31 research outputs found
On the Unlikelihood of D-Separation
Causal discovery aims to recover a causal graph from data generated by it;
constraint based methods do so by searching for a d-separating conditioning set
of nodes in the graph via an oracle. In this paper, we provide analytic
evidence that on large graphs, d-separation is a rare phenomenon, even when
guaranteed to exist, unless the graph is extremely sparse. We then provide an
analytic average case analysis of the PC Algorithm for causal discovery, as
well as a variant of the SGS Algorithm we call UniformSGS. We consider a set
of nodes, and generate a random DAG where
with i.i.d. probability if .
We provide upper bounds on the probability that a subset of
d-separates and , conditional on and being d-separable; our
upper bounds decay exponentially fast to as . For
the PC Algorithm, while it is known that its worst-case guarantees fail on
non-sparse graphs, we show that the same is true for the average case, and that
the sparsity requirement is quite demanding: for good performance, the density
must go to as even in the average case. For
UniformSGS, while it is known that the running time is exponential for existing
edges, we show that in the average case, that is the expected running time for
most non-existing edges as well
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
We introduce the Salesforce CausalAI Library, an open-source library for
causal analysis using observational data. It supports causal discovery and
causal inference for tabular and time series data, of both discrete and
continuous types. This library includes algorithms that handle linear and
non-linear causal relationships between variables, and uses multi-processing
for speed-up. We also include a data generator capable of generating synthetic
data with specified structural equation model for both the aforementioned data
formats and types, that helps users control the ground-truth causal process
while investigating various algorithms. Finally, we provide a user interface
(UI) that allows users to perform causal analysis on data without coding. The
goal of this library is to provide a fast and flexible solution for a variety
of problems in the domain of causality. This technical report describes the
Salesforce CausalAI API along with its capabilities, the implementations of the
supported algorithms, and experiments demonstrating their performance and
speed. Our library is available at
\url{https://github.com/salesforce/causalai}
REX: Rapid Exploration and eXploitation for AI Agents
In this paper, we propose an enhanced approach for Rapid Exploration and
eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have
inherent limitations, such as a heavy reliance on precise descriptions for
decision-making, and the lack of a systematic approach to leverage try-and-fail
procedures akin to traditional Reinforcement Learning (RL). REX introduces an
additional layer of rewards and integrates concepts similar to Upper Confidence
Bound (UCB) scores, leading to more robust and efficient AI agent performance.
This approach has the advantage of enabling the utilization of offline
behaviors from logs and allowing seamless integration with existing foundation
models while it does not require any model fine-tuning. Through comparative
analysis with existing methods such as Chain-of-Thoughts(CoT) and Reasoning viA
Planning(RAP), REX-based methods demonstrate comparable performance and, in
certain cases, even surpass the results achieved by these existing techniques.
Notably, REX-based methods exhibit remarkable reductions in execution time,
enhancing their practical applicability across a diverse set of scenarios
Relatório de estágio em farmácia comunitária
Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr
Synthesis and Chemical and Biological Comparison of Nitroxyl- and Nitric Oxide-Releasing Diazeniumdiolate-Based Aspirin Derivatives
Structural
modifications of nonsteroidal anti-inflammatory drugs
(NSAIDs) have successfully reduced the side effect of gastrointestinal
ulceration without affecting anti-inflammatory activity, but they
may increase the risk of myocardial infarction with chronic use. The
fact that nitroxyl (HNO) reduces platelet aggregation, preconditions
against myocardial infarction, and enhances contractility led us to
synthesize a diazeniumdiolate-based HNO-releasing aspirin and to compare
it to an NO-releasing analogue. Here, the decomposition mechanisms
are described for these compounds. In addition to protection against
stomach ulceration, these prodrugs exhibited significantly enhanced
cytotoxcity compared to either aspirin or the parent diazeniumdiolate
toward nonsmall cell lung carcinoma cells (A549), but they were not
appreciably toxic toward endothelial cells (HUVECs). The HNO-NSAID
prodrug inhibited cylcooxgenase-2 and glyceraldehyde 3-phosphate dehydrogenase
activity and triggered significant sarcomere shortening on murine
ventricular myocytes compared to control. Together, these anti-inflammatory,
antineoplasic, and contractile properties suggest the potential of
HNO-NSAIDs in the treatment of inflammation, cancer, or heart failure