425 research outputs found
Rainfall Distribution in Landfalling Tropical Cyclones
Accurate prediction of rainfall distribution in landfalling tropical cyclones (LTCs) is very important to disaster prevention but quite challenging to operational forecasters. This chapter will describe the rainfall distribution in LTCs, including both axisymmetric and asymmetric distributions and their major controlling parameters, such as environmental vertical wind shear, TC intensity and motion, and coastline. In addition to the composite results from many LTC cases, several case studies are also given to illustrate the predominant factors that are key to the asymmetric rainfall distribution in LTCs. Future directions in this area and potential ways to improve the operational forecasts of rainfall distribution in LTCs are also discussed briefly
Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
Clinical trials are essential for drug development but are extremely
expensive and time-consuming to conduct. It is beneficial to study similar
historical trials when designing a clinical trial. However, lengthy trial
documents and lack of labeled data make trial similarity search difficult. We
propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns
through self-supervision without annotating similar clinical trials.
Specifically, the meta-structure of trial documents (e.g., title, eligibility
criteria, target disease) along with clinical knowledge (e.g., UMLS knowledge
base https://www.nlm.nih.gov/research/umls/index.html) are leveraged to
automatically generate contrastive samples. Besides, Trial2Vec encodes trial
documents considering meta-structure thus producing compact embeddings
aggregating multi-aspect information from the whole document. We show that our
method yields medically interpretable embeddings by visualization and it gets a
15% average improvement over the best baselines on precision/recall for trial
retrieval, which is evaluated on our labeled 1600 trial pairs. In addition, we
prove the pre-trained embeddings benefit the downstream trial outcome
prediction task over 240k trials. Software ias available at
https://github.com/RyanWangZf/Trial2Vec.Comment: Findings of EMNLP 202
AutoTrial: Prompting Language Models for Clinical Trial Design
Clinical trials are critical for drug development. Constructing the
appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for
patient recruitment) is essential for the trial's success. Proper design of
clinical trial protocols should consider similar precedent trials and their
eligibility criteria to ensure sufficient patient coverage. In this paper, we
present a method named AutoTrial to aid the design of clinical eligibility
criteria using language models. It allows (1) controllable generation under
instructions via a hybrid of discrete and neural prompting, (2) scalable
knowledge incorporation via in-context learning, and (3) explicit reasoning
chains to provide rationales for understanding the outputs. Experiments on over
70K clinical trials verify that AutoTrial generates high-quality criteria texts
that are fluent and coherent and with high accuracy in capturing the relevant
clinical concepts to the target trial. It is noteworthy that our method, with a
much smaller parameter size, gains around 60% winning rate against the GPT-3.5
baselines via human evaluations.Comment: EMNLP 2023 Mai
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning
Clinical trials are essential to drug development but time-consuming, costly,
and prone to failure. Accurate trial outcome prediction based on historical
trial data promises better trial investment decisions and more trial success.
Existing trial outcome prediction models were not designed to model the
relations among similar trials, capture the progression of features and designs
of similar trials, or address the skewness of trial data which causes inferior
performance for less common trials.
To fill the gap and provide accurate trial outcome prediction, we propose
Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first
identifies trial topics to cluster the multi-sourced trial data into relevant
trial topics. It then generates trial embeddings and organizes them by topic
and time to create clinical trial sequences. With the consideration of each
trial sequence as a task, it uses a meta-learning strategy to achieve a point
where the model can rapidly adapt to new tasks with minimal updates. In
particular, the topic discovery module enables a deeper understanding of the
underlying structure of the data, while sequential learning captures the
evolution of trial designs and outcomes. This results in predictions that are
not only more accurate but also more interpretable, taking into account the
temporal patterns and unique characteristics of each trial topic. We
demonstrate that SPOT wins over the prior methods by a significant margin on
trial outcome benchmark data: with a 21.5\% lift on phase I, an 8.9\% lift on
phase II, and a 5.5\% lift on phase III trials in the metric of the area under
precision-recall curve (PR-AUC)
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
LLMs usually exhibit limitations in their ability to incorporate new
knowledge, the generation of hallucinations, and the transparency of their
decision-making process. In this paper, we explore how to prompt LLMs with
knowledge graphs (KG), working as a remedy to engage LLMs with up-to-date
knowledge and elicit the reasoning pathways from LLMs. Specifically, we build a
prompting pipeline that endows LLMs with the capability of comprehending KG
inputs and inferring with a combined implicit knowledge and the retrieved
external knowledge. In addition, we investigate eliciting the mind map on which
LLMs perform the reasoning and generate the answers. It is identified that the
produced mind map exhibits the reasoning pathways of LLMs grounded on the
ontology of knowledge, hence bringing the prospects of probing and gauging LLM
inference in production. The experiments on three question & answering datasets
also show that MindMap prompting leads to a striking empirical gain. For
instance, prompting a GPT-3.5 with MindMap yields an overwhelming performance
over GPT-4 consistently. We also demonstrate that with structured facts
retrieved from KG, MindMap can outperform a series of
prompting-with-document-retrieval methods, benefiting from more accurate,
concise, and comprehensive knowledge from KGs.Comment: 7 pages, 8 figures, 9 table
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Adversarial pruning compresses models while preserving robustness. Current
methods require access to adversarial examples during pruning. This
significantly hampers training efficiency. Moreover, as new adversarial attacks
and training methods develop at a rapid rate, adversarial pruning methods need
to be modified accordingly to keep up. In this work, we propose a novel
framework to prune a previously trained robust neural network while maintaining
adversarial robustness, without further generating adversarial examples. We
leverage concurrent self-distillation and pruning to preserve knowledge in the
original model as well as regularizing the pruned model via the Hilbert-Schmidt
Information Bottleneck. We comprehensively evaluate our proposed framework and
show its superior performance in terms of both adversarial robustness and
efficiency when pruning architectures trained on the MNIST, CIFAR-10, and
CIFAR-100 datasets against five state-of-the-art attacks. Code is available at
https://github.com/neu-spiral/PwoA/.Comment: Published at ICDM 2022 as a conference pape
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