Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Function

We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We derive loss functions that produce dual objectives with a sparse solution. We also show how to regularize convex losses to ensure such a dual sparsity-inducing property, and propose a general method to design screening tests for classification or regression based on ellipsoidal approximations of the optimal set. In addition to producing computational gains, our approach also allows us to compress a dataset into a subset of representative points.
Generative Synthesis of Insurance Datasets

One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets. In this work, we develop a workflow for synthesizing insurance datasets leveraging CTGAN, a recently proposed neural network architecture for generating tabular data. Applying the proposed workflow to publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling, we evaluate the synthesized datasets from a few perspectives: machine learning efficacy, distributions of variables, and stability of model parameters. This workflow is implemented via an R interface to promote adoption by researchers and data owners.
HABNet: Machine Learning, Remote Sensing Based Detection and Prediction of Harmful Algal Blooms

This paper describes the application of machine learning techniques to develop a state-of-the-art detection and prediction system for spatiotemporal events found within remote sensing data; specifically, Harmful Algal Bloom events (HABs). We propose an HAB detection system based on: a ground truth historical record of HAB events, a novel spatiotemporal datacube representation of each event (from MODIS and GEBCO bathymetry data) and a variety of machine learning architectures utilising state-of-the-art spatial and temporal analysis methods based on Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) components together with Random Forest and Support Vector Machine (SVM) classification methods. This work has focused specifically on the case study of the detection of Karenia Brevis Algae (K. brevis) HAB events within the coastal waters of Florida (over 2850 events from 2003 to 2018; an order of magnitude larger than any previous machine learning detection study into HAB events). The development of multimodal spatiotemporal datacube data structures and associated novel machine learning methods give a unique architecture for the automatic detection of environmental events. Specifically, when applied to the detection of HAB events it gives a maximum detection accuracy of 91% and a Kappa coefficient of 0.81 for the Florida data considered. A HAB forecast system was also developed where a temporal subset of each datacube was used to predict the presence of a HAB in the future. This system was not significantly less accurate than the detection system being able to predict with 86% accuracy up to 8 days in the future.
KoPA: Automated Kronecker Product Approximation

We consider the problem of matrix approximation and denoising induced by the Kronecker product decomposition. Specifically, we propose to approximate a given matrix by the sum of a few Kronecker products of matrices, which we refer to as the Kronecker product approximation (KoPA). Because the Kronecker product is an extension of the outer product from vectors to matrices, KoPA extends the low rank matrix approximation, and includes it as a special case. Comparing with the latter, KoPA also offers a greater flexibility, since it allows the user to choose the configuration, which are the dimensions of the two smaller matrices forming the Kronecker product. On the other hand, the configuration to be used is usually unknown, and needs to be determined from the data in order to achieve the optimal balance between accuracy and parsimony. We propose to use extended information criteria to select the configuration. Under the paradigm of high dimensional analysis, we show that the proposed procedure is able to select the true configuration with probability tending to one, under suitable conditions on the signal-to-noise ratio. We demonstrate the superiority of KoPA over the low rank approximations through numerical studies, and several benchmark image examples.
Is perturbation an effective restart strategy?

Premature convergence can be detrimental to the performance of search methods, which is why many search algorithms include restart strategies to deal with it. While it is common to perturb the incumbent solution with diversification steps of various sizes with the hope that the search method will find a new basin of attraction leading to a better local optimum, it is usually not clear how big the perturbation step should be. We introduce a new property of fitness landscapes termed "Neighbours with Similar Fitness" and we demonstrate that the effectiveness of a restart strategy depends on this property.
Magnetic Connectivity of the Ecliptic Plane within 0.5 au: Potential Field Source Surface Modeling of the First Parker Solar Probe Encounter

We compare magnetic field measurements taken by the FIELDS instrument on board Parker Solar Probe (PSP) during its first solar encounter to predictions obtained by potential field source surface (PFSS) modeling. Ballistic propagation is used to connect the spacecraft to the source surface. Despite the simplicity of the model, our results show striking agreement with PSP’s first observations of the heliospheric magnetic field from ∼0.5 au (107.5 R⊙) down to 0.16 au (35.7 R⊙). Further, we show the robustness of the agreement is improved both by allowing the photospheric input to the model to vary in time, and by advecting the field from PSP down to the PFSS model domain using in situ PSP/Solar Wind Electrons Alphas and Protons measurements of the solar wind speed instead of assuming it to be constant with longitude and latitude. We also explore the source surface height parameter (RSS) to the PFSS model, finding that an extraordinarily low source surface height (1.3–1.5 R⊙) predicts observed small-scale polarity inversions, which are otherwise washed out with regular modeling parameters. Finally, we extract field line traces from these models. By overlaying these on extreme ultraviolet images we observe magnetic connectivity to various equatorial and mid-latitude coronal holes, indicating plausible magnetic footpoints and offering context for future discussions of sources of the solar wind measured by PSP.
Global 21 cm Signal Extraction from Foreground and Instrumental Effects. II. Efficient and Self-consistent Technique for Constraining Nonlinear Signal Models

