pwc-archive/papers-with-abstracts huggingface dataset:
TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture
Authors: Shiyu Su, Qiaochu Zhang, Juzheng Liu, Mohsen Hassanpourghadi, Rezwan Rasul, Mike Shuo-Wei Chen
Date: 2021-12-15 00:00:00
Tasks: Quantization

A digital finite impulse response (FIR) filter design is fully synthesizable, thanks to the mature CAD support of digital circuitry. On the contrary, analog mixed-signal (AMS) filter design is mostly a manual process, including architecture selection, schematic design, and layout. This work presents a systematic design methodology to automate AMS FIR filter design using a time approximation architecture without any tunable passive component, such as switched capacitor or resistor. It not only enhances the flexibility of the filter but also facilitates design automation with reduced analog complexity. The proposed design flow features a hybrid approximation scheme that automatically optimize the filter's impulse response in light of time quantization effects, which shows significant performance improvement with minimum designer's efforts in the loop. Additionally, a layout-aware regression model based on an artificial neural network (ANN), in combination with gradient-based search algorithm, is used to automate and expedite the filter design. With the proposed framework, we demonstrate rapid synthesis of AMS FIR filters in 65nm process from specification to layout.
Learning Rigidity-based Flocking Control with Gaussian Processes
Authors: Manuela Gamonal, Thomas Beckers, George J. Pappas, Leonardo J. Colombo
Date: 2021-12-14 00:00:00
Tasks: Gaussian Processes

Flocking control of multi-agents system is challenging for agents with partially unknown dynamics. This paper proposes an online learning-based controller to stabilize flocking motion of double-integrator agents with additional unknown nonlinear dynamics by using Gaussian processes (GP). Agents interaction is described by a time-invariant infinitesimally minimally rigid undirected graph. We provide a decentralized control law that exponentially stabilizes the motion of the agents and captures Reynolds boids motion for swarms by using GPs as an online learning-based oracle for the prediction of the unknown dynamics. In particular the presented approach guarantees a probabilistic bounded tracking error with high probability.
Machine-Learning the Classification of Spacetimes
Authors: Yang-Hui He, Juan Manuel Pérez Ipiña
Date: 2022-01-05 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Data Visualization, Dimensionality Reduction

On the long-established classification problems in general relativity we take a novel perspective by adopting fruitful techniques from machine learning and modern data-science. In particular, we model Petrov's classification of spacetimes, and show that a feed-forward neural network can achieve high degree of success. We also show how data visualization techniques with dimensionality reduction can help analyze the underlying patterns in the structure of the different types of spacetimes.
What makes a reaction network "chemical"?
Authors: Stefan Müller, Christoph Flamm, Peter F. Stadler
Date: 2022-01-05 00:00:00

Reaction networks (RNs) comprise a set $X$ of species and a set $\mathscr{R}$ of reactions $Y\to Y'$, each converting a multiset of educts $Y\subseteq X$ into a multiset $Y'\subseteq X$ of products. RNs are equivalent to directed hypergraphs. However, not all RNs necessarily admit a chemical interpretation. Instead, they might contradict fundamental principles of physics such as the conservation of energy and mass or the reversibility of chemical reactions. The consequences of these necessary conditions for the stoichiometric matrix $\mathbf{S} \in \mathbb{R}^{X\times\mathscr{R}}$ have been discussed extensively in the literature. Here, we provide sufficient conditions for $\mathbf{S}$ that guarantee the interpretation of RNs in terms of balanced sum formulas and structural formulas, respectively. Chemically plausible RNs allow neither a perpetuum mobile, i.e., a "futile cycle" of reactions with non-vanishing energy production, nor the creation or annihilation of mass. Such RNs are said to be thermodynamically sound and conservative. For finite RNs, both conditions can be expressed equivalently as properties of $\mathbf{S}$. The first condition is vacuous for reversible networks, but it excludes irreversible futile cycles and - in a stricter sense - futile cycles that even contain an irreversible reaction. The second condition is equivalent to the existence of a strictly positive reaction invariant. Furthermore, it is sufficient for the existence of a realization in terms of sum formulas, obeying conservation of "atoms". In particular, these realizations can be chosen such that any two species have distinct sum formulas, unless $\mathbf{S}$ implies that they are "obligatory isomers". In terms of structural formulas, every compound is a labeled multigraph, in essence a Lewis formula, and reactions comprise only a rearrangement of bonds such that the total bond order is preserved.
Buy Now, Pay Later (BNPL)...On Your Credit Card
Authors: Benedict Guttman-Kenney, Christopher Firth, John Gathergood
Date: 2022-01-05 00:00:00

