pwc-archive/papers-with-abstracts huggingface dataset:
Biophysical interaction, nanotoxicology evaluation, and biocompatibility and biosafety of metal nanoparticles
Authors: Enrico Catalano
Date: 2021-08-04 00:00:00
Tasks: Specificity

Nanotechnology has been one of the fastest growing fields in the last three decades. Nanomaterials (sized 1-100 nm) has a wide spectrum of potential applications in many fields, applied as coating materials or in treatment and diagnosis. Nowadays, nanoparticles of both metallic and non-metallic origin are under investigation and development for applications in various fields of biology/therapeutics. Specifically, we show the correlations between the physicochemistry and biophysical specificity of metal nanoparticles and their uptake, transport, and biodistribution in cells, at the molecular, cellular, and whole organism level. Physiologically important metals are present in the human body with a wide range of biological activities. Some of these metals are magnesium, chromium, manganese, iron, cobalt, copper, zinc, selenium and molybdenum. Metals used in nanotechnology have to be biocompatible with the human system in terms of absorption, assimilation, excretion, and side effects. These metals are synthesized in the form of nanoparticles by different physical and chemical methods. Nanotoxicological studies of metal nanoparticles are intended to determine whether and to what extent their properties may pose a threat to the environment and to human beings. An overview of metal and metal oxide nanoparticles, their applications, and the potential for human exposure is provided, and it is integrated by a discussion of general principles of nanoparticle-induced toxicity and methods for toxicity testing of nanomaterials. This review wants to focus on establishing metal nanoparticles of physiological importance to be the best candidates for future nanotechnological tools and medicines, owing to the acceptability and safety in the human body. This can only be successful if these particles are synthesized with a better biocompatibility and low or no toxicity.
Identification of Incomplete Preferences
Authors: Luca Rigotti, Arie Beresteanu
Date: 2021-08-13 00:00:00
Tasks: Discrete Choice Models

We provide a sharp identification region for discrete choice models where consumers' preferences are not necessarily complete even if only aggregate choice data is available. Behavior is modeled using an upper and a lower utility for each alternative so that non-comparability can arise. The identification region places intuitive bounds on the probability distribution of upper and lower utilities. We show that the existence of an instrumental variable can be used to reject the hypothesis that the preferences of all consumers are complete. We apply our methods to data from the 2018 mid-term elections in Ohio.
The Use of Quantile Methods in Economic History
Authors: Damian Clarke, Manuel Llorca Jaña, Daniel Pailañir
Date: 2021-08-13 00:00:00
Tasks: quantile regression, regression

Quantile regression and quantile treatment effect methods are powerful econometric tools for considering economic impacts of events or variables of interest beyond the mean. The use of quantile methods allows for an examination of impacts of some independent variable over the entire distribution of continuous dependent variables. Measurement in many quantative settings in economic history have as a key input continuous outcome variables of interest. Among many other cases, human height and demographics, economic growth, earnings and wages, and crop production are generally recorded as continuous measures, and are collected and studied by economic historians. In this paper we describe and discuss the broad utility of quantile regression for use in research in economic history, review recent quantitive literature in the field, and provide an illustrative example of the use of these methods based on 20,000 records of human height measured across 50-plus years in the 19th and 20th centuries. We suggest that there is considerably more room in the literature on economic history to convincingly and productively apply quantile regression methods.
Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing
Authors: Soheil Ghili, Vineet Kumar
Date: 2021-08-12 00:00:00

This paper studies the effects of economies of density in transportation markets, focusing on ridesharing. Our theoretical model predicts that (i) economies of density skew the supply of drivers away from less dense regions, (ii) the skew will be more pronounced for smaller platforms, and (iii) rideshare platforms do not find this skew efficient and thus use prices and wages to mitigate (but not eliminate) it. We then develop a general empirical strategy with simple implementation and limited data requirements to test for spatial skew of supply from demand. Applying our method to ride-level, multi-platform data from New York City (NYC), we indeed find evidence for a skew of supply toward busier areas, especially for smaller platforms. We discuss the implications of our analysis for business strategy (e.g., spatial pricing) and public policy (e.g., consequences of breaking up or downsizing a rideshare platform)
Partisan affect and political outsiders
Authors: Fernanda Herrera
Date: 2021-08-12 00:00:00

