The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment

Recently, the high penetration of mobile devices and internet access offers a new source of fine-grained user behavior data (aka “alternative data”) to improve the financial credit risk assessment. This paper conducts a comprehensive evaluation of the value of alternative data on microloan platforms with a large field experiment. Our machine-learning-based empirical analyses demonstrate that alternative data can significantly improve the prediction accuracy of borrowers’ default behavior and increase platform profits. Cellphone usage and mobility trace information perform the best among the multiple sources of alternative data. Moreover, we find that our proposed framework helps financial institutions extend their service to more lower-income and less-educated loan applicants from less-developed geographical areas – those historically disadvantaged population who have been largely neglected in the past. Our study demonstrates the tremendous potential of leveraging alternative data to alleviate such inequality in the financial service markets, while in the meantime achieving higher platform revenues

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design

Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit

We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model’s ability to triangulate (i.e., infer the sensitive group affiliation by using only permissible features), which is often deemed “troublesome” in fair machine learning research, in a positive way to increase both borrower welfare and lender profits while improving fairness. We train and test our model on a dataset from a real-world microloan company. Our model significantly outperforms regular deep neural networks without adversaries and the most popular credit risk model XGBoost, in terms of both improving borrowers’ welfare and lenders’ profits. Our empirical findings also suggest that the traditional AUC metric cannot reflect a model\u27s performance on the borrowers’ welfare and lenders’ profits. Our framework is ready to be customized for other microloan firms, and can be easily adapted to many other decision-making scenarios

Efficacy and Safety of Prophylactic Vaccines against Cervical HPV Infection and Diseases among Women: A Systematic Review & MetaAnalysis

Background: We conducted a systematic review and meta-analysis to assess efficacy and safety of prophylactic HPV vaccines against cervical cancer precursor events in women. Methods: Randomized-controlled trials of HPV vaccines were identified from MEDLINE, Cochrane Central Register of Controlled Trials, conference abstracts and references of identified studies, and assessed by two independent reviewers. Efficacy data were synthesized using fixed-effect models, and evaluated for heterogeneity using I-2 statistic. Results: Seven unique trials enrolling 44,142 females were included. The fixed-effect Relative Risk (RR) and 95% confidence intervals were 0.04 (0.01-0.11) and 0.10 (0.03-0.38) for HPV-16 and HPV 18-related CIN2+ in the per-protocol populations (PPP). The corresponding RR was 0.47 (0.36-0.61) and 0.16 (0.08-0.34) in the intention-to-treat populations (ITT). Efficacy against CIN1+ was similar in scale in favor of vaccine. Overall vaccines were highly efficacious against 6-month persistent infection with HPV 16 and 18, both in the PPP cohort (RR: 0.06 [0.04-0.09] and 0.05 [0.03-0.09], respectively), and the ITT cohorts (RR: 0.15 [0.10-0.23] and 0.24 [0.14-0.42], respectively). There was limited prophylactic effect against CIN2+ and 6-month persistent infections associated with non-vaccine oncogenic HPV types. The risk of serious adverse events (RR: 1.00, 0.91-1.09) or vaccine-related serious adverse events (RR: 1.82; 0.79-4.20) did not differ significantly between vaccine and control groups. Data on abnormal pregnancy outcomes were underreported. Conclusions: Prophylactic HPV vaccines are safe, well tolerated, and highly efficacious in preventing persistent infections and cervical diseases associated with vaccine-HPV types among young females. However, long-term efficacy and safety needs to be addressed in future trials

