pwc-archive/papers-with-abstracts huggingface dataset:
Model-based assessment of sampling protocols for infectious disease genomic surveillance
Authors: Sebastian Contreras, Karen Y. Oróstica, Anamaria Daza-Sanchez, Joel Wagner, Philipp Dönges, David Medina-Ortiz, Matias Jara, Ricardo Verdugo, Carlos Conca, Viola Priesemann, Álvaro Olivera-Nappa
Date: 2023-01-19 00:00:00

Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.
Genealogical transition in the noisy $N$-Branching Random Walk. How stronger selection may promote genetic diversity
Authors: Emmanuel Schertzer, Alejandro H. Wences
Date: 2023-01-18 00:00:00
Tasks: Diversity

We consider an extension of the noisy $N$-Branching Random Walk that models the evolution of a population subject to natural selection. We show the existence of a critical value for the noise which separates the limiting genealogical structure into two regimes, which we respectively call the semi-pulled and the fully-pulled regimes. In the fully-pulled regime, the genealogy converges to a discrete time Poisson-Dirichlet coalescent. In the semi-pulled regime, the genealogy converges to the Bolthausen-Sznitman coalescent. We discuss some interesting biological consequences of this result. In particular, our model predicts a non-monotone relation between the selection strength and the effective population size.
Warlock: an automated computational workflow for simulating spatially structured tumour evolution
Authors: Maciej Bak, Blair Colyer, Veselin Manojlović, Robert Noble
Date: 2023-01-18 00:00:00
Tasks: Model Selection

A primary goal of modern cancer research is to characterize tumour growth and evolution, to improve clinical forecasting and individualized treatment. Agent-based models support this endeavour but existing models either oversimplify spatial structure or are mathematically intractable. Here we present warlock, an open-source automated computational workflow for fast, efficient simulation of intratumour population genetics in any of a diverse set of spatial structures. Warlock encapsulates a deme-based oncology model (demon), designed to bridge the divide between agent-based simulations and analytical population genetics models, such as the spatial Moran process. Model output can be readily compared to multi-region and single-cell sequencing data for model selection or biological parameter inference. An interface for High Performance Computing permits hundreds of simulations to be run in parallel. We discuss prior applications of this workflow to investigating human cancer evolution.
Fixed-point iterative algorithm for SVI model
Authors: Shuzhen Yang, Wenqing Zhang
Date: 2023-01-19 00:00:00
Tasks: model

The stochastic volatility inspired (SVI) model is widely used to fit the implied variance smile. Presently, most optimizer algorithms for the SVI model have a strong dependence on the input starting point. In this study, we develop an efficient iterative algorithm for the SVI model based on a fixed-point and least-square optimizer. Furthermore, we present the convergence results in certain situations for this novel iterative algorithm. Compared with the quasi-explicit SVI method, we demonstrate the advantages of the fixed-point iterative algorithm using simulation and market data.
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease
Authors: Fatemeh Haghighi, Soumitra Ghosh, Hai Ngu, Sarah Chu, Han Lin, Mohsen Hejrati, Baris Bingol, Somaye Hashemifar
Date: 2023-01-11 00:00:00
Tasks: Deep Learning, Self-Supervised Learning

Parkinson's Disease (PD) is the second most common neurodegenerative disease in humans. PD is characterized by the gradual loss of dopaminergic neurons in the Substantia Nigra (SN). Counting the number of dopaminergic neurons in the SN is one of the most important indexes in evaluating drug efficacy in PD animal models. Currently, analyzing and quantifying dopaminergic neurons is conducted manually by experts through analysis of digital pathology images which is laborious, time-consuming, and highly subjective. As such, a reliable and unbiased automated system is demanded for the quantification of dopaminergic neurons in digital pathology images. Recent years have seen a surge in adopting deep learning solutions in medical image processing. However, developing high-performing deep learning models hinges on the availability of large-scale, high-quality annotated data, which can be expensive to acquire, especially in applications like digital pathology image analysis. To this end, we propose an end-to-end deep learning framework based on self-supervised learning for the segmentation and quantification of dopaminergic neurons in PD animal models. To the best of our knowledge, this is the first deep learning model that detects the cell body of dopaminergic neurons, counts the number of dopaminergic neurons, and provides characteristics of individual dopaminergic neurons as a numerical output. Extensive experiments demonstrate the effectiveness of our model in quantifying neurons with high precision, which can provide a faster turnaround for drug efficacy studies, better understanding of dopaminergic neuronal health status, and unbiased results in PD pre-clinical research. As part of our contributions, we also provide the first publicly available dataset of histology digital images along with expert annotations for the segmentation of TH-positive DA neuronal soma.
Secrecy Sum Rate Maximization for a MIMO-NOMA Uplink Transmission in 6G networks
Authors: Yunus Dursun, Kaidi Wang, Zhiguo Ding
Date: 2022-08-01 00:00:00
Proceeding: Physical Communication 2022 8

