pwc-archive/papers-with-abstracts huggingface dataset:
The housing problem and revealed preference theory: duality and an application
Authors: Ivar Ekeland, Alfred Galichon
Date: 2021-02-04 00:00:00

This paper exhibits a duality between the theory of Revealed Preference of Afriat and the housing allocation problem of Shapley and Scarf. In particular, it is shown that Afriat's theorem can be interpreted as a second welfare theorem in the housing problem. Using this duality, the revealed preference problem is connected to an optimal assignment problem, and a geometrical characterization of the rationalizability of experiment data is given. This allows in turn to give new indices of rationalizability of the data, and to define weaker notions of rationalizability, in the spirit of Afriat's efficiency index.
Dual theory of choice with multivariate risks
Authors: Alfred Galichon, Marc Henry
Date: 2021-02-04 00:00:00
Tasks: Attribute

We propose a multivariate extension of Yaari's dual theory of choice under risk. We show that a decision maker with a preference relation on multidimensional prospects that preserves first order stochastic dominance and satisfies comonotonic independence behaves as if evaluating prospects using a weighted sum of quantiles. Both the notions of quantiles and of comonotonicity are extended to the multivariate framework using optimal transportation maps. Finally, risk averse decision makers are characterized within this framework and their local utility functions are derived. Applications to the measurement of multi-attribute inequality are also discussed.
The VAR at Risk
Authors: Alfred Galichon
Date: 2021-02-04 00:00:00

I show that the structure of the firm is not neutral in respect to regulatory capital budgeted under rules which are based on the Value-at-Risk.
Reconfigurable Intelligent Surface Aided Mobile Edge Computing
Authors: Tong Bai, Cunhua Pan, Chao Han, Lajos Hanzo
Date: 2021-02-04 00:00:00
Tasks: Edge-computing

Given the proliferation of wireless sensors and smart mobile devices, an explosive escalation of the volume of data is anticipated. However, restricted by their limited physical sizes and low manufacturing costs, these wireless devices tend to have limited computational capabilities and battery lives. To overcome this limitation, wireless devices may offload their computational tasks to the nearby computing nodes at the network edge in mobile edge computing (MEC). At the time of writing, the benefits of MEC systems have not been fully exploited, predominately because the computation offloading link is still far from perfect. In this article, we propose to enhance MEC systems by exploiting the emerging technique of reconfigurable intelligent surfaces (RIS), which are capable of `reconfiguring' the wireless propagation environments, hence enhancing the offloading links. The benefits of RISs can be maximized by jointly optimizing both the RISs as well as the communications and computing resource allocations of MEC systems. Unfortunately, this joint optimization imposes new research challenges on the system design. Against this background, this article provides an overview of RIS-assisted MEC systems and highlights their four use cases as well as their design challenges and solutions. Finally, their performance is characterized with the aid of a specific case study, followed by a range of future research ideas.
The Econometrics and Some Properties of Separable Matching Models
Authors: Alfred Galichon, Bernard Salanié
Date: 2021-02-04 00:00:00
Tasks: Econometrics

We present a class of one-to-one matching models with perfectly transferable utility. We discuss identification and inference in these separable models, and we show how their comparative statics are readily analyzed.
Sustainable Border Control Policy in the COVID-19 Pandemic: A Math Modeling Study
Authors: Zhen Zhu, Enzo Weber, Till Strohsal, Duaa Serhan
Date: 2020-08-28 00:00:00
Tasks: Math

