pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder
Authors: Ziqi Ke, Haris Vikalo
Date: 2020-11-16 00:00:00
Tasks: Computational Efficiency, Denoising, General Classification

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation. They are rendered difficult due to a growing number of emitter types and varied effects of real-world channels upon the radio signal. Existing spectrum monitoring techniques are capable of acquiring massive amounts of radio and real-time spectrum data using compact sensors deployed in a variety of settings. However, state-of-the-art methods that use such data to classify emitter types and detect communication schemes struggle to achieve required levels of accuracy at a computational efficiency that would allow their implementation on low-cost computational platforms. In this paper, we present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals, and infer modulation or technology type using the learned features. The algorithm utilizes a compact neural network architecture readily implemented on a low-cost computational platform while exceeding state-of-the-art accuracy. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals, often demonstrating superior performance compared to state-of-the-art methods.
Occams Razor for Big Data? On Detecting Quality in Large Unstructured Datasets
Authors: Birgitta Dresp-Langley, Ole Kristian Ekseth, Jan Fesl, Seiichi Gohshi, Marc Kurz, Hans-Werner Sehring
Date: 2020-11-12 00:00:00
Tasks: Clustering

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony or Occams Razor in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns, generate new information, or store and further process large amounts of sensor data is then reviewed; examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence aimed at coping with the big data deluge in the near future.
Uncertainty estimation for molecular dynamics and sampling
Authors: Giulio Imbalzano, Yongbin Zhuang, Venkat Kapil, Kevin Rossi, Edgar A. Engel, Federico Grasselli, Michele Ceriotti
Date: 2020-11-10 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Uncertainty Quantification

Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale and complexity. Given the interpolative nature of these models, the reliability of predictions depends on the position in phase space, and it is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during the training of the model. When using a machine-learning potential to sample a finite-temperature ensemble, the uncertainty on individual configurations translates into an error on thermodynamic averages, and provides an indication for the loss of accuracy when the simulation enters a previously unexplored region. Here we discuss how uncertainty quantification can be used, together with a baseline energy model, or a more robust although less accurate interatomic potential, to obtain more resilient simulations and to support active-learning strategies. Furthermore, we introduce an on-the-fly reweighing scheme that makes it possible to estimate the uncertainty in the thermodynamic averages extracted from long trajectories. We present examples covering different types of structural and thermodynamic properties, and systems as diverse as water and liquid gallium.
Evaluation of Error and Correlation-Based Loss Functions For Multitask Learning Dimensional Speech Emotion Recognition
Authors: Bagus Tris Atmaja, Masato Akagi
Date: 2020-03-24 00:00:00
Tasks: Emotion Recognition, regression, Speech Emotion Recognition

The choice of a loss function is a critical part in machine learning. This paper evaluated two different loss functions commonly used in regression-task dimensional speech emotion recognition, an error-based and a correlation-based loss functions. We found that using correlation-based loss function with a concordance correlation coefficient (CCC) loss resulted better performance than error-based loss function with a mean squared error (MSE) loss, in terms of the averaged CCC score. The results are consistent with two input feature sets and two datasets. The scatter plots of test prediction by those two loss functions also confirmed the results measured by CCC scores.
Exploratory Projection to Latent Structure Models for use in Transcriptomic Analysis
Authors: Edward Tjörnhammar, Richard Tjörnhammar
Date: 2020-01-17 00:00:00

In this paper, we ask if it is possible to increase the interpretability in multivariate analysis by aligning and projecting covariates onto comparative subspaces. We demonstrate our method as well as the interpretative power of PLS decomposed models and how robust interpretability can lead to quantitative insights. We discuss the statistical properties of the PLS weights, $p$-values associated with specific axes, as well as their alignment properties. The applicability of this approach within life science is also demonstrated by applying it to three use cases of publically available datasets. Further we present hierarchical pathway enrichment results stemming from aligned $p$-values, which are compared with results derived from enrichment analysis, as an external validation of our method. We find that the method can uncover known results from genomics for all of the studied use cases, i.e. microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients.
Audio Dequantization Using (Co)Sparse (Non)Convex Methods
Authors: Pavel Záviška, Pavel Rajmic, Ondřej Mokrý
Date: 2020-10-30 00:00:00
Tasks: Audio Dequantization