We present the completion of a data analysis pipeline that self-consistently separates global 21 cm signals from large systematics using a pattern recognition technique. This pipeline will be used for both ground and space-based hydrogen cosmology instruments. In the first paper of this series, we obtain optimal basis vectors from signal and foreground training sets to linearly fit both components with the minimal number of terms that best extracts the signal given its overlap with the foreground. In this second paper, we utilize the spectral constraints derived in the first paper to calculate the full posterior probability distribution of any signal parameter space of choice. The spectral fit provides the starting point for a Markov Chain Monte Carlo (MCMC) engine that samples the signal without traversing the foreground parameter space. At each MCMC step, we marginalize over the weights of all linear foreground modes and suppress those with unimportant variations by applying priors gleaned from the training set. This method drastically reduces the number of MCMC parameters, augmenting the efficiency of exploration, circumvents the need for selecting a minimal number of foreground modes, and allows the complexity of the foreground model to be greatly increased to simultaneously describe many observed spectra without requiring extra MCMC parameters. Using two nonlinear signal models, one based on the Experiment to Detect the Global Epoch-of-Reionization Signature (EDGES) observations and the other on phenomenological frequencies and temperatures of theoretically expected extrema, we demonstrate the success of this methodology by recovering the input parameters from multiple randomly simulated signals at low radio frequencies (10–200 MHz), while rigorously accounting for realistically modeled beam-weighted foregrounds.
Quality Selection in Two-Sided Markets : A Constrained Price Discrimination Approach ∗ PRELIMINARY AND INCOMPLETE

Online platforms collect rich information about participants, and then share this information back with participants to improve market outcomes. In this paper we study the following information disclosure problem of a two-sided market: how much of its available information about sellers’ quality should the platform share with buyers to maximize its revenue? One key innovation in our analysis is to reduce the study of optimal information disclosure policies to a constrained price discrimination problem. The information shared by the platform induces a “menu” of equilibrium prices and sellers’ expected qualities. Optimization over feasible menus yields a price discrimination problem. The problem is constrained because feasible menus are only those that can arise in the equilibrium of the two sided-market for some information disclosure policy. We analyze this constrained price discrimination problem, and apply our insights to two distinct two-sided market models: one in which the platform chooses prices and sellers choose quantities (similar to ride-sharing), and one in which sellers We would like to thank Dirk Bergemann, Kostas Bimpikis, Nicole Immorlica, Yiangos Papanastasiou, Mike Ostrovsky, as well as seminar participants at Simons Institute, Stanford, INFORMS RMP, and INFORMS annual meeting for helpful comments and advice. Department of Management Science and Engineering, Stanford University, Stanford, CA 94305, USA. e-mail: rjohari@stanford.edu Graduate School of Business, Stanford University, Stanford, CA 94305, USA. e-mail: barl@stanford.edu Graduate School of Business, Stanford University, Stanford, CA 94305, USA. e-mail: gweintra@stanford.edu choose prices (similar to e-commerce). We provide conditions under which a simple information structure of banning a certain portion of sellers from the platform, and not sharing any information about the remaining participating sellers maximizes the platforms revenue.
Characterization of non-collagenous extracellular matrix proteins in cardiac and aortic valve remodelling

