Time-series Generation by Contrastive Imitation

Consider learning a generative model for time-series data. The sequential setting poses a unique challenge: Not only should the generator capture the conditional dynamics of (stepwise) transitions, but its open-loop rollouts should also preserve the joint distribution of (multi-step) trajectories. On one hand, autoregressive models trained by MLE allow learning and computing explicit transition distributions, but suffer from compounding error during rollouts. On the other hand, adversarial models based on GAN training alleviate such exposure bias, but transitions are implicit and hard to assess. In this work, we study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy, where the reinforcement signal is provided by a global (but stepwise-decomposable) energy model trained by contrastive estimation. At training, the two components are learned cooperatively, avoiding the instabilities typical of adversarial objectives. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality. By expressly training a policy to imitate sequential behavior of time-series features in a dataset, this approach embodies "generation by imitation". Theoretically, we illustrate the correctness of this formulation and the consistency of the algorithm. Empirically, we evaluate its ability to generate predictively useful samples from real-world datasets, verifying that it performs at the standard of existing benchmarks

Invariant Causal Imitation Learning for Generalizable Policies

Consider learning an imitation policy on the basis of demonstrated behavior from multiple environments, with an eye towards deployment in an unseen environment. Since the observable features from each setting may be different, directly learning individual policies as mappings from features to actions is prone to spurious correlations -- and may not generalize well. However, the expert's policy is often a function of a shared latent structure underlying those observable features that is invariant across settings. By leveraging data from multiple environments, we propose Invariant Causal Imitation Learning (ICIL), a novel technique in which we learn a feature representation that is invariant across domains, on the basis of which we learn an imitation policy that matches expert behavior. To cope with transition dynamics mismatch, ICIL learns a shared representation of causal features (for all training environments), that is disentangled from the specific representations of noise variables (for each of those environments). Moreover, to ensure that the learned policy matches the observation distribution of the expert's policy, ICIL estimates the energy of the expert's observations and uses a regularization term that minimizes the imitator policy's next state energy. Experimentally, we compare our methods against several benchmarks in control and healthcare tasks and show its effectiveness in learning imitation policies capable of generalizing to unseen environments

Feature importance in multi-dimensional tissue-engineering datasets: random forest assisted optimization of experimental variables for collagen scaffolds

Ice-templated collagen-based tissue-engineering scaffolds are ideal for controlled tissue regeneration since they mimic the micro-environment experienced in vivo. The structure and properties of scaffolds are fine-tuned during fabrication by controlling a number of experimental parameters. However, this parameter space is large and complex, rendering the interpretation of results and selection of optimal parameters to be challenging in practice. This paper investigates the impact of a cross section of this parameter space (drying conditions and solute environment) on the scaffold microstructure. Qualitative assessment revealed the previously unreported impact of drying temperature and pressure on pore wall roughness, and confirmed the influence of collagen concentration, solvent type, and solute addition on pore morphology. For quantitative comparison, we demonstrate the novel application of random forest regression to analyze multi-dimensional biomaterials datasets, and predict microstructural attributes for a scaffold. Using these regression models, we assessed the relative importance of the input experimental parameters on quantitative pore measurements. Collagen concentration and pH were found to be the largest factors in determining pore size and connectivity. Furthermore, circular dichroism peak intensities were also revealed to be a good predictor for structural variations, which is a parameter that has not previously been investigated for its effect on a scaffold microstructure. Thus, this paper demonstrates the potential for predictive models such as random forest regressors to discover novel relationships in biomaterials datasets. These relationships between parameters (such as circular dichroism spectra and pore connectivity) can therefore also be used to identify and design further avenues of investigation within biomaterials

DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction

Leveraging labelled data from multiple domains to enable prediction in another domain without labels is a significant, yet challenging problem. To address this problem, we introduce the framework DAPDAG (\textbf{D}omain \textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and propose to learn an auto-encoder that undertakes inference on population statistics given features and reconstructing a directed acyclic graph (DAG) as an auxiliary task. The underlying DAG structure is assumed invariant among observed variables whose conditional distributions are allowed to vary across domains led by a latent environmental variable $E$. The encoder is designed to serve as an inference device on $E$ while the decoder reconstructs each observed variable conditioned on its graphical parents in the DAG and the inferred $E$. We train the encoder and decoder jointly in an end-to-end manner and conduct experiments on synthetic and real datasets with mixed variables. Empirical results demonstrate that reconstructing the DAG benefits the approximate inference. Furthermore, our approach can achieve competitive performance against other benchmarks in prediction tasks, with better adaptation ability, especially in the target domain significantly different from the source domains

Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study.

