pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Represent Programs with Heterogeneous Graphs
Authors: Kechi Zhang, Wenhan Wang, Huangzhao Zhang, Ge Li, Zhi Jin
Date: 2020-12-08 00:00:00
Tasks: Code Comment Generation, Comment Generation

Program source code contains complex structure information, which can be represented in structured data forms like trees or graphs. To acquire the structural information in source code, most existing researches use abstract syntax trees (AST). A group of works add additional edges to ASTs to convert source code into graphs and use graph neural networks to learn representations for program graphs. Although these works provide additional control or data flow information to ASTs for downstream tasks, they neglect an important aspect of structure information in AST itself: the different types of nodes and edges. In ASTs, different nodes contain different kinds of information like variables or control flow, and the relation between a node and all its children can also be different. To address the information of node and edge types, we bring the idea of heterogeneous graphs to learning on source code and present a new formula of building heterogeneous program graphs from ASTs with additional type information for nodes and edges. We use the ASDL grammar of programming language to define the node and edge types of program graphs. Then we use heterogeneous graph neural networks to learn on these graphs. We evaluate our approach on two tasks: code comment generation and method naming. Both tasks require reasoning on the semantics of complete code snippets. Experiment results show that our approach outperforms baseline models, including homogeneous graph-based models, showing that leveraging the type information of nodes and edges in program graphs can help in learning program semantics.
Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance
Authors: A. Barakat, P. Bianchi, W. Hachem, Sh. Schechtman
Date: 2020-12-07 00:00:00
Tasks: Stochastic Optimization

In this paper, a general stochastic optimization procedure is studied, unifying several variants of the stochastic gradient descent such as, among others, the stochastic heavy ball method, the Stochastic Nesterov Accelerated Gradient algorithm (S-NAG), and the widely used Adam algorithm. The algorithm is seen as a noisy Euler discretization of a non-autonomous ordinary differential equation, recently introduced by Belotto da Silva and Gazeau, which is analyzed in depth. Assuming that the objective function is non-convex and differentiable, the stability and the almost sure convergence of the iterates to the set of critical points are established. A noteworthy special case is the convergence proof of S-NAG in a non-convex setting. Under some assumptions, the convergence rate is provided under the form of a Central Limit Theorem. Finally, the non-convergence of the algorithm to undesired critical points, such as local maxima or saddle points, is established. Here, the main ingredient is a new avoidance of traps result for non-autonomous settings, which is of independent interest.
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
Authors: Patrick J. Fox, Shangqing Huang, Joshua Isaacson, Xiangyang Ju, Benjamin Nachman
Date: 2020-12-08 00:00:00
Tasks: parameter estimation

Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.
TrimNet: learning molecular representation from triplet messages for biomedicine
Authors: Pengyong Li, Yuquan Li, Chang-Yu Hsieh, Shengyu Zhang, Xianggen Liu, Huanxiang Liu, Sen Song, Xiaojun Yao
Date: 2020-11-04 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, molecular representation, Prediction, Property Prediction, Representation Learning, Triplet

Motivation: Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction identification. A key challenge is to learn useful molecular representation. In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine-learning methods, which requires expert knowledge and is often labor-intensive.  Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction.

Results: In this study, we proposed a graph-based approach that employed a novel triplet message mechanism to learn molecular representation efficiently,  named triplet message networks (TrimNet).  We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction,  including the quantum properties,  bioactivity,  physiology,  and compound-protein interaction (CPI) prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.
Uniformly-Damped Binomial Filters: Five-percent Maximum Overshoot Optimal Response Design
Authors: Oluwasegun A. Somefun, Kayode F. Akingbade, Folasade M. Dahunsi
Date: 2020-07-02 00:00:00

In this paper, the five-percent maximum overshoot design of uniformly-damped binomial filters (transfer-functions) is introduced. First, the butterworth filter response is represented as a damped-binomial filter response. To extend the maximum-overshoot response of the second-order butterworth to higher orders, the binomial theorem is extended to the uniformly-damped binomial theorem. It is shown that the five-percent uniformly-damped binomial filter is a compromise between the butterworth filter and the standard binomial filter, with respect to the filter-approximation problem in the time and frequency domain. Finally, this paper concludes that in applications of interest, such as step-tracking, where both strong filtering and a fast, smooth transient-response, with negligible overshoot are desired, the response of the normalized five-percent uniformly-damped binomial form is a candidate replacement for both the butterworth and standard binomial filter forms.
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNING
Authors: Cheng-Hao Tu, Cheng-En Wu, Chu-Song Chen
Date: 2020-12-07 00:00:00
Tasks: Computational Efficiency, Continual Learning, Multi-Task Learning

