pwc-archive/papers-with-abstracts huggingface dataset:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Authors: Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
Date: 2021-01-11 00:00:00
Tasks: BIG-bench Machine Learning

Differentially private (DP) machine learning allows us to train models on private data while limiting data leakage. DP formalizes this data leakage through a cryptographic game, where an adversary must predict if a model was trained on a dataset D, or a dataset D' that differs in just one example.If observing the training algorithm does not meaningfully increase the adversary's odds of successfully guessing which dataset the model was trained on, then the algorithm is said to be differentially private. Hence, the purpose of privacy analysis is to upper bound the probability that any adversary could successfully guess which dataset the model was trained on.In our paper, we instantiate this hypothetical adversary in order to establish lower bounds on the probability that this distinguishing game can be won. We use this adversary to evaluate the importance of the adversary capabilities allowed in the privacy analysis of DP training algorithms.For DP-SGD, the most common method for training neural networks with differential privacy, our lower bounds are tight and match the theoretical upper bound. This implies that in order to prove better upper bounds, it will be necessary to make use of additional assumptions. Fortunately, we find that our attacks are significantly weaker when additional (realistic)restrictions are put in place on the adversary's capabilities.Thus, in the practical setting common to many real-world deployments, there is a gap between our lower bounds and the upper bounds provided by the analysis: differential privacy is conservative and adversaries may not be able to leak as much information as suggested by the theoretical bound.
Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform
Authors: Monica Arul, Ahsan Kareem
Date: 2021-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Event Detection, Time Series, Time Series Analysis

Detection of thunderstorms is important to the wind hazard community to better understand extreme winds field characteristics and associated wind induced load effects on structures. This paper contributes to this effort by proposing a new course of research that uses machine learning techniques, independent of wind statistics based parameters, to autonomously identify and separate thunderstorms from large databases containing high frequency sampled continuous wind speed measurements. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of shape of their time series. This novel shape based representation when combined with machine learning algorithms yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1 year of data from 14 ultrasonic anemometers that are a part of an extensive in situ wind monitoring network in the Northern Mediterranean ports. A collective total of 235 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.
Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation
Authors: Lingkun Luo, Liming Chen, Shiqiang Hu
Date: 2021-01-09 00:00:00
Tasks: Domain Adaptation, regression

Domain adaptation (DA) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of data distributions between the two domains. State-of-the-art DA methods have so far focused on the search of a latent shared feature space where source and target domain data can be aligned either statistically and/or geometrically. In this paper, we propose a novel unsupervised DA method, namely Discriminative Noise Robust Sparse Orthogonal Label Regression-based Domain Adaptation (DOLL-DA). The proposed DOLL-DA derives from a novel integrated model which searches a shared feature subspace where source and target domain data are, through optimization of some repulse force terms, discriminatively aligned statistically, while at same time regresses orthogonally data labels thereof using a label embedding trick. Furthermore, in minimizing a novel Noise Robust Sparse Orthogonal Label Regression(NRS_OLR) term, the proposed model explicitly accounts for data outliers to avoid negative transfer and introduces the property of sparsity when regressing data labels. Due to the character restriction. Please read our detailed abstract in our paper.
Text analysis in financial disclosures
Authors: Sridhar Ravula
Date: 2021-01-06 00:00:00
Tasks: Financial Analysis

Financial disclosure analysis and Knowledge extraction is an important financial analysis problem. Prevailing methods depend predominantly on quantitative ratios and techniques, which suffer from limitations like window dressing and past focus. Most of the information in a firm's financial disclosures is in unstructured text and contains valuable information about its health. Humans and machines fail to analyze it satisfactorily due to the enormous volume and unstructured nature, respectively. Researchers have started analyzing text content in disclosures recently. This paper covers the previous work in unstructured data analysis in Finance and Accounting. It also explores the state of art methods in computational linguistics and reviews the current methodologies in Natural Language Processing (NLP). Specifically, it focuses on research related to text source, linguistic attributes, firm attributes, and mathematical models employed in the text analysis approach. This work contributes to disclosure analysis methods by highlighting the limitations of the current focus on sentiment metrics and highlighting broader future research areas
Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation
Authors: Jenn-Bing Ong, Wee-Keong Ng, Ivan Tjuawinata, Chao Li, Jielin Yang, Sai None Myne, Huaxiong Wang, Kwok-Yan Lam, C. -C. Jay Kuo
Date: 2021-01-04 00:00:00
Tasks: Dimensionality Reduction, Management, Tensor Networks

