pwc-archive/papers-with-abstracts huggingface dataset:
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
Authors: José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros
Date: 2021-12-29 00:00:00
Tasks: Management, Recommendation Systems

Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, configurations, traffic flows). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real networks. As a result, such models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in its unprecedented generalization capabilities when applied to other networks and configurations unseen during training, which is a critical feature for achieving practical data-driven solutions for networking. This article comprises a brief tutorial on GNNs and their possible applications to communication networks. To showcase the potential of this technology, we present two use cases with state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, we delve into the key open challenges and opportunities yet to be explored in this novel research area.
PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs
Authors: Ajinkya K Mulay, Sean Lane, Erin Hennes
Date: 2021-12-29 00:00:00
Tasks: Sensitivity, Transfer Learning

Statistical power estimation for studies with multiple model parameters is inherently a multivariate problem. Power for individual parameters of interest cannot be reliably estimated univariately since correlation and variance explained relative to one parameter will impact the power for another parameter, all usual univariate considerations being equal. Explicit solutions in such cases, especially for models with many parameters, are either impractical or impossible to solve, leaving researchers to the prevailing method of simulating power. However, the point estimates for a vector of model parameters are uncertain, and the impact of inaccuracy is unknown. In such cases, sensitivity analysis is recommended such that multiple combinations of possible observable parameter vectors are simulated to understand power trade-offs. A limitation to this approach is that it is computationally expensive to generate sufficient sensitivity combinations to accurately map the power trade-off function in increasingly high-dimensional spaces for the models that social scientists estimate. This paper explores the efficient estimation and graphing of statistical power for a study over varying model parameter combinations. We propose a simple and generalizable machine learning inspired solution to cut the computational cost to less than 10% of the brute force method while providing F1 scores above 90%. We further motivate the impact of transfer learning in learning power manifolds across varying distributions.
Feature matching as improved transfer learning technique for wearable EEG
Authors: Elisabeth R. M. Heremans, Huy Phan, Amir H. Ansari, Pascal Borzée, Bertien Buyse, Dries Testelmans, Maarten De Vos
Date: 2021-12-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Staging, Transfer Learning

Objective: With the rapid rise of wearable sleep monitoring devices with non-conventional electrode configurations, there is a need for automated algorithms that can perform sleep staging on configurations with small amounts of labeled data. Transfer learning has the ability to adapt neural network weights from a source modality (e.g. standard electrode configuration) to a new target modality (e.g. non-conventional electrode configuration). Methods: We propose feature matching, a new transfer learning strategy as an alternative to the commonly used finetuning approach. This method consists of training a model with larger amounts of data from the source modality and few paired samples of source and target modality. For those paired samples, the model extracts features of the target modality, matching these to the features from the corresponding samples of the source modality. Results: We compare feature matching to finetuning for three different target domains, with two different neural network architectures, and with varying amounts of training data. Particularly on small cohorts (i.e. 2 - 5 labeled recordings in the non-conventional recording setting), feature matching systematically outperforms finetuning with mean relative differences in accuracy ranging from 0.4% to 4.7% for the different scenarios and datasets. Conclusion: Our findings suggest that feature matching outperforms finetuning as a transfer learning approach, especially in very low data regimes. Significance: As such, we conclude that feature matching is a promising new method for wearable sleep staging with novel devices.
Utilizing gradient approximations to optimize data selection protocols for tumor growth model calibration
Authors: Allison L. Lewis, Kathleen M. Storey, Heyrim Cho, Anna C. Zittle
Date: 2021-12-25 00:00:00
Tasks: Decision Making

The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly more prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models correspondingly requires more detailed clinical data. This raises questions about how much data should be collected and when, in order to minimize the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using a gradient-based score function to determine the optimal data collection times for model calibration. The novel score function introduced in this work eliminates the need for a weight parameter used in a previous study's score function, while still yielding accurate and efficient model calibration using even fewer scans on a sample set of synthetic data, simulating tumors of varying levels of radiosensitivity. We also conduct a robust analysis of the calibration accuracy and certainty, using both error and uncertainty metrics. Unlike the error analysis of the previous study, the inclusion of uncertainty analysis in this work|as a means for deciding when the algorithm can be terminated|provides a more realistic option for clinical decision-making, since it does not rely on data that will be collected later in time.
DRF Codes: Deep SNR-Robust Feedback Codes
Authors: Mahdi Boloursaz Mashhadi, Deniz Gunduz, Alberto Perotti, Branislav Popovic
Date: 2021-12-22 00:00:00
Tasks: Decoder, Scheduling

