pwc-archive/papers-with-abstracts huggingface dataset:
IMG2SMI: Translating Molecular Structure Images to Simplified Molecular-input Line-entry System
Authors: Daniel Campos, Heng Ji
Date: 2021-09-03 00:00:00
Tasks: Image Captioning

Like many scientific fields, new chemistry literature has grown at a staggering pace, with thousands of papers released every month. A large portion of chemistry literature focuses on new molecules and reactions between molecules. Most vital information is conveyed through 2-D images of molecules, representing the underlying molecules or reactions described. In order to ensure reproducible and machine-readable molecule representations, text-based molecule descriptors like SMILES and SELFIES were created. These text-based molecule representations provide molecule generation but are unfortunately rarely present in published literature. In the absence of molecule descriptors, the generation of molecule descriptors from the 2-D images present in the literature is necessary to understand chemistry literature at scale. Successful methods such as Optical Structure Recognition Application (OSRA), and ChemSchematicResolver are able to extract the locations of molecules structures in chemistry papers and infer molecular descriptions and reactions. While effective, existing systems expect chemists to correct outputs, making them unsuitable for unsupervised large-scale data mining. Leveraging the task formulation of image captioning introduced by DECIMER, we introduce IMG2SMI, a model which leverages Deep Residual Networks for image feature extraction and an encoder-decoder Transformer layers for molecule description generation. Unlike previous Neural Network-based systems, IMG2SMI builds around the task of molecule description generation, which enables IMG2SMI to outperform OSRA-based systems by 163% in molecule similarity prediction as measured by the molecular MACCS Fingerprint Tanimoto Similarity. Additionally, to facilitate further research on this task, we release a new molecule prediction dataset. including 81 million molecules for molecule description generation
Low-Dispersive Permittivity Measurement Based on Transmitted Power Only
Authors: Seyed Hossein Mirjahanmardi, Omar Ramahi
Date: 2021-09-09 00:00:00

This paper presents a complex permittivity measurement method for low-dispersive materials as a function of frequency. The introduced method relies only on transmitted power signals which are collected using a spectrum analyzer/power meter, removing the need for phase measurements and a vector network analyzer. This method provides a very good accuracy along with easy and inexpensive permittivity measurements.
Integrating Low-Complexity and Flexible Sensing into Communication Systems
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo
Date: 2021-09-09 00:00:00

Integrating sensing into standardized communication systems can potentially benefit many consumer applications that require both radio frequency functions. However, without an effective sensing method, such integration may not achieve the expected gains of cost and energy efficiency. Existing sensing methods, which use communication payload signals, either have limited sensing performance or suffer from high complexity. In this paper, we develop a novel and flexible sensing framework which has a complexity only dominated by a Fourier transform and also provides the flexibility in adapting for different sensing needs. We propose to segment a whole block of echo signal evenly into sub-blocks; adjacent ones are allowed to overlap. We design a virtual cyclic prefix (VCP) for each sub-block that allows us to employ two common ways of removing communication data symbols and generate two types of range-Doppler maps (RDMs) for sensing. We perform a comprehensive analysis of the signal components in the RDMs, proving that their interference-plus-noise (IN) terms are approximately Gaussian distributed. The statistical properties of the distributions are derived, which leads to the analytical comparisons between the two RDMs as well as between the prior and our sensing methods. Moreover, the impact of the lengths of sub-block, VCP and overlapping signal on sensing performance is analyzed. Criteria for designing these lengths for better sensing performance are also provided. Extensive simulations validate the superiority of the proposed sensing framework over prior methods in terms of signal-to-IN ratios in RDMs, detecting performance and flexibility.
Stochastic Maximum-Likelihood DOA Estimation and Source Enumeration in the Presence of Nonuniform Noise
Authors: Mahmood Karimi
Date: 2021-09-09 00:00:00

