pwc-archive/papers-with-abstracts huggingface dataset:
A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models
Authors: Martin Mugnier
Date: 2022-03-16 00:00:00
Tasks: Combinatorial Optimization, regression

This paper introduces a new fixed effects estimator for linear panel data models with clustered time patterns of unobserved heterogeneity. The method avoids non-convex and combinatorial optimization by combining a preliminary consistent estimator of the slope coefficient, an agglomerative pairwise-differencing clustering of cross-sectional units, and a pooled ordinary least squares regression. Asymptotic guarantees are established in a framework where $T$ can grow at any power of $N$, as both $N$ and $T$ approach infinity. Unlike most existing approaches, the proposed estimator is computationally straightforward and does not require a known upper bound on the number of groups. As existing approaches, this method leads to a consistent estimation of well-separated groups and an estimator of common parameters asymptotically equivalent to the infeasible regression controlling for the true groups. An application revisits the statistical association between income and democracy.
Interactions between the individual and the group level in organizations: The case of learning and autonomous group adaptation
Authors: Dario Blanco-Fernandez, Stephan Leitner, Alexandra Rausch
Date: 2022-03-17 00:00:00

Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and adaptation. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group adaptation either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group adaptation too far might backfire and decrease performance significantly.
Dynamic groups in complex task environments: To change or not to change a winning team?
Authors: Darío Blanco-Fernández, Stephan Leitner, Alexandra Rausch
Date: 2022-03-17 00:00:00

Organisations rely upon group formation to solve complex tasks, and groups often adapt to the demands of the task they face by changing their composition periodically. Previous research comes to ambiguous results regarding the effects of group adaptation on task performance. This paper aims to understand the impact of group adaptation, defined as a process of periodically changing a group's composition, on complex task performance and considers the moderating role of individual learning and task complexity in this relationship. We base our analyses on an agent-based model of adaptive groups in a complex task environment based on the NK-framework. The results indicate that reorganising well-performing groups might be beneficial, but only if individual learning is restricted. However, there are also cases in which group adaptation might unfold adverse effects. We provide extensive analyses that shed additional light on and, thereby, help explain the ambiguous results of previous research.
Feature-informed Latent Space Regularization for Music Source Separation
Authors: Yun-Ning Hung, Alexander Lerch
Date: 2022-03-17 00:00:00
Tasks: Multi-Task Learning, Music Source Separation, Transfer Learning

The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labels, etc. in training and possibly during inference. The available datasets for source separation do not usually provide these additional annotations. In this work, we explore transfer learning strategies to incorporate VGGish features with a state-of-the-art source separation model; VGGish features are known to be a very condensed representation of audio content and have been successfully used in many MIR tasks. We introduce three approaches to incorporate the features, including two latent space regularization methods and one naive concatenation method. Experimental results show that our proposed approaches improve several evaluation metrics for music source separation.
To train or not to train adversarially: A study of bias mitigation strategies for speaker recognition
Authors: Raghuveer Peri, Krishna Somandepalli, Shrikanth Narayanan
Date: 2022-03-17 00:00:00
Tasks: Face Recognition, Fairness, Multi-Task Learning, Speaker Recognition

Speaker recognition is increasingly used in several everyday applications including smart speakers, customer care centers and other speech-driven analytics. It is crucial to accurately evaluate and mitigate biases present in machine learning (ML) based speech technologies, such as speaker recognition, to ensure their inclusive adoption. ML fairness studies with respect to various demographic factors in modern speaker recognition systems are lagging compared to other human-centered applications such as face recognition. Existing studies on fairness in speaker recognition systems are largely limited to evaluating biases at specific operating points of the systems, which can lead to false expectations of fairness. Moreover, there are only a handful of bias mitigation strategies developed for speaker recognition systems. In this paper, we systematically evaluate the biases present in speaker recognition systems with respect to gender across a range of system operating points. We also propose adversarial and multi-task learning techniques to improve the fairness of these systems. We show through quantitative and qualitative evaluations that the proposed methods improve the fairness of ASV systems over baseline methods trained using data balancing techniques. We also present a fairness-utility trade-off analysis to jointly examine fairness and the overall system performance. We show that although systems trained using adversarial techniques improve fairness, they are prone to reduced utility. On the other hand, multi-task methods can improve the fairness while retaining the utility. These findings can inform the choice of bias mitigation strategies in the field of speaker recognition.
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds
Authors: Viktoria Heimann, Andreas Spruck, André Kaup
Date: 2022-03-17 00:00:00
Tasks: Point Cloud Super Resolution, point cloud upsampling, Super-Resolution

