pwc-archive/papers-with-abstracts huggingface dataset:
Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image
Authors: Alon Mamistvalov, Yonina C. Eldar
Date: 2021-03-01 00:00:00
Tasks: compressed sensing

The most common technique for generating B-mode ultrasound (US) images is delay and sum (DAS) beamforming, where the signals received at the transducer array are sampled before an appropriate delay is applied. This necessitates sampling rates exceeding the Nyquist rate and the use of a large number of antenna elements to ensure sufficient image quality. Recently we proposed methods to reduce the sampling rate and the array size relying on image recovery using iterative algorithms, based on compressed sensing (CS) and the finite rate of innovation (FRI) frameworks. Iterative algorithms typically require a large number of iterations, making them difficult to use in real-time. Here, we propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm, resulting in an efficient and interpretable deep network. The inputs to our network are the subsampled beamformed signals after summation and delay in the frequency domain, requiring only a subset of the US signal to be stored for recovery. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance. Using \emph{in vivo} data we demonstrate that the proposed method yields high-quality images while reducing the data volume traditionally used up to 36 times. In terms of image resolution and contrast, our technique outperforms previously suggested methods as well as DAS and minimum-variance (MV) beamforming, paving the way to real-time applicable recovery methods.
A Deep Emulator for Secondary Motion of 3D Characters
Authors: Mianlun Zheng, Yi Zhou, Duygu Ceylan, Jernej Barbič
Date: 2021-03-01 00:00:00
Proceeding: CVPR 2021 1

Fast and light-weight methods for animating 3D characters are desirable in various applications such as computer games. We present a learning-based approach to enhance skinning-based animations of 3D characters with vivid secondary motion effects. We design a neural network that encodes each local patch of a character simulation mesh where the edges implicitly encode the internal forces between the neighboring vertices. The network emulates the ordinary differential equations of the character dynamics, predicting new vertex positions from the current accelerations, velocities and positions. Being a local method, our network is independent of the mesh topology and generalizes to arbitrarily shaped 3D character meshes at test time. We further represent per-vertex constraints and material properties such as stiffness, enabling us to easily adjust the dynamics in different parts of the mesh. We evaluate our method on various character meshes and complex motion sequences. Our method can be over 30 times more efficient than ground-truth physically based simulation, and outperforms alternative solutions that provide fast approximations.
Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements
Authors: Lara Hoffmann, Ines Fortmeier, Clemens Elster
Date: 2021-03-01 00:00:00
Tasks: Ensemble Learning, Form, Uncertainty Quantification

Uncertainty quantification by ensemble learning is explored in terms of an application from computational optical form measurements. The application requires to solve a large-scale, nonlinear inverse problem. Ensemble learning is used to extend a recently developed deep learning approach for this application in order to provide an uncertainty quantification of its predicted solution to the inverse problem. By systematically inserting out-of-distribution errors as well as noisy data the reliability of the developed uncertainty quantification is explored. Results are encouraging and the proposed application exemplifies the ability of ensemble methods to make trustworthy predictions on high dimensional data in a real-world application.
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
Authors: Avia Efrat, Uri Shaham, Dan Kilman, Omer Levy
Date: 2021-03-01 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Sentence, World Knowledge

Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).
Offshore Software Maintenance Outsourcing Predicting Clients Proposal using Supervised Learning
Authors: Atif Ikram, Masita Abdul Jalil, Amir Bin Ngah, Ahmad Salman Khan, Tahir Iqbal
Date: 2021-03-01 00:00:00

In software engineering, software maintenance is the process of correction, updating, and improvement of software products after handed over to the customer. Through offshore software maintenance outsourcing clients can get advantages like reduce cost, save time, and improve quality. In most cases, the OSMO vendor generates considerable revenue. However, the selection of an appropriate proposal among multiple clients is one of the critical problems for OSMO vendors. The purpose of this paper is to suggest an effective machine learning technique that can be used by OSMO vendors to assess or predict the OSMO client proposal. The dataset is generated through a survey of OSMO vendors working in a developing country. The results showed that supervised learning-based classifiers like Na\"ive Bayesian, SMO, Logistics apprehended 69.75, 81.81, and 87.27 percent testing accuracy respectively. This study concludes that supervised learning is the most suitable technique to predict the OSMO client's proposal.
Multiple Convolutional Features in Siamese Networks for Object Tracking
Authors: Zhenxi Li, Guillaume-Alexandre Bilodeau, Wassim Bouachir
Date: 2021-03-01 00:00:00
Tasks: Object, Object Tracking

Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem, and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks. The source code and trained models are available at https://github.com/zhenxili96/MFST.
Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces
Authors: Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban, Marta Karczewicz, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen
Date: 2021-02-27 00:00:00

Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed primarily for YUV 4:2:0 format, where U and V components are subsampled by considering the human visual system. This paper investigates various DLEC designs to support YUV 4:2:0 format by comparing their performance against the main profiles of HEVC and VVC standards under a common evaluation framework. Moreover, a new transform network architecture is proposed to improve the efficiency of coding YUV 4:2:0 data. The experimental results on YUV 4:2:0 datasets show that the proposed architecture significantly outperforms naive extensions of existing architectures designed for RGB format and achieves about 10% average BD-rate improvement over the intra-frame coding in HEVC.
Pricing high-dimensional Bermudan options with hierarchical tensor formats
Authors: Christian Bayer, Martin Eigel, Leon Sallandt, Philipp Trunschke
Date: 2021-03-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented. It is shown that the "curse of dimensionality" can be alleviated for the computation of Bermudan option prices with the Monte Carlo least-squares approach as well as the dual martingale method, both using high-dimensional tensorized polynomial expansions. This discretization allows for a simple and computationally cheap evaluation of conditional expectations. Complexity estimates are provided as well as a description of the optimization procedures in the tensor train format. Numerical experiments illustrate the favourable accuracy of the proposed methods. The dynamical programming method yields results comparable to recent Neural Network based methods.
Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators
Authors: Teresa Chouzouris, Nicolas Roth, Caglar Cakan, Klaus Obermayer
Date: 2021-02-17 00:00:00

We apply the framework of optimal nonlinear control to steer the dynamics of a whole-brain network of FitzHugh-Nagumo oscillators. Its nodes correspond to the cortical areas of an atlas-based segmentation of the human cerebral cortex, and the inter-node coupling strengths are derived from Diffusion Tensor Imaging data of the connectome of the human brain. Nodes are coupled using an additive scheme without delays and are driven by background inputs with fixed mean and additive Gaussian noise. Optimal control inputs to nodes are determined by minimizing a cost functional that penalizes the deviations from a desired network dynamic, the control energy, and spatially non-sparse control inputs. Using the strength of the background input and the overall coupling strength as order parameters, the network's state-space decomposes into regions of low and high activity fixed points separated by a high amplitude limit cycle all of which qualitatively correspond to the states of an isolated network node. Along the borders, however, additional limit cycles, asynchronous states and multistability can be observed. Optimal control is applied to several state-switching and network synchronization tasks, and the results are compared to controllability measures from linear control theory for the same connectome. We find that intuitions from the latter about the roles of nodes in steering the network dynamics, which are solely based on connectome features, do not generally carry over to nonlinear systems, as had been previously implied. Instead, the role of nodes under optimal nonlinear control critically depends on the specified task and the system's location in state space. Our results shed new light on the controllability of brain network states and may serve as an inspiration for the design of new paradigms for non-invasive brain stimulation.
Spectral nature of soiling and its impact on multi-junction based concentrator systems
Authors: Eduardo F. Fernandez, Daniel Chemisana, Leonardo Micheli, Florencia Almonacid
Date: 2021-03-02 00:00:00

