pwc-archive/papers-with-abstracts huggingface dataset:
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning
Authors: Ahmed Rasheed, Muhammad Shahzad Younis, Farooq Ahmad, Junaid Qadir, Muhammad Kashif
Date: 2021-03-05 00:00:00
Tasks: Time Series, Time Series Analysis

Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors. Future planning of commodity pricing is achievable by forecasting their future price anticipated by the current circumstances. This paper presents a wheat price forecasting methodology, which uses the price, weather, production, and consumption trends for wheat prices taken over the past few years and analyzes them with the help of advance neural networks architecture Long Short Term Memory (LSTM) networks. The proposed methodology presented significantly improved results versus other conventional machine learning and statistical time series analysis methods.
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Authors: Wilkie Olin-Ammentorp, Yury Sokolov, Maxim Bazhenov
Date: 2021-03-05 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications. Efficient implementations of RL techniques could allow for agents deployed in edge-use cases to gain novel abilities, such as improved navigation, understanding complex situations and critical decision making. Towards this goal, we describe a flexible architecture to carry out reinforcement learning on neuromorphic platforms. This architecture was implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics. Our study proposes a usable energy efficient solution for real-world RL applications and demonstrates applicability of the neuromorphic platforms for RL problems.
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
Authors: Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang
Date: 2021-03-05 00:00:00
Tasks: Denoising, Dictionary Learning, Grayscale Image Denoising, Image Denoising

Deep learning based methods hold state-of-the-art results in image denoising, but remain difficult to interpret due to their construction from poorly understood building blocks such as batch-normalization, residual learning, and feature domain processing. Unrolled optimization networks propose an interpretable alternative to constructing deep neural networks by deriving their architecture from classical iterative optimization methods, without use of tricks from the standard deep learning tool-box. So far, such methods have demonstrated performance close to that of state-of-the-art models while using their interpretable construction to achieve a comparably low learned parameter count. In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising performance in both low and high parameter count regimes. Specifically, we show that the proposed model outperforms the state-of-the-art denoising models when scaled to similar parameter count. In addition, we leverage the model's interpretable construction to propose an augmentation of the network's thresholds that enables state-of-the-art blind denoising performance and near-perfect generalization on noise-levels unseen during training.
Hierarchical Gumbel Attention Network for Text-based Person Search
Authors: Kecheng Zheng, Wu Liu, Jiawei Liu, Zheng-Jun Zha, Tao Mei
Date: 2020-10-10 00:00:00
Tasks: Image Retrieval, Image to text, Image-to-Text Retrieval, Person Search, Retrieval, Sentence, Text based Person Retrieval, Text based Person Search, Text Retrieval

Text-based person search aims to retrieve the pedestrian images that best match a given textual description from gallery images. Previous methods utilize the soft-attention mechanism to infer the semantic alignments between the regions of image and the corresponding words in sentence. However, these methods may fuse the irrelevant multi-modality features together which cause matching redundancy problem. In this work, we propose a novel hierarchical Gumbel attention network for text-based person search via Gumbel top-k re-parameterization algorithm. Specifically, it adaptively selects the strong semantically relevant image regions and words/phrases from images and texts for precise alignment and similarity calculation. This hard selection strategy is able to fuse the strong-relevant multi-modality features for alleviating the problem of matching redundancy. Meanwhile, a Gumbel top-k reparameterization algorithm is designed as a low-variance, unbiased gradient estimator to handle the discreteness problem of hard attention mechanism by an end-to-end manner. Moreover, a hierarchical adaptive matching strategy is employed by the model from three different granularities, i.e., word-level, phrase-level, and sentencelevel, towards fine-grained matching. Extensive experimental results demonstrate the state-of-the-art performance. Compared the existed best method, we achieve the 8.24% Rank-1 and 7.6% mAP relative improvements in the text-to-image retrieval task, and 5.58% Rank-1 and 6.3% mAP relative improvements in the image-to-text retrieval task on CUHK-PEDES dataset, respectively
The impact of state capacity on the cross-country variations in COVID-19 vaccination rates
Authors: Dragan Tevdovski, Petar Jolakoski, Viktor Stojkoski
Date: 2021-03-08 00:00:00

The initial period of vaccination shows strong heterogeneity between countries' vaccinations rollout, both in the terms of the start of the vaccination process and in the dynamics of the number of people that are vaccinated. A predominant thesis in the ongoing debate on the drivers of this observed heterogeneity is that a key determinant of the swift and extensive vaccine rollout is state capacity. Here, we utilize two measures that quantify different aspects of the state capacity: i) the external capacity (measured through the soft power and the economic power of the country) and ii) the internal capacity (measured via the country's government effectiveness) and investigate their relationship with the coronavirus vaccination outcome in the initial period (up to 30th January 2021). By using data on 189 countries and a two-step Heckman approach, we find that the economic power of the country and its soft power are robust determinants of whether a country has started with the vaccination process. In addition, the government effectiveness is a key factor that determines vaccine roll-out. Altogether, our findings are in line with the hypothesis that state capacity determines the observed heterogeneity between countries in the initial period of COVID-19 vaccines rollout.
Evidence suggests that SARS-CoV-2 rapid antigen tests provide benefits for epidemic control -- observations from Austrian schools
Authors: Jitka Polechová, Kory D. Johnson, Pavel Payne, Alex Crozier, Mathias Beiglböck, Pavel Plevka, Eva Schernhammer
Date: 2021-03-08 00:00:00

