pwc-archive/papers-with-abstracts huggingface dataset:
Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Authors: Rao Muhammad Umer, Gian Luca Foresti, Christian Micheloni
Date: 2020-09-07 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and high-resolution (HR) outputs. These existing SR methods do not take into account the image observation (physical) model and thus require a large number of network's trainable parameters with a great volume of training data. To address these issues, we propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Extensive experimental results on various super-resolution benchmarks demonstrate that our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks
Authors: Thomas Fel, David Vigouroux, Rémi Cadène, Thomas Serre
Date: 2020-09-07 00:00:00
Tasks: Image Classification

A plethora of methods have been proposed to explain how deep neural networks reach their decisions but comparatively, little effort has been made to ensure that the explanations produced by these methods are objectively relevant. While several desirable properties for trustworthy explanations have been formulated, objective measures have been harder to derive. Here, we propose two new measures to evaluate explanations borrowed from the field of algorithmic stability: mean generalizability MeGe and relative consistency ReCo. We conduct extensive experiments on different network architectures, common explainability methods, and several image datasets to demonstrate the benefits of the proposed measures.In comparison to ours, popular fidelity measures are not sufficient to guarantee trustworthy explanations.Finally, we found that 1-Lipschitz networks produce explanations with higher MeGe and ReCo than common neural networks while reaching similar accuracy. This suggests that 1-Lipschitz networks are a relevant direction towards predictors that are more explainable and trustworthy.
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
Authors: Levi McClenny, Ulisses Braga-Neto
Date: 2020-09-07 00:00:00

Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of "stiff" PDEs. In this paper, we propose a fundamentally new way to train PINNs adaptively, where the adaptation weights are fully trainable and applied to each training point individually, so the neural network learns autonomously which regions of the solution are difficult and is forced to focus on them. The self-adaptation weights specify a soft multiplicative soft attention mask, which is reminiscent of similar mechanisms used in computer vision. The basic idea behind these SA-PINNs is to make the weights increase as the corresponding losses increase, which is accomplished by training the network to simultaneously minimize the losses and maximize the weights. In addition, we show how to build a continuous map of self-adaptive weights using Gaussian Process regression, which allows the use of stochastic gradient descent in problems where conventional gradient descent is not enough to produce accurate solutions. Finally, we derive the Neural Tangent Kernel matrix for SA-PINNs and use it to obtain a heuristic understanding of the effect of the self-adaptive weights on the dynamics of training in the limiting case of infinitely-wide PINNs, which suggests that SA-PINNs work by producing a smooth equalization of the eigenvalues of the NTK matrix corresponding to the different loss terms. In numerical experiments with several linear and nonlinear benchmark problems, the SA-PINN outperformed other state-of-the-art PINN algorithm in L2 error, while using a smaller number of training epochs.
Scalar Coupling Constant Prediction Using Graph Embedding Local Attention Encoder
Authors: Caiqing Jian, Xinyu Cheng, Jian Zhang, Lihui Wang
Date: 2020-09-07 00:00:00
Tasks: Graph Embedding

Scalar coupling constant (SCC) plays a key role in the analysis of three-dimensional structure of organic matter, however, the traditional SCC prediction using quantum mechanical calculations is very time-consuming. To calculate SCC efficiently and accurately, we proposed a graph embedding local self-attention encoder (GELAE) model, in which, a novel invariant structure representation of the coupling system in terms of bond length, bond angle and dihedral angle was presented firstly, and then a local self-attention module embedded with the adjacent matrix of a graph was designed to extract effectively the features of coupling systems, finally, with a modified classification loss function, the SCC was predicted. To validate the superiority of the proposed method, we conducted a series of comparison experiments using different structure representations, different attention modules, and different losses. The experimental results demonstrate that, compared to the traditional chemical bond structure representations, the rotation and translation invariant structure representations proposed in this work can improve the SCC prediction accuracy; with the graph embedded local self-attention, the mean absolute error (MAE) of the prediction model in the validation set decreases from 0.1603 Hz to 0.1067 Hz; using the classification based loss function instead of the scaled regression loss, the MAE of the predicted SCC can be decreased to 0.0963 HZ, which is close to the quantum chemistry standard on CHAMPS dataset.
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
Authors: Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Klaus Mueller
Date: 2020-09-06 00:00:00
Tasks: Active Learning, Feature Importance