Imported COVID-19 cases, if unchecked, can jeopardize the effort of domestic containment. We aim to find out what sustainable border control options for different entities (e.g., countries, states) exist during the reopening phases, given their own choice of domestic control measures and new technologies such as contact tracing. We propose a SUIHR model, which represents an extension to the discrete time SIR models. The model focuses on studying the spreading of virus predominantly by asymptomatic and pre-symptomatic patients. Imported risk and (1-tier) contact tracing are both built into the model. Under plausible parameter assumptions, we seek sustainable border control policies, in combination with sufficient internal measures, which allow entities to confine the virus without the need to revert back to more restrictive life styles or to rely on herd immunity. When the base reproduction number of COVID-19 exceeds 2.5, even 100% effective contact tracing alone is not enough to contain the spreading. For an entity that has completely eliminated the virus domestically, and resumes "normal", very strict pre-departure screening and test and isolation upon arrival combined with effective contact tracing can only delay another outbreak by 6 months. However, if the total net imported cases are non-increasing, and the entity employs a confining domestic control policy, then the total new cases can be contained even without border control.
Active Disturbance Rejection Control Design with Suppression of Sensor Noise Effects in Application to DC-DC Buck Power Converter
Authors: Krzysztof Łakomy, Rafal Madonski, Bin Dai, Jun Yang, Piotr Kicki, Maral Ansari, Shihua Li
Date: 2020-09-07 00:00:00

The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this work, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a DC-DC buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise over-amplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications.
Deep Learning for Short-Term Voltage Stability Assessment of Power Systems
Authors: Meng Zhang, Jiazheng Li, Yang Li, Runnan Xu
Date: 2021-02-04 00:00:00

To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications
Authors: Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng, Jinhong Yuan
Date: 2020-09-03 00:00:00
Tasks: Denoising, Philosophy

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.
Koopman Operator Dynamical Models: Learning, Analysis and Control
Authors: Petar Bevanda, Stefan Sosnowski, Sandra Hirche
Date: 2021-02-04 00:00:00

The Koopman operator allows for handling nonlinear systems through a (globally) linear representation. In general, the operator is infinite-dimensional - necessitating finite approximations - for which there is no overarching framework. Although there are principled ways of learning such finite approximations, they are in many instances overlooked in favor of, often ill-posed and unstructured methods. Also, Koopman operator theory has long-standing connections to known system-theoretic and dynamical system notions that are not universally recognized. Given the former and latter realities, this work aims to bridge the gap between various concepts regarding both theory and tractable realizations. Firstly, we review data-driven representations (both unstructured and structured) for Koopman operator dynamical models, categorizing various existing methodologies and highlighting their differences. Furthermore, we provide concise insight into the paradigm's relation to system-theoretic notions and analyze the prospect of using the paradigm for modeling control systems. Additionally, we outline the current challenges and comment on future perspectives.
AoI Minimization in Status Update Control with Energy Harvesting Sensors
Authors: Mohammad Hatami, Markus Leinonen, Marian Codreanu
Date: 2020-09-09 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control systems. We consider an IoT status update system with multiple users, multiple energy harvesting sensors, and a wireless edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users send requests to the edge node where a cache contains the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send a status update or retrieves the aged measurement from the cache. We aim at finding the best actions of the edge node to minimize the age of information of the served measurements. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: model-based value iteration and model-free Q-learning methods. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors' battery levels only via the status updates. The case under transmission limitations is also addressed. Furthermore, properties of an optimal policy are analytically characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods significantly reduce the average cost compared to several baselines.
Boundary Stabilization and Observation of an Unstable Heat Equation in a General Multi-dimensional Domain
Authors: Hongyinping Feng, Pei-Hua Lang, Jiankang Liu
Date: 2021-02-04 00:00:00

In this paper, we consider the exponential stabilization and observation of an unstable heat equation in a general multi-dimensional domain by combining the finite-dimensional spectral truncation technique and the recently developed dynamics compensation approach. In contrast to the unstable one-dimensional partial differential equation (PDE), such as the transport equation, wave equation and the heat equation, that can be treated by the well-known PDE backstepping method, stabilization of unstable PDE in a general multi-dimensional domain is still a challenging problem. We treat the stabilization and observation problems separately. A dynamical state feedback law is proposed firstly to stabilize the unstable heat equation exponentially and then a state observer is designed via a boundary measurement. Both the stability of the closed-loop system and the well-posedness of the observer are proved. Some of the theoretical results are validated by the numerical simulations.
Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNN
Authors: Kai Cui; Atanas Boev; Elena Alshina; Eckehard Steinbach
Date: 2020-12-08 00:00:00
Tasks: Color Image Denoising, Demosaicking, Denoising, Image Denoising, Image Reconstruction, Image Restoration