We examine the effects of introducing a political outsider to the nomination process leading to an election. To this end, we develop a sequential game where politicians -- insiders and outsiders -- make a platform offer to a party, and parties in turn decide which offer to accept; this process conforms the voting ballot. Embedded in the evaluation of a party-candidate match is partisan affect, a variable comprising the attitudes of voters towards the party. Partisan affect may bias the electorate's appraisal of a match in a positive or negative way. We characterize the conditions that lead to the nomination of an outsider and determine whether her introduction as a potential candidate has any effect on the winning policy and on the welfare of voters. We find that the victory of an outsider generally leads to policy polarization, and that partisan affect has a more significant effect on welfare than ideology extremism.
Feature learning for efficient ASR-free keyword spotting in low-resource languages
Authors: Ewald van der Westhuizen, Herman Kamper, Raghav Menon, John Quinn, Thomas Niesler
Date: 2021-08-13 00:00:00
Tasks: Dynamic Time Warping, Humanitarian, Keyword Spotting

We consider feature learning for efficient keyword spotting that can be applied in severely under-resourced settings. The objective is to support humanitarian relief programmes by the United Nations in parts of Africa in which almost no language resources are available. For rapid development in such languages, we rely on a small, easily-compiled set of isolated keywords. These keyword templates are applied to a large corpus of in-domain but untranscribed speech using dynamic time warping (DTW). The resulting DTW alignment scores are used to train a convolutional neural network (CNN) which is orders of magnitude more computationally efficient and suitable for real-time application. We optimise this neural network keyword spotter by identifying robust acoustic features in this almost zero-resource setting. First, we incorporate information from well-resourced but unrelated languages using a multilingual bottleneck feature (BNF) extractor. Next, we consider features extracted from an autoencoder (AE) trained on in-domain but untranscribed data. Finally, we consider correspondence autoencoder (CAE) features which are fine-tuned on the small set of in-domain labelled data. Experiments in South African English and Luganda, a low-resource language, show that BNF and CAE features achieve a 5% relative performance improvement over baseline MFCCs. However, using BNFs as input to the CAE results in a more than 27% relative improvement over MFCCs in ROC area-under-the-curve (AUC) and more than twice as many top-10 retrievals. We show that, using these features, the CNN-DTW keyword spotter performs almost as well as the DTW keyword spotter while outperforming a baseline CNN trained only on the keyword templates. The CNN-DTW keyword spotter using BNF-derived CAE features represents an efficient approach with competitive performance suited to rapid deployment in a severely under-resourced scenario.
Multilingual training set selection for ASR in under-resourced Malian languages
Authors: Ewald van der Westhuizen, Trideba Padhi, Thomas Niesler
Date: 2021-08-13 00:00:00
Tasks: Humanitarian, speech-recognition, Speech Recognition

We present first speech recognition systems for the two severely under-resourced Malian languages Bambara and Maasina Fulfulde. These systems will be used by the United Nations as part of a monitoring system to inform and support humanitarian programmes in rural Africa. We have compiled datasets in Bambara and Maasina Fulfulde, but since these are very small, we take advantage of six similarly under-resourced datasets in other languages for multilingual training. We focus specifically on the best composition of the multilingual pool of speech data for multilingual training. We find that, although maximising the training pool by including all six additional languages provides improved speech recognition in both target languages, substantially better performance can be achieved by a more judicious choice. Our experiments show that the addition of just one language provides best performance. For Bambara, this additional language is Maasina Fulfulde, and its introduction leads to a relative word error rate reduction of 6.7%, as opposed to a 2.4% relative reduction achieved when pooling all six additional languages. For the case of Maasina Fulfulde, best performance was achieved when adding only Luganda, leading to a relative word error rate improvement of 9.4% as opposed to a 3.9% relative improvement when pooling all six languages. We conclude that careful selection of the out-of-language data is worthwhile for multilingual training even in highly under-resourced settings, and that the general assumption that more data is better does not always hold.
Joint Spatio-Temporal Discretisation of Nonlinear Active Cochlear Models
Authors: T. Dang, V. Sethu, E. Ambikairajah, J. Epps, H. Li
Date: 2021-08-12 00:00:00