We propose a new active learning (AL) framework, Active Learning++, which can utilize an annotator's labels as well as its rationale. Annotators can provide their rationale for choosing a label by ranking input features based on their importance for a given query. To incorporate this additional input, we modified the disagreement measure for a bagging-based Query by Committee (QBC) sampling strategy. Instead of weighing all committee models equally to select the next instance, we assign higher weight to the committee model with higher agreement with the annotator's ranking. Specifically, we generated a feature importance-based local explanation for each committee model. The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model. This approach is applicable to any kind of ML model using model-agnostic techniques to generate local explanation such as LIME. With a simulation study, we show that our framework significantly outperforms a QBC based vanilla AL framework.
Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing
Authors: Sheshank Shankar, Rishank Kanaparti, Ayush Chopra, Rohan Sukumaran, Parth Patwa, Myungsun Kang, Abhishek Singh, Kevin P. McPherson, Ramesh Raskar
Date: 2020-09-04 00:00:00
Tasks: Time Series, Time Series Analysis

As we await a vaccine, social-distancing via efficient contact tracing has emerged as the primary health strategy to dampen the spread of COVID-19. To enable efficient digital contact tracing, we present a novel system to estimate pair-wise individual proximity, via a joint model of Bluetooth Low Energy (BLE) signals with other on-device sensors (accelerometer, magnetometer, gyroscope). We explore multiple ways of interpreting the sensor data stream (time-series, histogram, etc) and use several statistical and deep learning methods to learn representations for sensing proximity. We report the normalized Decision Cost Function (nDCF) metric and analyze the differential impact of the various input signals, as well as discuss various challenges associated with this task.
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
Authors: Alexander Wang, Mengye Ren, Richard S. Zemel
Date: 2020-08-27 00:00:00
Tasks: Novel Concepts, Retrieval

Sketch drawings capture the salient information of visual concepts. Previous work has shown that neural networks are capable of producing sketches of natural objects drawn from a small number of classes. While earlier approaches focus on generation quality or retrieval, we explore properties of image representations learned by training a model to produce sketches of images. We show that this generative, class-agnostic model produces informative embeddings of images from novel examples, classes, and even novel datasets in a few-shot setting. Additionally, we find that these learned representations exhibit interesting structure and compositionality.
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
Authors: Enrui Zhang, Minglang Yin, George Em. Karniadakis
Date: 2020-09-02 00:00:00

We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials. We focus on the problem with a background in elasticity imaging, where one seeks to identify the nonhomogeneous mechanical properties of soft tissue based on the full-field displacement measurements under quasi-static loading. In our model, we apply two independent neural networks, one for approximating the solution of the corresponding forward problem, and the other for approximating the unknown material parameter field. As a proof of concept, we validate our model on a prototypical plane strain problem for incompressible hyperelastic tissue. The results show that the PINNs are effective in accurately recovering the unknown distribution of mechanical properties. By employing two neural networks in our model, we extend the capability of material identification of PINNs to include nonhomogeneous material parameter fields, which enables more flexibility of PINNs in representing complex material properties.
Extrapolating continuous color emotions through deep learning
Authors: Vishaal Ram, Laura P. Schaposnik, Nikos Konstantinou, Eliz Volkan, Marietta Papadatou-Pastou, Banu Manav, Domicele Jonauskaite, Christine Mohr
Date: 2020-08-05 00:00:00
Tasks: Deep Learning

By means of an experimental dataset, we use deep learning to implement an RGB extrapolation of emotions associated to color, and do a mathematical study of the results obtained through this neural network. In particular, we see that males typically associate a given emotion with darker colors while females with brighter colors. A similar trend was observed with older people and associations to lighter colors. Moreover, through our classification matrix, we identify which colors have weak associations to emotions and which colors are typically confused with other colors.
A Variational Algorithm for Quantum Neural Networks
Authors: Antonio Macaluso, Luca Clissa, Stefano Lodi, Claudio Sartori
Date: 2020-06-15 00:00:00
Tasks: Descriptive, General Classification

