pwc-archive/papers-with-abstracts huggingface dataset:
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy
Authors: Thomas Weng, Sujay Bajracharya, YuFei Wang, Khush Agrawal, David Held
Date: 2021-11-10 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

We address the problem of goal-directed cloth manipulation, a challenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for motion estimation in video, can also provide an effective representation for corresponding cloth poses across observation and goal images. We introduce FabricFlowNet (FFN), a cloth manipulation policy that leverages flow as both an input and as an action representation to improve performance. FabricFlowNet also elegantly switches between bimanual and single-arm actions based on the desired goal. We show that FabricFlowNet significantly outperforms state-of-the-art model-free and model-based cloth manipulation policies that take image input. We also present real-world experiments on a bimanual system, demonstrating effective sim-to-real transfer. Finally, we show that our method generalizes when trained on a single square cloth to other cloth shapes, such as T-shirts and rectangular cloths. Video and other supplementary materials are available at: https://sites.google.com/view/fabricflownet.
The Impact of Changes in Resolution on the Persistent Homology of Images
Authors: Teresa Heiss, Sarah Tymochko, Brittany Story, Adélie Garin, Hoa Bui, Bea Bleile, Vanessa Robins
Date: 2021-11-10 00:00:00
Tasks: Topological Data Analysis

Digital images enable quantitative analysis of material properties at micro and macro length scales, but choosing an appropriate resolution when acquiring the image is challenging. A high resolution means longer image acquisition and larger data requirements for a given sample, but if the resolution is too low, significant information may be lost. This paper studies the impact of changes in resolution on persistent homology, a tool from topological data analysis that provides a signature of structure in an image across all length scales. Given prior information about a function, the geometry of an object, or its density distribution at a given resolution, we provide methods to select the coarsest resolution yielding results within an acceptable tolerance. We present numerical case studies for an illustrative synthetic example and samples from porous materials where the theoretical bounds are unknown.
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets
Authors: Samyak Prajapati, Japman Singh Monga, Shaanya Singh, Amrit Raj, Yuvraj Singh Champawat, Chandra Prakash
Date: 2021-11-10 00:00:00
Tasks: Image Augmentation, Image Segmentation, Semantic Segmentation, Super-Resolution

Since the onset of the COVID-19 pandemic in 2020, millions of people have succumbed to this deadly virus. Many attempts have been made to devise an automated method of testing that could detect the virus. Various researchers around the globe have proposed deep learning based methodologies to detect the COVID-19 using Chest X-Rays. However, questions have been raised on the presence of bias in the publicly available Chest X-Ray datasets which have been used by the majority of the researchers. In this paper, we propose a 2 staged methodology to address this topical issue. Two experiments have been conducted as a part of stage 1 of the methodology to exhibit the presence of bias in the datasets. Subsequently, an image segmentation, super-resolution and CNN based pipeline along with different image augmentation techniques have been proposed in stage 2 of the methodology to reduce the effect of bias. InceptionResNetV2 trained on Chest X-Ray images that were augmented with Histogram Equalization followed by Gamma Correction when passed through the pipeline proposed in stage 2, yielded a top accuracy of 90.47% for 3-class (Normal, Pneumonia, and COVID-19) classification task.
Theoretical and Empirical Analysis of a Fast Algorithm for Extracting Polygons from Signed Distance Bounds
Authors: Nenad Markuš, Mirko Sužnjević
Date: 2021-11-10 00:00:00

Recently there has been renewed interest in signed distance bound representations due to their unique properties for 3D shape modelling. This is especially the case for deep learning-based bounds. However, it is beneficial to work with polygons in most computer-graphics applications. Thus, in this paper we introduce and investigate an asymptotically fast method for transforming signed distance bounds into polygon meshes. This is achieved by combining the principles of sphere tracing (or ray marching) with traditional polygonization techniques, such as Marching Cubes. We provide theoretical and experimental evidence that this approach is of the $O(N^2\log N)$ computational complexity for a polygonization grid with $N^3$ cells. The algorithm is tested on both a set of primitive shapes as well as signed distance bounds generated from point clouds by machine learning (and represented as neural networks). Given its speed, implementation simplicity and portability, we argue that it could prove useful during the modelling stage as well as in shape compression for storage. The code is available here: https://github.com/nenadmarkus/gridhopping
Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section
Authors: Huaqian Wu, Nicolas Souedet, Zhenzhen You, Caroline Jan, Cédric Clouchoux, Thierry Delzescaux
Date: 2021-11-10 00:00:00
Tasks: Deep Learning, Instance Segmentation, Semantic Segmentation