We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code. At the encoder, parity symbols are generated by a long short term memory (LSTM) network based on the message as well as the past forward channel outputs observed by the transmitter in a noisy fashion. The decoder uses a bi-directional LSTM architecture along with a signal to noise ratio (SNR)-aware attention NN to decode the message. The proposed code overcomes two major shortcomings of the previously proposed DNN-based codes over channels with passive output feedback: (i) the SNR-aware attention mechanism at the decoder enables reliable application of the same trained NN over a wide range of SNR values; (ii) curriculum training with batch-size scheduling is used to speed up and stabilize training while improving the SNR-robustness of the resulting code. We show that the DRF codes significantly outperform state-of-the-art in terms of both the SNR-robustness and the error rate in additive white Gaussian noise (AWGN) channel with feedback. In fading channels with perfect phase compensation at the receiver, DRF codes learn to efficiently exploit knowledge of the instantaneous fading amplitude (which is available to the encoder through feedback) to reduce the overhead and complexity associated with channel estimation at the decoder. Finally, we show the effectiveness of DRF codes in multicast channels with feedback, where linear feedback codes are known to be strictly suboptimal.
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Authors: Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah, Ali Bahrami Rad, Reza Sameni, Gari D. Clifford
Date: 2021-12-28 00:00:00
Tasks: Transfer Learning

Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress Disorder, or PTSD - and significantly boost performance on a small database of rare individuals. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without significant TWA, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology. A DNN, trained on over 70,000 patients to classify 25 different rhythms, was modified the output layer to a binary class (TWA or no-TWA, or equivalently, PTSD or no-PTSD), and transfer learning was performed on the artificial ECG. In a final transfer learning step, the DNN was trained and cross-validated on ECG from 12 PTSD and 24 controls for all combinations of using the three databases. Main results: The best performing approach (AUROC = 0.77, Accuracy = 0.72, F1-score = 0.64) was found by performing both transfer learning steps, using the pre-trained arrhythmia DNN, the artificial data and the real PTSD-related ECG data. Removing the artificial data from training led to the largest drop in performance. Removing the arrhythmia data from training provided a modest, but significant, drop in performance. The final model showed no significant drop in performance on the artificial data, indicating no overfitting. Significance: In healthcare, it is common to only have a small collection of high-quality data and labels, or a larger database with much lower quality (and less relevant) labels. The paradigm presented here, involving model-based performance boosting, provides a solution through transfer learning on a large realistic artificial database, and a partially relevant real database.
Adversarial Neural Networks for Error Correcting Codes
Authors: Hung T. Nguyen, Steven Bottone, Kwang Taik Kim, Mung Chiang, H. Vincent Poor
Date: 2021-12-21 00:00:00
Tasks: Decoder

Error correcting codes are a fundamental component in modern day communication systems, demanding extremely high throughput, ultra-reliability and low latency. Recent approaches using machine learning (ML) models as the decoders offer both improved performance and great adaptability to unknown environments, where traditional decoders struggle. We introduce a general framework to further boost the performance and applicability of ML models. We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words, and, hence, guides the decoding models to recover transmitted codewords. Our framework is game-theoretic, motivated by generative adversarial networks (GANs), with the decoder and discriminator competing in a zero-sum game. The decoder learns to simultaneously decode and generate codewords while the discriminator learns to tell the differences between decoded outputs and codewords. Thus, the decoder is able to decode noisy received signals into codewords, increasing the probability of successful decoding. We show a strong connection of our framework with the optimal maximum likelihood decoder by proving that this decoder defines a Nash equilibrium point of our game. Hence, training to equilibrium has a good possibility of achieving the optimal maximum likelihood performance. Moreover, our framework does not require training labels, which are typically unavailable during communications, and, thus, seemingly can be trained online and adapt to channel dynamics. To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
Point spread function estimation for blind image deblurring problems based on framelet transform
Authors: Reza Parvaz
Date: 2021-12-21 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

One of the most important issues in the image processing is the approximation of the image that has been lost due to the blurring process. These types of matters are divided into non-blind and blind problems. The second type of problem is more complex in terms of calculations than the first problems due to the unknown of original image and point spread function estimation. In the present paper, an algorithm based on coarse-to-fine iterative by $l_0-\alpha l_1$ regularization and framelet transform is introduced to approximate the spread function estimation. Framelet transfer improves the restored kernel due to the decomposition of the kernel to different frequencies. Also in the proposed model fraction gradient operator is used instead of ordinary gradient operator. The proposed method is investigated on different kinds of images such as text, face, natural. The output of the proposed method reflects the effectiveness of the proposed algorithm in restoring the images from blind problems.
Automatic Pharma News Categorization
Authors: Stanislaw Adaszewski, Pascal Kuner, Ralf J. Jaeger
Date: 2021-12-28 00:00:00

We use a text dataset consisting of 23 news categories relevant to pharma information science, in order to compare the fine-tuning performance of multiple transformer models in a classification task. Using a well-balanced dataset with multiple autoregressive and autocoding transformation models, we compare their fine-tuning performance. To validate the winning approach, we perform diagnostics of model behavior on mispredicted instances, including inspection of category-wise metrics, evaluation of prediction certainty and assessment of latent space representations. Lastly, we propose an ensemble model consisting of the top performing individual predictors and demonstrate that this approach offers a modest improvement in the F1 metric.
Deep neural networks for solving forward and inverse problems of (2+1)-dimensional nonlinear wave equations with rational solitons
Authors: Zijian Zhou, Li Wang, Zhenya Yan
Date: 2021-12-28 00:00:00