Data privacy is an important issue for organizations and enterprises to securely outsource data storage, sharing, and computation on clouds / fogs. However, data encryption is complicated in terms of the key management and distribution; existing secure computation techniques are expensive in terms of computational / communication cost and therefore do not scale to big data computation. Tensor network decomposition and distributed tensor computation have been widely used in signal processing and machine learning for dimensionality reduction and large-scale optimization. However, the potential of distributed tensor networks for big data privacy preservation have not been considered before, this motivates the current study. Our primary intuition is that tensor network representations are mathematically non-unique, unlinkable, and uninterpretable; tensor network representations naturally support a range of multilinear operations for compressed and distributed / dispersed computation. Therefore, we propose randomized algorithms to decompose big data into randomized tensor network representations and analyze the privacy leakage for 1D to 3D data tensors. The randomness mainly comes from the complex structural information commonly found in big data; randomization is based on controlled perturbation applied to the tensor blocks prior to decomposition. The distributed tensor representations are dispersed on multiple clouds / fogs or servers / devices with metadata privacy, this provides both distributed trust and management to seamlessly secure big data storage, communication, sharing, and computation. Experiments show that the proposed randomization techniques are helpful for big data anonymization and efficient for big data storage and computation.
Output Regulation of Linear Aperiodic Sampled-Data Systems
Authors: Himadri Basu, Francesco Ferrante, Se Young Yoon
Date: 2021-01-12 00:00:00

This paper deals with the output regulation problem of a linear time-invariant system in the presence of sporadically available measurement streams. A regulator with a continuous intersample injection term is proposed, where the intersample injection is provided by a linear dynamical system and the state of which is reset with the arrival of every new measurement updates. The resulting system is augmented with a timer triggering an instantaneous update of the new measurement and the overall system is then analyzed in a hybrid system framework. With the Lyapunov based stability analysis, we offer sufficient conditions to ensure the objectives of the output regulation problem are achieved under intermittency of the measurement streams. Then, from the solution to linear matrix inequalities, a numerically tractable regulator design procedure is presented. Finally, with the help of an illustrative example, the effectiveness of the theoretical results are validated.
Anisotropic field-of-views in radial imaging
Authors: Peder E. Z. Larson, Paul T. Gurney, Dwight G. Nishimura
Date: 2021-01-12 00:00:00

Radial imaging techniques, such as projection-reconstruction (PR), are used in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and short-imaging. They are robust to flow and motion, have diffuse aliasing patterns, and support short readouts and echo times. One drawback is that standard implementations do not support anisotropic field-of-view (FOV) shapes, which are used to match the imaging parameters to the object or region-of-interest. A set of fast, simple algorithms for 2-D and 3-D PR, and 3-D cones acquisitions are introduced that match the sampling density in frequency space to the desired FOV shape. Tailoring the acquisitions allows for reduction of aliasing artifacts in undersampled applications or scan time reductions without introducing aliasing in fully-sampled applications. It also makes possible new radial imaging applications that were previously unsuitable, such as imaging elongated regions or thin slabs. 2-D PR longitudinal leg images and thin-slab, single breath-hold 3-D PR abdomen images, both with isotropic resolution, demonstrate these new possibilities. No scan time to volume efficiency is lost by using anisotropic FOVs. The acquisition trajectories can be computed on a scan by scan basis.
Direction-Dependent Turning Leads to Anisotropic Diffusion and Persistence
Authors: Nadia Loy, Thomas Hillen, Kevin John Painter
Date: 2021-01-12 00:00:00

Cells and organisms follow aligned structures in their environment, a process that can generate persistent migration paths. Kinetic transport equations are a popular modelling tool for describing biological movements at the mesoscopic level, yet their formulations usually assume a constant turning rate. Here we relax this simplification, extending to include a turning rate that varies according to the anisotropy of a heterogeneous environment. We extend known methods of parabolic and hyperbolic scaling and apply the results to cell movement on micro-patterned domains. We show that inclusion of orientation dependence in the turning rate can lead to persistence of motion in an otherwise fully symmetric environment, and generate enhanced diffusion in structured domains.
Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves
Authors: Giovanni Soldi, Nicola Forti, Domenico Gaglione, Paolo Braca, Leonardo M. Millefiori, Stefano Marano, Peter Willett, Krishna Pattipati
Date: 2021-01-12 00:00:00