BACKGROUND: Brazil ranks second worldwide in total number of COVID-19 cases and deaths. Understanding the possible socioeconomic and ethnic health inequities is particularly important given the diverse population and fragile political and economic situation. We aimed to characterise the COVID-19 pandemic in Brazil and assess variations in mortality according to region, ethnicity, comorbidities, and symptoms. METHODS: We conducted a cross-sectional observational study of COVID-19 hospital mortality using data from the SIVEP-Gripe (Sistema de Informação de Vigilância Epidemiológica da Gripe) dataset to characterise the COVID-19 pandemic in Brazil. In the study, we included hospitalised patients who had a positive RT-PCR test for severe acute respiratory syndrome coronavirus 2 and who had ethnicity information in the dataset. Ethnicity of participants was classified according to the five categories used by the Brazilian Institute of Geography and Statistics: Branco (White), Preto (Black), Amarelo (East Asian), Indígeno (Indigenous), or Pardo (mixed ethnicity). We assessed regional variations in patients with COVID-19 admitted to hospital by state and by two socioeconomically grouped regions (north and central-south). We used mixed-effects Cox regression survival analysis to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation. FINDINGS: Of 99 557 patients in the SIVEP-Gripe dataset, we included 11 321 patients in our study. 9278 (82·0%) of these patients were from the central-south region, and 2043 (18·0%) were from the north region. Compared with White Brazilians, Pardo and Black Brazilians with COVID-19 who were admitted to hospital had significantly higher risk of mortality (hazard ratio [HR] 1·45, 95% CI 1·33-1·58 for Pardo Brazilians; 1·32, 1·15-1·52 for Black Brazilians). Pardo ethnicity was the second most important risk factor (after age) for death. Comorbidities were more common in Brazilians admitted to hospital in the north region than in the central-south, with similar proportions between the various ethnic groups. States in the north had higher HRs compared with those of the central-south, except for Rio de Janeiro, which had a much higher HR than that of the other central-south states. INTERPRETATION: We found evidence of two distinct but associated effects: increased mortality in the north region (regional effect) and in the Pardo and Black populations (ethnicity effect). We speculate that the regional effect is driven by increasing comorbidity burden in regions with lower levels of socioeconomic development. The ethnicity effect might be related to differences in susceptibility to COVID-19 and access to health care (including intensive care) across ethnicities. Our analysis supports an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect Pardo and Black Brazilians, as well as the population of poorer states, from their higher risk of dying of COVID-19. FUNDING: None

Diabetes mellitus and necrotizing fasciitis – a deadly combination; case report

Necrotizing fasciitis is a rapidly destructive affliction of soft tissues, with a mortality rate that may reach 73% of the cases. It is characterized by a progressive inflammation and extended necrosis of the subcutaneous tissue and the fascia. Necrotizing fasciitis was first described in 1848, and later in 1920 Meleney identified 20 patients in China in which the infection was presumably triggered by hemolytic streptococcus, linking pathological bacteria to the condition. In 1952, Wilson coined the term necrotizing fasciitis although without successfully identifying the specific pathological bacteria involved. In most cases, both risk and aggravating factors are present, the main risk factors being diabetes mellitus, liver cirrhosis, renal failure, and immunosuppressant states. Location may vary, but most frequently the disease occurs in the limbs, the trunk, and the perineum. Treatment depends on the location and the time of diagnosis and may range from large incisions with extensive debridement to organ amputations such as those of the limbs or breasts. Treatment is complex and expensive, and besides surgery, includes the administration of broad-spectrum antibiotics, anti-inflammatory drugs, intensive therapy support, and long-term hospitalizations. The prognosis is guarded. The present case entails a 56-year old female patient who presented with many risk factors favoring the occurrence of necrotizing fasciitis, namely diabetes mellitus, liver cirrhosis (decompensated with ascites and portal encephalopathy phenomena), untreated hepatitis B infection, chronic renal failure with diabetic nephrotic syndrome, and obesity