Rapid antigen tests detect proteins at the surface of virus particles, identifying the disease during its infectious phase. In contrast, PCR tests detect viral genomes; they can thus diagnose COVID-19 before the infectious phase but also react to remnants of the virus genome, even weeks after live virus ceases to be detectable in the respiratory tract. Furthermore, the logistics for administering the tests are different, with rapid antigen tests being much easier to administer at-scale. In this article, we discuss the relative advantages of the different testing procedures and summarise evidence that shows that using antigen tests 2-3 times per week could become a powerful tool to suppress the COVID-19 pandemic. We also discuss the results of recent large-scale rapid antigen testing in Austrian schools. While our report on testing predates Delta, we have updated the review with recent data on viral loads in breakthrough infections and more information about testing efficacy, especially in children.
Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects
Authors: Hugo Kruiniger
Date: 2021-03-08 00:00:00

In this paper we first propose a root-n-consistent Conditional Maximum Likelihood (CML) estimator for all the common parameters in the panel logit AR(p) model with strictly exogenous covariates and fixed effects. Our CML estimator (CMLE) converges in probability faster and is more easily computed than the kernel-weighted CMLE of Honor\'e and Kyriazidou (2000). Next, we propose a root-n-consistent CMLE for the coefficients of the exogenous covariates only. We also discuss new CMLEs for the panel logit AR(p) model without covariates. Finally, we propose CMLEs for multinomial dynamic panel logit models with and without covariates. All CMLEs are asymptotically normally distributed.
A note on local uniqueness of equilibria: How isolated is a local equilibrium?
Authors: Stefano Matta
Date: 2021-03-08 00:00:00

The motivation of this note is to show how singular values affect local unique- ness. More precisely, Theorem 3.1 shows how to construct a neighborhood (a ball) of a regular equilibrium whose diameter represents an estimate of local uniqueness, hence providing a measure of how isolated a (local) unique equilibrium can be. The result, whose relevance in terms of comparative statics is evident, is based on reasonable and natural assumptions and hence is applicable in many different settings, ranging from pure exchange economies to non-cooperative games.
Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles
Authors: Mattia Bernetti, Kathleen B. Hall, Giovanni Bussi
Date: 2021-03-08 00:00:00
Tasks: Translation

Small-angle X-ray scattering (SAXS) experiments are increasingly used to probe RNA structure. A number of \emph{forward models} that relate measured SAXS intensities and structural features, and that are suitable to model either explicit-solvent effects or solute dynamics, have been proposed in the past years. Here we introduce an approach that integrates atomistic molecular dynamics simulations and SAXS experiments to reconstruct RNA structural ensembles while simultaneously accounting for both RNA conformational dynamics and explicit-solvent effects. Our protocol exploits SAXS pure-solute forward models and enhanced sampling methods to sample an heterogenous ensemble of structures, with no information towards the experiments provided on-the-fly. The generated structural ensemble is then reweighted through the maximum entropy principle so as to match reference SAXS experimental data at multiple ionic conditions. Importantly, accurate explicit-solvent forward models are used at this reweighting stage. We apply this framework to the GTPase-associated center, a relevant RNA molecule involved in protein translation, in order to elucidate its ion-dependent conformational ensembles. We show that (a) both solvent and dynamics are crucial to reproduce experimental SAXS data and (b) the resulting dynamical ensembles contain an ion-dependent fraction of extended structures.
The economic impact of weather and climate
Authors: Richard S. J. Tol
Date: 2021-02-25 00:00:00

I propose a new conceptual framework to disentangle the impacts of weather and climate on economic activity and growth: A stochastic frontier model with climate in the production frontier and weather shocks as a source of inefficiency. I test it on a sample of 160 countries over the period 1950-2014. Temperature and rainfall determine production possibilities in both rich and poor countries; positively in cold countries and negatively in hot ones. Weather anomalies reduce inefficiency in rich countries but increase inefficiency in poor and hot countries; and more so in countries with low weather variability. The climate effect is larger that the weather effect.
Tripartite and Sign Consensus for Clustering Balanced Social Networks
Authors: Giulia De Pasquale, Maria Elena Valcher
Date: 2021-03-08 00:00:00
Tasks: Clustering