Non-orthogonal multiple access (NOMA), as a well-qualified candidate for sixth-generation (6G) mobile networks, has been attracting remarkable research interests due to high spectral efficiency and massive connectivity. The aim of this study is to maximize the secrecy sum rate (SSR) for a multiple-input multiple-output (MIMO)-NOMA uplink network under the maximum total transmit power and quality of service (QoS) constraints. Thanks to the generalized singular value decomposition method, the SSR of NOMA is compared with conventional orthogonal multiple access and other baseline algorithms in different MIMO scenarios. Due to the subtractive and non-convex nature of the SSR problem, the first-order Taylor approximation is exploited to transform the original problem into a suboptimal concave problem. Simulation results are provided and compared with some other benchmarks to evaluate the efficacy of the proposed method.
Fairness without Demographics through Knowledge Distillation
Authors: Junyi Chai, Taeuk Jang ~Taeuk_Jang1, Xiaoqian Wang
Date: 2022-11-01 00:00:00
Proceeding: NIPS 2022 11
Tasks: Fairness, Knowledge Distillation

Most of existing work on fairness assumes available demographic information in the training set. In practice, due to legal or privacy concerns, when demographic information is not available in the training set, it is crucial to find alternative objectives to ensure fairness. Existing work on fairness without demographics follows Rawlsian Max-Min fairness objectives. However, such constraints could be too strict to improve group fairness, and could lead to a great decrease in accuracy. In light of these limitations, in this paper, we propose to solve the problem from a new perspective, i.e., through knowledge distillation. Our method uses soft label from an overfitted teacher model as an alternative, and we show from preliminary experiments that soft labelling is beneficial for improving fairness. We analyze theoretically the fairness of our method, and we show that our method can be treated as an error-based reweighing. Experimental results on three datasets show that our method outperforms state-of-the-art alternatives, with notable improvements in group fairness and with relatively small decrease in accuracy.
FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns
Authors: Yitong Duan, Lei Wang, Qizhong Zhang, Jian Li
Date: 2022-06-28 00:00:00
Proceeding: AAAI Conference on Artificial Intelligence 2022 6
Tasks: Stock Price Prediction

As an asset pricing model in economics and finance, factor model has been widely used in quantitative investment. Towards building more effective factor models, recent years have witnessed the paradigm shift from linear models to more flexible nonlinear data-driven machine learning models. However, due to low signal-to-noise ratio of the financial data, it is quite challenging to learn effective factor models. In this paper, we propose a novel factor model, FactorVAE, as a probabilistic model with inherent randomness for noise modeling. Essentially, our model integrates the dynamic factor model (DFM) with the variational autoencoder (VAE) in machine learning, and we propose a prior-posterior learning method based on VAE, which can effectively guide the learning of model by approximating an optimal posterior factor model with future information. Particularly, considering that risk modeling is important for the noisy stock data, FactorVAE can estimate the variances from the distribution over the latent space of VAE, in addition to predicting returns. The experiments on the real stock market data demonstrate the effectiveness of FactorVAE, which outperforms various baseline methods.
StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic
Authors: Omama Hamad, Ali Hamdi, Sayed Hamdi, Khaled Shaban
Date: 2022-08-22 00:00:00
Proceeding: Big Data and Cognitive Computing 2022 8
Tasks: Benchmarking, Stance Detection, Transfer Learning

In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months, from March 2020 to May 2021, using the Twitter API. The tweets were manually annotated into the classes agree, disagreeor neutral. We performed benchmarking on the dataset using state-of-the-art and traditional machine learning models. Specifically, we trained deep learning models—bidirectional encoder representations from transformers, long short-term memory, convolutional neural networks, attention-based biLSTM and Naive Bayes SVM—in addition to naive Bayes, logistic regression, support vector machines, decision trees, K-nearest neighbor and random forest. The average accuracy in the 10-fold cross-validation of these models ranged from 75% to 84.8% and from 52.6% to 68% for binary and multi-class stance classifications, respectively. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education.
Glasses Detection Using Convolutional Neural Networks
Authors: Li Shao, Ronghang Zhu, Qijun Zhao
Date: 2016-09-21 00:00:00
Proceeding: Conference paper 2016 9
Tasks: Face Identification, Face Recognition, image-classification, Image Classification, object-detection, Object Detection, Person Identification