We provide the first economic research on `buy now, pay later' (BNPL): an unregulated FinTech credit product enabling consumers to defer payments into interest-free instalments. We study BNPL using UK credit card transaction data. We document consumers charging BNPL transactions to their credit card. Charging of BNPL to credit cards is most prevalent among younger consumers and those living in the most deprived geographies. Charging a $0\%$ interest, amortizing BNPL debt to credit cards - where typical interest rates are $20\%$ and amortization schedules decades-long - raises doubts on these consumers' ability to pay for BNPL. This prompts a regulatory question as to whether consumers should be allowed to refinance their unsecured debt.
Influence of trip distance and population density on intra-city mobility patterns in Tokyo during COVID-19 pandemic
Authors: Kazufumi Tsuboi, Naoya Fujiwara, Ryo Itoh
Date: 2022-01-05 00:00:00

This study investigates the influence of infection cases of COVID-19 and two non-compulsory lockdowns on human mobility within the Tokyo metropolitan area. Using the data of hourly staying population in each 500m$\times$500m cell and their city-level residency, we show that long-distance trips or trips to crowded places decrease significantly when infection cases increase. The same result holds for the two lockdowns, although the second lockdown was less effective. Hence, Japanese non-compulsory lockdowns influence mobility in a similar way to the increase in infection cases. This means that they are accepted as alarm triggers for people who are at risk of contracting COVID-19.
A hybrid approach to targeting social assistance
Authors: Lendie Follett, Heath Henderson
Date: 2022-01-04 00:00:00

Proxy means testing (PMT) and community-based targeting (CBT) are two of the leading methods for targeting social assistance in developing countries. In this paper, we present a hybrid targeting method that incorporates CBT's emphasis on local information and preferences with PMT's reliance on verifiable indicators. Specifically, we outline a Bayesian framework for targeting that resembles PMT in that beneficiary selection is based on a weighted sum of sociodemographic characteristics. We nevertheless propose calibrating the weights to preference rankings from community targeting exercises, implying that the weights used by our method reflect how potential beneficiaries themselves substitute sociodemographic features when making targeting decisions. We discuss several practical extensions to the model, including a generalization to multiple rankings per community, an adjustment for elite capture, a method for incorporating auxiliary information on potential beneficiaries, and a dynamic updating procedure. We further provide an empirical illustration using data from Burkina Faso and Indonesia.
Preparing urban mobility for the future of work
Authors: Nicholas S. Caros, Jinhua Zhao
Date: 2022-01-04 00:00:00

A gradual growth in flexible work over many decades has been suddenly and dramatically accelerated by the COVID-19 pandemic. The share of flexible work days in the United States is forecasted to grow from 4\% in 2018 to over 26\% by 2022. This rapid and unexpected shift in the nature of work will have a profound effect on the demand for, and supply of, urban transportation. Understanding how people make decisions around where and with whom to work will be critical for predicting future travel patterns and designing mobility systems to serve flexible commuters. To that end, this paper establishes a formal taxonomy for describing possible flexible work arrangements, the stakeholders involved and the relationships between them. An analytical framework is then developed for adapting existing transportation models to incorporate the unique dynamics of flexible work location choice. Several examples are provided to demonstrate how the new taxonomy and analytical framework can be applied across a broad set of scenarios. Finally, a critical research agenda is proposed to create both the empirical knowledge and methodological tools to prepare urban mobility for the future of work.
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification
Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh
Date: 2022-01-05 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Motor Imagery

Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important component of the Brain-Computer Interface (BCI) system that helps motor-disabled people interact with the outside world via external devices. The main issue in developing the EEG based BCI is the informative confusion due to the non-stationary characteristics of EEG data. In this work, an innovative idea of transforming an EEG signal into the weight vector of an unsupervised neural network called the autoencoder is proposed for the first time to solve that problem. Separate autoencoders are trained for the individual EEG data. The weight vectors are then optimized for the individual EEG signals. The EEG signals are thus represented in a new domain that is in the form of weight vectors of the individual autoencoder. The weight vectors are then used to extract features such as autoregressive coefficients (ARs), Shannon entropy (SE), and wavelet leader. A window-based feature extraction technique is implemented to capture the local features of the EEG data. Finally, extracted features are classified using a classifier network. The proposed approach is tested on two publicly accessible EEG datasets (BCI competition-III and Competition-IV) to ensure that it is as successful as and superior to the previously published methods. The proposed technique achieves a mean accuracy of 95.33 % for dataset-IIIa from BCI-III and a mean accuracy of 97% for dataset-IIa from BCI-IV for four-class EEG-based MI classification. The experimental outcomes show that the proposed approach is a promising way to increase BCI performance.
Multi-Person Passive WiFi Indoor Localization with Intelligent Reflecting Surface
Authors: Ganlin Zhang, Dongheng Zhang, Ying He, Jinbo Chen, Fang Zhou, Yan Chen
Date: 2022-01-05 00:00:00
Tasks: Indoor Localization

The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter
Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh, Ebrahim Ghaderpour
Date: 2022-01-05 00:00:00
Tasks: Brain Computer Interface, Denoising, EEG, EEG Denoising, Electroencephalogram (EEG)

Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain--computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 $\pm$ 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.
Blankets, Heat, and Why Free Energy Has Not Illuminated the Workings of the Brain
Authors: Donald Spector, Daniel Graham
Date: 2022-01-05 00:00:00

What can we hope to learn about brains from the free energy principle? In adopting the "primordial soup" physical model, Bruineberg et al. perpetuate the unsupported notion that the free energy principle has a meaningful physical--and neuronal--interpretation. We examine how minimization of free energy arises in physical contexts, and what this can and cannot tell us about brains.
The credit spread curve. I: Fundamental concepts, fitting, par-adjusted spread, and expected return
Authors: Richard J. Martin
Date: 2022-01-04 00:00:00

The notion of a credit spread curve is fundamental in fixed income investing, but in practice it is not `given' and needs to be constructed from bond prices either for a particular issuer, or for a sector rating-by-rating. Rather than attempting to fit spreads -- and as we discuss here, the Z-spread is unsuitable -- we fit parametrised survival curves. By deriving a valuation formula for a risky bond, we explain and avoid the problem that bonds with a high dollar price trade at a higher yield or spread than those with low dollar price (at the same maturity point), even though they do not necessarily offer better value. In fact, a concise treatment of this effect is elusive, and much of the academic literature on risky bond pricing, including a well-known paper by Duffie and Singleton (1997), is fundamentally incorrect. We then proceed to show how to calculate carry, rolldown and relative value for bonds/CDS. Also, once curve construction has been programmed and automated we can run it historically and assess the way a curve has moved over time. This provides the necessary grounding for econometric and arbitrage-free models of curve dynamics, which will be pursued in later work, as well as assessing how the perceived relative value of a particular instrument varies over time.
Stablecoins: Survivorship, Transactions Costs and Exchange Microstructure
Authors: Bruce Mizrach
Date: 2022-01-05 00:00:00

Stable coins are not very stable. Cash collateralized coins are more stable, but the overall failure rate is similar to tokens that are not designed to be stable. USD Coin, Tether and Dai have the largest Ethereum market shares, and they have an average velocity nearly three times higher than M1. Centralized and decentralized exchanges are the most active nodes and largest holders on the blockchain. Four of the top ten tokens have Herfindahl indices higher than the U.S. banking system. Median gas fees for Tether rose more than twelve times over the last two years, and nearly twenty times for USD Coin. Transactions of under 50,000 USD can generally be done more cheaply offchain. 24 hour exchange turnover in Tether is nearly 60 billion USD. This is comparable to the daily volume at the NYSE and eight times the daily flow in money market mutual funds. Narrow bid-ask spreads and depth have attracted HFT participation approaching 50%
SAR Image Despeckling Using Continuous Attention Module
Authors: Jaekyun Ko, Sanghwan Lee
Date: 2021-12-02 00:00:00
Proceeding: journal 2021 12
Tasks: Decoder, Denoising, Image Denoising, Image Restoration, Representation Learning, Sar Image Despeckling

