pwc-archive/papers-with-abstracts huggingface dataset:
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation
Authors: Wolfgang Ganglberger, Abigail A. Bucklin, Ryan A. Tesh, Madalena Da Silva Cardoso, Haoqi Sun, Michael J. Leone, Luis Paixao, Ezhil Panneerselvam, Elissa M. Ye, B. Taylor Thompson, Oluwaseun Akeju, David Kuller, Robert J. Thomas, M. Brandon Westover
Date: 2021-02-24 00:00:00
Tasks: Anomaly Detection

Objective: Sleep related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to automatically detect sleep apnea from a simple, easy-to-wear device. The objective is to automatically detect abnormal respiration and estimate the Apnea-Hypopnea-Index (AHI) with a wearable respiratory device, compared to an SpO2 signal or polysomnography using a large (n = 412) dataset serving as ground truth. Methods: Simultaneously recorded polysomnographic (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%)-feature, one allowing a time lag of 30 seconds between the two signals. Results: Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, 0.82, and areas under the precision-recall curves of 0.48, 0.32, 0.51 for the models using respiration and SpO2, respiration-only, and SpO2-only respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively. Conclusions: A wearable respiratory effort signal with or without SpO2 predicted AHI accurately. Given the large dataset and rigorous testing design, we expect our models are generalizable to evaluating respiration in a variety of environments, such as at home and in critical care.
PySensors: A Python Package for Sparse Sensor Placement
Authors: Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz
Date: 2021-02-20 00:00:00
Tasks: Classification, General Classification

PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-driven sparse sensor placement optimization for reconstruction (SSPOR) and sparse sensor placement optimization for classification (SSPOC). In this work we provide a brief description of the mathematical algorithms and theory for sparse sensor optimization, along with an overview and demonstration of the features implemented in PySensors (with code examples). We also include practical advice for user and a list of potential extensions to PySensors. Software is available at https://github.com/dynamicslab/pysensors.
Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation
Authors: Shreyan Chowdhury, Gerhard Widmer
Date: 2021-02-26 00:00:00
Tasks: Diversity, Domain Adaptation, Information Retrieval, Music Information Retrieval, Retrieval, Unsupervised Domain Adaptation

Emotion and expressivity in music have been topics of considerable interest in the field of music information retrieval. In recent years, mid-level perceptual features have been suggested as means to explain computational predictions of musical emotion. We find that the diversity of musical styles and genres in the available dataset for learning these features is not sufficient for models to generalise well to specialised acoustic domains such as solo piano music. In this work, we show that by utilising unsupervised domain adaptation together with receptive-field regularised deep neural networks, it is possible to significantly improve generalisation to this domain. Additionally, we demonstrate that our domain-adapted models can better predict and explain expressive qualities in classical piano performances, as perceived and described by human listeners.
Learning to Make Compiler Optimizations More Effective
Authors: Rahim Mammadli, Marija Selakovic, Felix Wolf, Michael Pradel
Date: 2021-02-24 00:00:00

Because loops execute their body many times, compiler developers place much emphasis on their optimization. Nevertheless, in view of highly diverse source code and hardware, compilers still struggle to produce optimal target code. The sheer number of possible loop optimizations, including their combinations, exacerbates the problem further. Today's compilers use hard-coded heuristics to decide when, whether, and which of a limited set of optimizations to apply. Often, this leads to highly unstable behavior, making the success of compiler optimizations dependent on the precise way a loop has been written. This paper presents LoopLearner, which addresses the problem of compiler instability by predicting which way of writing a loop will lead to efficient compiled code. To this end, we train a neural network to find semantically invariant source-level transformations for loops that help the compiler generate more efficient code. Our model learns to extract useful features from the raw source code and predicts the speedup that a given transformation is likely to yield. We evaluate LoopLearner with 1,895 loops from various performance-relevant benchmarks. Applying the transformations that our model deems most favorable prior to compilation yields an average speedup of 1.14x. When trying the top-3 suggested transformations, the average speedup even increases to 1.29x. Comparing the approach with an exhaustive search through all available code transformations shows that LoopLearner helps to identify the most beneficial transformations in several orders of magnitude less time.
The NPU System for the 2020 Personalized Voice Trigger Challenge
Authors: Jingyong Hou, Li Zhang, Yihui Fu, Qing Wang, Zhanheng Yang, Qijie Shao, Lei Xie
Date: 2021-02-26 00:00:00
Tasks: Keyword Spotting, Small-Footprint Keyword Spotting, Speaker Verification