Glasses detection plays an important role in face recognition and soft biometrices for person identification. However, automatic glasses detection is still a challenging problem under real application scenarios, because face variations, light conditions, and self-occlusion, have significant influence on its performance. Inspired by the success of Deep Convolutional Neural Networks (DCNN) on face recognition, object detection and image classification, we propose a glasses detection method based on DCNN. Specifically, we devise a Glasses Network (GNet), and pre-train it as a face identification network with a large number of face images. The pre-trained GNet is finally fine-tuned as a glasses detection network by using another set of facial images wearing and not wearing glasses. Evaluation experiments have been done on two public databases, Multi-PIE and LFW. The results demonstrate the superior performance of the proposed method over competing methods.
Gazing at Social Interactions Between Foraging and Decision Theory
Authors: Alessandro D'Amelio, Giuseppe Boccignone
Date: 2022-03-30 00:00:00
Proceeding: Frontiers in Neurorobotics 2022 3
Tasks: Decision Making, Gaze Prediction

Finding the underlying principles of social attention in humans seems to be essential for the design of the interaction between natural and artificial agents. Here, we focus on the computational modeling of gaze dynamics as exhibited by humans when perceiving socially relevant multimodal information. The audio-visual landscape of social interactions is distilled into a number of multimodal patches that convey different social value, and we work under the general frame of foraging as a tradeoff between local patch exploitation and landscape exploration. We show that the spatio-temporal dynamics of gaze shifts can be parsimoniously described by Langevin-type stochastic differential equations triggering a decision equation over time. In particular, value-based patch choice and handling is reduced to a simple multi-alternative perceptual decision making that relies on a race-to-threshold between independent continuous-time perceptual evidence integrators, each integrator being associated with a patch.
Sparse Winning Tickets are Data-Efficient Image Recognizers
Authors: Mukund Varma T, Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang
Date: 2022-11-01 00:00:00
Proceeding: NIPS 2022 11

Improving the performance of deep networks in data-limited regimes has warranted much attention. In this work, we empirically show that “winning tickets” (small sub-networks) obtained via magnitude pruning based on the lottery ticket hypothesis, apart from being sparse are also effective recognizers in data-limited regimes. Based on extensive experiments, we find that in low data regimes (datasets of 50-100 examples per class), sparse winning tickets substantially outperform the original dense networks. This approach, when combined with augmentations or fine-tuning from a self-supervised backbone network, shows further improvements in performance by as much as 16% (absolute) on low-sample datasets and long-tailed classification. Further, sparse winning tickets are more robust to synthetic noise and distribution shifts compared to their dense counterparts. Our analysis of winning tickets on small datasets indicates that, though sparse, the networks retain density in the initial layers and their representations are more generalizable.
Long-Term Macroeconomic Effects of Climate Change: A Cross-Country Analysis
Authors: Matthew E. Kahn, Kamiar Mohaddes, Ryan N. C. Ng, M. Hashem Pesaran, Mehdi Raissi and Jui-Chung Yang
Date: 2019-08-01 00:00:00
Proceeding: 2019/08 2019 8
Tasks: counterfactual

We study the long-term impact of climate change on economic activity across countries, using a 
stochastic growth model where labor productivity is affected by country-specific climate 
variables—defined as deviations of temperature and precipitation from their historical norms. 
Using a panel data set of 174 countries over the years 1960 to 2014, we find that per-capita real 
output growth is adversely affected by persistent changes in the temperature above or below its 
historical norm, but we do not obtain any statistically significant effects for changes in 
precipitation. Our counterfactual analysis suggests that a persistent increase in average global 
temperature by 0.04°C per year, in the absence of mitigation policies, reduces world real GDP 
per capita by more than 7 percent by 2100. On the other hand, abiding by the Paris Agreement, 
thereby limiting the temperature increase to 0.01°C per annum, reduces the loss substantially to 
about 1 percent. These effects vary significantly across countries depending on the pace of 
temperature increases and variability of climate conditions. We also provide supplementary 
evidence using data on a sample of 48 U.S. states between 1963 and 2016, and show that climate 
change has a long-lasting adverse impact on real output in various states and economic sectors, 
and on labor productivity and employment.
JEL Classification Numbers: C33, O40, O44, O51, Q51, Q54.
Keywords: Climate change, economic growth, adaptation, counterfactual analysis. 
Author’s E-Mail Address: mkahn10@jhu.edu; km418@cam.ac.uk; rncn2@cam.ac.uk; 
pesaran@usc.edu; mraissi@imf.org; jc.yang@mx.nthu.edu.tw.
The Impact of Weather on Local Government Spending
Authors: Bo Zhao
Date: 2022-11-22 00:00:00
Proceeding: journal 2022 11