Cell individualization has a vital role in digital pathology image analysis. Deep Learning is considered as an efficient tool for instance segmentation tasks, including cell individualization. However, the precision of the Deep Learning model relies on massive unbiased dataset and manual pixel-level annotations, which is labor intensive. Moreover, most applications of Deep Learning have been developed for processing oncological data. To overcome these challenges, i) we established a pipeline to synthesize pixel-level labels with only point annotations provided; ii) we tested an ensemble Deep Learning algorithm to perform cell individualization on neurological data. Results suggest that the proposed method successfully segments neuronal cells in both object-level and pixel-level, with an average detection accuracy of 0.93.
Structure from Silence: Learning Scene Structure from Ambient Sound
Authors: Ziyang Chen, Xixi Hu, Andrew Owens
Date: 2021-11-10 00:00:00

From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene. We ask whether these ambient sounds convey information about 3D scene structure and, if so, whether they provide a useful learning signal for multimodal models. To study this, we collect a dataset of paired audio and RGB-D recordings from a variety of quiet indoor scenes. We then train models that estimate the distance to nearby walls, given only audio as input. We also use these recordings to learn multimodal representations through self-supervision, by training a network to associate images with their corresponding sounds. These results suggest that ambient sound conveys a surprising amount of information about scene structure, and that it is a useful signal for learning multimodal features.
Advances in Neural Rendering
Authors: Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhoefer, Vladislav Golyanik
Date: 2021-11-10 00:00:00
Tasks: Inverse Rendering, Neural Rendering

Synthesizing photo-realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input. Collectively, these inputs define the actual scene and what is rendered, and are referred to as the scene representation (where a scene consists of one or more objects). Example scene representations are triangle meshes with accompanied textures (e.g., created by an artist), point clouds (e.g., from a depth sensor), volumetric grids (e.g., from a CT scan), or implicit surface functions (e.g., truncated signed distance fields). The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering. Neural rendering is closely related, and combines ideas from classical computer graphics and machine learning to create algorithms for synthesizing images from real-world observations. Neural rendering is a leap forward towards the goal of synthesizing photo-realistic image and video content. In recent years, we have seen immense progress in this field through hundreds of publications that show different ways to inject learnable components into the rendering pipeline. This state-of-the-art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D-consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene. In addition to methods that handle static scenes, we cover neural scene representations for modeling non-rigidly deforming objects...
Geometric analysis of differential-algebraic equations via linear control theory
Authors: Yahao Chen, Witold Respondek
Date: 2020-02-03 00:00:00

We consider linear differential-algebraic equations DAEs and the Kronecker canonical form KCF of the corresponding matrix pencils. We also consider linear control systems and their Morse canonical form MCF. For a linear DAE, a procedure named explicitation is proposed, which attaches to any linear DAE a linear control system defined up to a coordinates change, a feedback transformation and an output injection. Then we compare subspaces associated to a DAE in a geometric way with those associated (also in a geometric way) to a control system, namely, we compare the Wong sequences of DAEs and invariant subspaces of control systems. We prove that the KCF of linear DAEs and the MCF of control systems have a perfect correspondence and that their invariants are related. In this way, we connect the geometric analysis of linear DAEs with the classical geometric linear control theory. Finally, we propose a concept named internal equivalence for DAEs and discuss its relation with internal regularity, i.e., the existence and uniqueness of solutions.
OSSEM: one-shot speaker adaptive speech enhancement using meta learning
Authors: Cheng Yu, Szu-Wei Fu, Tsun-An Hsieh, Yu Tsao, Mirco Ravanelli
Date: 2021-11-10 00:00:00
Tasks: Meta-Learning, Speech Enhancement

Although deep learning (DL) has achieved notable progress in speech enhancement (SE), further research is still required for a DL-based SE system to adapt effectively and efficiently to particular speakers. In this study, we propose a novel meta-learning-based speaker-adaptive SE approach (called OSSEM) that aims to achieve SE model adaptation in a one-shot manner. OSSEM consists of a modified transformer SE network and a speaker-specific masking (SSM) network. In practice, the SSM network takes an enrolled speaker embedding extracted using ECAPA-TDNN to adjust the input noisy feature through masking. To evaluate OSSEM, we designed a modified Voice Bank-DEMAND dataset, in which one utterance from the testing set was used for model adaptation, and the remaining utterances were used for testing the performance. Moreover, we set restrictions allowing the enhancement process to be conducted in real time, and thus designed OSSEM to be a causal SE system. Experimental results first show that OSSEM can effectively adapt a pretrained SE model to a particular speaker with only one utterance, thus yielding improved SE results. Meanwhile, OSSEM exhibits a competitive performance compared to state-of-the-art causal SE systems.
Maximum-distance Race Strategies for a Fully Electric Endurance Race Car
Authors: Jorn van Kampen, Thomas Herrmann, Mauro Salazar
Date: 2021-11-10 00:00:00

