234 research outputs found
A New Approach to Synthesize of 4-Phenacylideneflavene Derivatives and to Evaluate Their Cytotoxic Effects on HepG2 Cell Line
In this study, a convenient approach and green procedure for the synthesis of 4-phenacylideneflavenes has been developed from the reaction between 2,4-dihydroxybenzaldehyde and substituted acetophenones using boric acid as a catalyst in polyethylene glycol 400. Seven 4-phenacylideneflavenes were synthetized and their structures were confirmed by NMR and mass spectral analyses. Meanwhile, their possible mechanism of formation was also discussed. These products were found to have potential cytotoxic effect on HepG2 cell line with IC50 values from 12.5 to 50 µM
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Offline reinforcement learning (RL) has received considerable attention in
recent years due to its attractive capability of learning policies from offline
datasets without environmental interactions. Despite some success in the
single-agent setting, offline multi-agent RL (MARL) remains to be a challenge.
The large joint state-action space and the coupled multi-agent behaviors pose
extra complexities for offline policy optimization. Most existing offline MARL
studies simply apply offline data-related regularizations on individual agents,
without fully considering the multi-agent system at the global level. In this
work, we present OMIGA, a new offline m ulti-agent RL algorithm with implicit
global-to-local v alue regularization. OMIGA provides a principled framework to
convert global-level value regularization into equivalent implicit local value
regularizations and simultaneously enables in-sample learning, thus elegantly
bridging multi-agent value decomposition and policy learning with offline
regularizations. Based on comprehensive experiments on the offline multi-agent
MuJoCo and StarCraft II micro-management tasks, we show that OMIGA achieves
superior performance over the state-of-the-art offline MARL methods in almost
all tasks
Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents
The escalating complexity of software systems and accelerating development
cycles pose a significant challenge in managing code errors and implementing
business logic. Traditional techniques, while cornerstone for software quality
assurance, exhibit limitations in handling intricate business logic and
extensive codebases. To address these challenges, we introduce the Intelligent
Code Analysis Agent (ICAA), a novel concept combining AI models, engineering
process designs, and traditional non-AI components. The ICAA employs the
capabilities of large language models (LLMs) such as GPT-3 or GPT-4 to
automatically detect and diagnose code errors and business logic
inconsistencies. In our exploration of this concept, we observed a substantial
improvement in bug detection accuracy, reducing the false-positive rate to 66\%
from the baseline's 85\%, and a promising recall rate of 60.8\%. However, the
token consumption cost associated with LLMs, particularly the average cost for
analyzing each line of code, remains a significant consideration for widespread
adoption. Despite this challenge, our findings suggest that the ICAA holds
considerable potential to revolutionize software quality assurance,
significantly enhancing the efficiency and accuracy of bug detection in the
software development process. We hope this pioneering work will inspire further
research and innovation in this field, focusing on refining the ICAA concept
and exploring ways to mitigate the associated costs
High-dimensional quantile mediation analysis with application to a birth cohort study of mother-newborn pairs
MOTIVATION: There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum.
RESULTS: The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method\u27s efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight.
AVAILABILITY AND IMPLEMENTATION: Our method offers a publicly available and user-friendly function qHIMA(), which can be accessed through the R package HIMA at https://CRAN.R-project.org/package=HIMA
HIMA2: High-dimensional mediation analysis and its application in epigenome-wide DNA methylation data
Mediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150-3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849-911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
In this paper, we introduce a large Multi-Attribute and Language Search
dataset for text-based person retrieval, called MALS, and explore the
feasibility of performing pre-training on both attribute recognition and
image-text matching tasks in one stone. In particular, MALS contains 1,510,330
image-text pairs, which is about 37.5 times larger than prevailing CUHK-PEDES,
and all images are annotated with 27 attributes. Considering the privacy
concerns and annotation costs, we leverage the off-the-shelf diffusion models
to generate the dataset. To verify the feasibility of learning from the
generated data, we develop a new joint Attribute Prompt Learning and Text
Matching Learning (APTM) framework, considering the shared knowledge between
attribute and text. As the name implies, APTM contains an attribute prompt
learning stream and a text matching learning stream. (1) The attribute prompt
learning leverages the attribute prompts for image-attribute alignment, which
enhances the text matching learning. (2) The text matching learning facilitates
the representation learning on fine-grained details, and in turn, boosts the
attribute prompt learning. Extensive experiments validate the effectiveness of
the pre-training on MALS, achieving state-of-the-art retrieval performance via
APTM on three challenging real-world benchmarks. In particular, APTM achieves a
consistent improvement of +6.96%, +7.68%, and +16.95% Recall@1 accuracy on
CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Safe offline RL is a promising way to bypass risky online interactions
towards safe policy learning. Most existing methods only enforce soft
constraints, i.e., constraining safety violations in expectation below
thresholds predetermined. This can lead to potentially unsafe outcomes, thus
unacceptable in safety-critical scenarios. An alternative is to enforce the
hard constraint of zero violation. However, this can be challenging in offline
setting, as it needs to strike the right balance among three highly intricate
and correlated aspects: safety constraint satisfaction, reward maximization,
and behavior regularization imposed by offline datasets. Interestingly, we
discover that via reachability analysis of safe-control theory, the hard safety
constraint can be equivalently translated to identifying the largest feasible
region given the offline dataset. This seamlessly converts the original trilogy
problem to a feasibility-dependent objective, i.e., maximizing reward value
within the feasible region while minimizing safety risks in the infeasible
region. Inspired by these, we propose FISOR (FeasIbility-guided Safe Offline
RL), which allows safety constraint adherence, reward maximization, and offline
policy learning to be realized via three decoupled processes, while offering
strong safety performance and stability. In FISOR, the optimal policy for the
translated optimization problem can be derived in a special form of weighted
behavior cloning. Thus, we propose a novel energy-guided diffusion model that
does not require training a complicated time-dependent classifier to extract
the policy, greatly simplifying the training. We compare FISOR against
baselines on DSRL benchmark for safe offline RL. Evaluation results show that
FISOR is the only method that can guarantee safety satisfaction in all tasks,
while achieving top returns in most tasks.Comment: ICLR 2024, 30pages, 11 figure
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