pwc-archive/papers-with-abstracts huggingface dataset:
Mechanistic Framework of Global Value Chains
Authors: Sourish Dutta
Date: 2021-01-09 00:00:00

Indeed, the global production (as a system of creating values) is eventually forming like a gigantic and complex network/web of value chains that explains the transitional structures of global trade and development of the global economy. It's truly a new wave of globalisation, and we term it as the global value chains (GVCs), creating the nexus among firms, workers and consumers around the globe. The emergence of this new scenario asks: how an economy's firms, producers and workers connect in the global economy. And how are they capturing the gains out of it in terms of different dimensions of economic development? This GVC approach is very crucial for understanding the organisation of the global industries and firms. It requires the statics and dynamics of diverse players involved in this complex global production network. Its broad notion deals with different global issues (including regional value chains also) from the top down to the bottom up, founding a scope for policy analysis (Gereffi & Fernandez-Stark 2011). But it is true that, as Feenstra (1998) points out, any single computational framework is not sufficient to quantification this whole range of economic activities. We should adopt an integrative framework for accurate projection of this dynamic multidimensional phenomenon.
Locational Marginal Pricing: Towards a Free Market in Power
Authors: Martin Higgins
Date: 2021-03-05 00:00:00

Nothing has done more to empower the free market, enterprise, and meritocracy than the spread of electricity and power to everyone. The power system has been the precursor to the greatest period of innovation in our history and has meant that visionaries with revolutionary ideas can compete with those with capital, political power, and means. Electricity, therefore, has been the great equalising force of the last 150 years, enhancing the productivity of the masses and granting prosperity to whole swathes of our nation. Whilst electricity has been one of the single largest innovations in enhancing the power of free markets, it is somewhat ironic that the way power is sold to consumers is largely unfree. The market is highly regulated, centralised, and is often used for political football by cynical politicians on both sides of the political spectrum. Introducing Locational Marginal Pricing into the UK grid system will increase economic freedom in the consumer markets for power, reduce prices for the poorest in the UK, decrease transmission losses, increase the permeation of low carbon generation in the grid, and incentivise investment in the UK's Northern Powerhouse initiative.
Smart Rewritings of the Basic Equations for Quantitative Non-Linear Inverse Scattering
Authors: Martina T. Bevacqua, Tommaso Isernia
Date: 2020-12-30 00:00:00

Nonlinearity arising from mutual interactions is one of the two main difficulties to be addressed in inverse scattering. In this paper, we review and describe under a common rationale some approaches which have been introduced in literature in order to counteract nonlinearity. In particular, we focus on possible rewritings of the Lippman Schwinger basic equation such to reduce the degree of nonlinearity of inverse scattering problem. In detail, three different rewritings are discussed and compared by emphasizing similarities and the differences, and in the same rewriting spirit, we also summarize and discuss the Virtual Experiments framework. Then, some possible joint exploitations of the above concepts are introduced, discussed and tested against numerical examples.
Learning Local Feature Descriptors for Multiple Object Tracking
Authors: Dmytro Mykheievskyi, Dmytro Borysenko, Viktor Porokhonskyy
Date: 2020-11-20 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking, Person Re-Identification

The present study aims at learning class-agnostic embedding, which is suitable for Multiple Object Tracking (MOT). We demonstrate that the learning of local feature descriptors could provide a sufficient level of generalization. Proposed embedding function exhibits on-par performance with its dedicated person re-identification counterparts in their target domain and outperforms them in others. Through its utilization, our solutions achieve state-of-the-art performance in a number of MOT benchmarks, which includes CVPR'19 Tracking Challenge.
CUPR: Contrastive Unsupervised Learning for Person Re-identification
Authors: Khadija Khaldi, Shishir K Shah
Date: 2021-02-18 00:00:00
Tasks: Clustering, Contrastive Learning, Domain Adaptation, Person Re-Identification

Most of the current person re-identification (Re-ID) algorithms require a large labeled training dataset to
obtain better results. For example, domain adaptation-based approaches rely heavily on limited real-world
data to alleviate the problem of domain shift. However, such assumptions are impractical and rarely hold,
since the data is not freely accessible and requires expensive annotation. To address this problem, we propose
a novel pure unsupervised learning approach using contrastive learning (CUPR). Our framework is a simple
iterative approach that learns strong high-level features from raw pixels using contrastive learning and then
performs clustering to generate pseudo-labels. We demonstrate that CUPR outperforms the unsupervised and
semi-supervised state-of-the-art methods on Market-1501 and DukeMTMC-reID datasets.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
Authors: Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
Date: 2017-12-25 00:00:00
Tasks: Causal Inference, Conformal Prediction, counterfactual, Matrix Completion, Time Series, Time Series Analysis, valid