Soiling, which consists of dust, dirt and particles accumulated on the surface of conventional or concentrator photovoltaic modules, absorbs, scatters, and reflects part of the incoming sunlight. Therefore, it reduces the amount of energy converted by the semiconductor solar cells. This work focuses on the effect of soiling on the spectral performance of multi-junction (MJ) cells, widely used in concentrator photovoltaic (CPV) applications. Novel indexes, useful to quantify the spectral impact of soiling are introduced, and their meanings are discussed. The results of a one-year experimental investigation conducted in Spain are presented and are used to discuss how soiling impacts each of the subcells of a MJ cell, as well as the cell current-matching. Results show that soiling affects the current balance among the junctions, i.e. the transmittance losses have found to be around 4% higher in the top than in the middle subcell. The spectral nature of soiling has demonstrated to increase the annual spectral losses of around 2%. Ideal conditions for the mitigation of soiling are also discussed and found to be in blue-rich environments, where the higher light intensity at the shorter wavelengths can limit the impact of soiling on the overall production of the CPV system.
Turbulence explains the accelerations of an eagle in natural flight
Authors: Kasey M. Laurent, Bob Fogg, Tobias Ginsburg, Casey Halverson, Michael Lanzone, Tricia A. Miller, David W. Winkler, Gregory P. Bewley
Date: 2021-02-19 00:00:00

Turbulent winds and gusts fluctuate on a wide range of timescales from milliseconds to minutes and longer, a range that overlaps the timescales of avian flight behavior, yet the importance of turbulence to avian behavior is unclear. By combining wind speed data with the measured accelerations of a golden eagle (Aquila chrysaetos) flying in the wild, we show that the eagle's accelerations can be explained by a linear interaction with turbulence for timescales between about 1/2 and 10 s. These timescales are comparable to those of typical eagle behaviors, corresponding to between about 1 and 25 wingbeats, and to those of turbulent gusts both larger than the eagle's wingspan and smaller than large-scale atmospheric phenomena such as convection cells. The eagle's accelerations exhibit power spectra and intermittent activity characteristic of turbulence, and increase in proportion to the turbulence intensity. Intermittency results in accelerations that are occasionally several times stronger than gravity, and much larger than the ones we experience while driving, for instance. These imprints of turbulence on the bird's movements need to be further explored to understand the energetics of birds and other volant lifeforms, to improve our own methods of flying through ceaselessly turbulent environments, and to engage airborne wildlife as distributed probes of the changing conditions in the atmosphere.
Probabilistic Conformance for Cyber-Physical Systems
Authors: Yu Wang, Mojtaba Zarei, Borzoo Bonakdarpoor, Miroslav Pajic
Date: 2020-08-03 00:00:00
Tasks: Model Predictive Control

In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatically transfer to the other, or one system can safely replace the other in practice. In this work, we study the probabilistic conformance of cyber-physical systems (CPS). We propose a notion of (approximate) probabilistic conformance for sets of complex specifications expressed by the Signal Temporal Logic (STL). Based on a novel statistical test, we develop the first statistical verification methods for the probabilistic conformance of a wide class of CPS. Using this method, we verify the conformance of the startup time of the widely-used full and simplified model of Toyota powertrain systems, the settling time of model-predictive-control-based and neural-network-based automotive lane-keeping controllers, as well as the maximal voltage deviation of full and simplified power grid systems.
Commentary on World Development Report 2020: Trading for Development in the Age of Global Value Chains
Authors: Rajkumar Byahut, Sourish Dutta, Chidambaran G. Iyer, Manikantha Nataraj
Date: 2021-03-02 00:00:00

The importance of trade to an economy needs no emphasis. You sell products or services that you are competitive at and buy those where you are not. Experience of countries such as South Korea and China demonstrate that resources required for development can be garnered through trade; thus, motivating many countries to embrace trade as a means for development. Simultaneously, emergence of 'Global Value Chain' or 'GVC' as they are popularly known has changed the way we trade. Though the concept of GVC was introduced in the early 2000s, there are examples of global value chains before the 1980s. However, the scale of the phenomenon and the way in which technological change, by lowering trade costs, has allowed fragmentation of production was not possible before (Hernandez et al., 2014). In this context, the World Bank has recently published its 'World Development Report 2020: Trading for Development in the Age of Global Value Chains' (WDR). The report prescribes that GVCs still offer developing countries a clear path to progress and that developing countries can achieve better outcomes by pursuing market-oriented reforms specific to their stage of development.
Implementation of a cost-benefit analysis of Demand-Responsive Transport with a Multi-Agent Transport Simulation
Authors: Conny Grunicke, Jan Christian Schlüter, Jani-Pekka Jokinen
Date: 2020-11-25 00:00:00

