Neurosymbolic Grounding for Compositional World Models

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces two new tools: (i) neurosymbolic scene encodings, which represent each entity in a scene using a real vector computed using a neural encoder, as well as a vector of composable symbols describing attributes of the entity, and (ii) a neurosymbolic attention mechanism that binds these entities to learned rules of interaction. Cosmos is end-to-end differentiable; also, unlike traditional neurosymbolic methods that require representations to be manually mapped to symbols, it computes an entity's symbolic attributes using vision-language foundation models. Through an evaluation that considers two different forms of CG on an established blocks-pushing domain, we show that the framework establishes a new state-of-the-art for CG in world modeling

Task Programming: Learning Data Efficient Behavior Representations

Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video tracking data. To reduce annotation effort, we present TREBA: a method to learn annotation-sample efficient trajectory embedding for behavior analysis, based on multi-task self-supervised learning. The tasks in our method can be efficiently engineered by domain experts through a process we call "task programming", which uses programs to explicitly encode structured knowledge from domain experts. Total domain expert effort can be reduced by exchanging data annotation time for the construction of a small number of programmed tasks. We evaluate this trade-off using data from behavioral neuroscience, in which specialized domain knowledge is used to identify behaviors. We present experimental results in three datasets across two domains: mice and fruit flies. Using embeddings from TREBA, we reduce annotation burden by up to a factor of 10 without compromising accuracy compared to state-of-the-art features. Our results thus suggest that task programming can be an effective way to reduce annotation effort for domain experts

Learning Differentiable Programs with Admissible Neural Heuristics

We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires optimizing over a combinatorial space of program "architectures". We frame this optimization problem as a search in a weighted graph whose paths encode top-down derivations of program syntax. Our key innovation is to view various classes of neural networks as continuous relaxations over the space of programs, which can then be used to complete any partial program. This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search. We instantiate our approach on top of the A-star algorithm and an iteratively deepened branch-and-bound search, and use these algorithms to learn programmatic classifiers in three sequence classification tasks. Our experiments show that the algorithms outperform state-of-the-art methods for program learning, and that they discover programmatic classifiers that yield natural interpretations and achieve competitive accuracy

Learning Differentiable Programs with Admissible Neural Heuristics

We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires optimizing over a combinatorial space of program "architectures". We frame this optimization problem as a search in a weighted graph whose paths encode top-down derivations of program syntax. Our key innovation is to view various classes of neural networks as continuous relaxations over the space of programs, which can then be used to complete any partial program. This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search. We instantiate our approach on top of the A-star algorithm and an iteratively deepened branch-and-bound search, and use these algorithms to learn programmatic classifiers in three sequence classification tasks. Our experiments show that the algorithms outperform state-of-the-art methods for program learning, and that they discover programmatic classifiers that yield natural interpretations and achieve competitive accuracy.Comment: 9 pages, published in NeurIPS 202

Sexual Dysfunction as a Marker of Cardiovascular Disease in Males With 50 or More Years of Type 1 Diabetes

OBJECTIVE Vascular dysfunction is a major contributor to diabetes complications. It is also the primary physiologic cause of erectile dysfunction and considered an independent predictor of cardiovascular disease (CVD) in males over age 40 years. A cohort of individuals with 50 or more years of type 1 diabetes, Joslin Medalists, have low rates of small but not large vessel complications. This study aims to identify the prevalence and longitudinal association of sexual dysfunction (SD) with CVD in Joslin Medalists. RESEARCH DESIGN AND METHODS Description and association of self-assessment of SD in males of the Medalist cohort by self-reported sexual problems with CVD. SD is validated through the use of the abbreviated International Index of Erectile Dysfunction (IIEF). RESULTS Of 301 males in the Medalist Study, 69.8% reported a history of SD. Unadjusted risk factors included elevated glycated hemoglobin (HbA1c) (P = 0.02), elevated BMI (P = 0.03), higher total cholesterol (P = 0.02), lower HDL (P 0.05) with SD. Current report of SD (IIEF score ≤17) in a subset of Medalists was significantly correlated with self-reported longitudinal SD. CONCLUSIONS SD in those with extreme-duration type 1 diabetes is independently associated with CVD, representing a large-vessel pattern. The findings suggest that SD may predict CVD in those with type 1 diabetes of long duration. These individuals have also been found to be relatively free of microvascular complications

A Rash Decision. The Hazards of the Wrongful Use of Adrenaline

Anaphylaxis is life-threatening and should be addressed urgently. Its treatment is not without side effects and an accurate diagnosis must be made to prevent potential harm by the wrongful use of medication. A 46-year-old woman with hypertension treated with angiotensin converting enzyme inhibitor (ACEI) presented to the emergency department with non-pitting oedema of the face and limbs. A hasty diagnosis of anaphylaxis was made and intravenous adrenaline administered. The patient developed a myocardial infarction caused by coronary artery spasm that required invasive intervention. The initial clinical picture was resolved when the ACEI was discontinued unmasking a case of ACEI-induced angioedema. The correct differentiation of these two apparently similar clinical entities is of utmost importance in the management of emergency department patients.info:eu-repo/semantics/publishedVersio