Modeling Latent Variable Uncertainty for Loss-based Learning

We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.Comment: ICML201

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals

Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover meaningful counterfactuals and efficiently label them, with minimal human labeling cost. Most existing methods either completely rely on human-annotated labels, an expensive process which limits the scale of counterfactual data, or implicitly assume label invariance, which may mislead the model with incorrect labels. In this paper, we present a novel framework that utilizes counterfactual generative models to generate a large number of diverse counterfactuals by actively sampling from regions of uncertainty, and then automatically label them with a learned pairwise classifier. Our key insight is that we can more correctly label the generated counterfactuals by training a pairwise classifier that interpolates the relationship between the original example and the counterfactual. We demonstrate that with a small amount of human-annotated counterfactual data (10%), we can generate a counterfactual augmentation dataset with learned labels, that provides an 18-20% improvement in robustness and a 14-21% reduction in errors on 6 out-of-domain datasets, comparable to that of a fully human-annotated counterfactual dataset for both sentiment classification and question paraphrase tasks

Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting

A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses. In this paper, we formalize diversity of representation in generative LLMs. We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes. We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal. This finding motivated a new prompting technique called collective-critique and self-voting (CCSV) to self-improve people diversity of LLMs by tapping into its diversity reasoning capabilities, without relying on handcrafted examples or prompt tuning. Extensive empirical experiments with both human and automated evaluations show that our proposed approach is effective at improving people and culture diversity, and outperforms all baseline methods by a large margin.Comment: To appear at EMNLP 2023 main conferenc

Nitric oxide from inflammatory origin impairs neural stem cell proliferation by inhibiting epidermal growth factor receptor signaling

Neuroinflammation is characterized by activation of microglial cells, followed by production of nitric oxide (NO), which may have different outcomes on neurogenesis, favoring or inhibiting this process. In the present study, we investigated how the inflammatory mediator NO can affect proliferation of neural stem cells (NSCs), and explored possible mechanisms underlying this effect. We investigated which mechanisms are involved in the regulation of NSC proliferation following treatment with an inflammatory stimulus (lipopolysaccharide plus IFN-gamma), using a culture system of subventricular zone (SVZ)-derived NSCs mixed with microglia cells obtained from wild-type mice (iNOS(+/+)) or from iNOS knockout mice (iNOS(-/-)). We found an impairment of NSC cell proliferation in iNOS(+/+) mixed cultures, which was not observed in iNOS(-/-) mixed cultures. Furthermore, the increased release of NO by activated iNOS(+/+) microglial cells decreased the activation of the ERK/MAPK signaling pathway, which was concomitant with an enhanced nitration of the EGF receptor. Preventing nitrogen reactive species formation with MnTBAP, a scavenger of peroxynitrite (ONOO-), or using the ONOO- degradation catalyst FeTMPyP cell proliferation and ERK signaling were restored to basal levels in iNOS(+/+) mixed cultures. Moreover, exposure to the NO donor NOC-18 (100 mu M), for 48 h, inhibited SVZ-derived NSC proliferation. Regarding the antiproliferative effect of NO, we found that NOC-18 caused the impairment of signaling through the ERK/MAPK pathway, which may be related to increased nitration of the EGF receptor in NSC. Using MnTBAP nitration was prevented, maintaining ERK signaling, rescuing NSC proliferation. We show that NO from inflammatory origin leads to a decreased function of the EGF receptor, which compromised proliferation of NSC. We also demonstrated that NO-mediated nitration of the EGF receptor caused a decrease in its phosphorylation, thus preventing regular proliferation signaling through the ERK/MAPK pathway.Foundation for Science and Technology, (FCT, Portugal); COMPETE; FEDER [PEst-C/SAU/LA0001/2013-2014, PEst-OE/EQB/LA0023/2013-2014, PTDC/SAU-NEU/102612/2008, PTDC/NEU-OSD/0473/2012]; FCT, Portugal [SERH/BPD/78901/2011, SERH/BD/38127/2007, SFRH/BD/77903/2011, SFRH/BD/79308/2011]info:eu-repo/semantics/publishedVersio

A Minimal Model of Metabolism Based Chemotaxis

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis

A self-controlled case series to assess the effectiveness of beta blockers for heart failure in reducing hospitalisations in the elderly

Background: To determine the suitability of using the self-controlled case series design to assess improvements in health outcomes using the effectiveness of beta blockers for heart failure in reducing hospitalisations as the example. Methods: The Australian Government Department of Veterans' Affairs administrative claims database was used to undertake a self-controlled case-series in elderly patients aged 65 years or over to compare the risk of a heart failure hospitalisation during periods of being exposed and unexposed to a beta blocker. Two studies, the first using a one year period and the second using a four year period were undertaken to determine if the estimates varied due to changes in severity of heart failure over time. Results: In the one year period, 3,450 patients and in the four year period, 12, 682 patients had at least one hospitalisation for heart failure. The one year period showed a non-significant decrease in hospitalisations for heart failure 4-8 months after starting beta-blockers, (RR, 0.76; 95% CI (0.57-1.02)) and a significant decrease in the 8-12 months post-initiation of a beta blocker for heart failure (RR, 0.62; 95% CI (0.39, 0.99)). For the four year study there was an increased risk of hospitalisation less than eight months post-initiation and significant but smaller decrease in the 8-12 month window (RR, 0.90; 95% CI (0.82, 0.98)). Conclusions: The results of the one year observation period are similar to those observed in randomised clinical trials indicating that the self-controlled case-series method can be successfully applied to assess health outcomes. However, the result appears sensitive to the study periods used and further research to understand the appropriate applications of this method in pharmacoepidemiology is still required. The results also illustrate the benefits of extending beta blocker utilisation to the older age group of heart failure patients in which their use is common but the evidence is sparse.Emmae N Ramsay, Elizabeth E Roughead, Ben Ewald, Nicole L Pratt and Philip Rya

International Veterinary Epilepsy Task Force Consensus Proposal: Outcome of therapeutic interventions in canine and feline epilepsy

Common criteria for the diagnosis of drug resistance and the assessment of outcome are needed urgently as a prerequisite for standardized evaluation and reporting of individual therapeutic responses in canine epilepsy. Thus, we provide a proposal for the definition of drug resistance and partial therapeutic success in canine patients with epilepsy. This consensus statement also suggests a list of factors and aspects of outcome, which should be considered in addition to the impact on seizures. Moreover, these expert recommendations discuss criteria which determine the validity and informative value of a therapeutic trial in an individual patient and also suggest the application of individual outcome criteria. Agreement on common guidelines does not only render a basis for future optimization of individual patient management, but is also a presupposition for the design and implementation of clinical studies with highly standardized inclusion and exclusion criteria. Respective standardization will improve the comparability of findings from different studies and renders an improved basis for multicenter studies. Therefore, this proposal provides an in-depth discussion of the implications of outcome criteria for clinical studies. In particular ethical aspects and the different options for study design and application of individual patient-centered outcome criteria are considered