Heart failure (HF) and aortic valve stenosis (AS) are complex disorders affected by functional alterations and actively regulated remodelling of the heart and the aortic valve, respectively. In addition to structural proteins, such as collagens and elastin, the extracellular matrix (ECM) in the heart and the aortic valve comprises non-collagenous factors that are not strictly involved in the architecture but may modulate cardiac and valvular remodelling. In the present study the expression of non-collagenous fibrosisand calcification-related ECM proteins was investigated in HF-associated cardiac remodelling from different origins as well as in fibrocalcific aortic valve disease leading to AS. The experimental models of pressure overload, myocardial infarction (MI) and chronic renal failure were used to study the cardiac expression of bone morphogenetic protein (BMP)-2, BMP4, bone sialoprotein, matrix Gla protein (MGP), osteoactivin, osteopontin, periostin and/or pleiotrophin in vivo in cardiac remodelling. Human aortic valves, obtained from patients undergoing valve replacement, were studied to characterize the valvular expression of BMP-2, BMP-4, bone sialoprotein, MGP, osteoactivin, osteopontin, osteoprotegerin, periostin, pleiotrophin, and thrombospondins (TSPs) 1-4 in the different stages of fibrocalcific aortic valve disease. Left ventricular (LV) MGP expression was upregulated in vivo in non-uremic cardiac remodelling. In vitro results indicate that angiotensin II elevates MGP expression in cardiomyocytes and fibroblasts. Periostin gene expression was induced in cardiac but not in aortic valve remodelling and the cardiac induction in chronic renal insufficiency was associated with LV hypertrophy and blood pressure as well as the cardiac gene expression of other fibrosis-related genes. Bone sialoprotein and osteopontin were expressed in the aortic valves in parallel with calcification, and also in distinct models of cardiac remodelling. Osteoprotegerin protein expression in stenotic valves was weak regardless of a simultaneous increase in gene expression. BMPs were downregulated in AS and no change in LV gene expression was detected in uremic cardiac remodelling. All the studied TSPs were expressed in human aortic valves, and especially the expression of TSP-2 was shown to increase in fibrocalcific aortic valves simultaneously with decreased activation of the Akt/nuclear factor (NF)-κB-pathway. This study delineates distinct expression patterns of non-collagenous ECM proteins in pathological tissue remodelling in the heart and in the aortic valve, and thus emphasizes the role of ECM proteins as an important modulator of cardiac and aortic valve remodelling.
All-Digital Massive MIMO Uplink and Downlink Rates under a Fronthaul Constraint

We characterize the rate achievable in a bidirectional quasi-static link where several user equipments communicate with a massive multiple-input multiple-output base station (BS). In the considered setup, the BS operates in full-digital mode, the physical size of the antenna array is limited, and there exists a rate constraint on the fronthaul interface connecting the (possibly remote) radio head to the digital baseband processing unit. Our analysis enables us to determine the optimal resolution of the analog-to- digital and digital-to-analog converters as well as the optimal number of active antenna elements to be used in order to maximize the transmission rate on the bidirectional link, for a given constraint on the outage probability and on the fronthaul rate. We investigate both the case in which perfect channel-state information is available, and the case in which channel-state information is acquired through pilot transmission, and is, hence, imperfect. For the second case, we present a novel rate expression that relies on the generalized mutual-information framework.
Estimating Large Mixed-Frequency Bayesian VAR Models

We discuss the issue of estimating large-scale vector autoregressive (VAR) models with stochastic volatility in real-time situations where data are sampled at different frequencies. In the case of a large VAR with stochastic volatility, the mixed-frequency data warrant an additional step in the already computationally challenging Markov Chain Monte Carlo algorithm used to sample from the posterior distribution of the parameters. We suggest the use of a factor stochastic volatility model to capture a time-varying error covariance structure. Because the factor stochastic volatility model renders the equations of the VAR conditionally independent, settling for this particular stochastic volatility model comes with major computational benefits. First, we are able to improve upon the mixed-frequency simulation smoothing step by leveraging a univariate and adaptive filtering algorithm. Second, the regression parameters can be sampled equation-by-equation in parallel. These computational features of the model alleviate the computational burden and make it possible to move the mixed-frequency VAR to the high-dimensional regime. We illustrate the model by an application to US data using our mixed-frequency VAR with 20, 34 and 119 variables.
Deep Learning Based Segmentation Free License Plate Recognition Using Roadway Surveillance Camera Images

Smart automated traffic enforcement solutions have been gaining popularity in recent years. These solutions are ubiquitously used for seat-belt violation detection, red-light violation detection and speed violation detection purposes. Highly accurate license plate recognition is an indispensable part of these systems. However, general license plate recognition systems require high resolution images for high performance. In this study, we propose a novel license plate recognition method for general roadway surveillance cameras. Proposed segmentation free license plate recognition algorithm utilizes deep learning based object detection techniques in the character detection and recognition process. Proposed method has been tested on 2000 images captured on a roadway.
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms

In this paper, we introduce a unified framework for analyzing a large family of Q-learning algorithms, based on switching system perspectives and ODE-based stochastic approximation. We show that the nonlinear ODE models associated with these Q-learning algorithms can be formulated as switched linear systems, and analyze their asymptotic stability by leveraging existing switching system theories. Our approach provides the first O.D. E. analysis of the asymptotic convergence of various Q-learning algorithms, including asynchronous Q-learning and averaging Q-learning. We also extend the approach to analyze Q-learning with linear function approximation and derive a new sufficient condition for its convergence.
Application of a multi-sector parabolic equation approach to compute acoustic scattering by non-canonically shaped impenetrable objects

