pwc-archive/papers-with-abstracts huggingface dataset:
Emergence of Hopf bifurcation in an extended SIR dynamic
Authors: Arash Roostaei, Hadi Barzegar, Fakhteh Ghanbarnejad
Date: 2021-07-22 00:00:00

In this paper, the SIR dynamics is extended by considering another compartmental which represents hospitalization of the critical cases. So a system of differential equations with four blocks is considered when there is intensive care unit (ICU) to cure critical cases. Outgoing rate of survived infected individuals is divided into $nI$ and $\frac{bI}{I+b}$. The second term represents the rate of critical cases who enter ICUs. It is proved that there are forward, backward and Hopf bifurcations in different regimes of parameters.
Clinica: an open source software platform for reproducible clinical neuroscience studies
Authors: Ninon Burgos, Mauricio Díaz, Michael Bacci, Simona Bottani, Omar El-Rifai, Sabrina Fontanella, Pietro Gori, Jérémy Guillon, Alexis Guyot, Ravi Hassanaly, Thomas Jacquemont, Pascal Lu, Arnaud Marcoux, Tristan Moreau, Jorge Samper-González, Marc Teichmann, Elina Thibeau--Sutre, Ghislain Vaillant, Junhao Wen, Adam Wild, Marie-Odile Habert, Stanley Durrleman, Alexandre Routier, Olivier Colliot
Date: 2021-07-21 00:00:00
Tasks: BIG-bench Machine Learning, Diffusion  MRI, Management, Philosophy

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform reproducible evaluations of their methods, and iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI and PET data), as well as tools for statistics, machine learning and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS and NIFD). Processed data include image-valued scalar fields (e.g. tissue probability maps), meshes, surface-based scalar fields (e.g. cortical thickness maps) or scalar outputs (e.g. regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
Everything You Always Wanted to Know About XVA Model Risk but Were Afraid to Ask
Authors: Lorenzo Silotto, Marco Scaringi, Marco Bianchetti
Date: 2021-07-21 00:00:00

Valuation adjustments, collectively named XVA, play an important role in modern derivatives pricing to take into account additional price components such as counterparty and funding risk premia. They are an exotic price component carrying a significant model risk and computational effort even for vanilla trades. We adopt an industry-standard realistic and complete XVA modelling framework, typically used by XVA trading desks, based on multi-curve time-dependent volatility G2++ stochastic dynamics calibrated on real market data, and a multi-step Monte Carlo simulation including both variation and initial margins. We apply this framework to the most common linear and non-linear interest rates derivatives, also comparing the MC results with XVA analytical formulas. Within this framework, we identify the most relevant model risk sources affecting the precision of XVA figures and we measure the corresponding computational effort. In particular, we show how to build a parsimonious and efficient MC time simulation grid able to capture the spikes arising in collateralized exposure during the margin period of risk. As a consequence, we also show how to tune accuracy vs performance, leading to sufficiently robust XVA figures in a reasonable time, a very important feature for practical applications. Furthermore, we provide a quantification of the XVA model risk stemming from the existence of a range of different parameterizations according to the EU prudent valuation regulation. Finally, this work also serves as an handbook containing step-by-step instructions for the implementation of a complete, realistic and robust modelling framework of collateralized exposure and XVA.
Capital Requirements and Claims Recovery: A New Perspective on Solvency Regulation
Authors: Cosimo Munari, Lutz Wilhelmy, Stefan Weber
Date: 2021-07-22 00:00:00
Tasks: Management