Neural Super-Resolution for Real-time Rendering with Radiance Demodulation

Rendering high-resolution images in real-time applications (e.g., video games, virtual reality) is time-consuming, thus super-resolution technology becomes more and more crucial in real-time rendering. However, it is still challenging to preserve sharp texture details, keep the temporal stability and avoid the ghosting artifacts in the real-time rendering super-resolution. To this end, we introduce radiance demodulation into real-time rendering super-resolution, separating the rendered image or radiance into a lighting component and a material component, due to the fact that the light component tends to be smoother than the rendered image and the high-resolution material component with detailed textures can be easily obtained. Therefore, we perform the super-resolution only on the lighting component and re-modulate with the high-resolution material component to obtain the final super-resolution image. In this way, the texture details can be preserved much better. Then, we propose a reliable warping module by explicitly pointing out the unreliable occluded regions with a motion mask to remove the ghosting artifacts. We further enhance the temporal stability by designing a frame-recurrent neural network to aggregate the previous and current frames, which better captures the spatial-temporal correlation between reconstructed frames. As a result, our method is able to produce temporally stable results in real-time rendering with high-quality details, even in the highly challenging 4 $\times$ 4 super-resolution scenarios

Contactless Haptic Display Through Magnetic Field Control

Haptic rendering enables people to touch, perceive, and manipulate virtual objects in a virtual environment. Using six cascaded identical hollow disk electromagnets and a small permanent magnet attached to an operator's finger, this paper proposes and develops an untethered haptic interface through magnetic field control. The concentric hole inside the six cascaded electromagnets provides the workspace, where the 3D position of the permanent magnet is tracked with a Microsoft Kinect sensor. The driving currents of six cascaded electromagnets are calculated in real-time for generating the desired magnetic force. Offline data from an FEA (finite element analysis) based simulation, determines the relationship between the magnetic force, the driving currents, and the position of the permanent magnet. A set of experiments including the virtual object recognition experiment, the virtual surface identification experiment, and the user perception evaluation experiment were conducted to demonstrate the proposed system, where Microsoft HoloLens holographic glasses are used for visual rendering. The proposed magnetic haptic display leads to an untethered and non-contact interface for natural haptic rendering applications, which overcomes the constraints of mechanical linkages in tool-based traditional haptic devices

Efficient Point based Global Illumination on Intel MIC Architecture

International audiencePoint-Based Global Illumination (PBGI) is a popular rendering method in special effects and motion picture productions. The tree-cut computation is in general the most time consuming part of this algorithm, but it can be formulated for efficient parallel execution, in particular regarding wide-SIMD hardware. In this context, we propose several vectorization schemes, namely single, packet and hybrid, to maximize the utilization of modern CPU architectures. While for the single scheme, 16 nodes from the hierarchy are processed for a single receiver in parallel, the packet scheme handles one node for 16 receivers. These two schemes work well for scenes having smooth geometry and diffuse material. When the scene contains high frequency bumps maps and glossy reflections, we use a hybrid vectorization method. We conduct experiments on an Intel Many Integrated Corearchitecture and report preliminary results on several scenes, showing that up to a 3x speedup can be achieved when compared with non-vectorized execution

Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks

Objectives The interpretability of convolutional neural networks (CNNs) for classifying subsolid nodules (SSNs) is insufficient for clinicians. Our purpose was to develop CNN models to classify SSNs on CT images and to investigate image features associated with the CNN classification. Methods CT images containing SSNs with a diameter o

Fast Computation of Single Scattering in Participating Media with Refractive Boundaries using Frequency Analysis

International audienceMany materials combine a refractive boundary and a participating media on the interior. If the material has a low opacity, single scattering effects dominate in its appearance. Refraction at the boundary concentrates the incoming light, resulting in an important phenomenon called volume caustics. This phenomenon is hard to simulate. Previous methods used point-based light transport, but attributed point samples inefficiently, resulting in long computation time. In this paper, we use frequency analysis of light transport to allocate point samples efficiently. Our method works in two steps: in the first step, we compute volume samples along with their covariance matrices, encoding the illumination frequency content in a compact way. In the rendering step, we use the covariance matrices to compute the kernel size for each volume sample: small kernel for high-frequency single scattering, large kernel for lower frequencies. Our algorithm computes volume caustics with fewer volume samples, with no loss of quality. Our method is both faster and uses less memory than the original method. It is roughly twice as fast and uses one fifth of the memory. The extra cost of computing covariance matrices for frequency information is negligible