259 research outputs found
Feature-aware conditional GAN for category text generation
Category text generation receives considerable attentions since it is
beneficial for various natural language processing tasks. Recently, the
generative adversarial network (GAN) has attained promising performance in text
generation, attributed to its adversarial training process. However, there are
several issues in text GANs, including discreteness, training instability, mode
collapse, lack of diversity and controllability etc. To address these issues,
this paper proposes a novel GAN framework, the feature-aware conditional GAN
(FA-GAN), for controllable category text generation. In FA-GAN, the generator
has a sequence-to-sequence structure for improving sentence diversity, which
consists of three encoders including a special feature-aware encoder and a
category-aware encoder, and one relational-memory-core-based decoder with the
Gumbel SoftMax activation function. The discriminator has an additional
category classification head. To generate sentences with specified categories,
the multi-class classification loss is supplemented in the adversarial
training. Comprehensive experiments have been conducted, and the results show
that FA-GAN consistently outperforms 10 state-of-the-art text generation
approaches on 6 text classification datasets. The case study demonstrates that
the synthetic sentences generated by FA-GAN can match the required categories
and are aware of the features of conditioned sentences, with good readability,
fluency, and text authenticity.Comment: 27 pages, 8 figure
Pathological Evidence Exploration in Deep Retinal Image Diagnosis
Though deep learning has shown successful performance in classifying the
label and severity stage of certain disease, most of them give few evidence on
how to make prediction. Here, we propose to exploit the interpretability of
deep learning application in medical diagnosis. Inspired by Koch's Postulates,
a well-known strategy in medical research to identify the property of pathogen,
we define a pathological descriptor that can be extracted from the activated
neurons of a diabetic retinopathy detector. To visualize the symptom and
feature encoded in this descriptor, we propose a GAN based method to synthesize
pathological retinal image given the descriptor and a binary vessel
segmentation. Besides, with this descriptor, we can arbitrarily manipulate the
position and quantity of lesions. As verified by a panel of 5 licensed
ophthalmologists, our synthesized images carry the symptoms that are directly
related to diabetic retinopathy diagnosis. The panel survey also shows that our
generated images is both qualitatively and quantitatively superior to existing
methods.Comment: to appear in AAAI (2019). The first two authors contributed equally
to the paper. Corresponding Author: Feng L
The temporal lagged association between meteorological factors and malaria in 30 counties in south-west China: a multilevel distributed lag non-linear analysis
BACKGROUND: The association between malaria and meteorological factors is complex due to the lagged and non-linear pattern. Without fully considering these characteristics, existing studies usually concluded inconsistent findings. Investigating the lagged correlation pattern between malaria and climatic variables may improve the understanding of the association and generate possible better prediction models. This is especially beneficial to the south-west China, which is a high-incidence area in China. METHODS: Thirty counties in south-west China were selected, and corresponding weekly malaria cases and four weekly meteorological variables were collected from 2004 to 2009. The Multilevel Distributed Lag Non-linear Model (MDLNM) was used to study the temporal lagged correlation between weekly malaria and weekly meteorological factors. The counties were divided into two groups, hot and cold weathers, in order to compare the difference under different climatic conditions and improve reliability and generalizability within similar climatic conditions. RESULTS: Rainfall was associated with malaria cases in both hot and cold weather counties with a lagged correlation, and the lag range was relatively longer than those of other meteorological factors. Besides, the lag range was longer in hot weather counties compared to cold weather counties. Relative humidity was correlated with malaria cases at early and late lags in hot weather counties. Minimum temperature had a longer lag range and larger correlation coefficients for hot weather counties compared to cold weather counties. Maximum temperature was only associated with malaria cases at early lags. CONCLUSION: Using weekly malaria cases and meteorological information, this work studied the temporal lagged association pattern between malaria cases and meteorological information in south-west China. The results suggest that different meteorological factors show distinct patterns and magnitudes for the lagged correlation, and the patterns will depend on the climatic condition. Existing inconsistent findings for climatic factors’ lags could be due to either the invalid assumption of a single fixed lag or the distinct temperature conditions from different study sites. The lag pattern for meteorological factors should be considered in the development of malaria early warning system
Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction
In this study, we investigate in-context learning (ICL) in document-level
event argument extraction (EAE). The paper identifies key challenges in this
problem, including example selection, context length limitation, abundance of
event types, and the limitation of Chain-of-Thought (CoT) prompting in
non-reasoning tasks. To address these challenges, we introduce the
Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we
hypothesize and validate that LLMs learn task-specific heuristics from
demonstrations via ICL. Building upon this hypothesis, we introduce an explicit
heuristic-driven demonstration construction approach, which transforms the
haphazard example selection process into a methodical method that emphasizes
task heuristics. Additionally, inspired by the analogical reasoning of human,
we propose the link-of-analogy prompting, which enables LLMs to process new
situations by drawing analogies to known situations, enhancing their
adaptability. Extensive experiments show that our method outperforms the
existing prompting methods and few-shot supervised learning methods, exhibiting
F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset.
Furthermore, when applied to sentiment analysis and natural language inference
tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%,
indicating its effectiveness across different tasks
UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding
In the era of Large Language Models (LLMs), tremendous strides have been made
in the field of multimodal understanding. However, existing advanced algorithms
are limited to effectively utilizing the immense representation capabilities
and rich world knowledge inherent to these large pre-trained models, and the
beneficial connections among tasks within the context of text-rich scenarios
have not been sufficiently explored. In this work, we introduce UniDoc, a novel
multimodal model equipped with text detection and recognition capabilities,
which are deficient in existing approaches. Moreover, UniDoc capitalizes on the
beneficial interactions among tasks to enhance the performance of each
individual task. To implement UniDoc, we perform unified multimodal instruct
tuning on the contributed large-scale instruction following datasets.