In this paper, the problem of determining the number of signal sources impinging on an array of sensors and estimating their directions-of-arrival (DOAs) in the presence of spatially white nonuniform noise is considered. It is known that, in the case of nonuniform noise, the stochastic likelihood function cannot be concentrated with respect to the diagonal elements of noise covariance matrix. Therefore, the stochastic maximum-likelihood (SML) DOA estimation and source enumeration in the presence of nonuniform noise requires multidimensional search with very high computational complexity. Recently, two algorithms for estimating noise covariance matrix in the presence of nonuniform noise have been proposed in the literature. Using these new estimates of noise covariance matrix, an approach for obtaining the SML estimate of signal DOAs is proposed. In addition, new approaches are proposed for SML source enumeration with information criteria in the presence of nonuniform noise. The important feature of the proposed SML approaches for DOA estimation and source enumeration is that they have admissible computational complexity. In addition, some of them are robust against correlation between source signals. The performance of the proposed DOA estimation and source enumeration approaches are investigated using computer simulations.
A Novel Method to Estimate the Coordinates of LEDs in Wireless Optical Positioning Systems
Authors: Kehan Zhang, Zaichen Zhang, Bingcheng Zhu
Date: 2021-09-09 00:00:00

Traditional visible light positioning (VLP) systems estimate receivers' coordinates based on the known light-emitting diode (LED) coordinates. However, the LED coordinates are not always known accurately. Because of the structural changes of the buildings due to temperature, humidity or material aging, even measured by highly accurate laser range finders, the LED coordinates may change unpredictably. In this paper, we propose an easy and low-cost method to update the position information of the LEDs. We use two optical angle-of-arrival (AOA) estimators to detect the beam directions of the LEDs. Each AOA estimator has four differently oriented photodiodes (PDs). Considering the additive noises of the PDs, we derive the closed-form error expression for the proposed LED coordinates estimator. Both analytical and Monte Carlo experimental results show that the layout of the AOA estimators could affect the estimation error. These results may provide intuitive insights for the design of the optical indoor positioning systems.
Signal Reconstruction from Phase-only Measurements: Uniqueness Condition, Minimal Measurement Number and Beyond
Authors: Junren Chen, Michael K. Ng
Date: 2021-09-08 00:00:00

This paper studies the phase-only reconstruction problem of recovering a complex-valued signal $\textbf{x}$ in $\mathbb{C}^d$ from the phase of $\textbf{Ax}$ where $\textbf{A}$ is a given measurement matrix in $\mathbb{C}^{m\times d}$. The reconstruction, if possible, should be up to a positive scaling factor. By using the rank of discriminant matrices, uniqueness conditions are derived to characterize whether the underlying signal can be uniquely reconstructed. We are also interested in the problem of minimal measurement number. We show that at least $2d$ but no more than $4d-2$ measurements are needed for the reconstruction of all $\textbf{x}\in\mathbb{C}^d$, whereas the minimal measurement number is exactly $2d-1$ if we pursue the recovery of almost all signals. Moreover, when adapted to the phase-only reconstruction of $\textbf{x}\in\mathbb{R}^d$, our uniqueness conditions are more practical and general than existing ones. Our theoretical results can be straightforwardly extended to affine phase-only reconstruction where the phase of $\textbf{Ax}+\textbf{b}$ is observed for some $\textbf{b}\in\mathbb{C}^m$.
Assessing Machine Learning Approaches to Address IoT Sensor Drift
Authors: Haining Zheng, Antonio Paiva
Date: 2021-09-02 00:00:00
Tasks: BIG-bench Machine Learning

The proliferation of IoT sensors and their deployment in various industries and applications has brought about numerous analysis opportunities in this Big Data era. However, drift of those sensor measurements poses major challenges to automate data analysis and the ability to effectively train and deploy models on a continuous basis. In this paper we study and test several approaches from the literature with regard to their ability to cope with and adapt to sensor drift under realistic conditions. Most of these approaches are recent and thus are representative of the current state-of-the-art. The testing was performed on a publicly available gas sensor dataset exhibiting drift over time. The results show substantial drops in sensing performance due to sensor drift in spite of the approaches. We then discuss several issues identified with current approaches and outline directions for future research to tackle them.
MutualGraphNet: A novel model for motor imagery classification
Authors: Yan Li, Ning Zhong, David Taniar, Haolan Zhang
Date: 2021-09-02 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Graph Neural Network, model, Motor Imagery