Speckle removal process is inevitable in the restoration of synthetic aperture radar (SAR) images. Several variant methods have been proposed for enhancing SAR images over the past decades. However, in recent studies, convolutional neural networks (CNNs) have been widely applied in SAR image despeckling because of their versatility in representation learning. Nonetheless, a fair number of textures of the images are still lost when despeckling using simple CNN structures. To solve this problem, an encoder–decoder architecture was previously proposed. Although this architecture extracts features on different scales and has been shown to yield state-of-the-art performance, it still learns representation locally, resulting in missing overall information of convolutional features. Therefore, we herein introduce a new method for SAR image despeckling (SAR-CAM), which improves the performance of an encoder–decoder CNN architecture by using various attention modules. Moreover, a context block is introduced at the minimum scale to capture multiscale information. The model is trained via a data-driven approach using the gradient descent algorithm with a combination of modified despeckling gain and total variation loss function. Experiments performed on simulated and real SAR data demonstrate that the proposed method achieves significant improvements over state-of-the-art methodologies.
On Constrained Spectral Clustering and Its Applications
Authors: Xiang Wang, Buyue Qian, Ian Davidson
Date: 2012-01-25 00:00:00
Tasks: Clustering, Constrained Clustering, Transfer Learning, valid

Constrained clustering has been well-studied for algorithms such as $K$-means and hierarchical clustering. However, how to satisfy many constraints in these algorithmic settings has been shown to be intractable. One alternative to encode many constraints is to use spectral clustering, which remains a developing area. In this paper, we propose a flexible framework for constrained spectral clustering. In contrast to some previous efforts that implicitly encode Must-Link and Cannot-Link constraints by modifying the graph Laplacian or constraining the underlying eigenspace, we present a more natural and principled formulation, which explicitly encodes the constraints as part of a constrained optimization problem. Our method offers several practical advantages: it can encode the degree of belief in Must-Link and Cannot-Link constraints; it guarantees to lower-bound how well the given constraints are satisfied using a user-specified threshold; it can be solved deterministically in polynomial time through generalized eigendecomposition. Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation. We validate the effectiveness of our approach by empirical results on both artificial and real datasets. We also demonstrate an innovative use of encoding large number of constraints: transfer learning via constraints.
ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language
Authors: Cleison Correia de Amorim, Cleber Zanchettin
Date: 2022-01-06 00:00:00
Tasks: Sign Language Recognition

Sign language is an essential resource enabling access to communication and proper socioemotional development for individuals suffering from disabling hearing loss. As this population is expected to reach 700 million by 2050, the importance of the language becomes even more essential as it plays a critical role to ensure the inclusion of such individuals in society. The Sign Language Recognition field aims to bridge the gap between users and non-users of sign languages. However, the scarcity in quantity and quality of datasets is one of the main challenges limiting the exploration of novel approaches that could lead to significant advancements in this research area. Thus, this paper contributes by introducing two new datasets for the American Sign Language: the first is composed of the three-dimensional representation of the signers and, the second, by an unprecedented linguistics-based representation containing a set of phonological attributes of the signs.
Realistic Full-Body Anonymization with Surface-Guided GANs
Authors: Håkon Hukkelås, Morten Smebye, Rudolf Mester, Frank Lindseth
Date: 2022-01-06 00:00:00
Tasks: Diversity, Full-body anonymization

Recent work on image anonymization has shown that generative adversarial networks (GANs) can generate near-photorealistic faces to anonymize individuals. However, scaling up these networks to the entire human body has remained a challenging and yet unsolved task. We propose a new anonymization method that generates realistic humans for in-the-wild images. A key part of our design is to guide adversarial nets by dense pixel-to-surface correspondences between an image and a canonical 3D surface. We introduce Variational Surface-Adaptive Modulation (V-SAM) that embeds surface information throughout the generator. Combining this with our novel discriminator surface supervision loss, the generator can synthesize high quality humans with diverse appearances in complex and varying scenes. We demonstrate that surface guidance significantly improves image quality and diversity of samples, yielding a highly practical generator. Finally, we show that our method preserves data usability without infringing privacy when collecting image datasets for training computer vision models. Source code and appendix is available at: \href{https://github.com/hukkelas/full_body_anonymization}{github.com/hukkelas/full\_body\_anonymization}