Quantitative and qualitative experimental results show that UniDoc sets
state-of-the-art scores across multiple challenging benchmarks. To the best of
our knowledge, this is the first large multimodal model capable of simultaneous
text detection, recognition, spotting, and understanding
Topical Digitoxigenin for Wound Healing: A Feasibility Study
(1) Background: Cardiotonic steroids have been found to stimulate collagen synthesis and might be potential wound healing therapeutics. The objective of this study was to evaluate the feasibility of digitoxigenin and its topical formulation for wound healing; (2) Methods: In the in vitro study, the human dermal fibroblast cells were treated with digitoxigenin and collagen synthesis was assessed. In the in vivo study, digitoxigenin was applied to excisional full-thickness wounds in rats immediately after wounding and remained for three days, and wound open was evaluated over 10 days. A digitoxigenin formulation for topical administration was prepared, and the in vitro release and in vivo wound healing effect were investigated; (3) Results: The expression of procollagen in human dermal fibroblast was significantly increased with the exposure to 0.1 nM digitoxigenin. Topical application of digitoxigenin in olive oil or alginate solution for three days significantly decreased the wound open in rats. Similarly, topical administration of the developed digitoxigenin formulation for three days also significantly increased wound healing. No wound healing effects were observed at days 7 and 10 after wounding when digitoxigenin was not applied; and, (4) Conclusions: It was possible to deliver digitoxigenin using the developed formulation. However, the wound healing effect of digitoxigenin and its mechanisms need to be further investigated in future studies
Large Language Model Distilling Medication Recommendation Model
The recommendation of medication is a vital aspect of intelligent healthcare
systems, as it involves prescribing the most suitable drugs based on a
patient's specific health needs. Unfortunately, many sophisticated models
currently in use tend to overlook the nuanced semantics of medical data, while
only relying heavily on identities. Furthermore, these models face significant
challenges in handling cases involving patients who are visiting the hospital
for the first time, as they lack prior prescription histories to draw upon. To
tackle these issues, we harness the powerful semantic comprehension and
input-agnostic characteristics of Large Language Models (LLMs). Our research
aims to transform existing medication recommendation methodologies using LLMs.
In this paper, we introduce a novel approach called Large Language Model
Distilling Medication Recommendation (LEADER). We begin by creating appropriate
prompt templates that enable LLMs to suggest medications effectively. However,
the straightforward integration of LLMs into recommender systems leads to an
out-of-corpus issue specific to drugs. We handle it by adapting the LLMs with a
novel output layer and a refined tuning loss function. Although LLM-based
models exhibit remarkable capabilities, they are plagued by high computational
costs during inference, which is impractical for the healthcare sector. To
mitigate this, we have developed a feature-level knowledge distillation
technique, which transfers the LLM's proficiency to a more compact model.
Extensive experiments conducted on two real-world datasets, MIMIC-III and
MIMIC-IV, demonstrate that our proposed model not only delivers effective
results but also is efficient. To ease the reproducibility of our experiments,
we release the implementation code online
Revisiting the monetary transmission mechanism via banking from the perspective of credit creation
Many transmission channels of monetary policy have been proposed to enrich and deepen the understanding of its mechanisms. However, some channels have not been clarified, particularly for those unconventional quantitative policies implemented after 2008 financial crisis. In this paper, we develop a unified model of a credit economy where bank regulations and decisions and loanable funds market are placed at a central position, while stocks and flows are incorporated with each other to formulate banks' credit creation and circulation. We find that bank regulations can induce some new channels of monetary transmission by imposing credit constraints, including the new bank capital channel, the credit supply channel, the new bank balance sheet channel, and the new bank risk-taking channel. Comparing these channels with the traditional ones, we underscore the impact of bank regulations on monetary transmission. As aggregate demand can be decomposed into two monetary flows generated by money circulation and bank lending respectively, the direct channels of monetary transmission to aggregate demand can be renewed as follows: the money channel, the narrow money circulation channel, the new bank lending channel, and the repayment channel. In addition, based on the relevant data from the United States, we have conducted vector autoregressive (VAR) impulse response analysis to confirm the effectiveness of some direct channels. Our work not only aids in revisiting the monetary transmission from a credit view but also facilitates the assessment of efficiency of monetary policy
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
Model-based offline reinforcement learning (RL) has made remarkable progress,
offering a promising avenue for improving generalization with synthetic model
rollouts. Existing works primarily focus on incorporating pessimism for policy
optimization, usually via constructing a Pessimistic Markov Decision Process
(P-MDP). However, the P-MDP discourages the policies from learning in
out-of-distribution (OOD) regions beyond the support of offline datasets, which
can under-utilize the generalization ability of dynamics models. In contrast,
we propose constructing an Optimistic MDP (O-MDP). We initially observed the
potential benefits of optimism brought by encouraging more OOD rollouts.
Motivated by this observation, we present ORPO, a simple yet effective
model-based offline RL framework. ORPO generates Optimistic model Rollouts for
Pessimistic offline policy Optimization. Specifically, we train an optimistic
rollout policy in the O-MDP to sample more OOD model rollouts. Then we relabel
the sampled state-action pairs with penalized rewards and optimize the output
policy in the P-MDP. Theoretically, we demonstrate that the performance of
policies trained with ORPO can be lower-bounded in linear MDPs. Experimental
results show that our framework significantly outperforms P-MDP baselines by a
margin of 30%, achieving state-of-the-art performance on the widely-used
benchmark. Moreover, ORPO exhibits notable advantages in problems that require
generalization
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