Biologically inspired auditory models play an important role in developing effective audio representations that can be tightly integrated into speech and audio processing systems. Current computational models of the cochlea are typically expressed in terms of systems of differential equations and do not directly lend themselves for use in computational speech processing systems. Specifically, these models are spatially discrete and temporally continuous. This paper presents a jointly discretised (spatially and temporally discrete) model of the cochlea which allows for processing at fixed time intervals suited to discrete time speech and audio processing systems. The proposed model takes into account the active feedback mechanism in the cochlea, a core characteristic lacking in traditional speech processing front-ends, which endows it with significant dynamic range compression capability. This model is derived by jointly discretising an established semi-discretised (spatially discrete and temporally continuous) cochlear model in a state space form. We then demonstrate that the proposed jointly discretised implementation matches the semi-discrete model in terms of its characteristics and finally present stability analyses of the proposed model.
Local Patch Network with Global Attention for Infrared Small Target Detection
Authors: Fang Chen, Chenqiang Gao, Fangcen Liu, Yue Zhao, Yuxi Zhou, Deyu Meng, WangMeng Zuo
Date: 2021-08-13 00:00:00
Tasks: Deep Learning, Semantic Segmentation

Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually processed the image from the global view. However, the imaging locality of small targets and extreme class-imbalance between the target and background pixels were not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By leveraging both the global and local properties, the data-driven framework proposed in this paper has fused multi-scale features for small target detection. Extensive synthetic and real data experiments show that the proposed method achieves the state-of-the-art performance compared with existing both conventional and deep learning methods.
Multimodal Unrolled Robust PCA for Background Foreground Separation
Authors: Spencer Markowitz, Corey Snyder, Yonina C. Eldar, Minh N. Do
Date: 2021-08-13 00:00:00
Tasks: Rolling Shutter Correction

Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene. Typically, this is performed using consumer cameras because of their low cost, human interpretability, and high resolution. Yet, cameras and the BFS algorithms that process their data have common failure modes due to lighting changes, highly reflective surfaces, and occlusion. One solution is to incorporate an additional sensor modality that provides robustness to such failure modes. In this paper, we explore the ability of a cost-effective radar system to augment the popular Robust PCA technique for BFS. We apply the emerging technique of algorithm unrolling to yield real-time computation, feedforward inference, and strong generalization in comparison with traditional deep learning methods. We benchmark on the RaDICaL dataset to demonstrate both quantitative improvements of incorporating radar data and qualitative improvements that confirm robustness to common failure modes of image-based methods.
Towards artificially intelligent recycling Improving image processing for waste classification
Authors: Youpeng Yu, Ryan Grammenos
Date: 2021-08-09 00:00:00
Tasks: Classification, Data Augmentation, Management, Transfer Learning

The ever-increasing amount of global refuse is overwhelming the waste and recycling management industries. The need for smart systems for environmental monitoring and the enhancement of recycling processes is thus greater than ever. Amongst these efforts lies IBM's Wastenet project which aims to improve recycling by using artificial intelligence for waste classification. The work reported in this paper builds on this project through the use of transfer learning and data augmentation techniques to ameliorate classification accuracy. Starting with a convolutional neural network (CNN), a systematic approach is followed for selecting appropriate splitting ratios and for tuning multiple training parameters including learning rate schedulers, layers freezing, batch sizes and loss functions, in the context of the given scenario which requires classification of waste into different recycling types. Results are compared and contrasted using 10-fold cross validation and demonstrate that the model developed achieves a 91.21% test accuracy. Subsequently, a range of data augmentation techniques are then incorporated into this work including flipping, rotation, shearing, zooming, and brightness control. Results show that these augmentation techniques further improve the test accuracy of the final model to 95.40%. Unlike other work reported in the field, this paper provides full details regarding the training of the model. Furthermore, the code for this work has been made open-source and we have demonstrated that the model can perform successful real-time classification of recycling waste items using a standard computer webcam.
Harmonic Retrieval of CFO and Frame Misalignment for OFDM-based Inter-Satellite Links
Authors: Rıfat Volkan Şenyuva, Güneş Karabulut Kurt
Date: 2021-08-13 00:00:00
Tasks: Retrieval

As dense low Earth orbit (LEO) constellations are being planned, the need for accurate synchronization schemes in high-speed environments remains a challenging problem to tackle. To further improve synchronization accuracy in channeling environments, which can also be applied in the LEO networks, we present a new method for estimating the carrier frequency offset (CFO) and frame misalignment in orthogonal frequency division multiplexing (OFDM) based inter-satellite links. The proposed method requires the transmission of pilot symbols to exploit 2-D estimation of signal parameters via rotational invariance techniques (ESPRIT) and estimate the CFO and the frame misalignment. The Cramer-Rao lower bounds (CRLB) of the joint estimation of the CFO and frame misalignment are also derived. Numerical results show that the difference between the proposed method and the state-of-art method is less than 5dB at its worse.
Enhanced Accuracy Simulator for a Future Korean Nationwide eLoran System
Authors: Joon Hyo Rhee, Sanghyun Kim, Pyo-Woong Son, Jiwon Seo
Date: 2021-08-13 00:00:00