Image restoration is a critical component of image processing pipelines and for low-level computer vision tasks. Conventional image restoration approaches are mostly based on hand-crafted image priors. The inter-channel correlation of color images is not fully exploited. Motivated by the special characteristics of the inter-channel correlation (higher correlation for red/green and green/blue channels than for red/blue) in color images and general characteristics (green channel always shows the best image quality among the three color components) of distorted color images, in this paper, a 3-stage convolutional neural network (CNN) structure is proposed for color image restoration tasks. Since the green channel is found to have the best quality among all three channels, in the first stage, the network is designed to reconstruct the green component. Then, with the guidance of the reconstructed green channel from the first stage, the red and blue channels are reconstructed in the second stage with two parallel networks. Finally, the intermediate reconstructions from the previous stages are concatenated and further refined jointly. We demonstrate the capabilities of the proposed 3-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising. In addition, we integrate pixel-shuffle convolution into our scheme to improve the efficiency, and also introduce a quality-blind training strategy to simplify the training process for the compression artifacts reduction task. Extensive experimental results and analyses show that the proposed structure successfully exploits the spatial and inter-channel correlation of color images and outperforms the state-of-the-art image reconstruction approaches.
Progressive Image Super-Resolution via Neural Differential Equation
Authors: Seobin Park, Tae Hyun Kim
Date: 2021-01-22 00:00:00
Tasks: Image Restoration, Image Super-Resolution, Super-Resolution

We propose a new approach for the image super-resolution (SR) task that progressively restores a high-resolution (HR) image from an input low-resolution (LR) image on the basis of a neural ordinary differential equation. In particular, we newly formulate the SR problem as an initial value problem, where the initial value is the input LR image. Unlike conventional progressive SR methods that perform gradual updates using straightforward iterative mechanisms, our SR process is formulated in a concrete manner based on explicit modeling with a much clearer understanding. Our method can be easily implemented using conventional neural networks for image restoration. Moreover, the proposed method can super-resolve an image with arbitrary scale factors on continuous domain, and achieves superior SR performance over state-of-the-art SR methods.
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition
Authors: Liang Sun, Xiang Guan, Yang Yang, Lei Zhang
Date: 2020-10-12 00:00:00
Tasks: Fine-Grained Image Recognition, Fine-Grained Visual Recognition, Object Recognition

Fine-grained visual recognition, which aims to identify subcategories of the same base-level category, is a challenging task because
of its large intra-class variances and small inter-class variances. Human beings can perform object recognition task based on not only the visual appearance but also the knowledge from texts, as texts can point out the discriminative parts or characteristics which
are always the key to distinguishing different subcategories. This is an involuntary transfer from human textual attention to visual attention, suggesting that texts are able to assist fine-grained recognition. In this paper, we propose a Text-Embedded Bilinear (TEB)
model which incorporates texts as extra guidance for fine-grained recognition. Specially, we first conduct a text-embedded network to
embed text feature into the discriminative image feature learning to get a embedded feature. In addition, since the cross-layer part
feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other, we also extract a candidate
feature from the text encoder and embed it into the inter-layer feature of the image encoder to get an embedded candidate feature.
At last we utilize a cross-layer bilinear network to fuse the two embedded features. Comparing with state-of-the-art methods on
the widely used CUB-200-2011 dataset and Oxford Flowers-102 dataset for fine-grained image recognition, the experimental results
demonstrate our TEB model achieves the best performance.
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Authors: Xiruo Shi, Liutong Xu, Pengfei Wang, Yuanyuan Gao, Haifang Jian, Wu Liu
Date: 2020-10-12 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