The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1.7 million deaths, and put the world's most advanced healthcare systems under heavy stress. In many countries, drastic restrictive measures adopted by political authorities, such as national lockdowns, have not prevented the outbreak of new pandemic's waves. In this article, we propose an integrated detection-estimation-forecasting framework that, using publicly available data, is designed to: (i) learn relevant features of the pandemic (e.g., the infection rate); (ii) detect as quickly as possible the onset (or the termination) of an exponential growth of the contagion; and (iii) reliably forecast the pandemic evolution. The proposed solution is validated by analyzing the COVID-19 second and third waves in the USA.
Social Media, Content Moderation, and Technology
Authors: Yi Liu, Pinar Yildirim, Z. John Zhang
Date: 2021-01-12 00:00:00
Tasks: Marketing

This paper develops a theoretical model to study the economic incentives for a social media platform to moderate user-generated content. We show that a self-interested platform can use content moderation as an effective marketing tool to expand its installed user base, to increase the utility of its users, and to achieve its positioning as a moderate or extreme content platform. The optimal content moderation strategy differs for platforms with different revenue models, advertising or subscription. We also show that a platform's content moderation strategy depends on its technical sophistication. Because of imperfect technology, a platform may optimally throw away the moderate content more than the extreme content. Therefore, one cannot judge how extreme a platform is by just looking at its content moderation strategy. Furthermore, we show that a platform under advertising does not necessarily benefit from a better technology for content moderation, but one under subscription does. This means that platforms under different revenue models can have different incentives to improve their content moderation technology. Finally, we draw managerial and policy implications from our insights.
Non-Orthogonal Multiple Access and Network Slicing: Scalable Coexistence of eMBB and URLLC
Authors: Eduardo Noboro Tominaga, Hirley Alves, Richard Demo Souza, João Luiz Rebelatto, Matti Latva-aho
Date: 2021-01-12 00:00:00

The 5G systems will feature three generic services: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service. Under the network slicing, the radio resources can be shared in orthogonal and non-orthogonal schemes. Motivated by Industrial Internet of Things (IIoT) scenarios where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose the use of Non-Orthogonal Multiple Access (NOMA) to improve the number of URLLC users that are connected in the uplink to the same base station (BS), for both orthogonal and non-orthogonal network slicing with eMBB users. The multiple URLLC users transmit simultaneously and across multiple frequency channels. We set the reliability requirements for the two services and analyze their pair of sum rates. We show that, even with overlapping transmissions from multiple eMBB and URLLC users, the use of NOMA techniques allows us to guarantee the reliability requirements for both services.
Wild Randomness, and the application of Hyperbolic Diffusion in Financial Modelling
Authors: Will Hicks
Date: 2021-01-12 00:00:00

The application of the Cauchy distribution has often been discussed as a potential model of the financial markets. In particular the way in which single extreme, or "Black Swan", events can impact long term historical moments, is often cited. In this article we show how one can construct Martingale processes, which have marginal distributions that tend to the Cauchy distribution in the large volatility limit. This provides financial justification to approaches discussed by other authors, and highlights an example of how quantum probability can be used to construct non-Gaussian Martingales. We go on to illustrate links with hyperbolic diffusion, and discuss the insight this provides.
Quantify Change of Inertia and Its Distribution in High Renewable Power Grids Using PMU
Authors: Shutang You
Date: 2021-01-12 00:00:00

This paper proposed an approach to identify the change of inertia distribution in high renewable power systems. Using the footprints of electromechanical wave propagation at the distribution level, this approach provides a new and non-invasive way to aware the system inertia distribution for primary frequency response. Actual measurements and high renewable dynamic models validated effectiveness of the approach.
Multiple Idiopathic Cervical Root Resorption: A Challenge for a Transdisciplinary Medical-Dental Team
Authors: Emily Y. Chu, Janina Golob Deeb, Brian L. Foster, Evlambia Hajishengalis, Martha J. Somerman, Vivek Thumbigere-Math
Date: 2021-01-12 00:00:00
Tasks: Management