Conditional operations have received much attention in recent deep learning studies to facilitate the prediction accuracy of a model. A recent advance toward this direction is the conditional parametric convolutions (CondConv), which is proposed to exploit additional capacities provided by the deep model weights to enhance the performance, whereas the computational complexity of the model is much less influenced. CondConv employs input-dependent fusion parameters that can combine multiple columns of convolution kernels adaptively for performance improvement. At runtime, the columns of kernels are on-line combined into a single one, and thus the time complexity is much less than that of employing multiple columns in a convolution layer under the same capacity. Although CondConv is effective for the performance enhancement of a deep model, it is currently applied to individual tasks only. As it has the nice property of adding model weights with computational efficiency, we extend it for multi-task learning, where the tasks are presented incrementally. In this work, we introduce a sequential multi-task (or continual) learning approach based on the CondConv structures, referred to as CondConv-Continual. Experimental results show that the proposed approach is effective for unforgetting continual learning. Compared to current approaches, CondConv is advantageous to offer a regular and easy-to-implement way to enlarge the neural networks for acquiring additional capacity and provides a cross-referencing mechanism for different task models to achieve comparative results.
Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures
Authors: Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon
Date: 2020-12-08 00:00:00

In this work, we tackle the problem of hyperspectral (HS) unmixing by departing from the usual linear model and focusing on a Linear-Quadratic (LQ) one. The proposed algorithm, referred to as Successive Nonnegative Projection Algorithm for Linear Quadratic mixtures (SNPALQ), extends the Successive Nonnegative Projection Algorithm (SNPA), designed to address the unmixing problem under a linear model. By explicitly modeling the product terms inherent to the LQ model along the iterations of the SNPA scheme, the nonlinear contributions in the mixing are mitigated, thus improving the separation quality. The approach is shown to be relevant in a realistic numerical experiment.
Let's Vibrate with Vibration: Augmenting Structural Engineering with Low-Cost Vibration Sensing
Authors: Masfiqur Rahaman, MD. Nazmul Hasan Sakib, Nafisa Islam, Saiful Islam Salim, Uday Kamal, Raihan Rasheed, A. B. M. Alim Al Islam
Date: 2020-12-08 00:00:00

Using low-cost piezoelectric sensors to sense structural vibration exhibits great potential in augmenting structural engineering, which is yet to be explored in the literature to the best of our knowledge. Examples of such unexplored augmentation include classifying diverse structures (such as building, flyover, foot over-bridge, etc.), and relating the extent of vibration generated at different height of a structure and the associated height. Accordingly, to explore these cases, we develop a low-cost piezoelectric sensor-based vibration sensing system aiming to remotely collect real vibration data from diversified civil structures. We dig into our collected sensed data to classify five different types of structures through rigorous statistical and machine-learning-based analyses. Our analyses achieve a classification accuracy of up to 97% with an F1 score of 0.97. Nonetheless, in the rarely explored time domain, our analyses reveal a novel modality of relation between vibration generated at different heights of a structure and the associated height, which was explored in the frequency domain earlier in the literature with expensive sensors.
Are Men Less Generous to a Smarter Woman? Evidence from a Dictator Game Experiment
Authors: Yuki Takahashi
Date: 2020-12-08 00:00:00
Tasks: Attribute, Experimental Design

Although evidence suggests men are more generous to women than to men, it may stem from paternalism and could reverse when women excel in important skills for one's career success, such as cognitive skills. Using a dictator game, this paper studies whether male dictators allocate less to female receivers than to male receivers when these receivers have higher IQs than dictators. By exogenously varying the receivers' IQ relative to the dictators', I do not find evidence consistent with this hypothesis; if anything, male dictators allocate slightly more to female receivers with higher IQs than to male receivers with equivalent IQs. The results hold both in mean and distribution and are robust to the so-called ``beauty premium.'' Also, female dictators' allocations are qualitatively similar to male dictators. These findings suggest that women who excel in cognitive skills may not receive less favorable treatment than equally intelligent men in the labor market.
How Covid-19 Pandemic Changes the Theory of Economics?
Authors: Matti Estola
Date: 2020-12-08 00:00:00