With the increased use of virtual and augmented reality applications, the importance of point cloud data rises. High-quality capturing of point clouds is still expensive and thus, the need for point cloud super-resolution or point cloud upsampling techniques emerges. In this paper, we propose an interpolation scheme for color upsampling of three-dimensional color point clouds. As a point cloud represents an object's surface in three-dimensional space, we first conduct a local transform of the surface into a two-dimensional plane. Secondly, we propose to apply a novel Frequency-Selective Mesh-to-Mesh Resampling (FSMMR) technique for the interpolation of the points in 2D. FSMMR generates a model of weighted superpositions of basis functions on scattered points. This model is then evaluated for the final points in order to increase the resolution of the original point cloud. Evaluation shows that our approach outperforms common interpolation schemes. Visual comparisons of the jaguar point cloud underlines the quality of our upsampling results. The high performance of FSMMR holds for various sampling densities of the input point cloud.
National Radio Dynamic Zone Concept with Autonomous Aerial and Ground Spectrum Sensors
Authors: Sung Joon Maeng, Ismail Güvenç, Mihail Sichitiu, Brian A. Floyd, Rudra Dutta, Thomas Zajkowski, Özgür Özdemir, Magreth J. Mushi
Date: 2022-03-17 00:00:00

National radio dynamic zone (NRDZs) are intended to be geographically bounded areas within which controlled experiments can be carried out while protecting the nearby licensed users of the spectrum. An NRDZ will facilitate research and development of new spectrum technologies, waveforms, and protocols, in typical outdoor operational environments of such technologies. In this paper, we introduce and describe an NRDZ concept that relies on a combination of autonomous aerial and ground sensor nodes for spectrum sensing and radio environment monitoring (REM). We elaborate on key characteristics and features of an NRDZ to enable advanced wireless experimentation while also coexisting with licensed users. Some preliminary results based on simulation and experimental evaluations are also provided on out-of-zone leakage monitoring and real-time REMs.
One-Bit Phase Retrieval: More Samples Means Less Complexity?
Authors: Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Date: 2022-03-16 00:00:00
Tasks: Retrieval

The classical problem of phase retrieval has found a wide array of applications in optics, imaging and signal processing. In this paper, we consider the phase retrieval problem in a one-bit setting, where the signals are sampled using one-bit analog-to-digital converters (ADCs). A significant advantage of deploying one-bit ADCs in signal processing systems is their superior sampling rates as compared to their high-resolution counterparts. This leads to an enormous amount of one-bit samples gathered at the output of the ADC in a short period of time. We demonstrate that this advantage pays extraordinary dividends when it comes to convex phase retrieval formulations, namely that the often encountered matrix semi-definiteness constraints as well as rank constraints (that are computationally prohibitive to enforce), become redundant for phase retrieval in the face of a growing sample size. Several numerical results are presented to illustrate the effectiveness of the proposed methodologies.
Improved Analysis of Current-Steering DACs Using Equivalent Timing Errors
Authors: Daniel Beauchamp, Keith M. Chugg
Date: 2022-03-16 00:00:00

Current-steering (CS) digital-to-analog converters (DACs) generate analog signals by combining weighted current sources. Ideally, the current sources are combined at each switching instant simultaneously. However, this is not true in practice due to timing mismatch, resulting in nonlinear distortion. This work uses the equivalent timing error model, introduced by previous work, to analyze the signal-to-distortion ratio (SDR) resulting from these timing errors. Using a behavioral simulation model we demonstrate that our analysis is significantly more accurate than the previous methods. We also use our simulation model to investigate the effect of timing mismatch in partially-segmented CS-DACs, i.e., those comprised of both equally-weighted and binary-weighted current sources.
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part I: Stationary Signals
Authors: Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Date: 2022-03-16 00:00:00
Tasks: Quantization, Relation

One-bit quantization, which relies on comparing the signals of interest with given threshold levels, has attracted considerable attention in signal processing for communications and sensing. A useful tool for covariance recovery in such settings is the arcsine law, that estimates the normalized covariance matrix of zero-mean stationary input signals. This relation, however, only considers a zero sampling threshold, which can cause a remarkable information loss. In this paper, the idea of the arcsine law is extended to the case where one-bit analog-to-digital converters (ADCs) apply time-varying thresholds. Specifically, three distinct approaches are proposed, investigated, and compared, to recover the autocorrelation sequence of the stationary signals of interest. Additionally, we will study a modification of the Bussgang law, a famous relation facilitating the recovery of the cross-correlation between the one-bit sampled data and the zero-mean stationary input signal. Similar to the case of the arcsine law, the Bussgang law only considers a zero sampling threshold. This relation is also extended to accommodate the more general case of time-varying thresholds for the stationary input signals.
Optimization of vaccination for COVID-19 in the midst of a pandemic
Authors: Qi Luo, Ryan Weightman, Sean T. McQuade, Mateo Diaz, Emmanuel Trélat, William Barbour, Dan Work, Samitha Samaranayake, Benedetto Piccoli
Date: 2022-03-17 00:00:00

