pwc-archive/papers-with-abstracts huggingface dataset:
Challenges and Opportunities of Future Rural Wireless Communications
Authors: Yaguang Zhang, David J. Love, James V. Krogmeier, Christopher R. Anderson, Robert W. Heath, Dennis R. Buckmaster
Date: 2021-08-11 00:00:00

Broadband access is key to ensuring robust economic development and improving quality of life. Unfortunately, the communication infrastructure deployed in rural areas throughout the world lags behind its urban counterparts due to low population density and economics. This article examines the motivations and challenges of providing broadband access over vast rural regions, with an emphasis on the wireless aspect in view of its irreplaceable role in closing the digital gap. Applications and opportunities for future rural wireless communications are discussed for a variety of areas, including residential welfare, digital agriculture, and transportation. This article also comprehensively investigates current and emerging wireless technologies that could facilitate rural deployment. Although there is no simple solution, there is an urgent need for researchers to work on coverage, cost, and reliability of rural wireless access.
Distribution of Matter from Singularity with Spherical Symmetry Using Fick Diffusion
Authors: Ahmet Mecit Öztaş, Michael L. Smith
Date: 2006-05-12 00:00:00

A model is presented allowing calculation of energy and matter distribution in the Universe after expansion from singularity without introduction of expansion energy. Beginning with Fick's law of diffusion, we solve the Bessel function for spherical systems during expansion, presuming isotropic matter distribution in the Hubble flow. This function can be substituted with the Associated Legendre differential equation and when solved we discover useful parameters for those of energy and matter densities, diffusion and temperature. Though we can follow the decline of matter density over spacetime, we cannot suggest a precise value at singularity. This model may be useful though, as a starting point for modeling star formation rates, SFR, galaxy formation horizon lengths and regions devoid of matter where tracing the matter density decline over time is important.
Clustering Commodity Markets in Space and Time: Clarifying Returns, Volatility, and Trading Regimes Through Unsupervised Machine Learning
Authors: James Ming Chen, Mobeen Ur Rehman, Xuan Vinh Vo
Date: 2021-03-31 00:00:00
Proceeding: SSRN 2021 3
Tasks: Clustering

Unsupervised machine learning can interpret logarithmic returns and conditional volatility in commodity markets. k-means and hierarchical clustering can generate a financial ontology of markets for fuels, precious and base metals, and agricultural commodities. Manifold learning methods such as multidimensional scaling (MDS) and t-distributed stochastic neighbor embedding (t-SNE) enable the visualization of comovement and other financial relationships in three dimensions.

Different methods of unsupervised learning excel at different tasks. k-means clustering based on logarithmic returns works well with MDS to classify commodities and to create a spatial ontology of commodities trading, A strikingly different application involves k-means clustering of the matrix transpose, such that conditional volatility is evaluated by trading date rather than by commodity. This approach can isolate the two most calamitous temporal regimes of the past two decades: the global financial crisis of 2008-09 and the immediate reaction to the Covid-19 pandemic. Temporal clustering of trading days, unlike the corresponding spatial task of clustering commodities, is better visualized through t-SNE than through MDS.
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Authors: Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan, SiQi Zhou, Jacopo Panerati, Angela P. Schoellig
Date: 2021-08-13 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. Our review includes: learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safety of a learned control policy. As data- and learning-based robot control methods continue to gain traction, researchers must understand when and how to best leverage them in real-world scenarios where safety is imperative, such as when operating in close proximity to humans. We highlight some of the open challenges that will drive the field of robot learning in the coming years, and emphasize the need for realistic physics-based benchmarks to facilitate fair comparisons between control and reinforcement learning approaches.
Understanding Structural Vulnerability in Graph Convolutional Networks
Authors: Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang
Date: 2021-08-13 00:00:00

Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to adversarial attacks on the graph structure. Although multiple works have been proposed to improve their robustness against such structural adversarial attacks, the reasons for the success of the attacks remain unclear. In this work, we theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i.e., the weighted mean) of GCNs. Specifically, our analysis takes advantage of the breakdown point which can quantitatively measure the robustness of aggregation schemes. The key insight is that weighted mean, as the basic design of GCNs, has a low breakdown point and its output can be dramatically changed by injecting a single edge. We show that adopting the aggregation scheme with a high breakdown point (e.g., median or trimmed mean) could significantly enhance the robustness of GCNs against structural attacks. Extensive experiments on four real-world datasets demonstrate that such a simple but effective method achieves the best robustness performance compared to state-of-the-art models.
A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation
Authors: Zhihao Xu, Clifford M. Hurvich
Date: 2021-08-13 00:00:00

A unified frequency domain cross-validation (FDCV) method is proposed to obtain a heteroskedasticity and autocorrelation consistent (HAC) standard error. This method enables model/tuning parameter selection across both parametric and nonparametric spectral estimators simultaneously. The candidate class for this approach consists of restricted maximum likelihood-based (REML) autoregressive spectral estimators and lag-weights estimators with the Parzen kernel. Additionally, an efficient technique for computing the REML estimators of autoregressive models is provided. Through simulations, the reliability of the FDCV method is demonstrated, comparing favorably with popular HAC estimators such as Andrews-Monahan and Newey-West.
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices
Authors: Md Mohaimenuzzaman, Christoph Bergmeir, Bernd Meyer
Date: 2021-08-13 00:00:00
Tasks: Audio Classification, Classification, Deep Learning, image-classification, Image Classification, Model Compression, Network Pruning, Quantization

Deep learning has celebrated resounding successes in many application areas of relevance to the Internet of Things (IoT), such as computer vision and machine listening. These technologies must ultimately be brought directly to the edge to fully harness the power of deep learning for the IoT. The obvious challenge is that deep learning techniques can only be implemented on strictly resource-constrained edge devices if the models are radically downsized. This task relies on different model compression techniques, such as network pruning, quantization, and the recent advancement of XNOR-Net. This study examines the suitability of these techniques for audio classification on microcontrollers. We present an application of XNOR-Net for end-to-end raw audio classification and a comprehensive empirical study comparing this approach with pruning-and-quantization methods. We show that raw audio classification with XNOR yields comparable performance to regular full precision networks for small numbers of classes while reducing memory requirements 32-fold and computation requirements 58-fold. However, as the number of classes increases significantly, performance degrades, and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring approximately 8x more computation. We show that these insights are consistent between raw audio classification and image classification using standard benchmark sets. To the best of our knowledge, this is the first study to apply XNOR to end-to-end audio classification and evaluate it in the context of alternative techniques. All codes are publicly available on GitHub.
A Dataset for Answering Time-Sensitive Questions
Authors: Wenhu Chen, Xinyi Wang, William Yang Wang
Date: 2021-08-13 00:00:00
Tasks: Benchmarking

Time is an important dimension in our physical world. Lots of facts can evolve with respect to time. For example, the U.S. President might change every four years. Therefore, it is important to consider the time dimension and empower the existing QA models to reason over time. However, the existing QA datasets contain rather few time-sensitive questions, hence not suitable for diagnosing or benchmarking the model's temporal reasoning capability. In order to promote research in this direction, we propose to construct a time-sensitive QA dataset. The dataset is constructed by 1) mining time-evolving facts from WikiData and aligning them to their corresponding Wikipedia page, 2) employing crowd workers to verify and calibrate these noisy facts, 3) generating question-answer pairs based on the annotated time-sensitive facts. Our dataset poses challenges in the aspect of both temporal understanding and temporal reasoning. We evaluate different SoTA long-document QA systems like BigBird and FiD on our dataset. The best-performing model FiD can only achieve 46\% accuracy, still far behind the human performance of 87\%. We demonstrate that these models are still lacking the ability to perform consistent temporal reasoning. Therefore, we believe that our dataset could serve as a benchmark to develop NLP models more sensitive to temporal shifts. The dataset and code are released in~\url{https://github.com/wenhuchen/Time-Sensitive-QA}.
Spatio-Temporal Split Learning
Authors: Joongheon Kim, Seunghoon Park, Soyi Jung, Seehwan Yoo
Date: 2021-08-13 00:00:00