While there is a new and rapidly growing literature on the effects of climatic factors on economic 
and  social  outcomes, little  research has  been  conducted to  understand the  fiscal  impact  of 
weather, especially at the sub-state level. Using data from Massachusetts municipalities from 
1990 through 2019, this paper estimates government spending as a function of temperature and 
precipitation while controlling for municipality and year fixed effects and municipality -specific 
time  trends.  The  results  show  that  weather  has  statistically  significant  and  economically 
meaningful effects on local government spending. A 1 degree Fahrenheit increase in the average 
temperature results in a 3.2 percent increase in real per capita total general fund expenditures. 
Some government functions, such as public  works and general government, are affected more 
by  weather than others. The impact of weather may be persistent and heterogeneous across 
municipalities. There is some  evidence that municipalities adapt to rising temperatures  over 
time.
DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior
Authors: Patara Trirat, Jae-Gil Lee
Date: 2021-04-01 00:00:00
Proceeding: Proceedings of the Web Conference 2021 4
Tasks: TAR

Because traffic accidents cause huge social and economic losses, it is of prime importance to precisely predict the traffic accident risk for reducing future accidents. In this paper, we propose a Deep Fusion network for citywide Traffic Accident Risk prediction (DF-TAR) with dangerous driving statistics that contain the frequencies of various dangerous driving offences in each region. Our unique contribution is to exploit these statistics, obtained by processing the data from in-vehicle sensors, for modeling the traffic accident risk. Toward this goal, we first examine the correlation between dangerous driving offences and traffic accidents, and the analysis shows a strong correlation between them in terms of both location and time. Specifically, quick start (0.83), rapid acceleration (0.76), and sharp turn (0.76) are the top three offences that have the highest average correlation scores. We then train the DF-TAR model using the dangerous driving statistics as well as external environmental features. By extensive experiments on various frameworks, the DF-TAR model is shown to improve the accuracy of the baseline models by up to 54% by virtue of the integration of dangerous driving into the modeling of traffic accident risk.
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis
Authors: Allan Zhou, Moo Jin Kim, Lirui Wang, Pete Florence, Chelsea Finn
Date: 2023-01-18 00:00:00
Proceeding: CVPR 2023 1
Tasks: Data Augmentation, Imitation Learning, NeRF, Novel View Synthesis, Robotic Grasping

Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors. In this work, we introduce SPARTN (Synthetic Perturbations for Augmenting Robot Trajectories via NeRF): a fully-offline data augmentation scheme for improving robot policies that use eye-in-hand cameras. Our approach leverages neural radiance fields (NeRFs) to synthetically inject corrective noise into visual demonstrations, using NeRFs to generate perturbed viewpoints while simultaneously calculating the corrective actions. This requires no additional expert supervision or environment interaction, and distills the geometric information in NeRFs into a real-time reactive RGB-only policy. In a simulated 6-DoF visual grasping benchmark, SPARTN improves success rates by 2.8$\times$ over imitation learning without the corrective augmentations and even outperforms some methods that use online supervision. It additionally closes the gap between RGB-only and RGB-D success rates, eliminating the previous need for depth sensors. In real-world 6-DoF robotic grasping experiments from limited human demonstrations, our method improves absolute success rates by $22.5\%$ on average, including objects that are traditionally challenging for depth-based methods. See video results at \url{https://bland.website/spartn}.
Neural Architecture Search: Insights from 1000 Papers
Authors: Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, Frank Hutter
Date: 2023-01-20 00:00:00
Tasks: Natural Language Understanding, Neural Architecture Search, speech-recognition, Speech Recognition

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries.