pwc-archive/papers-with-abstracts huggingface dataset:
Joint Resource Allocation and Phase Shift Optimization for RIS-Aided eMBB/URLLC Traffic Multiplexing
Authors: Mohammed AL-Mekhlafi, Mohamed Amine Arfaoui, Mohamed Elhattab, Chadi Assi, Ali Ghrayeb
Date: 2021-08-05 00:00:00

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99\% URLLC packets admission rate compared to 95.6\% when there is no RIS, while also achieving up to 70\% enhancement on the eMBB sum rate.
Structure determination
Authors: Halima Mouhib, Bas Stringer, Hugo van Ingen, Jose Gavaldá-García, Katharina Waury, Sanne Abeln, K. Anton Feenstra
Date: 2021-08-05 00:00:00
Tasks: Cryogenic Electron Microscopy (cryo-EM), Protein Structure Prediction

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. The main emphasis of this work is to provide a background on experimental techniques for protein structure determination. The focus is set on X-ray crystallography and Nuclear Magnetic Resonance spectroscopy (NMR), which are by far the main methods used to determine the structure of soluble proteins. We will also introduce cryogenic Electron Microscopy (cryo-EM) and electron diffraction which are more suited to analyze membrane proteins and larger protein complexes. At the end, more qualitative techniques are summarized that are used to obtain insight on the overall structure and dynamics of proteins. Note that this introduction to protein structure determination aims at familiarizing the reader to different experimental techniques, their benefits and bottlenecks, but that a thorough mathematical and technical description of the concept is beyond the scope of this work.
Artificial intelligence and the future of diagnostic and therapeutic radiopharmaceutical development: in Silico smart molecular design
Authors: Bahar Ataeinia, Pedram Heidari
Date: 2021-08-05 00:00:00
Tasks: Diagnostic

Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence (AI) has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their utility and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.
A Global Nucleic Acid Observatory for Biodefense and Planetary Health
Authors: The Nucleic Acid Observatory Consortium
Date: 2021-08-05 00:00:00
Tasks: Diversity

The spread of pandemic viruses and invasive species can be catastrophic for human societies and natural ecosystems. SARS-CoV-2 demonstrated that the speed of our response is critical, as each day of delay permitted exponential growth and dispersion of the virus. Here we propose a global Nucleic Acid Observatory (NAO) to monitor the relative frequency of everything biological through comprehensive metagenomic sequencing of waterways and wastewater. By searching for divergences from historical baseline frequencies at sites throughout the world, NAO could detect any virus or invasive organism undergoing exponential growth whose nucleic acids end up in the water, even those previously unknown to science. Continuously monitoring nucleic acid diversity would provide us with universal early warning, obviate subtle bioweapons, and generate a wealth of sequence data sufficient to transform ecology, microbiology, and conservation. We call for the immediate construction of a global NAO to defend and illuminate planetary health.
On the origin of zombies: a modeling approach
Authors: Alisha Kumari, Elijah Reece, Kursad Tosun, Scott Greenhalgh
Date: 2021-08-04 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

A zombie apocalypse is one pandemic that would likely be worse than anything humanity has ever seen. However, despite the mechanisms for zombie uprisings in pop culture, it is unknown whether zombies, from an evolutionary point of view, can actually rise from the dead. To provide insight into this unknown, we created a mathematical model that predicts the trajectory of human and zombie populations during a zombie apocalypse. We parameterized our model according to the demographics of the US, the zombie literature, and then conducted an evolutionary invasion analysis to determine conditions that permit the evolution of zombies. Our results indicate a zombie invasion is theoretically possible, provided there is a sufficiently large ratio of transmission rate to the zombie death rate. While achieving this ratio is uncommon in nature, the existence of zombie ant fungus illustrates it is possible and thereby suggests that a zombie apocalypse among humans could occur.
Short-time implied volatility of additive normal tempered stable processes
Authors: Michele Azzone, Roberto Baviera
Date: 2021-08-05 00:00:00