The Global Positioning System (GPS) has become the most widely used positioning, navigation, and timing system. However, the vulnerability of GPS to radio frequency interference has attracted significant attention. After experiencing several incidents of intentional high-power GPS jamming trials by North Korea, South Korea decided to deploy the enhanced long-range navigation (eLoran) system, which is a high-power terrestrial radio-navigation system that can complement GPS. As the first phase of the South Korean eLoran program, an eLoran testbed system was recently developed and declared operational on June 1, 2021. Once its operational performance is determined to be satisfactory, South Korea plans to move to the second phase of the program, which is a nationwide eLoran system. For the optimal deployment of additional eLoran transmitters in a nationwide system, it is necessary to properly simulate the expected positioning accuracy of the said future system. In this study, we propose enhanced eLoran accuracy simulation methods based on a land cover map and transmitter jitter estimation. Using actual measurements over the country, the simulation accuracy of the proposed methods was confirmed to be approximately 10%-91% better than that of the existing Loran (i.e., Loran-C and eLoran) positioning accuracy simulators depending on the test locations.
Synergy Test for Antibacterial Activity: Towards the Research for a Consensus between the Fractional Inhibitory Concentration (Checkboard Method) and the Increase in Fold Area (Disc Diffusion Method)
Authors: Mbarga Manga Joseph Arsene
Date: 2021-08-12 00:00:00

Antibiotic resistance is a topical problem for both humans and animals and has been the subject of special monitoring for two decades. Several recent studies, including ours, have shown that this phenomenon is accentuated by the transfer of certain genetic elements and cross resistance acquisition, the latter or both resulting from the misuse of these antimicrobial drugs. A more careful use of antimicrobials, the search for new antibacterial compounds (including probiotics and phages) are the most recommended alternatives to overcome this situation. However, tests for modulations of antimicrobial activity can also play a major role. The main goal of synergy studies is to assess whether substances with antibacterial properties can improve the effectiveness of existing antimicrobials or give them a second life against resistant germs. Moreover, recent studies have demonstrated the ability of silver nanoparticles and extracts of certain plants to boost the effectiveness of certain antibiotics. such as ampicillin, benzylpenicillin, cefazolin, ciprofloxacin, nitrofurantoin, and kanamycin. Yet, from these studies we found that there was a serious problem with the interpretation of the results when using the disk method with determination of the increase in fold area. Indeed, the use of this method firstly requires the determination of the diameter of inhibition of the antibiotic alone; follow by the determination of the combination of antibiotic + modulating substance (MS) or extract.....
Statistical Learning in Speech: A Biologically Based Predictive Learning Model
Authors: John Rohrlich, Randall C. O'Reilly
Date: 2021-08-13 00:00:00

Infants, adults, non-human primates and non-primates all learn patterns implicitly, and they do so across modalities. The biological evidence supports the hypothesis that the mechanism for this learning is general but computationally local. We hypothesize that the mechanism itself is predictive error-driven learning. We build on recent work that advanced a biologically plausible model of error backpropagation learning which proposes that higher order thalamic nuclei provide a locale for a temporal difference between top-down predictions and an actual event outcome. Our neural network based on that work also models the auditory cortex hierarchy of core, belt and parabelt and the caudal-rostral axis within regions. We simulated two studies showing statistical learning in infants, a seminal study using synthesized speech and a more recent study using human speech. Before simulating these studies the network was trained on spoken sentences from the TIMIT corpus to emulate infant's experience listening to random speech. The implemented neural network, learning only by predicting the next brief speech segment, learned in both simulations to predict in-word syllables better than next-word syllables showing that prediction could be the basis for word segmentation and thus statistical learning.
A modified Susceptible-Infected-Recovered model for observed under-reported incidence data
Authors: Imelda Trejo, Nicolas Hengartner
Date: 2020-12-09 00:00:00

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that consistently, about 40-60% of the infections were not observed in the American outbreaks. The two exception are Mexico and Peru, with acute under-reporting in Mexico.