Protection of creditors is a key objective of financial regulation. Where the protection needs are high, i.e., in banking and insurance, regulatory solvency requirements are an instrument to prevent that creditors incur losses on their claims. The current regulatory requirements based on Value at Risk and Average Value at Risk limit the probability of default of financial institutions, but they fail to control the size of recovery on creditors' claims in the case of default. We resolve this failure by developing a novel risk measure, Recovery Value at Risk. Our conceptual approach can flexibly be extended and allows the construction of general recovery risk measures for various risk management purposes. By design, these risk measures control recovery on creditors' claims and integrate the protection needs of creditors into the incentive structure of the management. We provide detailed case studies and applications: We analyze how recovery risk measures react to the joint distributions of assets and liabilities on firms' balance sheets and compare the corresponding capital requirements with the current regulatory benchmarks based on Value at Risk and Average Value at Risk. We discuss how to calibrate recovery risk measures to historic regulatory standards. Finally, we show that recovery risk measures can be applied to performance-based management of business divisions of firms and that they allow for a tractable characterization of optimal tradeoffs between risk and return in the context of investment management.
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network Latency
Authors: Shuochao Yao, Jinyang Li, Dongxin Liu, Tianshi Wang, Shengzhong Liu, Huajie Shao, Tarek Abdelzaher
Date: 2020-11-16 00:00:00
Proceeding: The 18th Conference on Embedded Networked Sensor Systems 2020 11
Tasks: Compressive Sensing, Edge-computing, speech-recognition, Speech Recognition

With recent advances, neural networks have become a crucial building block in intelligent IoT systems and sensing applications. However, the excessive computational demand remains a serious impediment to their deployments on low-end IoT devices. With the emergence of edge computing, offloading grows into a promising technique to circumvent end-device limitations. However, transferring data between local and edge devices takes up a large proportion of time in existing offloading frameworks, creating a bottleneck for low-latency intelligent services. In this work, we propose a general framework, called deep compressive offloading. By integrating compressive sensing theory and deep learning, our framework can encode data for offloading into tiny sizes with negligible overhead on local devices and decode the data on the edge server, while offering theoretical guarantees on perfect reconstruction and lossless inference. By trading edge computing resources for data transmission time, our design can significantly reduce offloading latency with almost no accuracy loss. We build a deep compressive offloading system to serve state-of-the-art computer vision and speech
recognition services. With comprehensive evaluations, our system can consistently reduce end-to-end latency by 2× to 4× with 1% accuracy loss, compared to state-of-the-art neural network offloading systems. In conditions of limited network bandwidth or intensive background traffic, our system can further speed up the neural network inference by up to 35×1.
Social-Aware Incentive Mechanism for VehicularCrowdsensing by Deep Reinforcement Learning
Authors: Yinuo Zhao, Chi Harold Liu
Date: 2020-08-21 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2020 8
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Vehicular crowdsensing (VCS) takes the advantage of vehicles’ mobility and exploits both the crowd wisdom and sensing abilities offered by vehicle drivers’ carried smart mobile devices and on-board sensors to accomplish challenging sensing tasks. The daily roadway commutes of vehicle drivers may form “virtual” mobile communities, called Vehicular Social Networks (VSNs). It offers an opportunity to include social network effect into incentive mechanism design where a driver can benefit from others’ sensing strategy in one VSN. In this paper, we consider a non-cooperative VCS campaign where multiple vehicles are incentivized by dynamically priced tasks and social network effect. In order to maximize the overall utility of vehicle drivers, we propose a social-aware incentive mechanism by deep reinforcement learning (called DRL-SIM), to derive the optimal long term sensing strategy for all vehicles. Finally, numerical results are supplemented to show both the convergence and the effectiveness of DRL-SIM when compared with other baselines.
Unsupervised image segmentation using a simple MRF model with a new implementation scheme
Authors: Huawu Deng, David A. Clausi
Date: 2004-04-27 00:00:00
Proceeding: Pattern Recognition 37 2004 4
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

based on image features. The traditional two-component MRF model for segmentation requires training data to estimate
necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this
problem by introducing a function-based weighting parameter between the two components. Using this method, the simple
MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results.
Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture)
and achieves an improvement over the traditional method.
 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Local motion phases for learning multi-contact character movements
Authors: Sebastian Dorothee Starke, Yiwei Zhao, Taku Komura, Kazi A. Zaman
Date: 2020-06-01 00:00:00
Proceeding: 10/06 2020 6

Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion. In this paper, we propose a novel framework to learn fast and dynamic character interactions that involve multiple contacts between the body and an object, another character and the environment, from a rich, unstructured motion capture database. We use one-on-one basketball play and character interactions with the environment as examples. To achieve this task, we propose a novel feature called local motion phase, that can help neural networks to learn asynchronous movements of each bone and its interaction with external objects such as a ball or an environment. We also propose a novel generative scheme to reproduce a wide variation of movements from abstract control signals given by a gamepad, which can be useful for changing the style of the motion under the same context. Our scheme is useful for animating contact-rich, complex interactions for real-time applications such as computer games.
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications
Authors: Mehdi Mousavi, Rolando Estrada
Date: 2021-07-23 00:00:00
Tasks: Caustics Segmentation, Depth Completion, Object Detection, Physical Simulations, Semantic Segmentation, Synthetic Data Generation, Transparent Object Depth Estimation, Transparent Object Detection, Transparent objects

Transparent objects are a very challenging problem in computer vision. They are hard to segment or classify due to their lack of precise boundaries, and there is limited data available for training deep neural networks. As such, current solutions for this problem employ rigid synthetic datasets, which lack flexibility and lead to severe performance degradation when deployed on real-world scenarios. In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline. To address this issue, we present SuperCaustics, a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations. To validate our proposed system, we trained a deep neural network from scratch to segment transparent objects in difficult lighting scenarios. Our neural network achieved performance comparable to the state-of-the-art on a real-world dataset using only 10% of the training data and in a fraction of the training time. Further experiments show that a model trained with SuperCaustics can segment different types of caustics, even in images with multiple overlapping transparent objects. To the best of our knowledge, this is the first such result for a model trained on synthetic data. Both our open-source code and experimental data are freely available online.
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
Authors: Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
Date: 2021-07-22 00:00:00
Tasks: Distributed Optimization

In this paper we prove that Local (S)GD (or FedAvg) can optimize deep neural networks with Rectified Linear Unit (ReLU) activation function in polynomial time. Despite the established convergence theory of Local SGD on optimizing general smooth functions in communication-efficient distributed optimization, its convergence on non-smooth ReLU networks still eludes full theoretical understanding. The key property used in many Local SGD analysis on smooth function is gradient Lipschitzness, so that the gradient on local models will not drift far away from that on averaged model. However, this decent property does not hold in networks with non-smooth ReLU activation function. We show that, even though ReLU network does not admit gradient Lipschitzness property, the difference between gradients on local models and average model will not change too much, under the dynamics of Local SGD. We validate our theoretical results via extensive experiments. This work is the first to show the convergence of Local SGD on non-smooth functions, and will shed lights on the optimization theory of federated training of deep neural networks.
Multiclass versus Binary Differentially Private PAC Learning
Authors: Mark Bun, Marco Gaboardi, Satchit Sivakumar
Date: 2021-07-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: PAC learning

We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning. We apply this transformation to a recently proposed binary private PAC learner to obtain a private multiclass learner with sample complexity that has a polynomial dependence on the multiclass Littlestone dimension and a poly-logarithmic dependence on the number of classes. This yields an exponential improvement in the dependence on both parameters over learners from previous work. Our proof extends the notion of $\Psi$-dimension defined in work of Ben-David et al. [JCSS '95] to the online setting and explores its general properties.
On the Certified Robustness for Ensemble Models and Beyond
Authors: Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
Date: 2021-07-22 00:00:00
Proceeding: ICLR 2022 4

Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend against such attacks, both empirical and theoretical defense approaches have been extensively studied for a single ML model. In this work, we aim to analyze and provide the certified robustness for ensemble ML models, together with the sufficient and necessary conditions of robustness for different ensemble protocols. Although ensemble models are shown more robust than a single model empirically; surprisingly, we find that in terms of the certified robustness the standard ensemble models only achieve marginal improvement compared to a single model. Thus, to explore the conditions that guarantee to provide certifiably robust ensemble ML models, we first prove that diversified gradient and large confidence margin are sufficient and necessary conditions for certifiably robust ensemble models under the model-smoothness assumption. We then provide the bounded model-smoothness analysis based on the proposed Ensemble-before-Smoothing strategy. We also prove that an ensemble model can always achieve higher certified robustness than a single base model under mild conditions. Inspired by the theoretical findings, we propose the lightweight Diversity Regularized Training (DRT) to train certifiably robust ensemble ML models. Extensive experiments show that our DRT enhanced ensembles can consistently achieve higher certified robustness than existing single and ensemble ML models, demonstrating the state-of-the-art certified L2-robustness on MNIST, CIFAR-10, and ImageNet datasets.
Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification
Authors: Diya Sashidhar, J. Nathan Kutz
Date: 2021-07-22 00:00:00
Tasks: regression, Uncertainty Quantification

Dynamic mode decomposition (DMD) provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal, data. A diversity of regression techniques have been developed for producing the linear model approximation whose solutions are exponentials in time. For spatio-temporal data, DMD provides low-rank and interpretable models in the form of dominant modal structures along with their exponential/oscillatory behavior in time. The majority of DMD algorithms, however, are prone to bias errors from noisy measurements of the dynamics, leading to poor model fits and unstable forecasting capabilities. The optimized DMD algorithm minimizes the model bias with a variable projection optimization, thus leading to stabilized forecasting capabilities. Here, the optimized DMD algorithm is improved by using statistical bagging methods whereby a single set of snapshots is used to produce an ensemble of optimized DMD models. The outputs of these models are averaged to produce a bagging, optimized dynamic mode decomposition (BOP-DMD). BOP-DMD not only improves performance, it also robustifies the model and provides both spatial and temporal uncertainty quantification (UQ). Thus unlike currently available DMD algorithms, BOP-DMD provides a stable and robust model for probabilistic, or Bayesian forecasting with comprehensive UQ metrics.
Discovering Sparse Interpretable Dynamics from Partial Observations
Authors: Peter Y. Lu, Joan Ariño, Marin Soljačić
Date: 2021-07-22 00:00:00

Identifying the governing equations of a nonlinear dynamical system is key to both understanding the physical features of the system and constructing an accurate model of the dynamics that generalizes well beyond the available data. We propose a machine learning framework for discovering these governing equations using only partial observations, combining an encoder for state reconstruction with a sparse symbolic model. Our tests show that this method can successfully reconstruct the full system state and identify the underlying dynamics for a variety of ODE and PDE systems.
Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules
Authors: Kirill Karpov, Artem Mitrofanov, Vadim Korolev, Valery Tkachenko
Date: 2021-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Transfer Learning

The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. This methodology assumes that a model built on a sufficient amount of data captures general features of the chemical compound structure on which it was trained and that the further reuse of these features on a dataset with a lack of data will greatly improve the quality of the new model. In this paper, we develop this approach for small organic molecules, implementing transfer learning with graph convolutional neural networks. The paper shows a significant improvement in the performance of models for target properties with a lack of data. The effects of the dataset composition on model quality and the applicability domain of the resulting models are also considered.
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Authors: Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, Jonghye Woo
Date: 2021-07-22 00:00:00
Tasks: Bayesian Inference, Domain Generalization

In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of $p(x|y)$ and $p(y)$. However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. $p(x)$, which rests on an unrealistic assumption that $p(y)$ is invariant across domains. We thereby propose a novel variational Bayesian inference framework to enforce the conditional distribution alignment w.r.t. $p(x|y)$ via the prior distribution matching in a latent space, which also takes the marginal label shift w.r.t. $p(y)$ into consideration with the posterior alignment. Extensive experiments on various benchmarks demonstrate that our framework is robust to the label shift and the cross-domain accuracy is significantly improved, thereby achieving superior performance over the conventional IFL counterparts.