In this paper, we address two forms of consensus for multi-agent systems with undirected, signed, weighted, and connected communication graphs, under the assumption that the agents can be partitioned into three clusters, representing the decision classes on a given specific topic, for instance, the in favour, abstained and opponent agents. We will show that under some assumptions on the cooperative/antagonistic relationships among the agents, simple modifications of DeGroot's algorithm allow to achieve tripartite consensus(if the opinions of agents belonging to the same class all converge to the same decision) or sign consensus (if the opinions of the agents in the three clusters converge to positive, zero and negative values, respectively).
InFocus: A spatial coding technique to mitigate misfocus in near field LoS beamforming
Authors: Nitin Jonathan Myers, Robert W. Heath Jr
Date: 2020-06-08 00:00:00

Phased arrays, commonly used in IEEE 802.11ad and 5G radios, are capable of focusing radio frequency signals in a specific direction or a spatial region. Beamforming achieves such directional or spatial concentration of signals and enables phased array-based radios to achieve high data rates. Designing beams for millimeter wave and terahertz communication using massive phased arrays, however, is challenging due to hardware constraints and the wide bandwidth in these systems. For example, beams which are optimal at the center frequency may perform poor in wideband communication systems where the radio frequencies differ substantially from the center frequency. The poor performance in such systems is due to differences in the optimal beamformers corresponding to distinct radio frequencies within the wide bandwidth. Such a mismatch leads to a misfocus effect in near field systems and the beam squint effect in far field systems. In this paper, we investigate the misfocus effect and propose InFocus, a low complexity technique to construct beams that are well suited for massive wideband phased arrays. The beams are constructed using a carefully designed frequency modulated waveform in the spatial dimension. For the special case of beamforming along the boresight of an array, this waveform is analogous to the frequency modulated continuous wave (FMCW) chirp signal in radar. InFocus mitigates beam misfocus and beam squint when applied to near field and far field systems. Simulation results indicate that InFocus enables massive wideband phased array-based radios to achieve higher data rates than comparable beamforming solutions.
Deep learning, machine vision in agriculture in 2021
Authors: Ildar Rakhmatulin
Date: 2021-03-03 00:00:00
Tasks: Deep Learning

Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
Authors: Yijie Zhang, Tairan Liu, Manmohan Singh, Yilin Luo, Yair Rivenson, Kirill V. Larin, Aydogan Ozcan
Date: 2021-03-04 00:00:00
Tasks: Image Reconstruction

Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical set-up and can be easily integrated with existing swept-source or spectral domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in ~6.73 ms using a desktop computer, removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3x undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2x spectral undersampling. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.
Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory
Authors: Jason Ramapuram, Yan Wu, Alexandros Kalousis
Date: 2021-02-20 00:00:00
Proceeding: ICLR 2021 1
Tasks: Conditional Image Generation, Image Generation

Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32.
AudioVisual Speech Synthesis: A brief literature review
Authors: Efthymios Georgiou, Athanasios Katsamanis
Date: 2021-02-18 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

This brief literature review studies the problem of audiovisual speech synthesis, which is the problem of generating an animated talking head given a text as input. Due to the high complexity of this problem, we approach it as the composition of two problems. Specifically, that of Text-to-Speech (TTS) synthesis as well as the voice-driven talking head animation. For TTS, we present models that are used to map text to intermediate acoustic representations, e.g. mel-spectrograms, as well as models that generate voice signals conditioned on these intermediate representations, i.e vocoders. For the talking-head animation problem, we categorize approaches based on whether they produce human faces or anthropomorphic figures. An attempt is also made to discuss the importance of the choice of facial models in the second case. Throughout the review, we briefly describe the most important work in audiovisual speech synthesis, trying to highlight the advantages and disadvantages of the various approaches.
Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes
Authors: Ngaire Underhill, Andrew Weinert
Date: 2021-03-04 00:00:00
Tasks: Collision Avoidance

The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For many aviation safety studies, manned aircraft behavior is represented using dynamic Bayesian networks. The original statistical models were developed from 2008-2013 to support safety simulations for altitudes above 500 feet Above Ground Level (AGL). However, these models were not sufficient to assess the safety of smaller UAS operations below 500 feet AGL. In response, newer models with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different than models of fixed-wing aircraft to require type specific models. The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low altitude operations. We also address which models can be surrogates for noncooperative aircraft without transponders.
Neural Network-based Quantization for Network Automation
Authors: Marton Kajo, Stephen S. Mwanje, Benedek Schultz, Georg Carle
Date: 2021-03-04 00:00:00
Tasks: Anomaly Detection, Deep Learning, Management, Quantization

Deep Learning methods have been adopted in mobile networks, especially for network management automation where they provide means for advanced machine cognition. Deep learning methods utilize cutting-edge hardware and software tools, allowing complex cognitive algorithms to be developed. In a recent paper, we introduced the Bounding Sphere Quantization (BSQ) algorithm, a modification of the k-Means algorithm, that was shown to create better quantizations for certain network management use-cases, such as anomaly detection. However, BSQ required a significantly longer time to train than k-Means, a challenge which can be overcome with a neural network-based implementation. In this paper, we present such an implementation of BSQ that utilizes state-of-the-art deep learning tools to achieve a competitive training speed.