pwc-archive/papers-with-abstracts huggingface dataset:
Shaping oscillations via mixed feedback
Authors: W. Che, F. Forni
Date: 2021-03-25 00:00:00

We study the problem of controlling oscillations in closed loop by combining positive and negative feedback in a mixed configuration. We develop a complete design procedure to set the relative strength of the two feedback loops to achieve steady oscillations. The proposed design takes advantage of dominance theory and adopts classical harmonic balance and fast/slow analysis to regulate the frequency of oscillations. The design is illustrated on a simple two-mass system, a setting that reveals the potential of the approach for locomotion, mimicking approaches based on central pattern generators.
Spatial-SIR with Network Structure and Behavior: Lockdown Rules and the Lucas Critique
Authors: Alberto Bisin, Andrea Moro
Date: 2021-03-25 00:00:00

We introduce a model of the diffusion of an epidemic with demographically heterogeneous agents interacting socially on a spatially structured network. Contagion-risk averse agents respond behaviorally to the diffusion of the infections by limiting their social interactions. Schools and workplaces also respond by allowing students and employees to attend and work remotely. The spatial structure induces local herd immunities along socio-demographic dimensions, which significantly affect the dynamics of infections. We study several non-pharmaceutical interventions; e.g., i) lockdown rules, which set thresholds on the spread of the infection for the closing and reopening of economic activities; ii) neighborhood lockdowns, leveraging granular (neighborhood-level) information to improve the effectiveness public health policies; iii) selective lockdowns, which restrict social interactions by location (in the network) and by the demographic characteristics of the agents. Substantiating a "Lucas critique" argument, we assess the cost of naive discretionary policies ignoring agents and firms' behavioral responses.
A perturbed utility route choice model
Authors: Mogens Fosgerau, Mads Paulsen, Thomas Kjær Rasmussen
Date: 2021-03-25 00:00:00
Tasks: model, regression

We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint}. Substitution between routes depends on how much they overlap. {\tr The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network.
IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization
Authors: Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, F. Rafael M. Lima, Liang Yang, Petar Popovski, Zhiguo Ding, Constantinos B. Papadias
Date: 2020-12-07 00:00:00
Tasks: Diversity

A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. To this end, the IRSs are optimized to mitigate transmissions originated at the BS from the interfering polarization. The formulated optimization is transformed into quadratic constrained quadratic sub-problems, which makes it possible to obtain the optimal solution via interior-points methods. We also derive analytically a closed-form expression for the users' ergodic rates by considering large numbers of reflecting elements. This is followed by representative simulation examples and comprehensive discussions. The results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dual-polarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through diversity.
Multi-asset optimal execution and statistical arbitrage strategies under Ornstein-Uhlenbeck dynamics
Authors: Philippe Bergault, Fayçal Drissi, Olivier Guéant
Date: 2021-03-25 00:00:00

In recent years, academics, regulators, and market practitioners have increasingly addressed liquidity issues. Amongst the numerous problems addressed, the optimal execution of large orders is probably the one that has attracted the most research works, mainly in the case of single-asset portfolios. In practice, however, optimal execution problems often involve large portfolios comprising numerous assets, and models should consequently account for risks at the portfolio level. In this paper, we address multi-asset optimal execution in a model where prices have multivariate Ornstein-Uhlenbeck dynamics and where the agent maximizes the expected (exponential) utility of her PnL. We use the tools of stochastic optimal control and simplify the initial multidimensional Hamilton-Jacobi-Bellman equation into a system of ordinary differential equations (ODEs) involving a Matrix Riccati ODE for which classical existence theorems do not apply. By using \textit{a priori} estimates obtained thanks to optimal control tools, we nevertheless prove an existence and uniqueness result for the latter ODE, and then deduce a verification theorem that provides a rigorous solution to the execution problem. Using examples based on data from the foreign exchange and stock markets, we eventually illustrate our results and discuss their implications for both optimal execution and statistical arbitrage.
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning
Authors: Xue Zhang, Wangxin Xiao, Weijia Xiao
Date: 2020-02-15 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Drug Discovery, Network Embedding

Motivation: Accurately predicting essential genes using computational methods can greatly
reduce the effort in finding them via wet experiments at both time and resource scales, and further
accelerate the process of drug discovery. Several computational methods have been proposed for
predicting essential genes in model organisms by integrating multiple biological data sources
either via centrality measures or machine learning based methods. However, the methods aiming
to predict human essential genes are still limited and the performance still need improve. In
addition, most of the machine learning based essential gene prediction methods are lack of skills
to handle the imbalanced learning issue inherent in the essential gene prediction problem, which
might be one factor affecting their performance.
Results: We proposed a deep learning based method, DeepHE, to predict human essential genes
by integrating features derived from sequence data and protein-protein interaction (PPI) network.
A deep learning based network embedding method was utilized to automatically learn features
from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein
sequence for each gene. These two types of features were integrated to train a multilayer neural
network. A cost-sensitive technique was used to address the imbalanced learning problem when
training the deep neural network. The experimental results for predicting human essential genes
showed that our proposed method, DeepHE, can accurately predict human gene essentiality with
an average AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%,
and the accuracy higher than 90%. We also compared DeepHE with several widely used traditional
machine learning models (SVM, Naïve Bayes, Random Forest, Adaboost). The experimental
results showed that DeepHE greatly outperformed the compared machine learning models.
Conclusions: We demonstrated that human essential genes can be accurately predicted by
designing effective machine learning algorithm and integrating representative features captured
from available biological data. The proposed deep learning framework is effective for such task.
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models
Authors: Michael C. Burkhart, David M. Brandman, Brian Franco, Leigh R. Hochberg, Matthew T. Harrison
Date: 2020-05-01 00:00:00
Tasks: Brain Computer Interface, regression, Sequential Bayesian Inference, State Estimation, State Space Models