While tooth root resorption is a normal physiological process required for resorption and exfoliation of primary teeth, root resorption of adult teeth is largely pathological. This perspective focuses on multiple idiopathic cervical root resorption (MICRR), an aggressive form of external root resorption that occurs near the cemento-enamel junction (CEJ). The cause of MICRR remains elusive, however, it is mediated primarily by osteoclasts/odontoclasts. Accumulating case studies and experiments in animal models have provided insights into defining the etiologies and pathophysiological mechanisms for MICRR, which include: systemic conditions and syndromes, inherited genetic variants affecting osteoclast/odontoclast activity, altered periodontal structures, drug-induced root resorption and rebound effects after cessation of anti-resorptive treatment, chemotherapy, exposure to pets or viral infections, and other factors such as inflammatory conditions or trauma. To determine the causative factors, at minimum, a comprehensive health history should be collected for all patients by dental care providers, discussed with other health care providers and appropriate collaborations established. The examples highlighted in this perspective emphasize the need for transdisciplinary research collaborations coupled with integrated management strategies between medicine and dentistry in order to identify cause(s) early and improve clinical outcomes.
Experiential Learning Styles and Neurocognitive Phenomics
Authors: Włodzisław Duch
Date: 2021-01-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Phenomics is concerned with detailed description of all aspects of organisms, from their physical foundations at genetic, molecular and cellular level, to behavioural and psychological traits. Neuropsychiatric phenomics, endorsed by NIMH, provides such broad perspective to understand mental disorders. It is clear that learning sciences also need similar approach that will integrate efforts to understand cognitive processes from the perspective of the brain development, in temporal, spatial, psychological and social aspects. The brain is a substrate shaped by genetic, epigenetic, cellular and environmental factors including education, individual experiences and personal history, culture, social milieu. Learning sciences should thus be based on the foundation of neurocognitive phenomics. A brief review of selected aspects of such approach is presented, outlining new research directions. Central, peripheral and motor processes in the brain are linked to the inventory of the learning styles.
Efficient and flexible approach to ptychography using an optimization framework based on automatic differentiation
Authors: Jacob Seifert, Dorian Bouchet, Lars Loetgering, Allard P. Mosk
Date: 2020-10-05 00:00:00

Ptychography is a lensless imaging method that allows for wavefront sensing and phase-sensitive microscopy from a set of diffraction patterns. Recently, it has been shown that the optimization task in ptychography can be achieved via automatic differentiation (AD). Here, we propose an open-access AD-based framework implemented with TensorFlow, a popular machine learning library. Using simulations, we show that our AD-based framework performs comparably to a state-of-the-art implementation of the momentum-accelerated ptychographic iterative engine (mPIE) in terms of reconstruction speed and quality. AD-based approaches provide great flexibility, as we demonstrate by setting the reconstruction distance as a trainable parameter. Lastly, we experimentally demonstrate that our framework faithfully reconstructs a biological specimen.
Arbitraging Narrow Bracketers
Authors: Francesco Fallucchi, Marc Kaufmann
Date: 2021-01-12 00:00:00

Many important economic outcomes result from the combined effects of several choices, so the best option is not determined from each choice in isolation, but depends on how each choice alters total outcomes. We formally show that narrow bracketing -- treating choices in isolation -- can be distinguished from broad bracketing -- combining the choices -- if and only if there exist price variation across context: there is some bundle for which a person is willing to pay more in one choice than in another. In this case, a narrow bracketer can be arbitraged, buying the bundle when it is expensive and selling when it is cheap in simultaneous choices. We design and run two experiments to identify bracketing from price variation. In a between-subjects design where we vary the amount of work to generate price variation, we reject broad bracketing and fail to reject narrow bracketing. In a within-subject design we directly test bracketing by attempting to arbitrage our participants. For price variation coming from varying amounts of money, 50.3% of subjects are classified as narrow bracketers, and only 14.6% as broad bracketers, the remainder being inconsistent with both. This changes for price variation coming from violations of expected utility -- 13.6% narrow vs 46.3% broad -- and of the weak axiom of revealed preference -- 26.3% narrow vs 38.1% broad.
Transient Theoretical Analysis of Diffusion RLS Algorithm for Cyclostationary Colored Inputs
Authors: Wei Gao, Jie Chen, Cédric Richard
Date: 2021-01-12 00:00:00

Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been reported yet. In this letter, we conduct a theoretical analysis of the transient behavior of the DRLS algorithm for cyclostationary colored inputs, in the mean and mean-square error sense. The resulting analytical models allows us to thoroughly investigate the convergence behavior of the algorithm over adaptive networks in such complex scenarios. Simulation results support the accuracy and correctness of the theoretical findings.