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution. Open-source software for implementing our conformal inference methods is available.
Debiasing and $t$-tests for synthetic control inference on average causal effects
Authors: Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu
Date: 2018-12-27 00:00:00
Tasks: valid

We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a $K$-fold cross-fitting procedure for bias correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized $t$-statistic, which has an asymptotically pivotal $t$-distribution. Our $t$-test is easy to implement, provably robust against misspecification, and valid with stationary and non-stationary data. It demonstrates an excellent small sample performance in application-based simulations and performs well relative to other methods. We illustrate the usefulness of the $t$-test by revisiting the effect of carbon taxes on emissions.
Parameterised-Response Zero-Intelligence Traders
Authors: Dave Cliff
Date: 2021-03-21 00:00:00

I introduce PRZI (Parameterised-Response Zero Intelligence), a new form of zero-intelligence trader intended for use in simulation studies of the dynamics of continuous double auction markets. Like Gode & Sunder's classic ZIC trader, PRZI generates quote-prices from a random distribution over some specified domain of allowable quote-prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the _strategy_ for that trader. When s=0, a PRZI trader is identical to ZIC, with a uniform PMF; but when |s|=~1 the PRZI trader's PMF becomes maximally skewed to one extreme or the other of the price-range, thereby making its quote-prices more or less urgent, biasing the quote-price distribution toward or away from the trader's limit-price. To explore the co-evolutionary dynamics of populations of PRZI traders that dynamically adapt their strategies, I show results from long-term market experiments in which each trader uses a simple stochastic hill-climber algorithm to repeatedly evaluate alternative s-values and choose the most profitable at any given time. In these experiments the profitability of any particular s-value may be non-stationary because the profitability of one trader's strategy at any one time can depend on the mix of strategies being played by the other traders at that time, which are each themselves continuously adapting. Results from these market experiments demonstrate that the population of traders' strategies can exhibit rich dynamics, with periods of stability lasting over hundreds of thousands of trader interactions interspersed by occasional periods of change. Python source-code for the work reported here has been made publicly available on GitHub.
Review Dry and Non-Contact EEG Electrodes for 2010-2021 Years
Authors: Ildar Rakhmatulin; Yajian Gan
Date: 2021-03-23 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain or some of its individual areas using a special sensor/electrode. This method is used for the treatment and diagnosis of various diseases. The use of wet electrodes in this case does not seem viable, for several well-known reasons. As a result of this, a detailed analysis of modern EEG sensors developed over the past few years is carried out, which will allow researchers to choose this type of sensor more carefully and, as a result, conduct their research more competently. Due to the absence of any standards in the production and testing of dry EEG sensors, the main moment of this manuscript is a detailed description of the necessary steps for testing a dry electrode, which will allow researchers to maximize the potential of the sensor in the various type of research.
Generalized Domain Conditioned Adaptation Network
Authors: Shuang Li, Binhui Xie, Qiuxia Lin, Chi Harold Liu, Gao Huang, Guoren Wang
Date: 2021-03-23 00:00:00
Tasks: Attribute, Domain Adaptation

Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision. Recent advances in DA mainly proceed by aligning the source and target distributions. Despite the significant success, the adaptation performance still degrades accordingly when the source and target domains encounter a large distribution discrepancy. We consider this limitation may attribute to the insufficient exploration of domain-specialized features because most studies merely concentrate on domain-general feature learning in task-specific layers and integrate totally-shared convolutional networks (convnets) to generate common features for both domains. In this paper, we relax the completely-shared convnets assumption adopted by previous DA methods and propose Domain Conditioned Adaptation Network (DCAN), which introduces domain conditioned channel attention module with a multi-path structure to separately excite channel activation for each domain. Such a partially-shared convnets module allows domain-specialized features in low-level to be explored appropriately. Further, given the knowledge transferability varying along with convolutional layers, we develop Generalized Domain Conditioned Adaptation Network (GDCAN) to automatically determine whether domain channel activations should be separately modeled in each attention module. Afterward, the critical domain-specialized knowledge could be adaptively extracted according to the domain statistic gaps. As far as we know, this is the first work to explore the domain-wise convolutional channel activations separately for deep DA networks. Additionally, to effectively match high-level feature distributions across domains, we consider deploying feature adaptation blocks after task-specific layers, which can explicitly mitigate the domain discrepancy.
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Authors: Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens
Date: 2021-03-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Classification, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation, Transfer Learning