This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge. Our submitted system consists of two independently trained subsystems: a small footprint keyword spotting (KWS) system and a speaker verification (SV) system. For the KWS system, a multi-scale dilated temporal convolutional (MDTC) network is proposed to detect wake-up word (WuW). For SV system, Write something here. The KWS predicts posterior probabilities of whether an audio utterance contains WuW and estimates the location of WuW at the same time. When the posterior probability ofWuW reaches a predefined threshold, the identity information of triggered segment is determined by the SV system. On evaluation dataset, our submitted system obtains detection costs of 0.081and 0.091 in close talking and far-field tasks, respectively.
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
Authors: Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra
Date: 2021-02-26 00:00:00
Tasks: All, Graph Embedding, Network Embedding

While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes. We present PhUSION, a proximity-based unified framework for computing structural and positional node embeddings, which leverages well-established methods for calculating node proximity scores. Clarifying a point of contention in the literature, we show which step of PhUSION produces the different kinds of embeddings and what steps can be used by both. Moreover, by aggregating the PhUSION node embeddings, we obtain graph-level features that model information lost by previous graph feature learning and kernel methods. In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35 methods, we systematically reveal successful design choices for node and graph-level machine learning with embeddings.
PAC-Bayesian Policy Evaluation for Reinforcement Learning
Authors: Mahdi Milani Fard, Joelle Pineau, Csaba Szepesvari
Date: 2012-02-14 00:00:00
Tasks: Model Selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, largely depends on accuracy and correctness of these priors. PAC-Bayesian methods overcome this problem by providing bounds that hold regardless of the correctness of the prior distribution. This paper introduces the first PAC-Bayesian bound for the batch reinforcement learning problem with function approximation. We show how this bound can be used to perform model-selection in a transfer learning scenario. Our empirical results confirm that PAC-Bayesian policy evaluation is able to leverage prior distributions when they are informative and, unlike standard Bayesian RL approaches, ignore them when they are misleading.
Understanding the difficulty of training deep feedforward neural networks
Authors: Xavier Glorot, Yoshua Bengio
Date: 2010-05-13 00:00:00

Whereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.
A conditional version of the second fundamental theorem of asset pricing in discrete time
Authors: Lars Niemann, Thorsten Schmidt
Date: 2021-02-26 00:00:00

We consider a financial market in discrete time and study pricing and hedging conditional on the information available up to an arbitrary point in time. In this conditional framework, we determine the structure of arbitrage-free prices. Moreover, we characterize attainability and market completeness. We derive a conditional version of the second fundamental theorem of asset pricing, which, surprisingly, is not available up to now. The main tool we use are time consistency properties of dynamic nonlinear expectations, which we apply to the super- and subhedging prices. The results obtained extend existing results in the literature, where the conditional setting is considered in most cases only on finite probability spaces.
Girasol, a Sky Imaging and Global Solar Irradiance Dataset
Authors: Guillermo Terrén-Serrano, Adnan Bashir, Trilce Estrada, Manel Martínez-Ramón
Date: 2021-02-15 00:00:00

The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. We propose to achieve this using a data acquisition systems (DAQ) that simultaneously records sky imaging and Global Solar Irradiance (GSI) measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed as the \emph{Girasol Machine} (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on an outdoor tracker. The tracker updates its pan an tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal support next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.
Cross-verification and Persuasive Cheap Talk
Authors: Alp Atakan, Mehmet Ekmekci, Ludovic Renou
Date: 2021-02-26 00:00:00

We study a cheap-talk game where two experts first choose what information to acquire and then offer advice to a decision-maker whose actions affect the welfare of all. The experts cannot commit to reporting strategies. Yet, we show that the decision-maker's ability to cross-verify the experts' advice acts as a commitment device for the experts. We prove the existence of an equilibrium, where an expert's equilibrium payoff is equal to what he would obtain if he could commit to truthfully revealing his information.
Robust Adaptive Model Predictive Control of Quadrotors
Authors: Alexandre Didier, Anilkumar Parsi, Jeremy Coulson, Roy S. Smith
Date: 2021-02-26 00:00:00
Tasks: model, Model Predictive Control

Robust adaptive model predictive control (RAMPC) is a novel control method that combines robustness guarantees with respect to unknown parameters and bounded disturbances into a model predictive control scheme. However, RAMPC has so far only been developed in theory. The goal of this paper is to apply RAMPC to a physical quadrotor experiment. To the best of our knowledge this is the first time that RAMPC has been applied in practice using a state space formulation. In doing so, we highlight important practical challenges such as computation of $\lambda$-contractive polytopes and dealing with measurement noise, and propose modifications to RAMPC so that it can be applied on a quadrotor. We first simulate quadrotor flight with a direct and a decoupled control architecture in different scenarios. The scenarios include: (i) an unknown mass of the quadrotor as a package delivery scenario with wind as a bounded disturbance; and (ii) all rotor efficiencies drop as a power delivery problem. We then implement these scenarios on a physical quadrotor and present the experimental results.
Visual Word Embedding for Text Classification
Authors: Ignazio Gallo, Shah Nawaz, Nicola Landro, Riccardo La Grassa
Date: 2021-02-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, text-classification, Text Classification

