Deep Latent State Space Models for Time-Series Generation

Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences

Rethinking Feature Extraction: Gradient-based Localized Feature Extraction for End-to-End Surgical Downstream Tasks

Several approaches have been introduced to understand surgical scenes through downstream tasks like captioning and surgical scene graph generation. However, most of them heavily rely on an independent object detector and region-based feature extractor. Encompassing computationally expensive detection and feature extraction models, these multi-stage methods suffer from slow inference speed, making them less suitable for real-time surgical applications. The performance of the downstream tasks also degrades from inheriting errors of the earlier modules of the pipeline. This work develops a detector-free gradient-based localized feature extraction approach that enables end-to-end model training for downstream surgical tasks such as report generation and tool-tissue interaction graph prediction. We eliminate the need for object detection or region proposal and feature extraction networks by extracting the features of interest from the discriminative regions in the feature map of the classification models. Here, the discriminative regions are localized using gradient-based localization techniques (e.g. Grad-CAM). We show that our proposed approaches enable the real-time deployment of end-to-end models for surgical downstream tasks. We extensively validate our approach on two surgical tasks: captioning and scene graph generation. The results prove that our gradient-based localized feature extraction methods effectively substitute the detector and feature extractor networks, allowing end-to-end model development with faster inference speed, essential for real-time surgical scene understanding tasks. The code is publicly available at https://github.com/PangWinnie0219/GradCAMDownstreamTask

Patient choice of health care providers in China: primary care facilities versus hospitals

As China’s health system is faced with challenges of overcrowded hospitals, there is a great need to better understand the recent patterns and determinants of people’s choice between primary care facilities and hospitals for outpatient care. Based on recent individual-level data from the China Health and Retirement Longitudinal Survey (CHARLS) and official province-level data from China health statistical yearbooks, we examine the patterns of outpatient visits to primary care facilities versus hospitals among middle-aged and older individuals and explore both supply- and demand-side correlates that explain these patterns. We find that 53% of outpatient visits were paid to primary care facilities as opposed to hospitals in 2015, compared to 60% in 2011. Both supply and demand factors were associated with this decline. On the supply side, we find that the density of primary care facilities did not account for this decline, but higher densities of hospitals and licensed doctors were associated with lower use of primary care facilities. On the demand side, we find that individuals with higher socioeconomic status and greater health care needs were less likely to use primary health care facilities. Our findings suggest that a high concentration of health care professionals in hospitals diverts patients away from primary care facilities. Staffing the primary care facilities with a well-trained health care workforce is the key to a well-functioning primary care system. The findings also suggest a need to address demand-side inequality issues

Fabrication tolerant and broadband polarization splitter and rotator based on a taper-etched directional coupler.

We propose a fabrication tolerant polarization splitter and rotator (PSR) on the silicon-on-insulator platform based on the mode-coupling mechanism. The PSR consists of a silicon wire waveguide coupled to a taper-etched waveguide. Compared to previously reported PSRs based on directional couplers which are sensitive to fabrication variations, the partially etched taper structure can compensate for fabrication inaccuracies. In addition, the taper-etched geometry breaks both the horizontal and vertical symmetries of the waveguide, introducing an additional degree of design freedom to accommodate different upper cladding layers. The proposed PSR can be readily integrated in a planar waveguide circuit using e.g. SiO(2) cladding, making it compatible with typical metal back-end-of-line processes. Our simulation results show that the PSR has a low TM-to-TE polarization conversion loss of -0.09 dB in the C-band (or a conversion efficiency of 98%). A low TE-to-TE through insertion loss (-0.07 dB) and a very low polarization crosstalk (-30 dB) over a wide wavelength range exceeding 160 nm with a large fabrication tolerance (50 nm) are numerically demonstrated

Neural Functional Transformers

The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input by operating directly over their weight spaces. Nevertheless, constructing expressive and efficient neural functional architectures that can handle high-dimensional weight-space objects remains challenging. This paper uses the attention mechanism to define a novel set of permutation equivariant weight-space layers and composes them into deep equivariant models called neural functional Transformers (NFTs). NFTs respect weight-space permutation symmetries while incorporating the advantages of attention, which have exhibited remarkable success across multiple domains. In experiments processing the weights of feedforward MLPs and CNNs, we find that NFTs match or exceed the performance of prior weight-space methods. We also leverage NFTs to develop Inr2Array, a novel method for computing permutation invariant latent representations from the weights of implicit neural representations (INRs). Our proposed method improves INR classification accuracy by up to $+17\%$ over existing methods. We provide an implementation of our layers at https://github.com/AllanYangZhou/nfn

Stress induced effects for advanced polarization control in silicon photonics components

We review the use of the oxide cladding stress-induced photoelastic effect to modify the polarization dependent properties in silicon-on-insulator (SOI) waveguide components, and highlight characteristics particular to this high index contrast (HIC) systems. The birefringence in SOI waveguides has its origin in the electromagnetic boundary conditions at the waveguide boundaries, and can be further modified by the presence of stress in the waveguiding materials. With typical stress levels in SiO2 films, which are often used as the upper cladding, the waveguide effective index can be altered anisotropically up to the order of 10−3 for ridges with heights ranging from 1 μm to 5 μm. This effect can be used effectively to counter the waveguide geometrical birefringence, allowing the waveguide cross-section profiles to be optimized for design criteria other than null geometrical birefringence. Design strategies are developed for using stress engineering to achieve a variety of functions. Polarization insensitive arrayed waveguide gratings (AWGs), polarization insensitive ring resonators, and polarization splitters and filters are demonstrated using these design principles

AMBIVALENT IMPLICATIONS OF HEALTH CARE INFORMATION SYSTEMS: A STUDY IN THE BRAZILIAN PUBLIC HEALTH CARE SYSTEM

This article evaluates social implications of the ""SIGA"" Health Care Information System (HIS) in a public health care organization in the city of Sao Paulo. The evaluation was performed by means of an in-depth case study with patients and staff of a public health care organization, using qualitative and quantitative data. On the one hand, the system had consequences perceived as positive such as improved convenience and democratization of specialized treatment for patients and improvements in work organization. On the other hand, negative outcomes were reported, like difficulties faced by employees due to little familiarity with IT and an increase in the time needed to schedule appointments. Results show the ambiguity of the implications of HIS in developing countries, emphasizing the need for a more nuanced view of the evaluation of failures and successes and the importance of social contextual factors