Empirical studies have emphasized that the equity implied volatility is characterized by a negative skew inversely proportional to the square root of the time-to-maturity. We examine the short-time-to-maturity behavior of the implied volatility smile for pure jump exponential additive processes. An excellent calibration of the equity volatility surfaces has been achieved by a class of these additive processes with power-law scaling. The two power-law scaling parameters are $\beta$, related to the variance of jumps, and $\delta$, related to the smile asymmetry. It has been observed, in option market data, that $\beta=1$ and $\delta=-1/2$. In this paper, we prove that the implied volatility of these additive processes is consistent, in the short-time, with the equity market empirical characteristics if and only if $\beta=1$ and $\delta=-1/2$.
The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion
Authors: Spiridon Penev, Pavel V. Shevchenko, Wei Wu
Date: 2021-08-05 00:00:00

We quantify model risk of a financial portfolio whereby a multi-period mean-standard-deviation criterion is used as a selection criterion. In this work, model risk is defined as the loss due to uncertainty of the underlying distribution of the returns of the assets in the portfolio. The uncertainty is measured by the Kullback-Leibler divergence, i.e., the relative entropy. In the worst case scenario, the optimal robust strategy can be obtained in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented which allow us to compare the performance of this robust strategy with the optimal non-robust strategy. For illustration, we also quantify the model risk associated with an empirical dataset.
Evaporating primordial black holes as varying dark energy
Authors: Savvas Nesseris, Domenico Sapone, Spyros Sypsas
Date: 2019-11-11 00:00:00
Proceeding: Physics of the Dark Universe 2019 11

If light enough primordial black holes (PBH) account for dark matter, then its density decreases
with time as they lose mass via Hawking radiation. We show that this time-dependence of the matter
density can be formulated as an equivalent w(z) dark energy model and we study its implications
on the expansion history. Using our approach and comparing with the latest cosmological data,
including the supernovae type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background and
the Hubble expansion H(z) data, we place observational constraints on the PBH model. We find
that it is statistically consistent with ΛCDM according to the AIC statistical tool. Furthermore, we
entertain the idea of having a population of ultra-light PBHs, decaying around neutrino decoupling,
on top of the dark matter fluid and show how this offers a natural dark matter-radiation coupling
altering the expansion history of the Universe and alleviating the H0 tension.
Predicting Splicing from Primary Sequence with Deep Learning
Authors: Kishore Jaganathan, Sofia Kyriazopoulou Panagiotopoulou, Jeremy F. McRae, Siavash Fazel Darbandi, David Knowles, Yang I. Li, Jack A. Kosmicki, Juan Arbelaez, Wenwu Cui, Grace B. Schwartz, Eric D. Chow, Efstathios Kanterakis, Hong Gao, Amirali Kia, Serafim Batzoglou, Stephan J. Sanders, Kyle Kai-How Farh
Date: 2019-01-17 00:00:00
Proceeding: A Cell Press journal 2019 1
Tasks: Deep Learning, Specificity

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%–11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.
The Regression Discontinuity Design
Authors: Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
Date: 2019-06-10 00:00:00
Tasks: regression

This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods.
Simple Local Polynomial Density Estimators
Authors: Matias D. Cattaneo, Michael Jansson, Xinwei Ma
Date: 2018-11-28 00:00:00
Tasks: regression

This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require pre-binning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel discontinuity in density testing procedure, an important problem in regression discontinuity designs and other program evaluation settings. An illustrative empirical application is given. Two companion Stata and R software packages are provided.
Characteristic-Sorted Portfolios: Estimation and Inference
Authors: Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Ernst Schaumburg
Date: 2018-09-10 00:00:00
Tasks: valid

Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been paid to the statistical properties of the procedure. We develop a general framework for portfolio sorting by casting it as a nonparametric estimator. We present valid asymptotic inference methods and a valid mean square error expansion of the estimator leading to an optimal choice for the number of portfolios. In practical settings, the optimal choice may be much larger than the standard choices of 5 or 10. To illustrate the relevance of our results, we revisit the size and momentum anomalies.
Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs
Authors: Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Date: 2018-09-01 00:00:00
Tasks: regression, valid

Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smallest coverage error. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in \texttt{R} and \texttt{Stata} packages.
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs
Authors: Matias D. Cattaneo, Luke Keele, Rocio Titiunik, Gonzalo Vazquez-Bare
Date: 2018-08-13 00:00:00
Tasks: Causal Inference, regression

In non-experimental settings, the Regression Discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of "common trends" in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility.
ZPY: Open Source Synthetic Data for Computer Vision
Authors: Hugo Ponte, Norman Ponte, Sammie Crowder, Kory Stiger, Steven Pecht, Elena Ponte
Date: 2021-08-06 00:00:00
Proceeding: Github 2021 8
Tasks: Fairness, Synthetic Data Generation

Synthetic data presents a unique solution to the huge data requirements of computer vision with deep learning. In this work, we present zpy, an open source framework for creating synthetic data in Python. Built on top of the popular open source 3D toolset Blender, zpy is designed with accessibility and readability in mind. Open source synthetic data toolkits like zpy are the bridge between the more mature tools of the 3D workflow and the machine learning frameworks. We make the case for why open source synthetic data is important to solve issues such as fairness and bias by democratizing access to data. Finally, we explore the effect of different types of domain randomization on synthetic training data by fine tuning a CNN on small synthetic training datasets and testing the model on a holdout test dataset of real images. All code is available on GitHub at http://github.com/ZumoLabs/zpy
Deep Ensemble Learning-based Approach to Real-time Power System State Estimation
Authors: Narayan Bhusal, Raj Mani Shukla, Mukesh Gautam, Mohammed Benidris, Shamik Sengupta
Date: 2021-01-10 00:00:00
Tasks: Ensemble Learning, State Estimation

Power system state estimation (PSSE) is commonly formulated as weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, low carbon technologies (e.g., electric vehicles), and demand response programs. Appropriate PSSE approaches are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. This paper proposes a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and multivariate-linear regressor as meta-learner. Historical measurements and states are utilised to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data-acquisition. This paper adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. The results show that the proposed approach outperforms existing data-driven PSSE methods techniques.
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color
Authors: Yajun Xu, Shogo Arai, Fuyuki Tokuda, Kazuhiro Kosuge
Date: 2020-03-05 00:00:00
Proceeding: IEEE Access 2020 3
Tasks: 3D Instance Segmentation, Autonomous Driving, Instance Segmentation, Segmentation, Semantic Segmentation

3D Instance segmentation is a fundamental task in computer vision. Effective segmentation plays an important role in robotic tasks, augmented reality, autonomous driving, etc. With the ascendancy of convolutional neural networks in 2D image processing, the use of deep learning methods to segment 3D point clouds receives much attention. A great convergence of training loss often requires a large amount of human-annotated data, while making such a 3D dataset is time-consuming. This paper proposes a method for training convolutional neural networks to predict instance segmentation results using synthetic data. The proposed method is based on the SGPN framework. We replaced the original feature extractor with “dynamic graph convolutional neural networks” that learned how to extract local geometric features and proposed a simple and effective loss function, making the network more focused on hard examples. We experimentally proved that the proposed method significantly outperforms the state-of-the-art method in both Stanford 3D Indoor Semantics Dataset and our datasets.
Probing Toxic Content in Large Pre-Trained Language Models
Authors: Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, Dit-yan Yeung
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Probing Language Models, Sentence

Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems. We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates. The templates are prompted by a name of a social group followed by a cause-effect relation. We use PTLMs to predict masked tokens at the end of a sentence in order to examine how likely they enable toxicity towards specific communities. We shed the light on how such negative content can be triggered within unrelated and benign contexts based on evidence from a large-scale study, then we explain how to take advantage of our methodology to assess and mitigate the toxicity transmitted by PTLMs.
Active Learning of Driving Scenario Trajectories
Authors: Sanna Jarl, Linus Aronsson, Sadegh Rahrovani, Morteza Haghir Chehreghani
Date: 2021-08-06 00:00:00
Tasks: Active Learning, Autonomous Vehicles, Time Series, Time Series Analysis

Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.