Motor imagery classification is of great significance to humans with mobility impairments, and how to extract and utilize the effective features from motor imagery electroencephalogram(EEG) channels has always been the focus of attention. There are many different methods for the motor imagery classification, but the limited understanding on human brain requires more effective methods for extracting the features of EEG data. Graph neural networks(GNNs) have demonstrated its effectiveness in classifying graph structures; and the use of GNN provides new possibilities for brain structure connection feature extraction. In this paper we propose a novel graph neural network based on the mutual information of the raw EEG channels called MutualGraphNet. We use the mutual information as the adjacency matrix combined with the spatial temporal graph convolution network(ST-GCN) could extract the transition rules of the motor imagery electroencephalogram(EEG) channels data more effectively. Experiments are conducted on motor imagery EEG data set and we compare our model with the current state-of-the-art approaches and the results suggest that MutualGraphNet is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep
Authors: Ran Guan, Andi Zhang, Mengchao Li, Yongliang Wang
Date: 2021-09-01 00:00:00
Tasks: Gaussian Processes

In indoor positioning, signal fluctuation is highly location-dependent. However, signal uncertainty is one critical yet commonly overlooked dimension of the radio signal to be fingerprinted. This paper reviews the commonly used Gaussian Processes (GP) for probabilistic positioning and points out the pitfall of using GP to model signal fingerprint uncertainty. This paper also proposes Deep Gaussian Processes (DGP) as a more informative alternative to address the issue. How DGP better measures uncertainty in signal fingerprinting is evaluated via simulated and realistically collected datasets.
Multi-dimensional graph fractional Fourier transform and its application
Authors: Fang-Jia Yan, Bing-Zhao Li
Date: 2021-08-28 00:00:00
Tasks: Data Compression

Many multi-dimensional signals appear in the real world, such as digital images and data that has spatial and temporal dimensions. How to show the spectrum of these multi-dimensional signals correctly is a key challenge in the field of graph signal processing. This paper investigates the novel transform for multi-dimensional signals defined on Cartesian product graph and studies several related properties. Our work includes: (i) defining the multi-dimensional graph fractional Fourier transform (MGFRFT) based on Laplacian matrix and adjacency matrix; (ii) exploring the advantages of MGFRFT in processing multi-dimensional signals in terms of spectrum and computational time; (iii) applying the proposed transform to data compression to highlight the utility and effectiveness of it.
Noise-induced network bursts and coherence in a calcium-mediated neural network
Authors: Na Yu, Gurpreet Jagdev, Michelle Morgovsky
Date: 2021-09-08 00:00:00

Noise-induced population bursting has been widely identified to play important roles in the information process. We constructed a mathematical model for a random and sparse neural network where bursting can be induced from the resting state by the global stochastic stimulus. Importantly, the noise-induced bursting dynamics of this network are mediated by calcium conductance. We use two spectral measures to evaluate the network coherence in the context of network bursts, the spike trains of all neurons and the individual bursts of all neurons. Our results show that the coherence of the network is optimized by an optimal level of stochastic stimulus, which is known as coherence resonance (CR). We also demonstrate that the interplay of calcium conductance and noise intensity can modify the degree of CR.
Incorporating Computational Challenges into a Multidisciplinary Course on Stochastic Processes
Authors: Mark Jayson Cortez, Alan Eric Akil, Krešimir Josić, Alexander J. Stewart
Date: 2021-09-09 00:00:00