The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation∣state) is nonlinear. We argue that in many cases, a model for p(state∣observation) proves both easier to learn and more accurate for latent state estimation.

Approximating p(state∣observation) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p(observation∣state) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein–von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions.

When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p(observation∣state) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.
The FRET-based structural dynamics challenge -- community contributions to consistent and open science practices
Authors: Eitan Lerner, Benjamin Ambrose, Anders Barth, Victoria Birkedal, Scott C. Blanchard, Richard Borner, Thorben Cordes, Timothy D. Craggs, Taekjip Ha, Gilad Haran, Thorsten Hugel, Antonino Ingargiola, Achillefs Kapanidis, Don C. Lamb, Ted Laurence, Nam ki Lee, Edward A. Lemke, Emmanuel Margeat, Jens Michaelis, Xavier Michalet, Daniel Nettels, Thomas-Otavio Peulen, Benjamin Schuler, Claus A. M. Seidel, Hamid So-leimaninejad, Shimon Weiss
Date: 2020-06-04 00:00:00
Tasks: Position

Single-molecule F\"{o}rster resonance energy transfer (smFRET) has become a mainstream technique for probing biomolecular structural dynamics. The rapid and wide adoption of the technique by an ever-increasing number of groups has generated many improvements and variations in the technique itself, in methods for sample preparation and characterization, in analysis of the data from such experiments, and in analysis codes and algorithms. Recently, several labs that employ smFRET have joined forces to try to bring the smFRET community together in adopting a consensus on how to perform experiments and analyze results for achieving quantitative structural information. These recent efforts include multi-lab blind-tests to assess the accuracy and precision of smFRET between different labs using different procedures, the formal assembly of the FRET community and development of smFRET procedures to be considered for entries in the wwPDB. Here we delve into the different approaches and viewpoints in the field. This position paper describes the current "state-of-the field", points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and a list of resources that are openly available. To make further progress, we strongly encourage 'open science' practices. We hope that this position paper will provide a roadmap for newcomers to the field, as well as a reference for seasoned practitioners.
Semi-Supervised Regression with Co-Training
Authors: Zhi-Hua Zhou, Ming Li
Date: 2005-06-01 00:00:00
Tasks: BIG-bench Machine Learning, regression

In many practical machine learning and data min-ing  applications,  unlabeled  training  examples  arereadily available but labeled ones are fairly expen-sive  to  obtain.   Therefore,  semi-supervised  learn-ing  algorithms  such  asco-traininghave  attractedmuch attention. Previous research mainly focuseson semi-supervised classification.  In this paper, aco-training style semi-supervised regression algo-rithm,  i.e.COREG,  is  proposed.   This  algorithmuses twok-nearest neighbor regressors with differ-ent distance metrics, each of which labels the unla-beled data for the other regressor where the label-ing confidence is estimated through consulting theinfluence of the labeling of unlabeled examples onthe labeled ones.   Experiments show thatCOREGcan  effectively  exploit  unlabeled  data  to  improveregression estimates.
Marine Snow Removal Benchmarking Dataset
Authors: Reina Kaneko, Yuya Sato, Takumi Ueda, Hiroshi Higashi, Yuichi Tanaka
Date: 2021-03-26 00:00:00
Tasks: Benchmarking, Sand, Snow Removal

This paper introduces a new benchmarking dataset for marine snow removal of underwater images. Marine snow is one of the main degradation sources of underwater images that are caused by small particles, e.g., organic matter and sand, between the underwater scene and photosensors. We mathematically model two typical types of marine snow from the observations of real underwater images. The modeled artifacts are synthesized with underwater images to construct large-scale pairs of ground truth and degraded images to calculate objective qualities for marine snow removal and to train a deep neural network. We propose two marine snow removal tasks using the dataset and show the first benchmarking results of marine snow removal. The Marine Snow Removal Benchmarking Dataset is publicly available online.
Rethinking Graph Neural Architecture Search from Message-passing
Authors: Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, Qingming Huang
Date: 2021-03-26 00:00:00
Proceeding: CVPR 2021 1
Tasks: feature selection, Neural Architecture Search

Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space. The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations (feature filtering and neighbor aggregation) from message-passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors' statistics. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth. The searched network achieves remarkable improvement over state-of-the-art manual designed and search-based GNNs on five large-scale datasets at three classical graph tasks. Codes can be found at https://github.com/phython96/GNAS-MP.
DDR-Net: Learning Multi-Stage Multi-View Stereo With Dynamic Depth Range
Authors: Puyuan Yi, Shengkun Tang, Jian Yao
Date: 2021-03-26 00:00:00

To obtain high-resolution depth maps, some previous learning-based multi-view stereo methods build a cost volume pyramid in a coarse-to-fine manner. These approaches leverage fixed depth range hypotheses to construct cascaded plane sweep volumes. However, it is inappropriate to set identical range hypotheses for each pixel since the uncertainties of previous per-pixel depth predictions are spatially varying. Distinct from these approaches, we propose a Dynamic Depth Range Network (DDR-Net) to determine the depth range hypotheses dynamically by applying a range estimation module (REM) to learn the uncertainties of range hypotheses in the former stages. Specifically, in our DDR-Net, we first build an initial depth map at the coarsest resolution of an image across the entire depth range. Then the range estimation module (REM) leverages the probability distribution information of the initial depth to estimate the depth range hypotheses dynamically for the following stages. Moreover, we develop a novel loss strategy, which utilizes learned dynamic depth ranges to generate refined depth maps, to keep the ground truth value of each pixel covered in the range hypotheses of the next stage. Extensive experimental results show that our method achieves superior performance over other state-of-the-art methods on the DTU benchmark and obtains comparable results on the Tanks and Temples benchmark. The code is available at https://github.com/Tangshengku/DDR-Net.
Planar Surface Reconstruction from Sparse Views
Authors: Linyi Jin, Shengyi Qian, Andrew Owens, David F. Fouhey
Date: 2021-03-26 00:00:00
Proceeding: ICCV 2021 10
Tasks: Surface Reconstruction

The paper studies planar surface reconstruction of indoor scenes from two views with unknown camera poses. While prior approaches have successfully created object-centric reconstructions of many scenes, they fail to exploit other structures, such as planes, which are typically the dominant components of indoor scenes. In this paper, we reconstruct planar surfaces from multiple views, while jointly estimating camera pose. Our experiments demonstrate that our method is able to advance the state of the art of reconstruction from sparse views, on challenging scenes from Matterport3D. Project site: https://jinlinyi.github.io/SparsePlanes/
Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages
Authors: Nay San, Martijn Bartelds, Mitchell Browne, Lily Clifford, Fiona Gibson, John Mansfield, David Nash, Jane Simpson, Myfany Turpin, Maria Vollmer, Sasha Wilmoth, Dan Jurafsky
Date: 2021-03-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient data for pre-training such models, or are predominantly oral vernaculars without a standardised writing system, precluding fine-tuning. Query-by-example spoken term detection (QbE-STD) offers an alternative for iteratively indexing untranscribed speech corpora by locating spoken query terms. Using data from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we show that QbE-STD can be improved by leveraging representations developed for ASR (wav2vec 2.0: the English monolingual model and XLSR53 multilingual model). Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD. Nevertheless, we find that wav2vec 2.0 representations (either English or XLSR53) offer large improvements (56-86% relative) over state-of-the-art approaches on our endangered language datasets.
An efficient way for segmentation of Bangla characters in printed document using curved scanning
Authors: Ahnaf Farhan Rownak; Md. Fazle Rabby; Sabir Ismail; Md. Saiful Islam
Date: 2016-05-13 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Segmentation, Text Segmentation

The preeminent reason for poor output in Optical Character Recognition (OCR) for Bangla text is introduced by segmentation related error. Different shape of characters, connected characters, modifiers in top and bottom, overlapped region between consecutive characters are the main obstacle for effective segmentation for Bangla printed text. In this paper an efficient strategy is introduced to segment characters consisting overlapped region with other characters. The proposed strategy of our research have achieved 99.8% accuracy rate in line segmentation, 99.5% accuracy in word segmentation and 99% accuracy for character segmentation. The error introduced when two consecutive characters have multiple touching points.
Baking Neural Radiance Fields for Real-Time View Synthesis
Authors: Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, Paul Debevec
Date: 2021-03-26 00:00:00
Proceeding: ICCV 2021 10
Tasks: GPU, NeRF

Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints. However, NeRF's computational requirements are prohibitive for real-time applications: rendering views from a trained NeRF requires querying a multilayer perceptron (MLP) hundreds of times per ray. We present a method to train a NeRF, then precompute and store (i.e. "bake") it as a novel representation called a Sparse Neural Radiance Grid (SNeRG) that enables real-time rendering on commodity hardware. To achieve this, we introduce 1) a reformulation of NeRF's architecture, and 2) a sparse voxel grid representation with learned feature vectors. The resulting scene representation retains NeRF's ability to render fine geometric details and view-dependent appearance, is compact (averaging less than 90 MB per scene), and can be rendered in real-time (higher than 30 frames per second on a laptop GPU). Actual screen captures are shown in our video.