During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the health system. Using an age-structured population compartmental finite-dimensional optimal control model, our results suggest that the eldest to youngest vaccination policy is optimal to minimize deaths. Our model includes the possible infection of vaccinated populations. We apply our model to real-life data from the US Census for New Jersey and Florida, which have a significantly different population structure. We also provide various estimates of the number of lives saved by optimizing the vaccine schedule and compared to no vaccination.
Weak error rates of numerical schemes for rough volatility
Authors: Paul Gassiat
Date: 2022-03-17 00:00:00

Simulation of rough volatility models involves discretization of stochastic integrals where the integrand is a function of a (correlated) fractional Brownian motion of Hurst index $H \in (0,1/2)$. We obtain results on the rate of convergence for the weak error of such approximations, in the special cases when either the integrand is the fBm itself, or the test function is cubic. Our result states that the convergence is of order $(3H+ \frac{1}{2}) \wedge 1$ for exact left-point discretization, and of order $H+\frac{1}{2}$ for the hybrid scheme with well-chosen weights.
The Variable Volatility Elasticity Model from Commodity Markets
Authors: Fuzhou Gong, Ting Wang
Date: 2022-03-17 00:00:00
Tasks: model

In this paper, we propose and study a novel continuous-time model, based on the well-known constant elasticity of variance (CEV) model, to describe the asset price process. The basic idea is that the volatility elasticity of the CEV model can not be treated as a constant from the perspective of stochastic analysis. To address this issue, we deduce the price process of assets from the perspective of volatility elasticity, propose the constant volatility elasticity (CVE) model, and further derive a more general variable volatility elasticity (VVE) model. Moreover, our model can describe the positive correlation between volatility and asset prices existing in the commodity markets, while CEV model can only describe the negative correlation. Through the empirical research on the financial market, many assets, especially commodities, often show this positive correlation phenomenon in some time periods, which shows that our model has strong practical application value. Finally, we provide the explicit pricing formula of European options based on our model. This formula has an elegant form convenient to calculate, which is similarly to the renowned Black-Scholes formula and of great significance to the research of derivatives market.
Image Super-Resolution Improved by Edge Information
Authors: Eldrey Galindo, Helio Pedrini
Date: 2019-10-06 00:00:00
Proceeding: SMC 2019 10
Tasks: Image Super-Resolution, MS-SSIM, SSIM, Super-Resolution

As well as in other knowledge domains, deep learning techniques have revolutionized the development of image super-resolution approaches. State-of-the-art algorithms for this problem have employed convolutional neural networks in residual architectures with a number of layers and generic loss functions, such as L1 and Peak Signal-to-Noise Ratio (PSNR). These frameworks (architectures + loss functions) are generic and do not address the main characteristics of an image for human visual perception (luminance, contrast, and structure) resulting in better images, however, with noise mainly at the edges. In this work, we present an edge enhanced super-resolution (EESR) method using a novel residual neural network with focus on image edges and a mix of loss functions that use PSNR, L1, Multiple-Scale Structural Similarity (MS-SSIM), and a new loss function based on the pencil sketch technique. As main contribution, the proposed framework aims to leverage the limits of image super-resolution and presents an improvement of the results in terms of the SSIM metric and achieving competitive results for the PSNR metric.
Context-aware taxi dispatching at city-scale using deep reinforcement learning
Authors: Zhidan Liu, Jiangzhou Li, and Kaishun Wu, Member, IEEE
Date: 2021-05-26 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2021 5
Tasks: Action Generation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Abstract— Proactive taxi dispatching is of great importance to
balance taxi demand-supply gaps among different locations in
a city. Recent advances primarily rely on deep reinforcement
learning (DRL) to directly learn the optimal dispatching policy.
These works, however, are still not sufficiently efficient because
they overlook several pieces of valuable context information. As a
result, they may generate quite a few improper actions and
introduce unnecessary coordination costs. To improve existing
works, we present COX – a context-aware taxi dispatching
approach that incorporates rich contexts into DRL modeling for
more efficient taxi reallocations. Specifically, rather than simply
dividing the service area into grids, COX proposes a road connectivity aware clustering algorithm to divide the road network
graph into zones for practical taxi dispatching. In addition, COX
comprehensively analyzes zone-level taxi demands and supplies
through accurate taxi demand prediction and timely updates of
taxi statuses. COX improves the DRL modeling by integrating
these derived contexts, e.g., state representation with complete
demand/supply data and sequential action generation with full
coordination among idle taxis. In particular, we implement an
environment simulator to train and evaluate COX using a large
real-world taxi dataset. Extensive experiments show that COX
outperforms state-of-the-art approaches on various performance
metrics, e.g., on average improving the total order values by
6.74%, while reducing the number of unserved taxi orders and
passengers’ waiting time by 4.92% and 44.84%, respectively.
Machine Translation Of Bi-Lingual Hindi-English (Hinglish) text. 10th Machine Translation summit (MT Summit X),
Authors: R. Mahesh K. Sinha, Anil Thakur
Date: 2005-05-10 00:00:00
Proceeding: Computational Linguistics is the international scientific and professional society for people working on problems involving natural language and computation. 2005 5
Tasks: Machine Translation, Sentence, Translation