During its history, the ultimate goal of economics has been to develop similar frameworks for modeling economic behavior as invented in physics. This has not been successful, however, and current state of the process is the neoclassical framework that bases on static optimization. By using a static framework, however, we cannot model and forecast the time paths of economic quantities because for a growing firm or a firm going into bankruptcy, a positive profit maximizing flow of production does not exist. Due to these problems, we present a dynamic theory for the production of a profit-seeking firm where the adjustment may be stable or unstable. This is important, currently, because we should be able to forecast the possible future bankruptcies of firms due to the Covid-19 pandemic. By using the model, we can solve the time moment of bankruptcy of a firm as a function of several parameters. The proposed model is mathematically identical with Newtonian model of a particle moving in a resisting medium, and so the model explains the reasons that stop the motion too. The frameworks for modeling dynamic events in physics are thus applicable in economics, and we give reasons why physics is more important for the development of economics than pure mathematics. (JEL D21, O12) Keywords: Limitations of neoclassical framework, Dynamics of production, Economic force, Connections between economics and physics.
Efficient model selection in switching linear dynamic systems by graph clustering
Authors: Parisa Karimi, Mark Butala, Zhizhen Zhao, Farzad Kamalabadi
Date: 2020-12-08 00:00:00
Tasks: Clustering, Graph Clustering, Model Selection

The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes. In this paper, a computationally tractable graph representation is proposed for a switching linear dynamic system (SLDS) along with the solution of a minimum-sum optimization problem for clustering to reduce the switching mode cardinality offline, before collecting measurements. It is shown that upon perfect mode detection, the induced error caused by mode clustering can be quantified exactly in terms of the dissimilarity measures in the proposed graph structure. Numerical results verify that clustering based on the proposed framework effectively reduces model complexity given uncertain mode detection and that the induced error can be well approximated if the underlying assumptions are satisfied.
Quantification of mismatch error in randomly switching linear state-space models
Authors: Parisa Karimi, Zhizhen Zhao, Mark Butala, Farzad Kamalabadi
Date: 2020-12-08 00:00:00
Tasks: State Space Models

Switching Kalman Filters (SKF) are well known for their ability to solve the piecewise linear dynamic system estimation problem using the standard Kalman Filter (KF). Practical SKFs are heuristic, approximate filters that are not guaranteed to have optimal performance and require more computational resources than a single mode KF. On the other hand, applying a single mode mismatched KF to a switching linear dynamic system (SLDS) results in erroneous estimation. This paper aims to quantify the average error an SKF can eliminate compared to a mismatched, single mode KF in a known SLDS before collecting measurements. Mathematical derivations for the first and second moments of the estimators errors are provided and compared. One can use these derivations to quantify the average performance of filters beforehand and decide which filter to run in operation to have the best performance in terms of estimation error and computation complexity. We further provide simulation results that verify our mathematical derivations.
Frequency Sub-Sampling of Ultrasound Non-Destructive Measurements: Acquisition, Reconstruction and Performance
Authors: Jan Kirchhof, Sebastian Semper, Christoph W. Wagner, Eduardo Pérez, Florian Römer, Giovanni Del Galdo
Date: 2020-12-08 00:00:00

In ultrasound nondestructive testing, a widespread approach is to take synthetic aperture measurements from the surface of a specimen to detect and locate defects within it. Based on these measurements, imaging is usually performed using the Synthetic Aperture Focusing Technique (SAFT). However, SAFT is sub-optimal in terms of resolution and requires oversampling in time domain to obtain a fine grid for the Delay-and-Sum (DAS). On the other hand, parametric reconstruction algorithms give better resolution, but their usage for imaging becomes computationally expensive due to the size of the parameter space and the large amount of measurement data in realistic 3-D scenarios. In the literature, the remedies to this are twofold: First, the amount of measurement data can be reduced using state of the art sub-Nyquist sampling approaches to measure Fourier coefficients instead of time domain samples. Second, parametric reconstruction algorithms mostly rely on matrix-vector operations that can be implemented efficiently by exploiting the underlying model structure. In this paper, we propose and compare different strategies to choose the Fourier coefficients to be measured. Their asymptotic performance is compared by numerically evaluating the Cram\'er-Rao-Bound for the localizability of the defect coordinates. These subsampling strategies are then combined with an $\ell_1$-minimization scheme to compute 3-D reconstructions from the low-rate measurements. Compared to conventional DAS, this allows us to formulate a fully physically motivated forward model. To enable this, the projection operations of the forward model matrix are implemented matrix-free by exploiting the underlying 2-level Toeplitz structure. Finally, we show that high resolution reconstructions from as low as a single Fourier coefficient per scan are possible based on simulated data as well as on measurements.
Intersymbol and Intercarrier Interference in OFDM Systems: Unified Formulation and Analysis
Authors: Fernando Cruz-Roldán, Wallace A. Martins, Fausto García G., Marc Moonen, Paulo S. R. Diniz
Date: 2020-12-08 00:00:00