The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. In the experiments the methods are evaluated using the signal-to-distortion ratio (SDR) and PEMO-Q, a perceptually motivated metric.
PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation
Authors: Yicheng Wu, Vivek Boominathan, Huaijin Chen, Aswin Sankaranarayanan, and Ashok Veeraraghavan
Date: 2019-05-15 00:00:00
Tasks: Autonomous Driving, Depth Estimation, Monocular Depth Estimation

3DImaging is critical for a myriad of applications such as autonomous driving, robotics, virtual reality, and surveillance. The current state of art relies on active illumination based techniques such as LIDAR, radar, structured illumination or continuous-wave time-of-flight. However, many emerging applications, especially on mobile platforms, are severely power and energy constrained. Active approaches are unlikely to scale well for these applications and hence, there is a pressing need for robust passive 3D imaging technologies. Multi-camera systems provide state of the art performance for passive 3D imaging. In these systems, triangulation between corresponding points on multiple views of the scene allows for 3D estimation. Stereo and multi-view stereo approaches meet some of the needs mentioned above, and an increasing number of mobile platforms have been adopting such technology. Unfortunately, having multiple cameras within a single platform results in increased system cost as well as implementation complexity. The principal goal of this paper is to develop a passive, single-viewpoint 3D imaging system. We exploit the emerging
computational imaging paradigm, wherein the optics and the computational algorithm are co-designed to maximize performance
within operational constraints.
Named Entity Linking in #Tweets with KEA
Authors: Jörg Waitelonis, Harald Sack
Date: 2016-04-01 00:00:00
Tasks: Entity Disambiguation, Entity Linking

This paper presents the KEA system at the #Microposts 2016 NEEL Challenge. Its task is to recognize and type mentions from English microposts and link them to their corresponding entries in DBpedia. For this task, we have adapted our Named Entity Disambiguation tool originally designed for natural language text to the special requirements of noisy, terse, and poorly worded tweets containing
special functional terms and language.
A New Dataset and Proposed Convolutional Neural Network Architecture for Classification of American Sign Language Digits
Authors: Arda Mavi
Date: 2020-11-16 00:00:00

According to interviews with people who work with speech impaired persons, speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence. With this paper, to increase the quality of life of individuals with facilitating communication between individuals who use sign language and who do not know this language, a new American Sign Language (ASL) digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful created and published as Sign Language Digits Dataset on Kaggle Datasets web page, a proposal Convolutional Neural Network (CNN) architecture that can get 98% test accuracy on our dataset presented, and compared with the existing popular CNN models.
Argumentative Topology: Finding Loop(holes) in Logic
Authors: Sarah Tymochko, Zachary New, Lucius Bynum, Emilie Purvine, Timothy Doster, Julien Chaput, Tegan Emerson
Date: 2020-11-17 00:00:00
Tasks: Topological Data Analysis, Word Embeddings

Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the introduction of increasingly sophisticated text representations. While many of the new word embedding techniques can be shown to capture particular notions of sentiment or associative structures, we explore the ability of two different word embeddings to uncover or capture the notion of logical shape in text. To this end we present a novel framework that we call Topological Word Embeddings which leverages mathematical techniques in dynamical system analysis and data driven shape extraction (i.e. topological data analysis). In this preliminary work we show that using a topological delay embedding we are able to capture and extract a different, shape-based notion of logic aimed at answering the question "Can we find a circle in a circular argument?"
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
Authors: Jaemin Na, Heechul Jung, Hyung Jin Chang, Wonjun Hwang
Date: 2020-11-18 00:00:00
Proceeding: CVPR 2021 1
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidence-based learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017.
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
Authors: Kelly Geyer, Frederick Campbell, Andersen Chang, John Magnotti, Michael Beauchamp, Genevera I. Allen
Date: 2020-11-15 00:00:00
Tasks: Dimensionality Reduction

ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capability to record activity at a fast temporal resolution, ECoG experiments have allowed scientists to better understand how the human brain processes speech. By its nature, ECoG data is difficult for neuroscientists to directly interpret for two major reasons. Firstly, ECoG data tends to be large in size, as each individual experiment yields data up to several gigabytes. Secondly, ECoG data has a complex, higher-order nature. After signal processing, this type of data may be organized as a 4-way tensor with dimensions representing trials, electrodes, frequency, and time. In this paper, we develop an interpretable dimension reduction approach called Regularized Higher Order Principal Components Analysis, as well as an extension to Regularized Higher Order Partial Least Squares, that allows neuroscientists to explore and visualize ECoG data. Our approach employs a sparse and functional Candecomp-Parafac (CP) decomposition that incorporates sparsity to select relevant electrodes and frequency bands, as well as smoothness over time and frequency, yielding directly interpretable factors. We demonstrate the performance and interpretability of our method with an ECoG case study on audio and visual processing of human speech.
Principal Component Analysis and Factor Analysis for Feature Selection in Credit Rating
Authors: Shenghuan Yang, lonut Florescu, Md Tariqul Islam
Date: 2020-11-18 00:00:00
Tasks: feature selection

The credit rating is an evaluation of a company's credit risk that values the ability to pay back the debt and predict the likelihood of the debtor defaulting. There are various features influencing credit rating. Therefore, it is essential to select substantive features to explore the main reason for credit rating change. To address this issue, this paper exploited Principal Component Analysis and Factor Analysis as feature selection algorithms to select important features, summarized the similar features together, and obtained a minimum set of features for four sectors, Financial Sector, Energy Sector, Health Care Sector, Consumer Discretionary Sector. This paper used two data sets, Financial Ratio and Balance Sheet, with two mappings, Detailed Mapping, and Coarse Mapping, converting the target variable(credit rating) into categorical variable. To test the accuracy of credit rating prediction, Random Forest Classifier was used to test and train feature sets. The results showed that the accuracy of Financial Ratio feature sets was higher than that of Balance Sheet feature sets. In addition, Factor Analysis can reduce the number of features significantly to obtain almost the same accuracy that can decrease dramatically the time spent on analyzing data; we also summarized seven dominant factors and ten dominant factors affecting credit rating change in Financial Ratio and Balance Sheet by utilizing Factor Analysis, respectively, which can explain the reason of credit rating change better.
Adversarial collision attacks on image hashing functions
Authors: Brian Dolhansky, Cristian Canton Ferrer
Date: 2020-11-18 00:00:00

Hashing images with a perceptual algorithm is a common approach to solving duplicate image detection problems. However, perceptual image hashing algorithms are differentiable, and are thus vulnerable to gradient-based adversarial attacks. We demonstrate that not only is it possible to modify an image to produce an unrelated hash, but an exact image hash collision between a source and target image can be produced via minuscule adversarial perturbations. In a white box setting, these collisions can be replicated across nearly every image pair and hash type (including both deep and non-learned hashes). Furthermore, by attacking points other than the output of a hashing function, an attacker can avoid having to know the details of a particular algorithm, resulting in collisions that transfer across different hash sizes or model architectures. Using these techniques, an adversary can poison the image lookup table of a duplicate image detection service, resulting in undefined or unwanted behavior. Finally, we offer several potential mitigations to gradient-based image hash attacks.
FROST: Faster and more Robust One-shot Semi-supervised Training
Authors: Helena E. Liu, Leslie N. Smith
Date: 2020-11-18 00:00:00