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we aim to develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutional models.
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Authors: Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu
Date: 2021-03-23 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning (RL)

Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In particular, we still do not have agreeable ways to measure the difficulty or solvability of a task, given that each has fundamentally different actions, observations, dynamics, rewards, and can be tackled with diverse RL algorithms. In this work, we propose policy information capacity (PIC) -- the mutual information between policy parameters and episodic return -- and policy-optimal information capacity (POIC) -- between policy parameters and episodic optimality -- as two environment-agnostic, algorithm-agnostic quantitative metrics for task difficulty. Evaluating our metrics across toy environments as well as continuous control benchmark tasks from OpenAI Gym and DeepMind Control Suite, we empirically demonstrate that these information-theoretic metrics have higher correlations with normalized task solvability scores than a variety of alternatives. Lastly, we show that these metrics can also be used for fast and compute-efficient optimizations of key design parameters such as reward shaping, policy architectures, and MDP properties for better solvability by RL algorithms without ever running full RL experiments.
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
Authors: Steve Yadlowsky, Taedong Yun, Cory McLean, Alexander D'Amour
Date: 2021-03-23 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: regression, Vocal Bursts Intensity Prediction

Logistic regression remains one of the most widely used tools in applied statistics, machine learning and data science. However, in moderately high-dimensional problems, where the number of features $d$ is a non-negligible fraction of the sample size $n$, the logistic regression maximum likelihood estimator (MLE), and statistical procedures based the large-sample approximation of its distribution, behave poorly. Recently, Sur and Cand\`es (2019) showed that these issues can be corrected by applying a new approximation of the MLE's sampling distribution in this high-dimensional regime. Unfortunately, these corrections are difficult to implement in practice, because they require an estimate of the \emph{signal strength}, which is a function of the underlying parameters $\beta$ of the logistic regression. To address this issue, we propose SLOE, a fast and straightforward approach to estimate the signal strength in logistic regression. The key insight of SLOE is that the Sur and Cand\`es (2019) correction can be reparameterized in terms of the \emph{corrupted signal strength}, which is only a function of the estimated parameters $\widehat \beta$. We propose an estimator for this quantity, prove that it is consistent in the relevant high-dimensional regime, and show that dimensionality correction using SLOE is accurate in finite samples. Compared to the existing ProbeFrontier heuristic, SLOE is conceptually simpler and orders of magnitude faster, making it suitable for routine use. We demonstrate the importance of routine dimensionality correction in the Heart Disease dataset from the UCI repository, and a genomics application using data from the UK Biobank. We provide an open source package for this method, available at \url{https://github.com/google-research/sloe-logistic}.
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
Authors: Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han, Jing Liao, Bing Jiang, Wei Liu
Date: 2021-03-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Decoder, Texture Synthesis

User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (\ie, free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which relies on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip-connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks demonstrate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content.
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Authors: Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
Date: 2021-03-23 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Image Classification, Representation Learning, Saliency Detection, Self-Supervised Learning, Unsupervised Saliency Detection

Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image-e.g. a subject vs. a background-which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly effective "background augmentations", which encourage models to focus on semantically-relevant content by discouraging them from focusing on image backgrounds. Through a systematic investigation, we show that background augmentations lead to substantial improvements in performance across a spectrum of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety of tasks, e.g. $\sim$+1-2% gains on ImageNet, enabling performance on par with the supervised baseline. Further, we find the improvement in limited-labels settings is even larger (up to 4.2%). Background augmentations also improve robustness to a number of distribution shifts, including natural adversarial examples, ImageNet-9, adversarial attacks, ImageNet-Renditions. We also make progress in completely unsupervised saliency detection, in the process of generating saliency masks used for background augmentations.
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Authors: Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, Trevor Darrell
Date: 2021-03-23 00:00:00
Tasks: Data Augmentation, Image Augmentation

While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to data augmentation. Prior work demonstrates that models pretrained on datasets dissimilar to their target data, such as chest X-ray models trained on ImageNet, underperform models trained from scratch. Users that lack the resources to pretrain must use existing models with lower performance. This paper explores Hierarchical PreTraining (HPT), which decreases convergence time and improves accuracy by initializing the pretraining process with an existing pretrained model. Through experimentation on 16 diverse vision datasets, we show HPT converges up to 80x faster, improves accuracy across tasks, and improves the robustness of the self-supervised pretraining process to changes in the image augmentation policy or amount of pretraining data. Taken together, HPT provides a simple framework for obtaining better pretrained representations with less computational resources.