This paper proposes a novel split learning framework with multiple end-systems in order to realize privacypreserving deep neural network computation. In conventional split learning frameworks, deep neural network computation is separated into multiple computing systems for hiding entire network architectures. In our proposed framework, multiple computing end-systems are sharing one centralized server in split learning computation, where the multiple end-systems are with input and first hidden layers and the centralized server is with the other hidden layers and output layer. This framework, which is called as spatio-temporal split learning, is spatially separated for gathering data from multiple end-systems and also temporally separated due to the nature of split learning. Our performance evaluation verifies that our proposed framework shows nearoptimal accuracy while preserving data privacy.
Random Subspace Mixture Models for Interpretable Anomaly Detection
Authors: Cetin Savkli, Catherine Schwartz
Date: 2021-08-13 00:00:00
Tasks: Anomaly Detection, Attribute

We present a new subspace-based method to construct probabilistic models for high-dimensional data and highlight its use in anomaly detection. The approach is based on a statistical estimation of probability density using densities of random subspaces combined with geometric averaging. In selecting random subspaces, equal representation of each attribute is used to ensure correct statistical limits. Gaussian mixture models (GMMs) are used to create the probability densities for each subspace with techniques included to mitigate singularities allowing for the ability to handle both numerical and categorial attributes. The number of components for each GMM is determined automatically through Bayesian information criterion to prevent overfitting. The proposed algorithm attains competitive AUC scores compared with prominent algorithms against benchmark anomaly detection datasets with the added benefits of being simple, scalable, and interpretable.
Enhancing audio quality for expressive Neural Text-to-Speech
Authors: Abdelhamid Ezzerg, Adam Gabrys, Bartosz Putrycz, Daniel Korzekwa, Daniel Saez-Trigueros, David McHardy, Kamil Pokora, Jakub Lachowicz, Jaime Lorenzo-Trueba, Viacheslav Klimkov
Date: 2021-08-13 00:00:00
Tasks: Acoustic Modelling, Speech Synthesis, text-to-speech, Text to Speech

Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings. However, not all speaking styles are easy to model: highly expressive voices are still challenging even to recent TTS architectures since there seems to be a trade-off between expressiveness in a generated audio and its signal quality. In this paper, we present a set of techniques that can be leveraged to enhance the signal quality of a highly-expressive voice without the use of additional data. The proposed techniques include: tuning the autoregressive loop's granularity during training; using Generative Adversarial Networks in acoustic modelling; and the use of Variational Auto-Encoders in both the acoustic model and the neural vocoder. We show that, when combined, these techniques greatly closed the gap in perceived naturalness between the baseline system and recordings by 39% in terms of MUSHRA scores for an expressive celebrity voice.
Optical Adversarial Attack
Authors: Abhiram Gnanasambandam, Alex M. Sherman, Stanley H. Chan
Date: 2021-08-13 00:00:00
Tasks: Adversarial Attack

We introduce OPtical ADversarial attack (OPAD). OPAD is an adversarial attack in the physical space aiming to fool image classifiers without physically touching the objects (e.g., moving or painting the objects). The principle of OPAD is to use structured illumination to alter the appearance of the target objects. The system consists of a low-cost projector, a camera, and a computer. The challenge of the problem is the non-linearity of the radiometric response of the projector and the spatially varying spectral response of the scene. Attacks generated in a conventional approach do not work in this setting unless they are calibrated to compensate for such a projector-camera model. The proposed solution incorporates the projector-camera model into the adversarial attack optimization, where a new attack formulation is derived. Experimental results prove the validity of the solution. It is demonstrated that OPAD can optically attack a real 3D object in the presence of background lighting for white-box, black-box, targeted, and untargeted attacks. Theoretical analysis is presented to quantify the fundamental performance limit of the system.
An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images
Authors: Haomin Chen, T. Y. Alvin Liu, Catalina Gomez, Zelia Correa, Mathias Unberath
Date: 2021-08-13 00:00:00