Recent advances in one-shot semi-supervised learning have lowered the barrier for deep learning of new applications. However, the state-of-the-art for semi-supervised learning is slow to train and the performance is sensitive to the choices of the labeled data and hyper-parameter values. In this paper, we present a one-shot semi-supervised learning method that trains up to an order of magnitude faster and is more robust than state-of-the-art methods. Specifically, we show that by combining semi-supervised learning with a one-stage, single network version of self-training, our FROST methodology trains faster and is more robust to choices for the labeled samples and changes in hyper-parameters. Our experiments demonstrate FROST's capability to perform well when the composition of the unlabeled data is unknown; that is when the unlabeled data contain unequal numbers of each class and can contain out-of-distribution examples that don't belong to any of the training classes. High performance, speed of training, and insensitivity to hyper-parameters make FROST the most practical method for one-shot semi-supervised training. Our code is available at https://github.com/HelenaELiu/FROST.
Improved Grey System Models for Predicting Traffic Parameters
Authors: Gurcan Comert, Negash Begashaw, Nathan Huynh
Date: 2020-11-18 00:00:00
Tasks: Management, Time Series, Time Series Analysis

In transportation applications such as real-time route guidance, ramp metering, congestion pricing and special events traffic management, accurate short-term traffic flow prediction is needed. For this purpose, this paper proposes several novel \textit{online} Grey system models (GM): GM(1,1$|cos(\omega t)$), GM(1,1$|sin(\omega t)$, $cos(\omega t)$), and GM(1,1$|e^{-at}$,$sin(\omega t)$,$cos(\omega t)$). To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1,1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model. The evaluation is performed using loop detector and probe vehicle data from California, Virginia, and Oregon. Among the benchmark models, the error corrected Grey Verhulst model with Fourier outperformed the GM(1,1) model, linear time series, and non-linear time series models. In turn, the three proposed models, GM(1,1$|cos(\omega t)$), GM(1,1$|sin(\omega t)$,$cos(\omega t)$), and GM(1,1$|e^{-at}$,$sin(\omega t)$,$cos(\omega t)$), outperformed the Grey Verhulst model in prediction by at least $65\%$, $16\%$, and $11\%$, in terms of Root Mean Squared Error, and by $82\%$, $58\%$, and $42\%$, in terms of Mean Absolute Percentage Error, respectively. It is observed that the proposed Grey system models are more adaptive to location (e.g., perform well for all roadway types) and traffic parameters (e.g., speed, travel time, occupancy, and volume), and they do not require as many data points for training (4 observations are found to be sufficient).
Gradient Starvation: A Learning Proclivity in Neural Networks
Authors: Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, Guillaume Lajoie
Date: 2020-11-18 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Out-of-Distribution Generalization

We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks. Gradient Starvation arises when cross-entropy loss is minimized by capturing only a subset of features relevant for the task, despite the presence of other predictive features that fail to be discovered. This work provides a theoretical explanation for the emergence of such feature imbalance in neural networks. Using tools from Dynamical Systems theory, we identify simple properties of learning dynamics during gradient descent that lead to this imbalance, and prove that such a situation can be expected given certain statistical structure in training data. Based on our proposed formalism, we develop guarantees for a novel regularization method aimed at decoupling feature learning dynamics, improving accuracy and robustness in cases hindered by gradient starvation. We illustrate our findings with simple and real-world out-of-distribution (OOD) generalization experiments.
Detecting Hierarchical Changes in Latent Variable Models
Authors: Shintaro Fukushima, Kenji Yamanishi
Date: 2020-11-18 00:00:00
Tasks: Change Detection

This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable models: 1) the first level is the change in data distribution for fixed latent variables, 2) the second one is that in the distribution over latent variables, and 3) the third one is that in the number of latent variables. It is important to detect these changes because we can analyze the causes of changes by identifying which level a change comes from (change interpretability). This paper proposes an information-theoretic framework for detecting changes of the three levels in a hierarchical way. The key idea to realize it is to employ the MDL (minimum description length) change statistics for measuring the degree of change, in combination with DNML (decomposed normalized maximum likelihood) code-length calculation. We give a theoretical basis for making reliable alarms for changes. Focusing on stochastic block models, we employ synthetic and benchmark datasets to empirically demonstrate the effectiveness of our framework in terms of change interpretability as well as change detection.