Quantitative methods and mathematical modeling are playing an increasingly important role across disciplines. As a result, interdisciplinary mathematics courses are increasing in popularity. However, teaching such courses at an advanced level can be challenging. Students often arrive with different mathematical backgrounds, different interests, and divergent reasons for wanting to learn the material. Here we describe a course on stochastic processes in biology, delivered between September and December 2020 to a mixed audience of mathematicians and biologists. In addition to traditional lectures and homeworks, we incorporated a series of weekly computational challenges into the course. These challenges served to familiarize students with the main modeling concepts, and provide them with an introduction on how to implement them in a research-like setting. In order to account for the different academic backgrounds of the students, they worked on the challenges in small groups, and presented their results and code in a dedicated discussion class each week. We discuss our experience designing and implementing an element of problem-based learning in an applied mathematics course through computational challenges. We also discuss feedback from students, and describe the content of the challenges presented in the course. We provide all materials, along with example code for a number of challenges.
RNAglib: A Python Package for RNA 2.5D Graphs
Authors: Vincent Mallet, Carlos Oliver, Jonathan Broadbent, William L. Hamilton, Jérôme Waldispühl
Date: 2021-09-09 00:00:00
Tasks: BIG-bench Machine Learning

RNA 3D architectures are stabilized by sophisticated networks of (non-canonical) base pair interactions, which can be conveniently encoded as multi-relational graphs and efficiently exploited by graph theoretical approaches and recent progresses in machine learning techniques. RNAglib is a library that eases the use of this representation, by providing clean data, methods to load it in machine learning pipelines and graph-based deep learning models suited for this representation. RNAglib also offers other utilities to model RNA with 2.5D graphs, such as drawing tools, comparison functions or baseline performances on RNA applications. The method and data is distributed as a fully documented pip package. Availability: https://rnaglib.cs.mcgill.ca
Portfolio Theory and Security Investment Risk Analysis Using Coefficient of Variation: An Alternative to Mean-Variance Analysis
Authors: Julius O. Campeciño
Date: 2021-09-09 00:00:00

We provided proof here that coefficient of variation (CV) is a direct measure of risk using an equation that has been derived here for the first time. We also presented a method to generate a stock CV based on return that strongly correlates with stock price performance. Consequently, we found that the price growths of stocks with low but positive CV are approximately exponential which explains our finding here that the total return of US domestic stocks within $0 \le CV \le 1$ between Dec 2008 to Dec 2018 averaged at around 475% and outperformed the average total return of stocks within $CV > 1$ and $CV > 4$ by 144% and 2000%, respectively. From these observations, we posit that minimizing portfolio CV does not only minimize risk but also maximizes return. Minimizing risk by minimizing the standard deviation of return (volatility) as espoused by the Modern Portfolio Theory only resulted in a meager average total return of 15%, and the low-risk (low volatility) portfolio outperformed the high-risk portfolio by only 25%. These observations suggest that CV is a more reliable measure of risk than volatility.
Fast and robust template matching with majority neighbour similarity and annulus projection transformation
Authors: Jinxiang Lai, Liang Lei, Kaiyuan Deng, Runming Yan, Yang Ruan, Zhou Jinyun
Date: 2020-02-10 00:00:00
Proceeding: Pattern Recognition 2020 2
Tasks: Template Matching

In the paper, a novel fast and robust template matching method named A-MNS based on Majority Neighbour Similarity (MNS) and the annulus projection transformation (APT) is proposed. Its essence is the MNS, a useful, rotation-invariant, low computational cost and robust similarity measurement. The proposed method is theoretically demonstrated and experimentally evaluated as being able to estimate the rotation angle of the target object, overcome challenges such as background clutter, occlusion, arbitrary rotation transformation, and non-rigid deformation, while performing fast matching. Empirical results evaluated on the up-to-date benchmark show that A-MNS is 4.419 times faster than DDIS (the state-ofthe-art) and is also competitive in terms of its matching accuracy.
The Information Theory of Individuality
Authors: David Krakauer, Nils Bertschinger, Eckehard Olbrich, Nihat Ay, Jessica C. Flack
Date: 2014-12-08 00:00:00