The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding. The proposed approach computes the Word2Vec word embedding of a text document, quantizes the embedding, and arranges it into a 2D visual representation, as an RGB image. Finally, visual embedding is categorized with state-of-the-art image classification models. We achieved competitive performance on well-known benchmark text classification datasets. In addition, we evaluated our proposed approach in a multimodal setting that allows text and image information in the same feature space.
Modeling Insights from COVID-19 Incidence Data: Part II -- Why are compartment models so accurate?
Authors: Ryan Wilkinson, Marcus Roper
Date: 2020-12-24 00:00:00
Tasks: regression

The SIR-compartment model is among the simplest models that describe the spread of a disease through a population. The model makes the unrealistic assumption that the population through which the disease is spreading is well-mixed. Although real populations have heterogeneities in contacts not represented in the SIR model, it nevertheless well fits real U.S. state data at multiple points throughout the pandemic. Here we demonstrate mathematically how closely the simple continuous SIR model approximates a model which includes heterogeneous contacts, and provide insight onto how one can interpret parameters gleaned from regression in the context of heterogeneous dynamics.
Weighted Feature Fusion Based Emotional Recognition for Variable-length Speech using DNN
Authors: Sifan Wu1, Fei Li1, Pengyuan Zhang
Date: 2019-01-01 00:00:00
Tasks: Emotion Recognition

Emotion recognition plays an increasingly important role in human-computer interaction systems, which is a
key technology in multimedia communication. Because neural
networks can automatically learn the intermediate representation
of raw speech signal, currently, most methods use Convolutional
Neural Network (CNN) to extract information directly from
spectrograms, but this may result in the ineffective use of
information in hand-crafted features. In this work, a model
based on weighted feature fusion method is proposed for emotion
recognition of variable-length speech. Since the Chroma-based
features are closely related to speech emotions, our model can
effectively utilize the useful information in Chromaticity map
to improve the performance by combining CNN-based features
and Chroma-based features. We evaluated the model on the
Interactive Emotional Motion Capture (IEMOCAP) dataset and
achieved more than 5% increase in weighted accuracy (WA) and
unweighted accuracy (UA), comparing with the existing state-ofthe-art methods
LocalDrop: A Hybrid Regularization for Deep Neural Networks
Authors: Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama
Date: 2021-03-01 00:00:00

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutional neural networks (CNNs), including drop rates and weight matrices, has been developed based on the proposed upper bound of the local Rademacher complexity by the strict mathematical deduction. The analyses of dropout in FCNs and DropBlock in CNNs with keep rate matrices in different layers are also included in the complexity analyses. With the new regularization function, we establish a two-stage procedure to obtain the optimal keep rate matrix and weight matrix to realize the whole training model. Extensive experiments have been conducted to demonstrate the effectiveness of LocalDrop in different models by comparing it with several algorithms and the effects of different hyperparameters on the final performances.
PENet: Towards Precise and Efficient Image Guided Depth Completion
Authors: Mu Hu, Shuling Wang, Bin Li, Shiyu Ning, Li Fan, Xiaojin Gong
Date: 2021-03-01 00:00:00
Tasks: Depth Completion

Image guided depth completion is the task of generating a dense depth map from a sparse depth map and a high quality image. In this task, how to fuse the color and depth modalities plays an important role in achieving good performance. This paper proposes a two-branch backbone that consists of a color-dominant branch and a depth-dominant branch to exploit and fuse two modalities thoroughly. More specifically, one branch inputs a color image and a sparse depth map to predict a dense depth map. The other branch takes as inputs the sparse depth map and the previously predicted depth map, and outputs a dense depth map as well. The depth maps predicted from two branches are complimentary to each other and therefore they are adaptively fused. In addition, we also propose a simple geometric convolutional layer to encode 3D geometric cues. The geometric encoded backbone conducts the fusion of different modalities at multiple stages, leading to good depth completion results. We further implement a dilated and accelerated CSPN++ to refine the fused depth map efficiently. The proposed full model ranks 1st in the KITTI depth completion online leaderboard at the time of submission. It also infers much faster than most of the top ranked methods. The code of this work is available at https://github.com/JUGGHM/PENet_ICRA2021.
Generative Adversarial Transformers
Authors: Drew A. Hudson, C. Lawrence Zitnick
Date: 2021-03-01 00:00:00
Tasks: Disentanglement, Image Generation, Scene Generation

We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model's strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency. Further qualitative and quantitative experiments offer us an insight into the model's inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at https://github.com/dorarad/gansformer.