This paper presents a bi-level optimization framework to compute the maximum-distance stint and charging strategies for a fully electric endurance race car. Thereby, the lower level computes the minimum-stint-time Powertrain Operation (PO) for a given battery energy budget and stint length, whilst the upper level leverages that information to jointly optimize the stint length, charge time and number of pit stops, in order to maximize the driven distance in the course of a fixed-time endurance race. Specifically, we first extend a convex lap time optimization framework to capture multiple laps and force-based electric motor models, and use it to create a map linking the charge time and stint length to the achievable stint time. Second, we leverage the map to frame the maximum-race-distance problem as a mixed-integer second order conic program that can be efficiently solved to the global optimum with off-the-shelf optimization algorithms. Finally, we showcase our framework on a 6 h race around the Zandvoort circuit. Our results show that a flat-out strategy can be extremely detrimental, and that, compared to when the stints are optimized for a fixed number of pit stops, jointly optimizing the stints and number of pit stops can increase the driven distance of several laps.
Can phase change materials in building insulation improve self-consumption of residential rooftop solar? An Australian case study
Authors: Zahra Rahimpour, Gregor Verbic, Archie C. Chapman
Date: 2021-11-10 00:00:00
Tasks: energy management, Management

This work investigates the extent to which phase change material (PCM) in the building's envelope can be used as an alternative to battery storage systems to increase self-consumption of rooftop solar photovoltaic (PV) generation. In particular, we explore the electricity cost-savings and increase in PV self-consumption that can be achieved by using PCMs and the operation of the heating, ventilation, and air conditioning (HVAC) system optimised by a home energy management system (HEMS). In more detail, we consider a HEMS with an HVAC system, rooftop PV, and a PCM layer integrated into the building envelope. The objective of the HEMS optimisation is to minimise electricity costs while maximising PV self-consumption and maintaining the indoor building temperature in a preferred comfort range. Solving this problem is challenging due to PCM's nonlinear characteristics, and using methods that can deal with the resulting non-convexity of the optimisation problem, like dynamic programming is computationally expensive. Therefore, we use multi-timescale approximate dynamic programming (MADP) that we developed in our earlier work to explore a number of Australian PCM scenarios. Specifically, we analyse a large number of residential buildings across five Australian capital cities. We find that using PCM can reduce annual electricity costs by between 10.6% in Brisbane and 19% in Adelaide. However, somewhat surprisingly, using PCM reduces PV self-consumption by between 1.5% in Brisbane and 2.7% in Perth.
System Level Synthesis-based Robust Model Predictive Control through Convex Inner Approximation
Authors: Shaoru Chen, Nikolai Matni, Manfred Morari, Victor M. Preciado
Date: 2021-11-10 00:00:00
Tasks: Model Predictive Control

We propose a robust model predictive control (MPC) method for discrete-time linear time-invariant systems with norm-bounded additive disturbances and model uncertainty. In our method, at each time step we solve a finite time robust optimal control problem (OCP) which jointly searches over robust linear state feedback controllers and bounds the deviation of the system states from the nominal predicted trajectory. By leveraging the System Level Synthesis (SLS) framework, the proposed robust OCP is formulated as a convex quadratic program in the space of closed-loop system responses. When an adaptive horizon strategy is used, we prove the recursive feasibility of the proposed MPC controller and input-to-state stability of the origin for the closed-loop system. We demonstrate through numerical examples that the proposed method considerably reduces conservatism when compared with existing SLS-based and tube-based robust MPC methods, while also enjoying low computational complexity.
Inequitable Access to EV Charging Infrastructure
Authors: Hafiz Anwar Ullah Khan, Sara Price, Charalampos Avraam, Yury Dvorkin
Date: 2021-11-09 00:00:00