We consider biological individuality in terms of information theoretic and graphical principles. Our purpose is to extract through an algorithmic decomposition system-environment boundaries supporting individuality. We infer or detect evolved individuals rather than assume that they exist. Given a set of consistent measurements over time, we discover a coarse-grained or quantized description on a system, inducing partitions (which can be nested). Legitimate individual partitions will propagate information from the past into the future, whereas spurious aggregations will not. Individuals are therefore defined in terms of ongoing, bounded information processing units rather than lists of static features or conventional replication-based definitions which tend to fail in the case of cultural change. One virtue of this approach is that it could expand the scope of what we consider adaptive or biological phenomena, particularly in the microscopic and macroscopic regimes of molecular and social phenomena.
Waste Classification Algorithm
Authors: Neha Naveen, Michael Rodrigues, Srjana Srivatsa
Date: 2021-09-04 00:00:00
Proceeding: Overleaf 2021 9
Tasks: Classification

Waste sorting is a major environmental problem. Many have a hard time determining whether waste is organic, meaning food or natural material. Versus recyclable, which are able to be processed and used again. Computer Vision may be used to combat this problem. Our idea is to build and utilize a Convolutional Neural Network (CNN) to classify recyclable and organic waste. We use the Pytorch Machine Learning Library in our CNN. The model uses a simple CNN model with binary cross entropy to classify through images from our dataset as recyclable (R) or organic (O). The model is about 80-90% successful in its final state. In the majority of cases, it can quickly and successfully determine if an object is recyclable or organic.
Privacy-Preserving Collaborative Operation of Networked Microgrids with the Local Utility Grid Based on Enhanced Benders Decomposition
Authors: Zhiyi Li
Date: 2019-12-13 00:00:00
Proceeding: 期刊 2019 12
Tasks: Decision Making, Privacy Preserving, valid

Abstract—In this paper, a privacy-preserving decision-making 
the framework is developed for a collaborative operation of networked microgrids and the local utility grid, where the distribution system operator (DSO) interacts with microgrid master controllers (MCs) in a leader-followers fashion. First, the paper models the decision-making in the collaborative operation presented as an integrated mixed-integer second-order cone programming problem that satisfies various technical and operational requirements in a look-ahead manner. Then, the integrated optimization problem is decomposed into a master problem and a set of subproblems to decentralize the decision-making process of DSO and MCs. The master problem is solved by DSO for optimizing the local utility grid operation, and the subproblems are solved independently by MCs for optimizing the operation of individual microgrids. We propose an enhanced 
Benders decomposition algorithm to guide the iterations between the master problem and subproblems. In particular, an enhanced approach is introduced in the Benders decomposition algorithm for generating valid cutting planes from mixed-integer linear programming subproblems. Mathematically, the proposed algorithm can find the globally optimal solution after a finite number of iterations. Accordingly, DSO and MCs manage to collaborate on making operational decisions that would maximize the social welfare without compromising independent decision-
making and privacy provisions. Numerical experiments are 
conducted on a simplified two-bus system and a modified IEEE 
123-bus system. The results validate the efficiency, robustness, 
and scalability of the proposed collaborative decision-making 
framework in power systems.
Gifting in multi-agent reinforcement learning
Authors: Andrei Lupu, Doina Precup
Date: 2020-05-05 00:00:00
Proceeding: AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems 2020 5
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-agent reinforcement learning has generally been studied under an assumption inherited from classical reinforcement learning: that the reward function is the exclusive property of the environment, and is only altered by external factors. In this work, we break free of this assumption and introduce peer rewarding, in which agents can deliberately influence each others’ reward function. We formalize this more general setting and discuss its properties in depth. We also empirically study gifting, a peer rewarding mechanism which allows agents to reward other agents as part of their action space. We demonstrate that this approach can greatly improve learning progression in a resource appropriation setting and provide a preliminary analysis of the complex effects of gifting on the learning dynamics.