In the present communication-based society,
no natural language seems to have been left
untouched by the trends of code-mixing. For
different communicative purposes, a language
uses linguistic codes from other languages.
This gives rise to a mixed language which is
neither totally the host language nor the
foreign language. The mixed language poses a
new challenge to the problem of machine
translation. It is necessary to identify the
“foreign” elements in the source language and
process them accordingly. The foreign
elements may not appear in their original form
and may get morphologically transformed as
per the host language. Further, in a complex
sentence, a clause/utterance may be in the host
language while another clause/utterance may
be in the foreign language.
 Code-mixing of Hindi and English where
Hindi is the host language, is a common
phenomenon in day-to-day language usage in
Indian metropolis. The scenario is so common
that people have started considering this a
different variety altogether and calling it by
the name Hinglish. In this paper, we present a
mechanism for machine translation of
Hinglish to pure (standard) Hindi and pure
English forms.
Towards Energy and Time Efficient Resource Allocation in IoT-Fog-Cloud Environment
Authors: Huaiying Sun1, 2(B), Huiqun Yu1(B), and Guisheng Fan1(B)
Date: 2019-09-20 00:00:00
Proceeding: c Springer Nature Switzerland AG 2019 2019 9
Tasks: Cloud Computing, Scheduling

As the number of IoT devices with limited resources and the
corresponding observed data grow exponentially, the method of offloading all tasks to a remote data center becomes expensive, even inefficient.
How to optimize the energy consumption of application requests from
IoT devices satisfying the deadline constraint is also a challenge. Fog
computing is closer to users, featuring the lower service delay but less
resource than the remote cloud. Fog does not mean to replace cloud.
They are complementary to each other, and cooperation between them
is worth studying. The main points of this paper are: (1) Proposing
a general IoT-fog-cloud computing architecture that fully exploits the
advantages of fog and cloud. (2) Formulating the energy efficient computation offloading and dynamic resource scheduling (eoDS) problem, then
proposing an eoDS algorithm to solve the problem, reducing the energy
consumption and completion time of application requests (3) Compared
with cloud nodes, the mobility of fog nodes is higher. For this, we propose
the fog functional areas reconstruction method to adaptively deal with
the changing environment, improving the resource utilization of fog.
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath
Authors: Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo, Luigi Moretti, Giuseppe Pirlo
Date: 2022-03-15 00:00:00
Proceeding: Pattern Recognition 2022 3
Tasks: 8k, Audio Classification, COVID-19 Diagnosis, Data Augmentation, Dimensionality Reduction, Environmental Sound Classification, feature selection, Sound Classification

This study presents the Auditory Cortex ResNet (AUCO ResNet), it is a biologically inspired deep neural network especially designed for sound classification and more specifically for Covid-19 recognition from audio tracks of coughs and breaths. Differently from other approaches, it can be trained end-to-end thus optimizing (with gradient descent) all the modules of the learning algorithm: mel-like filter design, feature extraction, feature selection, dimensionality reduction and prediction. This neural network includes three attention mechanisms namely the squeeze and excitation mechanism, the convolutional block attention module, and the novel sinusoidal learnable attention. The attention mechanism is able to merge relevant information from activation maps at various levels of the network. The net takes as input raw audio files and it is able to fine tune also the features extraction phase. In fact, a Mel-like filter is designed during the training, thus adapting filter banks on important frequencies. AUCO ResNet has proved to provide state of art results on many datasets. Firstly, it has been tested on many datasets containing Covid-19 cough and breath. This choice is related to the fact that that cough and breath are language independent, allowing for cross dataset tests with generalization aims. These tests demonstrate that the approach can be adopted as a low cost, fast and remote Covid-19 pre-screening tool. The net has also been tested on the famous UrbanSound 8K dataset, achieving state of the art accuracy without any data preprocessing or data augmentation technique.