news, and events on social media to inform others. Many rescue agencies monitor this type of data regularly to identify disasters and reduce the risk of lives. However, it is impossible for humans to manually check the mass amount of data and identify disasters in real-time. For this purpose, many research works have been proposed to present words in machine-understandable representations and apply machine learning methods on the word representations to identify the sentiment of a text. The previous research methods provide a single representation or embedding of a word from a given document. However, the recent advanced contextual embedding method (BERT) constructs different vectors for the same word in different contexts. BERT embeddings have been successfully used in different natural language processing (NLP) tasks, yet there is no concrete analysis of how these representations are helpful in disaster-type tweet analysis. In this research work, we explore the efficacy of BERT embeddings on predicting disaster from Twitter data and compare these to traditional context-free word embedding methods (GloVe, Skip-gram, and FastText). We use both traditional machine learning methods and deep learning methods for this purpose. We provide both quantitative and qualitative results for this study. The results show that the BERT embeddings have the best results in disaster prediction task than the traditional word embeddings. Our codes are made freely accessible to the research community.
Quantum adaptive agents with efficient long-term memories
Authors: Thomas J. Elliott, Mile Gu, Andrew J. P. Garner, Jayne Thompson
Date: 2021-08-24 00:00:00

Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly -- they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum information processing. We uncover the most general form a quantum agent need adopt to maximise memory compression advantages, and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favourable scaling advantages relative to memory-minimal classical agents, particularly when information must be retained about events increasingly far into the past.