Algorithmic decision support is rapidly becoming a staple of personalized medicine, especially for high-stakes recommendations in which access to certain information can drastically alter the course of treatment, and thus, patient outcome; a prominent example is radiomics for cancer subtyping. Because in these scenarios the stakes are high, it is desirable for decision systems to not only provide recommendations but supply transparent reasoning in support thereof. For learning-based systems, this can be achieved through an interpretable design of the inference pipeline. Herein we describe an automated yet interpretable system for uveal melanoma subtyping with digital cytology images from fine needle aspiration biopsies. Our method embeds every automatically segmented cell of a candidate cytology image as a point in a 2D manifold defined by many representative slides, which enables reasoning about the cell-level composition of the tissue sample, paving the way for interpretable subtyping of the biopsy. Finally, a rule-based slide-level classification algorithm is trained on the partitions of the circularly distorted 2D manifold. This process results in a simple rule set that is evaluated automatically but highly transparent for human verification. On our in house cytology dataset of 88 uveal melanoma patients, the proposed method achieves an accuracy of 87.5% that compares favorably to all competing approaches, including deep "black box" models. The method comes with a user interface to facilitate interaction with cell-level content, which may offer additional insights for pathological assessment.
Online Fairness-Aware Learning with Imbalanced Data Streams
Authors: Vasileios Iosifidis, Wenbin Zhang, Eirini Ntoutsi
Date: 2021-08-13 00:00:00
Tasks: Fairness, Stock Price Prediction, valid

Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they "reject" minority instances at large due to their inability to effectively learn all classes. In this work, we propose \ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. \ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%] in kappa and [89.4\%-96.6\%] in statistical parity (fairness).
One-shot Transfer Learning for Population Mapping
Authors: Erzhuo Shao, Jie Feng, Yingheng Wang, Tong Xia, Yong Li
Date: 2021-08-13 00:00:00
Tasks: Population Mapping, Scheduling, Transfer Learning

Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due to the limitations of data collection, including infrastructure density, user privacy, and business security, such fine-grained data is hard to collect and usually, only coarse-grained data is available. Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem. To tackle this problem, existing methods mainly rely on sufficient fine-grained ground truth for training, which is not often available for the majority of cities. That limits the applications of these methods and brings the necessity to transfer knowledge between data-sufficient source cities to data-scarce target cities. In knowledge transfer scenario, we employ single reference fine-grained ground truth in target city, which is easy to obtain via remote sensing or questionnaire, as the ground truth to inform the large-scale urban structure and support the knowledge transfer in target city. By this approach, we transform the fine-grained population mapping problem into a one-shot transfer learning problem. In this paper, we propose a novel one-shot transfer learning framework PSRNet to transfer spatial-temporal knowledge across cities from the view of network structure, the view of data, and the view of optimization. Experiments on real-life datasets of 4 cities demonstrate that PSRNet has significant advantages over 8 state-of-the-art baselines by reducing RMSE and MAE by more than 25%. Our code and datasets are released in Github (https://github.com/erzhuoshao/PSRNet-CIKM).
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
Authors: Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan
Date: 2021-08-13 00:00:00
Tasks: Data Augmentation, Image Generation, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Representation Learning, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation. In addition, most existing semi-supervised approaches are usually not robust compared with the supervised counterparts, and also lack explicit modeling of geometric structure and semantic information, both of which limit the segmentation accuracy. In this work, we present SimCVD, a simple contrastive distillation framework that significantly advances state-of-the-art voxel-wise representation learning. We first describe an unsupervised training strategy, which takes two views of an input volume and predicts their signed distance maps of object boundaries in a contrastive objective, with only two independent dropout as mask. This simple approach works surprisingly well, performing on the same level as previous fully supervised methods with much less labeled data. We hypothesize that dropout can be viewed as a minimal form of data augmentation and makes the network robust to representation collapse. Then, we propose to perform structural distillation by distilling pair-wise similarities. We evaluate SimCVD on two popular datasets: the Left Atrial Segmentation Challenge (LA) and the NIH pancreas CT dataset. The results on the LA dataset demonstrate that, in two types of labeled ratios (i.e., 20% and 10%), SimCVD achieves an average Dice score of 90.85% and 89.03% respectively, a 0.91% and 2.22% improvement compared to previous best results. Our method can be trained in an end-to-end fashion, showing the promise of utilizing SimCVD as a general framework for downstream tasks, such as medical image synthesis, enhancement, and registration.