In this paper, the technical requirements to perform a cost-benefit analysis of a Demand Responsive Transport (DRT) service with the traffic simulation software MATSim are elaborated in order to achieve the long-term goal of assessing the introduction of a DRT service in G\"ottingen and the surrounding area. The aim was to determine if the software is suitable for a cost-benefit analysis while providing a user manual for building a basic simulation that can be extended with public transport and DRT. The main result is that the software is suitable for a cost-benefit analysis of a DRT service. In particular, the most important internal and external costs, such as usage costs of the various modes of transport and emissions, can be integrated into the simulation scenarios. Thus, the scenarios presented in this paper can be extended by data from a mobility study of G\"ottingen and its surroundings in order to achieve the long-term goal. This paper is aimed at transport economists and researchers who are not familiar with MATSim, to provide them with a guide for the first steps in working with a traffic simulation software.
A coplanar circumbinary protoplanetary disk in the TWA 3 triple M dwarf system
Authors: Ian Czekala, Álvaro Ribas, Nicolás Cuello, Eugene Chiang, Enrique Macías, Gaspard Duchêne, Sean M. Andrews, Catherine C. Espaillat
Date: 2021-02-23 00:00:00

We present sensitive ALMA observations of TWA 3, a nearby, young ($\sim$10 Myr) hierarchical system composed of three pre-main sequence M3--M4.5 stars. For the first time, we detected ${}^{12}$CO and ${}^{13}$CO $J$=2-1 emission from the circumbinary protoplanetary disk around TWA 3A. We jointly fit the protoplanetary disk velocity field, stellar astrometric positions, and stellar radial velocities to infer the architecture of the system. The Aa and Ab stars ($0.29\pm0.01\,M_\odot$ and $0.24\pm0.01\,M_\odot$, respectively) comprising the tight ($P=35$ days) eccentric ($e=0.63\pm0.01$) spectroscopic binary are coplanar with their circumbinary disk (misalignment $< 6^{\circ}$ with 68% confidence), similar to other short-period binary systems. From models of the spectral energy distribution, we found the inner radius of the circumbinary disk ($r_\mathrm{inner} = 0.50 - 0.75$ au) to be consistent with theoretical predictions of dynamical truncation $r_\mathrm{cav}/a_\mathrm{inner} \approx 3$. The outer orbit of the tertiary star B ($0.40\pm0.28\,M_\odot$, $a\sim65 \pm 18$ au, $e=0.3\pm0.2$) is not as well constrained as the inner orbit, however, orbits coplanar with the A system are still preferred (misalignment $ < 20^{\circ}$). To better understand the influence of the B orbit on the TWA 3A circumbinary disk, we performed SPH simulations of the system and found that the outer edge of the gas disk ($r_\mathrm{outer}=8.5\pm0.2$ au) is most consistent with truncation from a coplanar, circular or moderately eccentric orbit, supporting the preference from the joint orbital fit.
Was there a COVID-19 harvesting effect in Northern Italy?
Authors: Augusto Cerqua, Roberta Di Stefano, Marco Letta, Sara Miccoli
Date: 2021-03-02 00:00:00

We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, associated with the COVID-19 pandemic by looking at the excess mortality trends of an area that registered one of the highest death tolls in the world during the first wave, Northern Italy. We do not find any evidence of a sizable COVID-19 harvesting effect, neither in the summer months after the slowdown of the first wave nor at the beginning of the second wave. According to our estimates, only a minor share of the total excess deaths detected in Northern Italian municipalities over the entire period under scrutiny (February - November 2020) can be attributed to an anticipatory role of COVID-19. A slightly higher share is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect can only account for less than 20% of excess deaths. Furthermore, the lower mortality rates observed in these areas at the beginning of the second wave may be due to several factors other than a harvesting effect, including behavioral change and some degree of temporary herd immunity. The very limited presence of short-run mortality displacement restates the case for containment policies aimed at minimizing the health impacts of the pandemic.