Access to and affordability of electric vehicle (EV) charging infrastructure are the two prominent barriers for EV adoption. While major efforts are underway in the United States to roll-out public EV charging infrastructure, persistent social disparities in EV adoption call for interventions. In this paper, we analyze the existing EV charging infrastructure across New York City (NYC) to identify such socio-demographic and transportation features that correlate with the current distribution of EV charging stations. Our results demonstrate that population density is not correlated with the density of EV chargers, hindering New York's EV adoption and decarbonization goals. On the contrary, the distribution of EV charging stations is heavily skewed against low-income, Black-identifying, and disinvested neighborhoods in NYC, however, positively correlated to presence of highways in a zip code. The results underscore the need for policy frameworks that incorporate equity and justice in the roll-out of EV charging infrastructure.
Fitting ellipses to noisy measurements
Authors: Sebastian Dingler
Date: 2021-11-09 00:00:00

This work deals with fitting of ellipses to noisy measurements. The literature knows many different approaches for this. The main representatives are presented and discussed in this paper. Furthermore, the case is considered when outliers are present in the measurement data. Robust methods which are less sensitive to outliers are suitable for this case. All discussed methods are compared by a simulation. The code for the simulation is available for free use on github.com/sebdi/ellipse-fitting.
bootUR: An R Package for Bootstrap Unit Root Tests
Authors: Stephan Smeekes, Ines Wilms
Date: 2020-07-23 00:00:00
Tasks: Time Series, Time Series Analysis

Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in the R package bootUR.The package's backbone is the popular augmented Dickey-Fuller test paired with a union of rejections principle, which can be performed directly on single time series or multiple (including panel) time series. Accurate inference is ensured through the use of bootstrap methods. The package addresses the needs of both novice users, by providing user-friendly and easy-to-implement functions with sensible default options, as well as expert users, by giving full user-control to adjust the tests to one's desired settings. Our parallelized C++ implementation ensures that all unit root tests are scalable to datasets containing many time series.
The Local Economic Impact of Mineral Mining in Africa: Evidence from Four Decades of Satellite Imagery
Authors: Sandro Provenzano, Hannah Bull
Date: 2021-11-10 00:00:00

Using state-of-the-art techniques in computer vision, we analyze one million satellite images covering 12% of the African continent between 1984 and 2019 to track local development around 1,658 mineral deposits. We use stacked event studies and difference-in-difference models to estimate the impact of mine openings and closings. The magnitude of the effect of mine openings is considerable - after 15 years, urban areas within 20km of an opening mine almost double in size. We find strong evidence of a political resource curse at the local level. Although mining boosts the local economy in democratic countries, these gains are meager in autocracies and come at the expense of tripling the likelihood of conflict relative to prior the onset of mining. Furthermore, our results suggest that the growth acceleration in mining areas is only temporary and diminishes with the closure of the mine.
Going... going... wrong: a test of the level-k (and cognitive hierarchy) models of bidding behaviour
Authors: Itzhak Rasooly
Date: 2021-11-10 00:00:00

In this paper, we design and implement an experiment aimed at testing the level-k model of auctions. We begin by asking which (simple) environments can best disentangle the level-k model from its leading rival, Bayes-Nash equilibrium. We find two environments that are particularly suited to this purpose: an all-pay auction with uniformly distributed values, and a first-price auction with the possibility of cancelled bids. We then implement both of these environments in a virtual laboratory in order to see which theory can best explain observed bidding behaviour. We find that, when plausibly calibrated, the level-k model substantially under-predicts the observed bids and is clearly out-performed by equilibrium. Moreover, attempting to fit the level-k model to the observed data results in implausibly high estimated levels, which in turn bear no relation to the levels inferred from a game known to trigger level-k reasoning. Finally, subjects almost never appeal to iterated reasoning when asked to explain how they bid. Overall, these findings suggest that, despite its notable success in predicting behaviour in other strategic settings, the level-k model (and its close cousin cognitive hierarchy) cannot explain behaviour in auctions.
Flood Disasters and Health Among the Urban Poor
Authors: Michelle Escobar Carias, David Johnston, Rachel Knott, Rohan Sweeney
Date: 2021-11-09 00:00:00
Tasks: Nutrition

Billions of people live in urban poverty, with many forced to reside in disaster-prone areas. Research suggests that such disasters harm child nutrition and increase adult morbidity. However, little is known about impacts on mental health, particularly of people living in slums. In this paper we estimate the effects of flood disasters on the mental and physical health of poor adults and children in urban Indonesia. Our data come from the Indonesia Family Life Survey and new surveys of informal settlement residents. We find that urban poor populations experience increases in acute morbidities and depressive symptoms following floods, that the negative mental health effects last longer, and that the urban wealthy show no health effects from flood exposure. Further analysis suggests that worse economic outcomes may be partly responsible. Overall, the results provide a more nuanced understanding of the morbidities experienced by populations most vulnerable to increased disaster occurrence.