Quantum Computing leverages the laws of quantum mechanics to build computers endowed with tremendous computing power. The field is attracting ever-increasing attention from both academic and private sectors, as testified by the recent demonstration of quantum supremacy in practice. However, the intrinsic restriction to linear operations significantly limits the range of relevant use cases for the application of Quantum Computing. In this work, we introduce a novel variational algorithm for quantum Single Layer Perceptron. Thanks to the universal approximation theorem, and given that the number of hidden neurons scales exponentially with the number of qubits, our framework opens to the possibility of approximating any function on quantum computers. Thus, the proposed approach produces a model with substantial descriptive power, and widens the horizon of potential applications already in the NISQ era, especially the ones related to Quantum Artificial Intelligence. In particular, we design a quantum circuit to perform linear combinations in superposition and discuss adaptations to classification and regression tasks. After this theoretical investigation, we also provide practical implementations using various simulation environments. Finally, we test the proposed algorithm on synthetic data exploiting both simulators and real quantum devices.
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data Augmentation
Authors: Zhaoyi Gu, Lele Liao, Kai Chen, Jing Lu
Date: 2020-05-16 00:00:00
Tasks: blind source separation, Data Augmentation, Speaker Recognition, Speech Extraction

Extracting the desired speech from a mixture is a meaningful and challenging task. The end-to-end DNN-based methods, though attractive, face the problem of generalization. In this paper, we explore a sequential approach for target speech extraction by combining blind source separation (BSS) with the x-vector based speaker recognition (SR) module. Two promising BSS methods based on source independence assumption, independent low-rank matrix analysis (ILRMA) and multi-channel variational autoencoder (MVAE), are utilized and compared. ILRMA employs nonnegative matrix factorization (NMF) to capture spectral structures of source signals and MVAE utilizes the strong modeling power of deep neural networks (DNN). However, the investigation of MVAE has been limited to the training with very few speakers and the speech signals of test speakers are usually included. We extend the training of MVAE using clean speech signals of 500 speakers to evaluate its generalization to unseen speakers. To improve the correct extraction rate, two data augmentation strategies are implemented to train the SR module. The performance of the proposed cascaded approach is investigated with test data constructed with real room impulse responses under varied environments.
Alchemical Transformations for Single-Step Hydration Free Energy Calculations
Authors: Emilio Gallicchio
Date: 2020-05-13 00:00:00

We present a family of alchemical perturbation potentials that allow the calculation of hydration free energy of small to medium-sized molecules in a single perturbation step. We also present a general framework to optimize the parameters of the alchemical perturbation potentials based on avoiding first order pseudo phase transitions along the alchemical path. We illustrate the method for two compounds of increasing size and complexity: ethanol and 1-naphtol. In each case we show that convergence of the hydration free energy is achieved rapidly when conventional approaches fail.
Extracting a Semantic Database with Syntactic Relations for Finnish to Boost Resources for Endangered Uralic Languages
Authors: Mika Hämäläinen
Date: 2018-11-01 00:00:00
Tasks: Translation

This paper introduces the second version of SemFi, a semantic database for Finnish with syntactic relations. The previous version of SemFi has been used in poem generation, and thus it has application area in NLG applications. In addition to extending SemFi, this paper describes and evaluates its translation into four endangered Uralic languages, Skolt Sami, Erzya, Moksha and Komi-Zyrian, all of which are greatly under-resourced. The translated dataset is known as SemUr.
Adaptive Fuzzy Cerebellar Model Articulation Controller for Two-Wheeled Robot
Authors: Jen-Yang Chen, Kuei-Chih Lin, Pu-Sheng Tsai, Chuan-Hsi Liu, and Jing-Ming Ouyang
Date: 2012-02-07 00:00:00
Tasks: Position, Vocal Bursts Valence Prediction

In this paper, an adaptive Fuzzy Cerebellar Model Articulation Controller (FCMAC) is proposed to control two-wheeled robot at upright position. The dynamical model of the robot,LEGO Mindstorms NXT, is derived from Lagrange of kinetic and potential energies. Based on the developed model, an adaptive FCMAC is then designed. Adaptation laws are derived from the Lyapunov stability analysis. According to the stability analysis, the developed FCMAC guarantees that the system tracking performance and the error convergence can be assured in the closed-loop system. Finally, to compare the system performances we apply the proposed FCMAC and the PID(Proportional Integral Derivative) controller to balance the two-wheeled robot.
Symplectic Gaussian Process Regression of Hamiltonian Flow Maps
Authors: Katharina Rath, Christopher G. Albert, Bernd Bischl, Udo von Toussaint
Date: 2020-09-11 00:00:00
Tasks: Numerical Integration, regression, Time Series, Time Series Analysis