A new unified formulation for seven different orthogonal frequency-division multiplexing (OFDM) systems is presented. The proposed formulation relies on six parameters and encompasses conventional OFDM systems, with windowing in the transmitter and/or in the receiver, and also with a cyclic prefix (CP) or both a CP and cyclic suffix (CS). A new equivalent channel matrix that is useful for calculating both the received signal and the intersymbol and intercarrier interference power is defined and characterized. Unlike previous works, this new channel matrix formulates the channel convolution with no restrictions on the length of the channel impulse response. Moreover, it includes the overlap-and-add procedure performed in the transmitter of windowed OFDM systems. Furthermore, new theoretical expressions for the intersymbol and intercarrier interference and also for the signal-to-interference-plus-noise ratio are derived.
Business Cycles as Collective Risk Fluctuations
Authors: Victor Olkhov
Date: 2020-12-08 00:00:00

We suggest use continuous numerical risk grades [0,1] of R for a single risk or the unit cube in Rn for n risks as the economic domain. We consider risk ratings of economic agents as their coordinates in the economic domain. Economic activity of agents, economic or other factors change agents risk ratings and that cause motion of agents in the economic domain. Aggregations of variables and transactions of individual agents in small volume of economic domain establish the continuous economic media approximation that describes collective variables, transactions and their flows in the economic domain as functions of risk coordinates. Any economic variable A(t,x) defines mean risk XA(t) as risk weighted by economic variable A(t,x). Collective flows of economic variables in bounded economic domain fluctuate from secure to risky area and back. These fluctuations of flows cause time oscillations of macroeconomic variables A(t) and their mean risks XA(t) in economic domain and are the origin of any business and credit cycles. We derive equations that describe evolution of collective variables, transactions and their flows in the economic domain. As illustration we present simple self-consistent equations of supply-demand cycles that describe fluctuations of supply, demand and their mean risks.
Deep Learning for Landslide Recognition in Satellite Architecture
Authors: Trong-An Bui, Pei-Jun Lee, Kai-Yew Lum, Clarissa Loh, Kyo Tan
Date: 2020-08-05 00:00:00
Tasks: Deep Learning, Landslide segmentation

Using the optical camera in remote sensing is limited in various environmental conditions. This paper presents a system of combining deep learning and image transform algorithms to detect landslide location in satellite images. In the deep learning part, a convolution neural network is used to classify satellite images contain landslides. From landslide images classified, in order to accurately identify landslides under different lighting conditions, this paper proposes a transformation algorithm Hue - Bi-dimensional empirical mode decomposition (H-BEMD) to determine the landslide region and size. After the location of landslide is detected, we discover the size change of the landslide based on different time points. In this study, we record an accuracy of up to 96% in the classification process, and the accuracy of landslide location almost absolute.
Visual place recognition: A survey from deep learning perspective
Authors: Xiwu Zhang, Lei Wang, Yan Su
Date: 2020-11-28 00:00:00
Tasks: Deep Learning, Loop Closure Detection, Scene Understanding, Simultaneous Localization and Mapping, Survey, Visual Localization, Visual Place Recognition

Visual place recognition has attracted widespread research interest in multiple fields such as computer vision and robotics. Recently, researchers have employed advanced deep learning techniques to tackle this problem. While an increasing number of studies have proposed novel place recognition methods based on deep learning, few of them has provided a whole picture about how and to what extent deep learning has been utilized for this issue. In this paper, by delving into over 200 references, we present a comprehensive survey that covers various aspects of place recognition from deep learning perspective. We first present a brief introduction of deep learning and discuss its opportunities for recognizing places. After that, we focus on existing approaches built upon convolutional neural networks, including off-the-shelf and specifically designed models as well as novel image representations. We also discuss challenging problems in place recognition and present an extensive review of the corresponding datasets. To explore the future directions, we describe open issues and some new tools, for instance, generative adversarial networks, semantic scene understanding and multi-modality feature learning for this research topic. Finally, a conclusion is drawn for this paper.