Learning subtle discriminative features plays a significant role in fine-grained image classification. Existing methods usually extract
the distinguishable parts through the attention module for classification. Although these learned distinguishable parts contain valuable
features that are beneficial for classification, part of irrelevant features are also preserved, which may confuse the model to make a
correct classification, especially for the fine-grained tasks due to their similarities. How to keep the discriminative features while
removing confusable features from the distinguishable parts is an interesting yet changeling task. In this paper, we introduce a novel
classification approach, named Logical-based Feature Extraction Model (LAFE for short) to address this issue. The main advantage
of LAFE lies in the fact that it can explicitly add the significance of discriminative features and subtract the confusable features. Specifically, LAFE utilizes the region attention modules and channel attention modules to extract discriminative features and confusable features respectively. Based on this, two novel loss functions are designed to automatically induce attention over these features for fine-grained image classification. Our approach demonstrates its robustness, efficiency, and state-of-the-art performance on three benchmark datasets.
Category-specific Semantic Coherency Learning for Fine-grained Image Recognition
Authors: Shijie Wang, Zhihui Wang, Haojie Li, Wanli Ouyang
Date: 2020-10-12 00:00:00
Tasks: Attribute, Fine-Grained Image Recognition

Existing deep learning based weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative
regions from the high-level feature (HLF) maps directly. However, as HLF maps are derived based on spatial aggregation of convolution which is basically a pattern matching process that applies fixed filters, it is ineffective to model visual contents of same semantic but varying posture or perspective. We argue that this will cause the selected discriminative regions of same sub-category are not semantically corresponding and thus degrade the WFGIR performance. To address this issue, we propose an end-to-end Category-specific Semantic Coherency Network (CSC-Net) to semantically align the discriminative regions of the same subcategory. Specifically, CSCNet consists of: 1) Local-to-Attribute Projecting Module (LPM), which automatically learns a set of latent attributes via collecting the category-specific semantic details while eliminating the varying spatial distributions from the local regions. 2) Latent Attribute Aligning (LAA), which aligns the latent attributes to specific semantic via graph convolution based on their discriminability, to achieve category-specific semantic coherency; 3) Attribute-to-Local Resuming Module (ARM), which resumes the original Euclidean space of latent attributes and construct latent attribute aligned feature maps by a location-embedding graph unpooling operation. Finally, the new feature maps are used which applies the category-specific semantic coherency implicitly for more accurate discriminative regions localization. Extensive experiments verify that CSC-Net yields the best performance under the same settings with most competitive approaches, on CUB Bird, Stanford-Cars, and FGVC Aircraft datasets.
Active Ranking using Pairwise Comparisons
Authors: Kevin G. Jamieson, Robert D. Nowak
Date: 2011-09-16 00:00:00
Proceeding: NeurIPS 2011 12

This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of $n$ objects can be identified by standard sorting methods using $n log_2 n$ pairwise comparisons. We are interested in natural situations in which relationships among the objects may allow for ranking using far fewer pairwise comparisons. Specifically, we assume that the objects can be embedded into a $d$-dimensional Euclidean space and that the rankings reflect their relative distances from a common reference point in $R^d$. We show that under this assumption the number of possible rankings grows like $n^{2d}$ and demonstrate an algorithm that can identify a randomly selected ranking using just slightly more than $d log n$ adaptively selected pairwise comparisons, on average. If instead the comparisons are chosen at random, then almost all pairwise comparisons must be made in order to identify any ranking. In addition, we propose a robust, error-tolerant algorithm that only requires that the pairwise comparisons are probably correct. Experimental studies with synthetic and real datasets support the conclusions of our theoretical analysis.
Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer
Authors: Rafael Anicet Zanini, Esther Luna Colombini
Date: 2020-05-03 00:00:00
Tasks: Data Augmentation, Electromyography (EMG), Style Transfer

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient’s tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.