In this paper, we investigate the forward problems on the data-driven rational solitons for the (2+1)-dimensional KP-I equation and spin-nonlinear Schr\"odinger (spin-NLS) equation via the deep neural networks leaning. Moreover, the inverse problems of the (2+1)-dimensional KP-I equation and spin-NLS equation are studied via deep learning. The main idea of the data-driven forward and inverse problems is to use the deep neural networks with the activation function to approximate the solutions of the considered (2+1)-dimensional nonlinear wave equations by optimizing the chosen loss functions related to the considered nonlinear wave equations.
Adversarial Learning for Incentive Optimization in Mobile Payment Marketing
Authors: Xuanying Chen, Zhining Liu, Li Yu, Sen Li, Lihong Gu, Xiaodong Zeng, Yize Tan, Jinjie Gu
Date: 2021-12-28 00:00:00
Tasks: Marketing

Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, incentive allocations are commonly solved in a two-stage procedure. After training a response estimation model to estimate the users' mobile payment probabilities (MPP), a linear programming process is applied to obtain the optimal incentive allocation. However, the large amount of biased data in the training set, generated by the previous biased allocation policy, causes a biased estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign.
Solving time dependent Fokker-Planck equations via temporal normalizing flow
Authors: Xiaodong Feng, Li Zeng, Tao Zhou
Date: 2021-12-28 00:00:00

In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations. It is well known that solutions of such equations are probability density functions, and thus our approach relies on modelling the target solutions with the temporal normalizing flows. The temporal normalizing flow is then trained based on the TFP loss function, without requiring any labeled data. Being a machine learning scheme, the proposed approach is mesh-free and can be easily applied to high dimensional problems. We present a variety of test problems to show the effectiveness of the learning approach.
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
Authors: Bingqing Song, Haoran Sun, Wenqiang Pu, Sijia Liu, Mingyi Hong
Date: 2021-12-28 00:00:00
Tasks: Management

Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key tasks such as power control, beamforming and admission control. There are two popular training paradigms for such DNNs-based interference management models: supervised learning (i.e., fitting labels generated by an optimization algorithm) and unsupervised learning (i.e., directly optimizing some system performance measure). Although both of these paradigms have been extensively applied in practice, due to the lack of any theoretical understanding about these methods, it is not clear how to systematically understand and compare their performance. In this work, we conduct theoretical studies to provide some in-depth understanding about these two training paradigms. First, we show a somewhat surprising result, that for some special power control problem, the unsupervised learning can perform much worse than its supervised counterpart, because it is more likely to stuck at some low-quality local solutions. We then provide a series of theoretical results to further understand the properties of the two approaches. Generally speaking, we show that when high-quality labels are available, then the supervised learning is less likely to be stuck at a solution than its unsupervised counterpart. Additionally, we develop a semi-supervised learning approach which properly integrates these two training paradigms, and can effectively utilize limited number of labels to find high-quality solutions. To our knowledge, these are the first set of theoretical results trying to understand different training approaches in learning-based wireless communication system design.
AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
Authors: Ayan Chatterjee, Robin Walters, Zohair Shafi, Omair Shafi Ahmed, Michael Sebek, Deisy Gysi, Rose Yu, Tina Eliassi-Rad, Albert-László Barabási, Giulia Menichetti
Date: 2021-12-25 00:00:00
Tasks: Drug Discovery, Unsupervised Pre-training

Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We first unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Then, we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training, allowing us to limit the annotation imbalance and improve binding predictions for novel proteins and ligands. We illustrate the value of AI-Bind by predicting drugs and natural compounds with binding affinity to SARS-CoV-2 viral proteins and the associated human proteins. We also validate these predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. Overall, AI-Bind offers a powerful high-throughput approach to identify drug-target combinations, with the potential of becoming a powerful tool in drug discovery.
Approximation of functions with one-bit neural networks
Authors: C. Sinan Güntürk, Weilin Li
Date: 2021-12-16 00:00:00
Tasks: Quantization

The celebrated universal approximation theorems for neural networks roughly state that any reasonable function can be arbitrarily well-approximated by a network whose parameters are appropriately chosen real numbers. This paper examines the approximation capabilities of one-bit neural networks -- those whose nonzero parameters are $\pm a$ for some fixed $a\not=0$. One of our main theorems shows that for any $f\in C^s([0,1]^d)$ with $\|f\|_\infty<1$ and error $\varepsilon$, there is a $f_{NN}$ such that $|f(\boldsymbol{x})-f_{NN}(\boldsymbol{x})|\leq \varepsilon$ for all $\boldsymbol{x}$ away from the boundary of $[0,1]^d$, and $f_{NN}$ is either implementable by a $\{\pm 1\}$ quadratic network with $O(\varepsilon^{-2d/s})$ parameters or a $\{\pm \frac 1 2 \}$ ReLU network with $O(\varepsilon^{-2d/s}\log (1/\varepsilon))$ parameters, as $\varepsilon\to0$. We establish new approximation results for iterated multivariate Bernstein operators, error estimates for noise-shaping quantization on the Bernstein basis, and novel implementation of the Bernstein polynomials by one-bit quadratic and ReLU neural networks.