We present an approach to construct appropriate and efficient emulators for Hamiltonian flow maps. Intended future applications are long-term tracing of fast charged particles in accelerators and magnetic plasma confinement configurations. The method is based on multi-output Gaussian process regression on scattered training data. To obtain long-term stability the symplectic property is enforced via the choice of the matrix-valued covariance function. Based on earlier work on spline interpolation we observe derivatives of the generating function of a canonical transformation. A product kernel produces an accurate implicit method, whereas a sum kernel results in a fast explicit method from this approach. Both correspond to a symplectic Euler method in terms of numerical integration. These methods are applied to the pendulum and the H\'enon-Heiles system and results compared to an symmetric regression with orthogonal polynomials. In the limit of small mapping times, the Hamiltonian function can be identified with a part of the generating function and thereby learned from observed time-series data of the system's evolution. Besides comparable performance of implicit kernel and spectral regression for symplectic maps, we demonstrate a substantial increase in performance for learning the Hamiltonian function compared to existing approaches.
Machine Unlearning for Random Forests
Authors: Jonathan Brophy, Daniel Lowd
Date: 2020-09-11 00:00:00
Tasks: Machine Unlearning

Responding to user data deletion requests, removing noisy examples, or deleting corrupted training data are just a few reasons for wanting to delete instances from a machine learning (ML) model. However, efficiently removing this data from an ML model is generally difficult. In this paper, we introduce data removal-enabled (DaRE) forests, a variant of random forests that enables the removal of training data with minimal retraining. Model updates for each DaRE tree in the forest are exact, meaning that removing instances from a DaRE model yields exactly the same model as retraining from scratch on updated data. DaRE trees use randomness and caching to make data deletion efficient. The upper levels of DaRE trees use random nodes, which choose split attributes and thresholds uniformly at random. These nodes rarely require updates because they only minimally depend on the data. At the lower levels, splits are chosen to greedily optimize a split criterion such as Gini index or mutual information. DaRE trees cache statistics at each node and training data at each leaf, so that only the necessary subtrees are updated as data is removed. For numerical attributes, greedy nodes optimize over a random subset of thresholds, so that they can maintain statistics while approximating the optimal threshold. By adjusting the number of thresholds considered for greedy nodes, and the number of random nodes, DaRE trees can trade off between more accurate predictions and more efficient updates. In experiments on 13 real-world datasets and one synthetic dataset, we find DaRE forests delete data orders of magnitude faster than retraining from scratch while sacrificing little to no predictive power.
Accelerating 2PC-based ML with Limited Trusted Hardware
Authors: Muqsit Nawaz, Aditya Gulati, Kunlong Liu, Vishwajeet Agrawal, Prabhanjan Ananth, Trinabh Gupta
Date: 2020-09-11 00:00:00
Tasks: CPU

This paper describes the design, implementation, and evaluation of Otak, a system that allows two non-colluding cloud providers to run machine learning (ML) inference without knowing the inputs to inference. Prior work for this problem mostly relies on advanced cryptography such as two-party secure computation (2PC) protocols that provide rigorous guarantees but suffer from high resource overhead. Otak improves efficiency via a new 2PC protocol that (i) tailors recent primitives such as function and homomorphic secret sharing to ML inference, and (ii) uses trusted hardware in a limited capacity to bootstrap the protocol. At the same time, Otak reduces trust assumptions on trusted hardware by running a small code inside the hardware, restricting its use to a preprocessing step, and distributing trust over heterogeneous trusted hardware platforms from different vendors. An implementation and evaluation of Otak demonstrates that its CPU and network overhead converted to a dollar amount is 5.4$-$385$\times$ lower than state-of-the-art 2PC-based works. Besides, Otak's trusted computing base (code inside trusted hardware) is only 1,300 lines of code, which is 14.6$-$29.2$\times$ lower than the code-size in prior trusted hardware-based works.
Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse
Authors: Ancil Crayton, João Fonseca, Kanav Mehra, Michelle Ng, Jared Ross, Marcelo Sandoval-Castañeda, Rachel von Gnechten
Date: 2020-09-11 00:00:00

People often turn to social media to comment upon and share information about major global events. Accordingly, social media is receiving increasing attention as a rich data source for understanding people's social, political and economic experiences of extreme weather events. In this paper, we contribute two novel methodologies that leverage Twitter discourse to characterize narratives and identify unmet needs in response to Cyclone Amphan, which affected 18 million people in May 2020.
Systematic Generalization on gSCAN with Language Conditioned Embedding
Authors: Tong Gao, Qi Huang, Raymond J. Mooney
Date: 2020-09-11 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Systematic Generalization

Systematic Generalization refers to a learning algorithm's ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. Therefore, we propose a novel method that learns objects' contextualized embeddings with dynamic message passing conditioned on the input natural language and end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded-SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.