While parabolic equation (PE) methods have seen great use in ocean acoustic waveguide propagation, it is not as widely known that a multi-sector PE method, originally proposed by Levy and Zaporozhets [J. Acoust. Soc. Am. 103, 735–741 (1998)], can be applied to compute the scattered field about an object. Previous work was only able to benchmark the method with canonical geometries. In this talk, we demonstrate favorable agreement between free-field PE-based and finite-element based computations, the latter being taken as a benchmark, in two and three dimensions for non-canonically shaped impenetrable objects. Run-time scaling comparisons are also presented, which demonstrate the great advantage of the PE method in the high-frequency limit when a target is several or more wavelengths long. We also show how wide-angle PE and multiple-scattering corrections can be incorporated to treat concave scatterers. [Work sponsored by the Office of Naval Research. ]While parabolic equation (PE) methods have seen great use in ocean acoustic waveguide propagation, it is not as widely known that a multi-sector PE method, originally proposed by Levy and Zaporozhets [J. Acoust. Soc. Am. 103, 735–741 (1998)], can be applied to compute the scattered field about an object. Previous work was only able to benchmark the method with canonical geometries. In this talk, we demonstrate favorable agreement between free-field PE-based and finite-element based computations, the latter being taken as a benchmark, in two and three dimensions for non-canonically shaped impenetrable objects. Run-time scaling comparisons are also presented, which demonstrate the great advantage of the PE method in the high-frequency limit when a target is several or more wavelengths long. We also show how wide-angle PE and multiple-scattering corrections can be incorporated to treat concave scatterers. [Work sponsored by the Office of Naval Research. ]
An Interpretable Prediction Model for Obesity Prediction using EHR Data

Childhood obesity is a major public health challenge. Obesity in early childhood and adolescence can lead to obesity and other health problems in adulthood. Early prediction and identification of the children at a high-risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage this and other related health conditions. Existing predictive tools designed for childhood obesity primarily rely on traditional regression-type methods without exploiting longitudinal patterns of children’s data (ignoring data temporality). In this present, we present a machine learning model specifically designed for predicting future obesity patterns from generally available items on children’s medical history. To do this, we have used a large unaugmented EHR (Electronic Health Record) dataset from a major pediatric health system in the US. We adopt a general LSTM (long short-term memory) network architecture for our model for training over dynamic (sequential) EHR data. We then modified and extended the architecture to accept static (demographic) data related to each individual. We have additionally included a set embedding and attention layers to compute the attention scores of each hidden layer corresponding to each input timestep. The attention score for each timestep were computed as an average score given to all the features associated with the timestep. These attention scores added interpretability at both timestep-level and the features associated with the timesteps.
Towards Robust Image Classification Using Sequential Attention Models

In this paper we propose to augment a modern neural-network architecture with an attention model inspired by human perception. Specifically, we adversarially train and analyze a neural model incorporating a human inspired, visual attention component that is guided by a recurrent top-down sequential process. Our experimental evaluation uncovers several notable findings about the robustness and behavior of this new model. First, introducing attention to the model significantly improves adversarial robustness resulting in state-of-the-art ImageNet accuracies under a wide range of random targeted attack strengths. Second, we show that by varying the number of attention steps (glances/fixations) for which the model is unrolled, we are able to make its defense capabilities stronger, even in light of stronger attacks --- resulting in a ``computational race'' between the attacker and the defender. Finally, we show that some of the adversarial examples generated by attacking our model are quite different from conventional adversarial examples --- they contain global, salient and \emph{spatially coherent} structures coming from the target class that would be recognizable even to a human, and work by distracting the attention of the model away from the main object in the original image.
Algorithm for Finding the Maximum Clique Based on Continuous Time Quantum Walk

In this work, we consider the application of continuous time quantum walking(CTQW) to the Maximum Clique(MC) Problem. Performing CTQW on graphs will generate distinct periodic probability amplitude for different vertices. We will show that the intensity of the probability amplitude at frequency indeed implies the clique structure of some special kinds of graph. And recursive algorithms with time complexity $O(N^5)$ in classical computers for finding the maximum clique are proposed. We have experimented on random graphs where each edge exists with probabilities 0.3, 0.5 and 0.7. Although counter examples are not found for random graphs, whether these algorithms are universal is not known to us.